Role of Remote Sensing in Forest and National Greenhouse Gas Inventories

IPCC Expert Meeting: Role of Remote
Sensing in Forest and National Greenhouse
Gas Inventories
Hayama, Japan
23 – 25 October 2012

Task Force on National Greenhouse Gas Inventories

i

IPCC Expert Meeting ReportRole of Remote Sensing in Forest and National Greenhouse Gas Inventories
Supporting material prepared for consideration by the Intergovernmental Panel on Climate Change (IPCC). This
supporting material has not been subject to formal IPCC review processes. Neither the papers presented at theexpert meeting nor this report of its proceedings has been subjected to IPCC review.

The IPCC would like to thank Institute for Global Environmental Strategies (IGES) and the Government of Japanfor hosting this meeting and providing technical support.

Published by IGES, Hayama, Japan on behalf of the IPCC
© Intergovernmental Panel on Climate Change (IPCC), 2014
Please cite as:
IPCC (2014). Role of Remote Sensing in Forest and National Greenhouse Gas Inventories. Eds: Srivastava, N.,
Tanabe, K., Baasansuren, J. and Fukuda, M. Report of the IPCC Expert Meeting, Pub. IGES, Japan.
IPCC Task Force on National Greenhouse Gas Inventories (TFI)
Technical Support Unit
℅ Institute for Global Environmental Strategies
2108 -11, Kamiyamaguchi
Hayama, Kanagawa
JAPAN, 240-0115
E-mail: nggip-tsu@iges.or.jp
Fax: +81-46-855-3808
http://www.ipcc-nggip.iges.or.jp
Printed in Japan
ISBN 978-4-88788-172-3

IPCC Expert Meeting ReportiiRole of Remote Sensing in Forest and National Greenhouse Gas Inventories

IPCC Expert Meeting: Role of Remote
Sensing in Forest and National Greenhouse
Gas Inventories
Hayama, Japan
23 – 25 October 2012

iii

IPCC Expert Meeting ReportRole of Remote Sensing in Forest and National Greenhouse Gas Inventories

Contents
Foreword ............................................................................................................................................................... vi
Executive Summary ............................................................................................................................................ vii
Abbreviations ..................................................................................................................................................... viii
1

Introduction................................................................................................................................................... 1

2

Use of RS in Estimating GHG Emissions and Removals from Forests ................................................... 2
2.1

Introduction ............................................................................................................................................ 2

2.2

RS Information....................................................................................................................................... 7

2.2.1

Passive RS approaches including the use of spectral indices ...................................................................... 7

2.2.2

Active optical techniques such as Light Detection And Ranging (LiDAR) .................................................... 7

2.2.3

Radar techniques such as Synthetic Aperture Radar (SAR) ........................................................................ 7

2.3
2.3.1

National Forest Inventories (NFI) .................................................................................................................. 8

2.3.2

Use of ground data to validate RS ................................................................................................................ 9

2.4
3

Methods for estimating biomass, their relative advantages and disadvantages .................................. 11

3.1.1

Methods using optical images to determine forest strata............................................................................ 11

3.1.2

Methods using RS indices .......................................................................................................................... 12

3.1.3

LiDAR and SAR .......................................................................................................................................... 12

3.2

5

Drivers ................................................................................................................................................. 10

Biomass estimation using RS ................................................................................................................... 11
3.1

4

Ground data........................................................................................................................................... 8

Monitoring changes in biomass ........................................................................................................... 13

Future capabilities ...................................................................................................................................... 14
4.1

Technical developments which could be helpful .................................................................................. 14

4.2

New data possibilities and needs ........................................................................................................ 14

4.3

Emission and removal factors.............................................................................................................. 15

Conclusions ................................................................................................................................................ 16

References ........................................................................................................................................................... 17
Annex 1 Agenda ..................................................................................................................................................... I
Annex 2 Participants............................................................................................................................................ III
Annex 3 Presentations ........................................................................................................................................VI

IPCC Expert Meeting ReportivRole of Remote Sensing in Forest and National Greenhouse Gas Inventories

TablesTable 1 RS capabilities and forest monitoring needs for AD .................................................................................. 6
Table 2 EF information from RS and ground data.................................................................................................. 8
Table 3 Quantifying carbon stock change: expected capabilities in 5 years’ time................................................ 15
Table 4 Detection of degradation: expected capabilities in 5 years’ time ............................................................. 15

Equations
Equation 1 Estimation of CO2 fluxes from forests .................................................................................................. 2

Boxes
Box 1 Approaches to consistent representation of land in the 2006 IPCC Guidelines ............................................ 3v

IPCC Expert Meeting ReportRole of Remote Sensing in Forest and National Greenhouse Gas Inventories

Foreword
The IPCC’s Task Force on National Greenhouse Gas Inventories has, as part of its mandate, the objective of
encouraging users to adopt the IPCC methodological guidelines for estimating national inventories ofgreenhouse gases. This report is one of a series, developed through expert meetings, which aims to assist usersof the IPCC guidelines by addressing specific problem areas. Application of IPCC guidelines to estimation ofemissions and removals from forests poses a significant challenge to inventory compilers particularly with regard
to the difficulties with data collection, both current and time series, and with appropriate parameters for use in
GHG inventory compilation. Remote sensing technologies have the potential to address some of these issues
especially in light of considerable improvement in quality, coverage, availability and cost of remote sensing
products in recent years as well as much wider experience in their use by inventory compilers. IPCC meeting on
National Forest GHG Inventories - A Stocktaking, held in Yokohama Japan in February 20101, sought to addresssome of the challenges in the application of IPCC Guidelines to forest GHG inventories. Amongst otherrecommendations, it identified the need for another more focused expert meeting on technical issues in Forest
GHG Inventories that would, among other issues, address the use of remotely sensed data in forest GHG
inventories including on stratification, change assessment and methods for biomass estimation. IPCC Expert
Meeting: Role of Remote Sensing in Forest and National Greenhouse Gas Inventories, held in Hayama, Japanon 23-25 October 2012, built on the work done earlier in the Forest GHG Inventory Meeting by providing
extremely relevant and useful information on the application of remote sensing technologies in forest GHG
inventories and issues in their use besides identifying areas for future work. We, the Co-chairs of the Task Force
Bureau, would like to thank all those involved in this meeting, and would like to express our sincere thanks andappreciation to Institute for Global Environmental Strategies (IGES) and the Government of the Japan for theirsupport in hosting this meeting.

Thelma Krug
Co-Chair Task Force Bureau

Taka Hiraishi
Co-Chair Task Force Bureau

1 IPCC (2010). National Forest GHG Inventories – A Stock Taking. Eds: Eggleston, H. S., Srivastava, N., Tanabe, K., andBaasansuren, J. IPCC Expert Meeting Report, Pub. IGES, Japan.

IPCC Expert Meeting ReportviRole of Remote Sensing in Forest and National Greenhouse Gas Inventories

Executive Summary
The IPCC Expert Meeting: Role of Remote Sensing in Forest and National Greenhouse Gas Inventories
reviewed the use of remote sensing (RS) in forest greenhouse gas (GHG) inventories.
The meeting discussed three topics relating to the use of RS in forest GHG inventories: a) combination of remotesensing and ground-based observations; b) use of RS for biomass measurement; c) use of RS in assessing andmonitoring forest degradation. On each of these topics, the meeting discussed the information required to apply
the IPCC guidelines methods to forest GHG inventories; current RS capabilities and their anticipateddevelopment; and steps that could be taken by the RS community and GHG inventory experts to facilitate forest
GHG inventories.
The meeting concluded the following:a. While using RS to monitor forest areas can be a routine activity, given sufficient resources andcapacity, widespread monitoring of forest carbon stocks in many countries is still not widely applicable.
For those countries where RS methods can be applied, they will require access to remotely-sensed
data, resources and capacity development to provide these capabilities to countries that need them.
b. Currently the only widely accepted remote sensing-based method available to estimate biomass stocks
in forests is to use the remotely sensed data to estimate areas of each forest stratum and multiply thisarea by a biomass density obtained from ground based surveys and other ancillary data, if available.
Although methods for estimating biomass by using RS data jointly with field data for model calibrationand validation have been demonstrated in specific cases, they are not yet ready to be operationalized
in routine national forest monitoring in most countries.
c.

Detection of forest degradation using RS is a particular challenge due to different drivers ofdegradation that may impose distinct challenges in identification and assessment of changes. Of
particular relevance is the difficulty to assess the impact on carbon stocks from forest degradation
activities, including fire, logging and pest attacks, among others, and the extensive resources, bothfinancial and human, required for validating results of degradation.

d. There are a number of techniques under development and new satellites to be launched in the next few
years that will lead to increased capabilities of RS of forests. However, to speed up the development ofoperational techniques, closer collaboration of RS experts, terrestrial biosphere modellers andemission inventory experts is needed.

vii

IPCC Expert Meeting ReportRole of Remote Sensing in Forest and National Greenhouse Gas Inventories

Abbreviations
AD

Activity Data

AFOLU

Agriculture, Forestry and Other Land Use

ALOS PALSAR Advanced Land Observing Satellite Phased Array L-band Synthetic Aperture Radar
ASTER

Advanced Spaceborne Thermal Emission and Reflection Radiometer

CBERS

China Brazil Earth Resources Satellite

CO2

Carbon Dioxide

DBH

Diameter at Breast Height

DMC

Disaster Monitoring Constellation

EF

Emission Factor

ENVISAT

Environmental Satellite

ERS

European Remote Sensing Satellite

ESA

European Space Agency

ETM

Enhanced Thematic Mapper

GHG

Greenhouse Gas

GPG-LULUCF

Good Practice Guidance for Land Use, Land-Use Change and Forestry

HWP

Harvested Wood Products

ICESatIce, Cloud, and land Elevation Satellite

ILOVE

ISS-JEM LiDAR Observation of Vegetation Environment

IPCC

Intergovernmental Panel on Climate Change

IRS

Indian Remote Sensing Satellite

ISS-JEMInternational Space Station Japanese Experiment Module

JERS

Japanese Earth Resources Satellite

LiDAR

Light Detection And Ranging

IPCC Expert Meeting ReportviiiRole of Remote Sensing in Forest and National Greenhouse Gas Inventories
LULUCF

Land Use, Land-Use Change and Forestry

MERIS

Medium Resolution Imaging Spectrometer

MODIS

Moderate Resolution Imaging SpectroradiometerNASA

National Aeronautics and Space Administration

NFI

National Forest Inventory

REDD+

Reducing Emissions from Deforestation and forest Degradation and the role of conservation,
sustainable management of forests and enhancement of forest carbon stocks in developing
countries

RS

Remote Sensing

RSE

Relative Standard Error

SAR

Synthetic Aperture Radar

SPOT

Satellite Pour l'Observation de la Terre (Satellite for observation of Earth)

UNFCCC

United Nations Framework Convention on Climate Change

VIIRS

Visible Infrared Imaging Radiometer Suite

ix

IPCC Expert Meeting ReportRole of Remote Sensing in Forest and National Greenhouse Gas Inventories

1 Introduction
This report summarises the conclusions of the IPCC Expert Meeting: Role of Remote Sensing in Forest andNational GHG Inventories (“Forest RS Meeting”) which took place in Hayama, Japan, on 23-25 October 2012.
The meeting aimed to review the current state-of-play on the use of Remote Sensing (RS) and in-situ
observations in estimating greenhouse gas (GHG) emissions and removals from forests.
Many countries use remotely sensed data in conjunction with ground data to compile their GHG inventories for
the Land Use, Land-Use Change and Forestry (LULUCF) or Agriculture, Forestry and Other Land Use (AFOLU)2.
Since the 2006 IPCC Guidelines were produced, RS technologies have further continued to develop and there is
now considerably more experience in their use. In addition, the need for reliable estimates of GHG emissions
and removals from forests in developing countries has increased because of proposed GHG mitigation measures
such as the REDD+ process under the UNFCCC.
The 2006 IPCC Guidelines for National GHG Inventories (2006 IPCC Guidelines; IPCC (2006)) explicitly refer tothe use of RS data to obtain, amongst other things, maps of land-use at regular intervals, estimates of changes
in carbon stocks in above-ground biomass, and to assess areas and types of disturbances. However, there is no
detailed discussion of using remotely sensed data, its advantages and limitations in this field of application.
The Forest RS Meeting was held in two parts, with a series of presentations being followed by group discussions(see Agenda in Annex 1). Annex 2 gives the list of participants. The presentations are given in Annex 3.

2 LULUCF and Agriculture in the Good Practice Guidance on Land Use, Land-Use Change and Forestry (GPG-LULUCF;

IPCC (2003b)) were integrated into AFOLU Sector in the 2006 IPCC Guidelines.

1

IPCC Expert Meeting ReportRole of Remote Sensing in Forest and National Greenhouse Gas Inventories

2 Use of RS in estimating GHG emissions and removals from forests
2.1

Introduction

The general approach to estimate GHG emissions and removals in the IPCC Guidelines is to multiply Activity
Data (AD) by an Emission factor (EF)3 . For forests, the AD means typically the forest area.
The IPCC Guidelines consider the following carbon pools in any land-use category4 (Chapter 2, Volume 4, 2006
IPCC Guidelines), i.e., :Above-ground biomassBelow-ground biomass



Dead wood

Litter

Soil organic carbon

The CO2 emission factor reflects the change in the total carbon stocks in each one of these five pools associatedwith the activity in question. Changes in each pool are estimated by either the difference in carbon stock at two
points in time (Stock-Difference Method) or as carbon gains (e.g., through annual growth) minus carbon losses(e.g., through harvest, disturbances) (Gain-Loss Method)5. The total forest area should be sub-divided (stratified)
into areas of similar characteristics in order to apply a constant EF.
In summary, the annual net emissions (emissions/removals) from forests can be generally estimated using
Equation 1:
Equation 1 Estimation of CO2 fluxes from forests⁄,

Both RS and ground data can be used to provide AD and, to some extent, EFs, depending on which techniques
are applied. Historically, RS has contributed mostly to the estimation of AD, while the EFs are estimated

3 Conventionally an emission factor can be associated to an emission or to removal of a GHG from the atmosphere.
4

The 2006 IPCC Guidelines also provide guidance on Harvested Wood Products (HWP) pool. However, the annual change

in carbon stocks in the HWP pool can be reported as zero, if these are considered to be insignificant by the inventory
compiler (Section 12.2.1, Chapter 12, Volume 4(2) of the 2006 IPCC Guidelines). This is sometimes referred to as thedefault assumption of instantaneous oxidation of HWP.
5

Chapter 2, Volume 4 of 2006 Guidelines provides description and equations for the two methods.

IPCC Expert Meeting Report2Role of Remote Sensing in Forest and National Greenhouse Gas Inventories
predominantly from ground observations. Indeed, the IPCC Guidelines provide methodological guidance for
estimating EFs based on field data.
IPCC provides three Approaches to represent land, which is the basis for estimating forest-related AD (Box 1).
Box 1
Approaches to consistent representation of land in the 2006 IPCC Guidelines
The 2006 IPCC Guidelines describe three Approaches that may be used to consistently represent
areas of land-use categories across time. These are presented in the order of increasing information
content but are not hierarchical in the sense that moving to a different Approach does not imply an
increase or decrease in accuracy.
Approach 1 only identifies the net areas and net area changes of individual land-use categories ina country without providing any information on areas of specific conversions between land-use
categories (e.g., Forest Land Converted to Cropland) through time. Consequently, the exact locationor pattern of land-use conversions in a country is not known.
Approach 2 provides information on specific land-use conversions between land-use categories but
not on a spatially explicit basis. In Approach 2, information on the exact pattern of land-use
conversions involving specific land-use categories is known but not their geographical location.
Approach 3 allows for tracking of land-use conversions on a spatially explicit basis so that both theexact geographical location and pattern of land-use conversions between land-use categories are
known.
IPCC methods are applied by land-use category at the pool level then summed to give the emissions or
removals. IPCC methods do not necessarily require that countries have a national forest inventory in place,although the Stock-Difference Method does so. IPCC methods all require a forest classification/stratification andthe area of each stratum. IPCC EFs are described6 at three levels or Tiers of detail and complexity. The simplest
Tier 1 methods use global or regional default values for the required parameters and the users simply have toprovide AD (in the absence of national AD Tier 1methods can use AD from international datasets). Tier 2
methods, suitable for sources that contribute significantly to countries’ total emissions or removals or their trends,
generally use the same equations as Tier 1, but call for country-specific values for the EFs and any otherparameter required, as well as the national AD at finer resolution. Tier 3 methods are generally more complex
modelling or measurement approaches.

3

IPCC Expert Meeting ReportRole of Remote Sensing in Forest and National Greenhouse Gas Inventories
While default data and assumptions are available for the simplest Tier 1 methods, more accurate higher tiermethods (Tiers 2 & 3) require more detailed and accurate local information. For example, locally applicable
allometric equations can be used to go from typical forest inventory7 measurements to above-ground biomass;
below-ground biomass can then be estimated from above-ground biomass. AD on areas of land-use and landuse change may be obtained from RS or from ground-based data, e.g., rates of forest establishment orconversion to other land uses. Some AD is not area-related, e.g., firewood collection, or harvesting rates.
There is normally a need for some ancillary data. Ancillary data is supporting information used to link AD to theappropriate EF and help interpret GHG estimates, and may include forest maps, soil surveys, climatic data,
topography etc.
It is difficult to separate natural and anthropogenic fluxes in a globally consistent way and so the IPCC
Guidelines consider only fluxes from managed lands as a proxy for anthropogenic emissions and removals(IPCC, 2010). Therefore complete RS coverage (either by sampling or wall-to wall approaches) may not beneeded. However, the unmanaged land needs to be monitored since areas affected by disturbances, forexample, if followed by a land-use change, are then considered to be managed and therefore, emissions andremovals need to be estimated.
National GHG Inventories clearly need information at the national scale, stratified as necessary. Reporting at
finer spatial scales may also be needed for assessing the effects of drivers, or identifying projects (demonstration/projects). While deforestation is normally clear (conversion of forest land to other land-use category), forest
degradation is more difficult to define (IPCC, 2003a). However, for the purposes of GHG inventories, any change
in carbon stock in forest land in managed land is included as part of the inventory, regardless of the nature of thechange (e.g., degradation process, enhancement of stocks). The basic data needed to estimate emissions andremovals from forests following the approach adopted by the IPCC are:

Total area and AD. Total forest area, measured at the hectare or sub-hectare8 level is needed. Total
forest area should be appropriately stratified, e.g. by forest type, climate zone, soil type etc., to reflectvariation in EFs within the forest. Net changes in forest area due to deforestation and reforestation (AD)need to be estimated. This ideally should be done annually, but in the absence of yearly estimates of
total forest area, net annual changes can be estimated based on monitoring over longer time intervals.
Table 1 shows the current potential for RS technology to provide AD for different types of forest activity.



Forest Composition. At the national level, there is little operational RS capacity at present. National
Forest Inventories (NFI) and forest maps based on ground information provide this information.

7 A Forest Inventory is a statistically designed inventory of national forests, usually undertaken for resource management. It

is not the same as a GHG inventory, though the former may provide information useful for the latter, especially on emission
factors.
8 National forest definitions may use minimum forest areas down to 0.05 ha.

IPCC Expert Meeting Report4Role of Remote Sensing in Forest and National Greenhouse Gas Inventories


Biomass. Ideally, the carbon stock of each of the five IPCC carbon pools is needed. Usually inventory
compilers use allometric equations to estimate above-ground biomass from measurements. Belowground biomass is generally estimated by multiplying above-ground biomass with root-to-shoot ratio
although sometimes allometric equations are also used. For estimating C stocks of other pools,
generally IPCC default methods and assumptions are used.

Growth and Harvest. Typical growth rates are used to estimate CO2 uptake while data on the amounts
harvested provide estimate of emissions. Knowledge of the distinct harvesting practices allows toestimate transfers of carbon between pools. While clear-cutting and large scale harvesting may be
detectable, detecting and quantifying selective logging is more challenging using coarse and midresolution RS imagery.

5

IPCC Expert Meeting ReportRole of Remote Sensing in Forest and National Greenhouse Gas Inventories
Table 1 RS capabilities and forest monitoring needs for AD9
Deforestation

Activity

Degradation

Conversion of natural forest toplantationConservation, sustainable
management of forests andenhancement of forest carbon
stocksIssuesDefined as transition fromforest land to non-forest
land use.
Cost and availability ofdata.
Need to continue tomonitor land-use after
transition to estimate
subsequent emissions
and removals.



RS Capability
Operational10 but continuity of wallto-wall RS may be a problem due
to limited human and financial
resources. Historical data isneeded to monitor trends.

Need operational
definition which may
differ between countries,
but needs to beconsistently applied. The
sustained reduction ofcarbon stocks is the key
criteria in GHG
inventories.
Need to have groundtruth data at fine scales
to calibrate results.



Need ability to distinguishplantation from natural
regrowth.
Some planted forests
may resemble naturalforest regrowth.
A clear definition isneeded to guide whatneeds to be monitored.
Emphasis on carbon
stock change.
Needs land tenure maps.
Examples such as “MataAtlântica Project” inBrazil.



Potentially operational with needfor ancillary data.

Good operational capacity notavailable at present.
Need to use proxies.



Globally potential methods include
the use of surrogates (e.g.,
roading) for secondary forests, andthe use of intensive feature
extraction techniques.
Optimum resolution needs to be
determined.
Optical very high spatial resolution
imagery has demonstrated
potential; however operational
capacity is not available fornational level monitoring.

9 De Sy et al. (2012)
10 “Operational” is defined here as capable of being used for national reporting. This implies that appropriate sensors areavailable, at appropriate resolutions, with orbital/revisit characteristics to allow sufficient coverage at most latitudes to meetnational or regional reporting obligations in the time-frames required. It does not necessarily imply that this data is currently
available, or free-of-charge.

IPCC Expert Meeting Report6Role of Remote Sensing in Forest and National Greenhouse Gas Inventories

2.2

RS information

RS data can come from a variety of sources and techniques. Aircraft or satellites are normally used andtechniques can be grouped into the following methods:
2.2.1

Passive RS approaches including the use of spectral indices

Passive optical sensors on earth orbiting satellites have provided regular observations of earth’s forests since the1980s. Several passive optical sensors currently provide up to daily global coverage in multiple bands adequate
for identifying changes in land cover. These include the Landsat, ASTER, SPOT, IRS, DMC, and CBERS
missions which image Earth’s surface at a spatial resolution of 10-80 m. Other instruments, most notably MODIS,VIIRS, and MERIS provide more frequent (near-daily) observations at a more coarse resolution. Other satellites(e.g., IKONOS, RapidEye, and Quickbird) provide much higher spatial resolution (1-5 m), but more limited
spectral resolution, spatial extent, and revisit periods, making them less suitable for regular wall-to-wall mappingin most countries. Observations of all these sensors are obstructed by the presence of clouds. This restricts theiruse for land cover monitoring through repeat-observations in tropical regions, particularly during the wetter
seasons, unless diverse sources of remotely sensed data are available to fill in gaps in observation.
2.2.2

Active optical techniques such as Light Detection And Ranging (LiDAR)

Light Detection And Ranging (LiDAR) measures how light emitted from the sensor itself is echoed off the Earth’s
surface. Control of the illuminating beam and the relatively constant speed of light allow LiDAR instruments toestimate the distance between the sensor and the Earth’s surface from the time recorded between the emissionof a light pulse and its return to the sensor. LiDAR observations over forests can represent the top of the canopy
and the soil surface when only the first and last returns signals over an area are measured, or a vertical profile ofthe vegetation when the waveform of the return signal is measured. From these metrics, biophysicalcharacteristics of the canopy can be estimated, such as canopy height and density. These, in turn, may be used
to estimate timber volume or above-ground biomass. In forest applications LiDAR instruments most often emit
near-infrared radiation, which vegetation is highly reflective of. This implies that the LiDAR measurements, likepassive optical observations, are compromised by clouds. In contrast to the latter, LiDAR observations can bemade at night. Since the end of the ICESat mission (2003-2009), which was designed primarily for cryosphereobservations but proved also to be valuable for forest monitoring, LiDAR instruments are no longer active on
space-based systems (although new missions are planned for the coming years). Instead, LiDAR instruments
are commonly carried on aircraft, generating observations over a wide range of spatial resolutions (‘footprints’), in
specific sampled areas tailored to the specific mission.
2.2.3

Radar techniques such as Synthetic Aperture Radar (SAR)

Synthetic Aperture Radar (SAR) measurements have the advantage of not being compromised by the presenceof cloud, hazardous weather or night-time. Similar to LiDAR, radar observations measure the return of a signal
7

IPCC Expert Meeting ReportRole of Remote Sensing in Forest and National Greenhouse Gas Inventories
emitted by the sensor itself (i.e., they are ‘active’ sensors). Currently or recently operational SAR sensors onearth-orbiting platforms (e.g., ERS1/2 SAR, JERS-1, ENVISAT-SAR, ALOS PALSAR, and Cosmo Skymed SAR)
operate at wavelengths of 3 to 23 cm that exhibit signal saturation under high biomass conditions (> 150 Mg/ha),that are typical of tropical forests. As part of the BIOMASS mission, the European Space Agency (ESA) is
designing a P-band sensor, i.e., operating at a 70 cm wavelength (Le Toan 2011; RSE 115, 2850-2860), to
overcome this limitation. However, its launching is not anticipated before 2020.
Table 2 EF information from RS and ground data
EF



RS

Ground
data





2.3

Not currently operational but:o Potential for model-based estimate ofbiomass (Section 3.1)
o Potential for peat combustion andsubsidence from LiDAR
o LiDAR in combination with ground data
can help estimate biomass and growth
factors.

Ancillary Data








National Forest Inventories can give EF and 
removal factors. Need to ensure the coveragemeets reporting requirements.
Research studies can be used to derive EFs.
Non-CO2 EFs are obtained mainly fromresearch studies. Soil C change data ismainly from research although modelling issometimes used. For dead wood and litter
either default approach or modelling may be
more cost-effective than measurement.

Supporting information to help link AD andappropriate EF and to help interpret the GHG
estimates (e.g., forest type maps, soil surveys,
climatic data etc.)
Land-use after transition
Local data to guide interpretation (land surveyse.g., to interpret forests conversion)
Agriculture management practices
Stratification to help link AD to EFs
Allometric equations and root-to-shoot ratios toestimate biomass (above- and/or below-ground)

Ground data11

Typically these are repeated surveys that provide information on forest type, resources, and management
practices. They may generate forest maps that may be useful when stratifying forest lands, and may be avaluable source of information on EFs.
2.3.1

National Forest Inventories (NFI)

Traditional NFIs use sample plots where trees are measured following nationally established sampling strategies
and design. For example, plot design is standardised and measurements may include tree diameter at breast
height (DBH, typically 1.3 m) and tree height. They often focus on national commercial tree stocks rather thanchanges in forest carbon. Trees below a pre-determined diameter and non-commercial plants may not be
included. The sampling design may not always be suitable to detect deforestation, even if it occurs at high rates
11 Kissinger et al.

(2012)

IPCC Expert Meeting Report8Role of Remote Sensing in Forest and National Greenhouse Gas Inventories
due to the length of inventory cycle (normally every 10 years) and deforestation being a relatively rare event in
the forest landscape as a whole. Supplementary sampling would therefore be necessary to increase theaccuracy of the annual estimates.
Thus there are challenges in adapting traditional NFI to carbon stock change estimation. New NFI approaches
that incorporate RS to assist with forest stratification and sampling design can improve the results. One importantconsideration relates to the temporal and spatial coverage required to match the forest monitoring needs forannual or biannual reporting.
NFIs can in general provide EFs. In-situ tree measurements can be used to estimate biomass and changes inbiomass and hence, carbon stock changes and CO2 emissions. However, it is important to ensure that thecoverage meets reporting requirements. Various research studies can be used to derive EFs (non-CO2 EFs
come mainly from research studies). Where sub-national estimates are needed, the use of Tier 2 may likely beneeded to improve the accuracy of the estimates. However, the use of a Tier 2 method may be challenging tosome countries.
While the below-ground biomass pool can be estimated from the above-ground biomass, through a root-to-shootratio, typical NFI measurements do not always provide information on the other carbon pools. Soils present a
particular challenge: soil carbon change data is normally derived from research programmes, although modelling
approaches have also been used in some cases. Where forests occur on organic soils, emissions from the soil
may dominate if the water table is lowered. For dead wood and litter either the default approach in the IPCC
Guidelines or modelling may be more cost-effective than measurements.
Currently only about 16% of Annex I Parties to the UNFCCC use RS in GHG inventory compilation12. This ispossibly due to the fact that most countries have reliable and well established NFI in place, based on fieldsample plots.
2.3.2

Use of ground data to validate RS

Remotely sensed data normally needs ground data for calibration and validation. A key factor is the need forgood georeferencing of the ground plots to ensure they are matched to the correct RS pixel. Otherwise, large coregistration errors may occur. Consideration also needs to be given to linking the plot dimensions to RS datasets.
Multiscale/multistage approaches can be used.
Issues in the use of ground data combined with RS include:

12

9



How to upscale from plot scale information to global-scale estimationUse of allometric equations

http://unfccc.int/2860.php

IPCC Expert Meeting ReportRole of Remote Sensing in Forest and National Greenhouse Gas Inventories


Spatial coverage: wall-to-wall and sampling approaches:oSampling can be applied to both RS and ground-based measurements.

oIn the case of detecting biomass changes due to deforestation or intense degradation,
sampling can be intensified in “hot-spots”, i.e., areas with high deforestation rates oranthropogenic pressure on forests. The initial identification of hotspots can be done usingcoarse resolution RS imagery and then refined further using multi-temporal imagery.

Temporal coverage (can it be done annually or more frequently?):o

Depends on the spatial scale, level of automation, weather and availability of verification
information.



Relationship between NFI and RS in estimating biomass:o

NFIs have traditionally been used for forest resource information collection, conducted on
longer temporal cycles and therefore need interpolation for the purposes of national GHG
inventories.

o

RS can supplement traditional NFIs by providing area information at a higher frequency.

o

The role of ground-based data will tend to shift from traditional NFIs to providing EFs orcalibration information for RS.



2.4

Practicality and availability for the present and potential for the future

Drivers

In order to understand changes in forest cover/use and monitor the implementation of measures and policies, it
is important to also monitor drivers of degradation. For REDD+, the identification of drivers is an important part ofthe process. Examples of such drivers include:

Selective logging

Conversion of forests to plantations or crops for food and energy productionCharcoal production

Agricultural practices (e.g., crop- or grazing land expansion)Socio-economic pressure (e.g., road networks, settlement expansion)Fires and natural disturbances

Pollution (e.g., smoke, acid rain)

IPCC Expert Meeting Report10Role of Remote Sensing in Forest and National Greenhouse Gas Inventories

3 Biomass estimation using RS
There have been some demonstrations of biomass estimates from RS, normally coupled with ground information(Baccini et al., 2012; Saatchi et al., 2011). These have focussed on above-ground biomass, leaving out belowground biomass, dead organic matter (dead wood and litter) and soil organic carbon. These methods haveseveral limitations for use in routine inventories including high uncertainty and limited time-series availability. Thisis a developing field with new capabilities developing rapidly.

3.1

Methods for estimating biomass, their relative advantages and disadvantages

There are different basic techniques proposed to estimate biomass using RS:
Methods that use optical images to support the stratification of the forest area while using averagebiomass density from ground-based methods (“Stratify and Multiply”; e.g., Gibbs et al., 2007)



Methods that use spectral information from passive sensors to estimate biomass, with or without otherancillary or ground-data (e.g., Baccini et al., 2012: Saatchi et al. 2011; Foody et al., 2003)



Methods relying on the ability of LiDAR or SAR to capture forest structure information (e.g., Asner et al.,2012; Le Toan et al., 2004)

Currently, only the first method is operational in the sense that it can be routinely employed to produce annual
estimates in most countries. The others have only been demonstrated as research projects in particular
circumstances. It should be noted that the methods above can be combined. Biomass estimates from airborne orsatellite-based LiDAR measurements can be upscaled using a “Stratify and Multiply” approach (e.g., Asner et al.,2012) or through use of regression models driven with spectral data (e.g., Baccini et al., 2012). Data derived
from passive and optical sensors can also be combined to directly estimate above-ground biomass without
stratifying the landscape (Cartus et al., 2012).
3.1.1

Methods using optical images to determine forest strata

These methods use RS to estimate the area of each forest stratum after stratification. The RS data can be usedin conjunction with other geographic data, such as soil maps, altitude and climatic factors. The area can then be
multiplied by EFs determined from ground-based data, including from NFI (a “Stratify-and-Multiply” approach).
Traditional optical sensors for land cover mapping are normally used (mid-resolution or better, global scale
imagery with annual coverage). The current workhorses are Landsat ETM7, SPOT4 and SPOT5. It is anticipatedthat these will be supplemented by Landsat 8, Sentinel 1, 2, 3, CBERS, SPOT6 and SPOT7.

Traditional passive optical RS has some limitations that include:

11

IPCC Expert Meeting ReportRole of Remote Sensing in Forest and National Greenhouse Gas Inventories


Potentially limited ability to discriminate different land cover classes unless high spatial resolution
remotely sensed data is used. However, this may be constrained by cost and processing capacity.


3.1.2

Cloud cover and illumination effects.
Methods using RS indices

These methods need calibration using ground-based observations. Some countries are beginning to use thesemethods on a national scale for biomass estimation. However it is more suitable for some ecosystems thanothers and the interpretation of passive optical radiometric data is confounded by issues such as:Saturation13



LayeringNon-biomass components, such as the dead organic and soil organic carbon pool

3.1.3

LiDAR and SAR

LiDAR
LiDAR has the potential to overcome some of these issues but some practical difficulties remain:Difficulty to obtain permission to fly over forested areas with a LiDAR sensor in some countries(sovereignty issues)

Expensive to employ sufficient sampling intensity

Limited experience with time-series data for change estimation



Limited technical resources and capacityNo operational satellites (NASA may launch a satellite after 2016 but it is currently scoped as a single
mission so will not support long-term time series.)

SAR
SAR’s most compelling feature is its ability to obtain images even under cloud-cover conditions. The broad useof SAR data for biomass estimation is still under investigation. While different wavelengths are sensitive todifferent biomass features, there are saturation issues at high biomass levels typical of tropical forest
ecosystems.

13 Passive short wave infra-red imagery has demonstrated potential to overcome saturation issues because of its sensitivity

to structural attributes of vegetation such as stem density, crown size, and canopy shadowing, which correlate in somedegree with biomass (Baccini et al., 2012; Puhr et al., 2000; Tangki et al., 2008) . However, short wave infra-red imagery isless widely available than visible RS imagery.

IPCC Expert Meeting Report12Role of Remote Sensing in Forest and National Greenhouse Gas Inventories

3.2

Monitoring changes in biomass“Stratify-and-Multiply” is the only currently available technique for change detection at national scales.
Biomass change detection should be feasible in the future using remotely-sensed data in conjunction with amodel appropriately calibrated with field observations, rather than assigning carbon density values to a land
cover map based on RS. Outstanding issues for further consideration before this approach can be widely used
include: validation of changes detected; time-series consistency issues; and lack of appropriate ground data forvalidation of historical change.
RS is useful to identify areas that need more intensive investigation (“hot spot areas”). This will enhance
efficiency by focusing on the use of higher resolution, more resource intensive methods, or ground-based
investigations on areas where they are most needed.
There is a need for continuous reliable satellite systems as time series data is required to detect degradation.
Matching data from different satellites at different times is an additional source of uncertainty. In planning RS, it isimportant to consider the scale of temporal dynamics.

13

IPCC Expert Meeting ReportRole of Remote Sensing in Forest and National Greenhouse Gas Inventories

4 Future capabilities
Future techniques used will need to be at appropriate scales to monitor changes and both the spatial resolution
and the timing and frequency for RS need to be considered.

4.1

Technical developments which could be helpful
1. Use of hyperspectral data to detect species composition change. Hyperspectral sensors provide images
from a much larger set of spectral bands than the more common multispectral sensors (e.g., Landsat,
SPOT).
2. A constellation of matched satellites with Landsat type bands and moderate resolution could provide
adequate coverage for national annual reporting particularly for areas with persistent cloud coverespecially for developing countries.
3. Drones/UAVs could be used to produce very high resolution data, but this may lead to coverage issues(e.g., for broad territorial coverage) and may need permission to fly. Operational L-band SAR
observations are expected from 2014 with the launch of ALOS-2 that is carrying a PALSAR instrumentwith higher temporal and spatial resolution than the one carried on the original ALOS satellite launchedin 2006 and operational until 2011.
4. ISS-JEM LIDAR Observation of Vegetation Environment (ILOVE) to estimate forest height is understudy and expected to be installed on-board ISS in late 2010s.
5. Missions to measure CO2 concentrations. Such space borne observations will not be operational in 5
years and for country-scale measurements users will need to wait for future satellites. There are also
issues related to discrimination of anthropogenic and natural CO2 sources and forest fluxes from fluxes
from other economic sectors.

4.2

New data possibilities and needs
1. It is possible to produce global forest cover maps and some have already been generated. Such maps
could be produced to address specific needs of a range of users. In 2013, ESA will deliver global land
cover maps (for years 2000, 2005, 2010) under the Climate Change Initiative. These maps will allow toestimate the global extent of forests by forest types. It may be possible to generate several different
maps using different forest definitions. The use of the Land Cover Classification System (LCCS) should
be fostered in the land monitoring community14.

14 http://www.esa-cci.org/; Di Gregorio (2005)

IPCC Expert Meeting Report14Role of Remote Sensing in Forest and National Greenhouse Gas Inventories
2. Some countries have developed country-specific observation systems but these are still not broadly
available. Expansion of these systems requires resources, access to data and capacity building.
3. The ability to reliably estimate biomass from remotely sensed data would significantly improve forestrelated GHG estimation and reporting and may become available in the near future (refer to Section
3.1).

4.3

Emission and removal factors
1. There is a potential for using atmospheric concentration measurements to directly estimate emissions oremission rates from fires.
2. LiDAR and RADAR may be used to estimate regrowth (accuracy of maximum canopy height in LiDAR
footprint is 3m.). There needs to be a rigorous evaluation of the potential of RS observations for
estimation of removals of CO2 from the atmosphere.
Table 3 Quantifying carbon stock change: expected capabilities in 5 years’ time
Variable
Density
Fragmentation
Species detection
Crown cover
Height of trees
Canopy structureStand structure
Forest type detection
Photosynthetic capacity – biochemical concentration

Resolution

Current status

MediumMediumFine
MediumFine
Fine
MediumMediumMediumOperational
Operational
Case SpecificOperational
Operational
Case SpecificCase SpecificOperational
Operational

Resource
Requirement
Low
Low
High
Low
High
High
High
Low
Low

Table 4 Detection of degradation: expected capabilities in 5 years’ time
Variable
Canopy gap fraction
Health/Stress – water stress, etc.
Fire
Species diversity
Change in height of trees
Soil erosion/Suspended sediment
Note: Also includes all items in Table 3

Resolution

Current status

MediumFine
Medium
Fine
Fine
Fine

Operational
Case SpecificOperational
Case SpecificOperational
Case SpecificResource
Requirement
Low
High
Low
High
High
High

There remain a number of important variables that cannot be assessed and/or monitored from RS and this is
likely to remain the case in the foreseeable future. Further development activities may address some of these.
Issues. This could be facilitated by better communication between the RS community and terrestrial biospheremodellers. These variables include those associated with:15



Soil carbon, dead organic matter, litter and the below-ground biomass carbon pools;

Forest structure once the point of saturation is passed.

IPCC Expert Meeting ReportRole of Remote Sensing in Forest and National Greenhouse Gas Inventories

5 Conclusions
The meeting concluded that:a. Currently the only widely accepted remote-sensing based method to estimate changes in carbon
stocks in biomass is to use remotely sensed data to estimate the area of distinct forest strata in forest
land and develop emission factors for each stratum based on ground based surveys and/or NFI. The
product of the area and associated emission factor provides the emissions/removals associated withthe observed changes in carbon stocks. The following may be helpful:I.

Combining medium resolution data with more detailed spatial data on “hot spots” (areas of highrate of change) can reduce costs while providing quality results.

II.

The temporal resolution of forest monitoring is important. Constructing a consistent time series
for annual or biennial change estimation requires routine, repeated, RS data over many years.
This has been difficult to achieve without complementing with different data sources.

b. Using RS to monitor forest areas can be a routine activity, given sufficient resources and capacity.
c.

Methods for estimating biomass by using RS data jointly with field data for model calibration andvalidation have been demonstrated in specific cases but are not yet ready to be operationalized inroutine national forest monitoring. These methods show promise for the future, but they need to beverified and validated on a wide scale.

d. Detection of forest degradation from remotely sensed data may pose a particular challenge, and ishighly definition dependent. Different drivers of degradation have different impacts on the forest
structure which adds to the complexity of identification and change detection. Validating results ofdegradation requires extensive resources, both financial and human, to implement.
e. There are a number of techniques under development and new satellites to be launched in the next few
years that will lead to increased capabilities for the use of RS in forests.
f.

Widespread forest monitoring of carbon stocks and CO2 emissions and removals in many countries will
require access to remotely-sensed data, resources and capacity development.

g. To speed up the development of operational techniques, closer collaboration of RS experts, terrestrial
biosphere modellers and emission inventory experts is needed.

IPCC Expert Meeting Report16Role of Remote Sensing in Forest and National Greenhouse Gas Inventories

References
Asner, G.P., Clark, J. K., Mascaro, J., Galindo García, G. A., Chadwick, K. D., Navarrete Encinales, D. A., PaezAcosta, G., Cabrera Montenegro, E., Kennedy-Bowdoin, T. Duque, Á., Balaji, A., von Hildebrand, P.,
Maatoug, L., Phillips Bernal, J. F., Yepes Quintero, A. P., Knapp, D. E., García Dávila, M. C., Jacobson,J., and Ordóñez, M. F. (2012). High-resolution mapping of forest carbon stocks in the Colombian
Amazon, Biogeosciences, 9: 2683-2696. doi:10.5194/bg-9-2683-2012.
Baccini, A., Goetz, S. J., Walker, W. S., Laporte, N. T., Sun, M. et al. (2012). Estimated carbon dioxide emissions
from tropical deforestation improved by carbon-density maps. Nature Climate Change, 2: 182-185.
doi:10.1038/nclimate1354.
Cartus, O., Santoro, M., and Kellndorfer, J. (2012). Mapping of Forest Aboveground Biomass in the Northeastern
United States with ALOS PALSAR Dual-Polarization L-Band. Remote Sensing of Environment, 124:466478.
De Sy, V., Herold, M., Achard, F., Asner, G. P., Held, A., Kellndorfer, J., and Verbesselt, J. (2012). Synergies ofmultiple RS data sources for REDD + monitoring. Current Opinion in Environmental Sustainability, 4, 1–
11.
Di Gregorio, A. (2005). Land cover classification system: classification concepts and user manual: LCCS. Foodand Agriculture Organization of the United Nations.
Foody, G M, Boyd, D. S. and Cutler, M. E. J. (2003). Predictive relations of tropical forest biomass from Landset
TM data and their transferability between regions. Remote Sensing of Environment, 85: 463–74.
Gibbs, H.K., Brown, S., Niles, J.O, and Foley, J.A. (2007). Monitoring and estimating forest carbon stocks:
making REDD a reality. Environmental Resource Letters 2. IOP Publishing Ltd., UK. Available online at:
http://www.iop.org/EJ/article/1748-9326/2/4/045023/ erl7_4_045023.html
IPCC (2003a). Definitions and Methodological Options to Inventory Emissions from Direct Human-induced
Degradation of Forests and Devegetation of Other Vegetation Types, eds: Jim Penman, Michael
Gytarsky, Taka Hiraishi, Thelma Krug, Dina Kruger, Riitta Pipatti, Leandro Buendia, Kyoko Miwa, Todd
Ngara, Kiyoto Tanabe, and Fabian Wagner IPCC, Pub IGES, Japan 2003.
IPCC (2003b). Good Practice Guidance for Land Use, Land-Use Change and Forestry. Eds. Penman J.,
Gytarsky M., Hiraishi T., Krug T., Kruger D., Pipatti R., Buendia L., Miwa K., Ngara T., Tanabe K. &Wagner F. Intergovernmental Panel of Climate Change (IPCC), IPCC/IGES, Hayama, Japan.
IPCC (2006). 2006 IPCC Guidelines for National Greenhouse Gas Inventories. Prepared by the National
Greenhouse Gas Inventories Programme. Eds. Eggleston H.S., Buendia L., Miwa K., Ngara T. &Tanabe K. Published: IGES, Japan.
IPCC (2010). Revisiting the Use of Managed Land as a Proxy for Estimating National Anthropogenic Emissions
and Removals, eds: Eggleston H.S., Srivastava N., Tanabe K. and Baasansuren J. IPCC Expert
Meeting Report, Pub. IGES, Japan.

17

IPCC Expert Meeting ReportRole of Remote Sensing in Forest and National Greenhouse Gas Inventories
Kissinger, G., Herold, M. and De Sy, V. (2012). Drivers of Deforestation and Forest Degradation: A Synthesis
Report for REDD+ Policymakers (p. 26). Vancouver, BC, Canada.
Le Toan, T., Quegan, S., Woodward, I., Lomas, M., Delbart, N. and Picard C. (2004). Relating radar remotesensing of biomass to modeling of forest carbon budgets. Climate Change, 76: 379–402.
Le Toan T., Quegan, S., Davidson, M., Balzter, H., Paillou, P., Papathanassiou, K., Plummer, S., Saatchi, S.,
Shugart, H. and Ulander, L. (2011). The BIOMASS Mission: Mapping global forest biomass to better
understand the terrestrial carbon cycle. Remote Sensing of Environment, 115(2011)2850-2860.
Puhr, C. and Donoghue, D. (2000). RS of upland conifer plantations using Landsat TM data: a case study from
Galloway, South-West Scotland. International Journal of Remote Sensing, 21: 633–646.
Saatchi, S.S., Harris, N L, Brown, S., Lefsky, M., Mitchard, E. T. A, Salas, W., Zutta, B. R., Buermann, W., Lewis,
S. L., Hagen, S., Petrova, S., White, L., Silman M., and Morel., A. (2011). A Benchmark map of forest
carbon stocks in tropical regions across three continents. Proceedings of the National Academy of
Sciences, 2011: DOI: 10.1073/pnas.1019576108.
Tangki, H. and Chappell, N. (2008). Biomass variation across selectively logged forest within a 225-km2 region of
Borneo and its prediction by Landsat TM. Forest Ecology and Management, 256: 1960t Eco.

IPCC Expert Meeting Report18Role of Remote Sensing in Forest and National Greenhouse Gas Inventories

Annex 1 Agenda
IPCC Expert Meeting: Role of RS in Forest and National GHG Inventories
Hayama, Japan
23 – 25 October 2012
AGENDA
Tuesday 23rd October
09:00

Registration

09:30

Open Meeting
Welcome to Participants

Taka Hiraishi (IPCC TFI)
Thelma Krug (IPCC TFI)
Hideyuki Mori (Institute for Global Environmental Strategies)
Hiroshi Tsujihara (Ministry of the Environment, Japan)

Presentations
10:00

GFOI Methods and Guidance in the Use of RS and Ground Observations for Estimates of GHG
Emissions and Removals - Miriam Baltuck (Australia)
GFOI Methods and Guidance Document Overview - Jim Penman (UK)

10:50
11:10

COFFEE
Role of RS for GEOSS and IPCC - Yukio Haruyama (Japan)

12:50

JAXA’s Earth Observation Satellites and Forest Mapping - Masanobu Tsuji (Japan)
The Role of RS/GIS Applications for National Forest Monitoring Systems in the Context of REDD+ - Inge
Jonckheere (FAO)
LUNCH

14:00

Indonesia’s National Forest Monitoring System as Main System for REDD+ MRV- Ruandha Agung
Sugardiman (Indonesia)
RS Data-based Forest Inventories and Carbon Stock Reporting - Brice Mora (Netherlands)
GHG Inventories: The Case of Land Use, Land Use Change and Forestry(LULUCF) Mapping in Eastern and Southern Africa - Erick Khamala (Kenya)

15:40

COFFEE

16:00

Integration of Optical and SAR Data for Land Cover Classification - Hasi Bagan (Japan)
Amazon Biomass Estimation Using X and P Band SAR Data - Carlos Alberto Pires de Castro Filho(Brazil)
CLOSE

18:00

Wednesday 24th OctoberAnalysis of the Normalized Differential Vegetation Index (NDVI) for the Detection of Forest Degradation
09:15
Coverage in México - Meneses Tovar Carmen Lourdes (Mexico)
Using SPOT 5 Satellite Multispectral Imagery and LiDAR Data to Monitor Forest Carbon Stocks andStock Change in New Zealand - Deborah Burgess (New Zealand)
Forest Degradation : the DEGRAD Project at INPE - Thelma Krug (Brazil)
Use of Airborne and Satellite LiDAR for Estimating Forest Carbon Stock and its Changes - Yasumasa
I

IPCC Expert Meeting ReportRole of Remote Sensing in Forest and National Greenhouse Gas Inventories
Hirata (Japan)
11:00
11:20

COFFEE
Detecting Forest Degradation by Using Gramm-Schmidt Transformation - Carlos Humberto
Bahamondez (Chile)
Using RS Measurements Constrain Terrestrial Biosphere Model Predictions - Paul Moorcroft (US)

13:00
14:00

LUNCH
Obtaining Reliable Fine-Scale Estimates of Biomass Using Ground-Based Methods - John Raison(Australia)
Estimating Tropical Forest Biomass With Multisource RS Data: Predictions Between Regions - Mark
Cutler (UK)
Satellite-Based Carbon Stock Measurements in the Tropics - Pieter S. A. Beck (USA)

16:00

COFFEE
Global and Regional CO2 Flux Estimation Using Atmospheric CO2 Data Obtained by GOSAT, GOSAT-2,
and Other Future Satellites - Tsuneo Matsunaga (Japan)
High Spatial Resolution RS Improves Forest Carbon Stock Estimation in Dry Forests - Amon Murwira(Zimbabwe)
Biomass Dynamics in Mozambican Woodland - Casey Ryan (UK)

16:15

18:00

CLOSE

Thursday 25th October
Break-Out Groups- BOG 1: Use of RS for Biomass Measurements
09:15- BOG 2: Combination of RS and Ground Data to Improve National GHG Inventories- BOG 3: Use of RS for Degradation Measurements
11:15 COFFEE
11:30

Break-Out Groups Continue

13:00

LUNCH

14:00

Closing Plenary (Brief Report by BOGs). Summary of Conclusions

IPCC Expert Meeting ReportIIRole of Remote Sensing in Forest and National Greenhouse Gas Inventories

Annex 2 Participants
Australia

Miriam Baltuck
Commonwealth Scientific and Industrial Research
Organisation, Dept. of
Climate Change and Energy Efficiency
GPO 664 Canberra ACT, 2601
Australia
Tel: +61 413742271
Fax: +61 262558425
John Raison
Commonwealth Scientific and Industrial Research
Organisation (CSIRO)
PO Box 1700 Canberra, ACT 2601
Australia
Tel: +61 262464053

BrazilCarlos Alberto Pires de Castro Filho
INPE - National Institute for Space Research
Rua H9B, Apto 301, CTA, São José dos Campos - SP,
12228-611 Brazil
Tel: +55 12 39473593
Fax: +55 12 32086005
Thelma Krug (TFI Co-chair)
INPE-Instituto Nacional de Pesquisas Espaciais
Av. Dos Astronautas, 1758 - Jardim da Granja,
SJCampos - Sao Paulo, 12227-010 Brazil
Tel: +55 12 3208 6005
Fax: +55 12 3941 2077
Yosio Edemir Shimabukuro
INPE - National Institute for Space Research
Av. dos Astronautas, 1758, São José dos Campos, SP,12227-010 Brazil
Tel: +55 12 3208 6483
Fax: +55 12 3208 6488

Chile

Carlos Humberto Bahamondez
Instituto Forestal Valdivia Chile
Casilla 385 Valdivia Chile
Tel: +56 63 335224

China

Lingxi Zhou
Chinese Academy of Meteorological Sciences (CAMS),
China Meteorological Administration (CMA)
46 Zhongguancun Nandajie, Beijing
100081 China

III

Tel: +86 10 5899 5279
Fax: +86 10 6217 6414

Indonesia

Ruandha Agung Sugardiman
Ministry of Forestry
Manggala Wanabakti Block I 7th Floor,
Jalan Gatot Subroto, Senayan, Jakarta 10270
Indonesia
Tel: +62 215730293
Fax: +62 215734632

Japan

Hasi Bagan
National Institute for Environmental Studies - NIES
16-2 Onogawa, Tsukuba, Ibaraki, 305-8506 Japan
Tel: +81 29 850 2567
Fax: +81 29 850 2960
Yukio Haruyama
RS Technology Center of Japan (RESTEC)
Tokyu REIT Toranomon BLDG., 3-17-1, Toranomon,
Minato-ku, Tokyo 105-0001 Japan
Tel: +81 3 6435 6796
Fax: +81 3 5777 1585
Taka Hiraishi (TFI Co-chair)
C/o Institute for Global Environmental
Strategies (IGES)
2108-11 Kamiyamaguchi Hayama, Kanagawa,
240-0115 Japan
Tel: +81 46 855 3758
Fax: +81 46 855 3808
Yasumasa Hirata
Forestry and Forest Products Research Institute FFPRI
1 Matsunosato, Tsukuba, Ibaraki, 305-8687 Japan
Tel: +81 29 829 8330
Fax: +81 29 874 3720
Tsuneo Matsunaga
Environmental Information Analysis Section,
Center for Environmental Measurement and Analysis,
National Institute for Environmental Studies
16-2 Onogawa Tsukuba Ibaraki 305-8506 Japan
Tel: +81 29 850 2731
Fax: +81 29 850 2751
Masami Onoda
Institute for Global Environmental Strategies (IGES)

IPCC Expert Meeting ReportRole of Remote Sensing in Forest and National Greenhouse Gas Inventories
7-1-20-302 Shimorenjaku, Mitakashi, 181-0013 Tokyo
Japan
Tel: +81 80 4737 1162
Fax: +81 422 40 3121
Masanobu Tsuji
Japan Aerospace Exploration Agency (JAXA)
Tsukuba Space Center, JAXA, 2-1-1, Sengen, Tsukuba,
Ibaraki 305-8505 Japan
Tel: +81 50 3362 7646
Fax: +81 29 868 2961

Kenya

Erick Siminyu Khamala
Regional Centre for Mapping of Resources forDevelopment (RCMRD)
P.O BOX 632 - 00618, Nairobi Kenya
Tel: +254 20 2680722 / 748
Fax: +254 20 2680747 / 8561673

MexicoMeneses Tovar Carmen Lourdes
National Forest Commision
Periférico poniente N°5360.
Colonia San Juan de Ocotan. Zapopan-Jalisco
MexicoTel: +52 33 37777000 ext: 4201
Fax: +52 33 37777068

45019

Netherlands

Brice Mora
Wageningen University
Droevendaalsesteeg 3, Wageningen, 6708 PB
Netherlands
Tel: +31 6 53917087
Fax: +31 317 419000

New ZealandThe

Deborah Burgess
New Zealand Ministry for the Environment
23 Kate Sheppard Place, PO Box 10362, Wellington
6143 New ZealandTel: +64 4 439 7596
Fax: +64 4 439 7700

UK

Mark Cutler
School of the Environment,
University of Dundee
Dundee DD1 4HN UK
Tel: +44 1382 385446
Fax: +44 1382 388588
Jim Penman
Environment Institute, University College London

IPCC Expert Meeting Report

Pearson Building, Gower St, London
WC1E 6BT UK
Tel: +44 7766 145152
Casey Ryan
University of Edinburgh,
School of GeoSciences
Room 210 Crew Building, King's Buildings, Edinburgh,
EH9 3JN UK
Tel: +44 131 650 7722
(He did not attend the meeting but made apresentation via internet.)

USA

Pieter S. A. Beck
Woods Hole Research Center
149 Woods Hole Road, Falmouth, MA
02540 USA
Tel: +1 508 444 1507
Fax: +1 508 444 1807
Paul Moorcroft
Harvard University
26 Oxford St., OEB Dept. Harvard University,
Cambridge MA, 02138 USA
Tel: +1 617 320 4002
Anthony M. Filippi
Texas A&M University
Department of Geography, 3147 TAMU,
Texas A&M University, College Station, TX 77843-3147
USA
Tel: +1 979 845 5744
Fax: +1 979 862 4487

Zimbabwe

Amon Murwira
University of Zimbabwe, Department of Geography andEnvironmental Science
P.O. Box MP167, Mount Pleasant, Harare, Zimbabwe
Tel: +263 4 303211ext: 14200 / 13111

IGOs
FAO

Inge G.C. Jonckheere
Food and Agriculture Organization of the United Nations- FAO
Viale delle Terme di Caracalla, 00153 Rome Italy
Tel: +39 6 570 53896
Tel: +49 228 815 1999

IPCC TSU

C/o Institute for Global Environmental

IVRole of Remote Sensing in Forest and National Greenhouse Gas Inventories
Strategies (IGES)
2108-11 Kamiyamaguchi, Hayama, Kanagawa,
240-0115 Japan
Fax: +81 46 855 3808
Simon Eggleston
Tel: +81 46 855 3751
Kiyoto Tanabe
Tel: +81 46 855 3752
Nalin Srivastava
Tel: +81 46 855 3754
Baasansuren Jamsranjav
Tel: +81 46 855 3757
Maya Fukuda
Tel: +81 46 855 3753
Tiffany Troxler
Tel: +81 46 826 9618
Eriko Nakamura
Tel: +81 46 855 3750
Toru Matsumoto
Tel: +81 46 855 3746
Koh Mikuni
Tel: +81 46 855 3

UNFCCC

Jenny Wong
Climate Change Secretariat (UNFCCC)
Martin-Luther-King-Strasse 8, Bonn, 53175 Germany
Tel: +49 228 815 1601

V

IPCC Expert Meeting ReportRole of Remote Sensing in Forest and National Greenhouse Gas Inventories

Annex 3 Presentations15
1) GFOI Methods and Guidance in the Use of RS and Ground Observations for Estimates of GHG Emissions
and Removals - Miriam Baltuck (Australia)
2) GFOI Methods and Guidance Document Overview - Jim Penman (UK)
3) Role of RS for GEOSS and IPCC - Yukio Haruyama (Japan)
4) JAXA’s Earth Observation Satellites and Forest Mapping - Masanobu Tsuji (Japan)
5) The Role of RS/GIS Applications for National Forest Monitoring Systems in the Context of REDD+ - Inge
G.C. Jonckheere (FAO)
6) Indonesia’s National Forest Monitoring System as Main System for REDD+ MRV - Ruandha Agung
Sugardiman (Indonesia)7) RS Data-based Forest Inventories and Carbon Stock Reporting - Brice Mora (GOFC-GOLD)8) GHG Inventories: The Case of Land Use, Land Use Change and Forestry (LULUCF) Mapping in Easternand Southern Africa - Erick Khamala (Kenya)
9) Integration of Optical and SAR Data for Land Cover Classification - Hasi Bagan (Japan)
10) Amazon Biomass Estimation Using X and P Band SAR Data - Carlos Alberto Pires de Castro Filho(Brazil)11) Analysis of the Normalized Differential Vegetation Index (NDVI) for the Detection of Forest Degradation
Coverage in México - Meneses Tovar Carmen Lourdes (Mexico)
12) Using SPOT 5 Satellite Multispectral Imagery and LiDAR Data to Monitor Forest Carbon Stocks and Stock
Change in New Zealand - Deborah Burgess (New Zealand)13) Forest Degradation : the DEGRAD Project at INPE - Thelma Krug (Brazil)
14) Use of Airborne and Satellite LiDAR for Estimating Forest Carbon Stock and its Changes - Yasumasa
Hirata (Japan)
15) Detecting Forest Degradation by Using Gramm-Schmidt Transformation - Carlos Humberto Bahamondez(Chile)16) Using RS Measurements Constrain Terrestrial Biosphere Model Predictions - Paul Moorcroft (US)17) Obtaining Reliable Fine-Scale Estimates of Biomass Using Ground-Based Methods - John Raison(Australia)18) Estimating Tropical Forest Biomass With Multisource RS Data: Predictions Between Regions - Mark Cutler(UK)19) Satellite-Based Carbon Stock Measurements in the Tropics - Pieter S. A. Beck (USA)20) Global and Regional CO2 Flux Estimation Using Atmospheric CO2 Data Obtained by GOSAT, GOSAT-2,
and Other Future Satellites - Tsuneo Matsunaga (Japan)

15 All presentations are available on: http://www.ipcc-nggip.iges.or.jp/meeting/meeting.html

IPCC Expert Meeting ReportVIRole of Remote Sensing in Forest and National Greenhouse Gas Inventories
21) High Spatial Resolution RS Improves Forest Carbon Stock Estimation in Dry Forests - Amon Murwira(Zimbabwe)
22) Biomass Dynamics in Mozambican Woodland - Casey Ryan (UK)VII

IPCC Expert Meeting Report

GFOI Methods and Guidance in theuse of Remote Sensing and Ground
Observations for Estimates of GHG
Emissions and Removals
Miriam Baltuck
CSIRO/DCCEE
Hayama, Japan 23-25 October

GFOI Methods and Guidance Documentation
MGD Advisory Group
ChairmanJim Penman*/University College, London

Members
Stephen Briggs/ESA
Simon Eggleston/IPCCJohn Faundeen/USGS (USA)Martin Herold /University of Wageningen
Suthy Heth/Mekong River Commission
Alex Lotsch/World Bank
Thelma Krug/INPE (Brazil
Kenneth MacDicken/FAO
Douglas Muchoney/USGS (USA)
Orbita Roswintiarti/LAPAN (Indonesia)
Rob Waterworth/DCCEE (Australia)

GEO Support
Giovanni Rum/GEO Secretariat

GFOI MGD Lead Organiser
Miriam Baltuck** MGD Coordination TeamMGD Authors Team
Miriam Baltuck*
Rich Birdsey
Cris Brack
Peter Caccetta
Simon Eggleston
Giles Foody
Matt Hansen
Martin Herold
Dirk Hoekman
Leif Kastdalen
Josef Kellndorfer
Erik Naesset
Ron McRoberts
Brice Mora
Pontus Olofsson*
Keryn Paul
Jim Penman*
Shaun QueganJohn Raison*
Ake Rosenqvist
Steve Stehman
Rob Waterworth
Curtis Woodcock*
Mike Wulder

GFOI Methods and Guidance Documentation
Timeline to Completion
Completed Steps-2012
February-GFOI country co-leads agree that Australia will lead pursuit of this area.
May- First AG meeting one day session hosted by World Bank to assess ad hocdraft documents
for end-to-end completeness, identify gaps, redundancies, etc in the context of a single compiled
document. . Authors invited to participate in MGD writing.
June 26-28- the authors and AG met to discuss AG recommendations and develop document
outline and content and agree to writing assignments and nominal timeline to completion.
Current activity (as of October 2012)
June/July to October- electronic correspondence during rewrite.
Oct-- 1st cut draft to review by MGD Advisory Group
Oct/Nov/Dec –review by AG, revise in response to AG review
Future Steps-2012 to 2013
Dec/Jan-commence GEO and external review of documents; aim for completion by Jan 2013
February- MGD AG and Writing Team meet to revise in response to reviews and COP outcome
April/May-Completion of Methods and Guidance Book-beta version
May-August-“Field test” of Methods and Guidance Book (rigorous review by user community)
September--adjustments as appropriate. (Additional Writing Team meeting may be required)October-submission to GEO Plenary X
November-Final report approved by GEO Plenary X

GFOI Methods and Guidance Documentation
Chapter 1- Introduction (see previous presentation)
Chapter 2- Generic guidance for estimation of GHG emissions and removals for forests
2.1 Introduction
2.2. Description of REDD+ activities and their effects on GHG emissions and removals.
2.3 Generic description and guidance for each IPCC Tier.
2.4 Generic Methods for Estimating Activity Data
2.5 Generic Methods for Estimating Emissions and Removals Factors
2.6 Estimation of GHG emissions and removals
Chapter 3- Using remote sensing and ground observations to estimate GHG emissions andremovals caused by specific forest activities
3.1 Introduction
3.2 Activity data
3.3 Estimation of emissions and removals of carbon
3.4 Non-CO2 emissions (fire and soil)
3.5 Guidance on uncertainties
3.6 Advice on Reporting

GFOI Methods and Guidance Documentation
Chapter 2- Generic guidance for estimation of GHG emissions
and removals for forests
Provides generic guidance on using remotely sensed and groundbased data in combination to estimate GHG emissions andremovals for forests. The guidance in chapter 2 is intended foruse in chapter 3, which sets out how to apply these methods to
the REDD+ activities being discussed under the UNFCCC.
2.1 Introduction
Previews the topics covered in this chapter and outlines the gain-loss
and stock difference methods for estimating emissions or removals
as annual rates of change in carbon stocks and circumstances whichmight lead to use of one or the other approach.

GFOI Methods and Guidance Documentation
Chapter 2- Generic guidance for estimation of GHG emissions and removals forforests
2.2. Description of REDD+ activities and their effects on GHG emissions andremovals.
This section briefly describes each of the six forest activities relevant to REDD+
and the effects of these on GHG emissions and removals.
Deforestation
Forest degradation
Establishment of forest on non-forest landConservation, sustainable management of forests, and enhancement of forest
stocksConversion of native forests to plantations
Rehabilitation of degraded peat forests

GFOI Methods and Guidance Documentation
Chapter 2- Generic guidance for estimation of GHG emissions and removals forforests
2.3 Generic description and guidance for each IPCC Tier.
This Section provides an accessible introduction to the IPCC Good Practice Guidelines
including a description of the five principles underlying the IPCC , then builds on thisto outline the three Tiers of reporting and the circumstances under which they might
be selected.
While the IPCC guidelines provide methodologies relevant to all source and sinkcategories likely to be encountered in the monitoring and reporting of GHG emissions
and removals form forests, they do not refer to terms such as deforestation anddegredation. This section suggests how such activities being discussed by the
UNFCCC can be addressed by the IPCC Guidelines.
The section also provides useful detail on performing Quality Control/Quality
Assurance and obtaining appropriate reviews to assure validation and verification ofreporting material.

GFOI Methods and Guidance Documentation
Chapter 2- Generic guidance for estimation of GHG emissions and removals forforests
2.4 Generic Methods for Estimating Activity Data
Data sets associated with land-use and land-use change are often very large. Thisapplies to remote sensing (RS), but can also be the case with ground observations
such as forest inventories and soil surveys which will may contain observations frommany thousands of plots.
Using and combining such data sets to make greenhouse gas inventory estimates
meeting good practice requirements including those of consistency andcomparability poses particular challenges for data processing. These challenges are
addressed in this section which includes description of RS techniques including basic
preparation (e.g. selection of imaging technology(ies) image registration/orthorectification, radiometric and geometric corrections, mosaicking of multiple
images) and ground observations and their use in combination.
This section will also cover related uncertainties, as well as issues associated with
Tier 3 methods

GFOI Methods and Guidance Documentation
Chapter 2- Generic guidance for estimation of GHG emissions and removals forforests
2.5 Generic Methods for Estimating Emissions and Removals Factors
Although RS is usually associated with activity data there have been advances in its
use for direct estimation of biomass, and some techniques can be used to estimate
forest structure.
This section will discuss how such techniques can be used to best effect in
combination with ground based data.
The section offers guidance on selection of reporting tiers, selection of emission
factors, model choice, parameters etc and overall uncertainty estimate.

GFOI Methods and Guidance Documentation
Chapter 2- Generic guidance for estimation of GHG emissions and removals forforests
2.6 Estimation of GHG emissions and removals

This section will show how to bring together activity data andemissions and removal factors in a consistent fashion.
The section also addresses cross-cutting issues such as uncertainty
estimation, ensuring time series consistency, data gap filling,interoperability issues between different sources of RS data.
It is possible that the final document may deal with these issues
adequately in the previous section and in Ch 3, in which case thissection might be unnecessary.

GFOI Methods and Guidance Documentation
Chapter 3- Using remote sensing and ground observations to estimate GHG
emissions and removals caused by specific forest activities
Chapter 3 sets out how to apply the generic methods of Chapter 2 to the REDD+
activities being discussed under the UNFCCC.
Chapter 3 provides specific guidance for using remote sensing and groundobservations to construct or improve estimates of GHG emissions and removals.
For each of the six forest activities guidance is provided for each component(carbon pools and non-CO2 emissions) of the GHG estimates reported using IPCC
methodology.
-Above and below ground biomass-Deadwood and litter (dead organic matter)-Soil carbon-Non-CO2 from soils and fire

GFOI Methods and Guidance Documentation
Chapter 3- Using remote sensing and ground observations to estimate GHG
emissions and removals caused by specific forest activities, continued
For each combination of forest activity and GHG category guidance is providedon:• Choice of method for estimating GHG emissions & removals as outlined in the2006 IPCC Guidelines• Activity data (usually area, but also other factors affecting GHG emissions
such as disturbance history) consistent with the method chosen• Choice of emission and removal factors and other parameters• Other supporting data such as forest type, soil type, or climate that isuseful or necessary to implement the chosen methodology.
• Quantification of uncertainties and combining them in estimating GHG
emissionsGFOI Methods and Guidance Documentation
Chapter 3- Using remote sensing and ground observations toestimate GHG emissions and removals caused by specific forest
activities
Non-CO2 emissions (fire and soil)

This section will reference internationally available data
sets (e.g. on fire extent) as resources for analysis andestimation.
This section will include advice on establishing therelationship between soil/soil management data and soil
emissions.

GFOI Methods and Guidance Documentation
Chapter 3- Using remote sensing and ground observations to estimate
GHG emissions and removals caused by specific forest activities
Guidance on uncertainties
The MGD adopts an approach to uncertainty analysis consistent with thattaken by IPCC. This recognises that uncertainty estimates are an essential
element of a complete GHG inventory of emissions and removals.
The MGD will provide:•advice on a simple approach based on the activity data x emission factor
equation plus error propagation• generic advice on a more complex approach using monte-carlo analysis
or equivalent.

GFOI Methods and Guidance Documentation
Chapter 3- Using remote sensing and ground observations toestimate GHG emissions and removals caused by specific forest
activities
Advice on Reporting
This section provides a preview of likely reporting requirements.
Including likely COP 18 outcomes. It will be updated accordingly.
MRV reporting requirements will probably conform to the general
requirements of good practice set out in MGD Chapter 1, consistent
with a phased approach building on existing systems, the availabilityof financial support, and with the flexibility to allow for
improvement.

GFOI Methods and Guidance Documentation
Next Steps
Advice welcome!
Constructive Reviewers welcome!
MGD will be revised as new methodologies are adopted and new
requirements are defined.
MGD will have an online wiki presence with hyperlinks toreferences, documents, illustrations and Youtube demonstrations.
The intent is to make this document useful across the UNFCCC
community and beyond: The MGD will be available to any
organisation wishing to develop forest reporting capacity
consistent with IPCC GPG.

GFOI Methods and GuidanceDocument Overview
Jim Penman
Environment Institute
University College London

Hayama 23 to 25 October 2012

Global Forest Observations
Initiative
Established in 2010 by Group on Earth Observations(GEO). Five work areas:
1) Coordination of satellite data supply
2) Capacity Building3) Methods and Guidance Documentation

4) R&D
5) Administration and coordination

Conceptual Model
Deforestation

Results-based finance based on the
difference between actual emissions
in future periods and a reference
level requires:• Methodologies to estimate ofactual emissions and removals• Establishment of a reference levelwith the same coverage ofemissions and removals.
Implies need to consider historical aswell as current and future data, on aconsistent basis

Reference levelor benchmark

Outturn

t1

t2

Time

Coverage of REDD+• National (sub-national interim)• 5 plus activities: reduced emissions fromdeforestation, forest degradation, and the roleof conservation, sustainable management offorests and enhancement of forest carbon
stocks in developing countries; possible
extension to other activities that drive REDD+• 5 pools: above and below ground biomass,
deadwood, litter, soil organic matter• Related non-CO2 GHG emissions

Probable elements of a REDD+ MRV
decision in Doha
• Use of IPCC methods, further work by IPCC
• Transparency, consistency, completeness, accuracy,comparability and QA/QC• Stepped approach building on existing forest monitoring
systems and with sub-national approaches as an interim• Identification of changes in natural forest• Identification of uncertainties• Stepwise approach to incorporate better data for pools
and/or gases and to improve methodologies• Possible use of conservativeness• Request that availability of RS data be facilitated – mostpowerful in combination with ground based data
• Envisages technical assessment of reported data andreference levels

IPCC Guidance and Guidelines• IPCC first produced GHG inventory guidelines in 1995
and 1996, building on previous work by OECD
• KP implied the need for greater specificity, and hence
the IPCC Good Practice Guidance.
• IPCC methods are tiered to accommodate all national
circumstances. Higher tiers are called for key
categories, unless the resources called for are
disproportionate.
• Definition is that Good Practice inventories are thosewhich contain neither over-nor underestimates so far
as can be judged, and in which uncertainties are
reduced so far as is practicable

IPCC estimation methods• Gain-loss: emission factor x activity data; most widely
applicable (basis for IPCC default method)• Stock-difference: needs national forest inventory (NFI not necessarily cost effective to establish, andsupplementary sampling may be needed to detect
deforestation efficiently)• Some overlap – e.g. non-CO2 gases always require EF x
AD calculations; existing NFI can be used to establish EF
even if sampling not optimal for deforestation;
sampling to establish EF in the absence of an NFI may
be similar to supplementary sampling• MGR will consider different cases

IPCC GPG General Principles
IPCC good practice guidance entails adherence
to principles of:• Transparency• Completeness• Consistency• Comparability• Accuracy

GPG and land area identification• Three approaches:
1. Tracks land area by land use (no LUC matrix)
2. Adds LUC matrix, but not necessarily spatially
explicit
3. Spatially explicit matrix (sampling or completetally)• Guidance on sampling, use of RS and groundbased data and uncertainties• See http://www.ipcc-

nggip.iges.or.jp/public/2006gl/pdf/4_Volume4/V4_03_Ch3_R
epresentation.pdf

Relationship between GFOI, IPCC and
GOFC-GOLD• IPCC: operational guidance for greenhouse gas
inventories• GFOI: operational guidance for users andsuppliers of RS data for use in greenhouse gas
inventories consistent with IPCC guidance• GOFC-GOLD: continuously updated review ofapplicable science and technologies relevant
to IPCC and GFOI.

The MGD will…
• Be consistent with IPCC methods, and cross-reference
them throughout• Identify the steps that countries need to go through inmaking use of RS and ground based data in
combination• Communicate to RS data providers what the needs ofcountries are in this context• Be practical, operational, and understandable by nonspecialists• Use decision trees, appropriately cross-referenced to
IPCC methods• Draw on other relevant work, notably GOFC-Gold--//--

Role of Remote Sensing
for
GEOSS and IPCC
Yukio Haruyama
Remote Sensing Technology Center of Japan(RESTEC)
IPCC Expert Meeting: Role of Remote Sensing
in Forest and National GHG Inventories
23-25 October 2012
IGES, Hayama, Japan

Introduction•

TheIntergovermental PanelonClimateChangeForthAssessmentReport
(IPCCAR4,2007)suggeststhatdeforestation,forestdegradationandlandusechangecontributealmost20%ofglobalGHGemissions.
• ThePartiestotheUnitedNationsConventiononClimateChange
(UNFCCC)arecommittedtoprovidingnationalinventoriesofgreenhousegasemissionsfromhumanactivities,includinglanduse,landusechangeandforestry(LULUCF).
• TheUNFCCCisalsoconsideringpolicyapproachesandpositiveincentives
specifictotheforestsector(REDD+activities)asawaytoreduceglobal
GHGemissions.
• TheGrouponEarthObservations(GEO)ForestCarbonTracking(FCT)task,
whichhasbeenunderwaysince2008,focusesonaseriesofnational
demonstrators,andondevelopingmethodsandprotocolsfortheuseofobservationsinnationalforestmonitoringsystems.
• BasedonGEOFCTactivities,theGlobalForestObservationInitiative
(GFOI)hasbeendevelopedandtheImplementationPlanwasacceptedat
GEOVIIIinNovember2011.
Remote Sensing Technology Center of Japan

Global Earth Observation
System of Systems (GEOSS)¾ GEOSS is a comprehensive, coordinated, and sustained
observations of the Earth system.
¾ GEOSS will enhance the development and use of Earthobservations in 9 societal benefit areas.

GEOSS

3

GEOSShasbeenbuildingupon3Pillars
1. CoordinatedDataAccess2. OpenDataPolicy
3. PoliticalVisibility

U.S. Department of State, Washington DC
July 31, 2003

The GEO Forest Carbon Tracking Task
GEOestablishedin2008theForestCarbonTracking(FCT)task
demonstratethatcoordinatedObservationsfromsatellites,
validatedbyinsitumeasurementsandproperlylinkedtocarbon
modelingcanprovidereliable,accurate,consistentand
continuousinformationtoaddressthemonitoringcomponentofnationalMRVs.
TheFCToverallgoalsare
1. to showthefeasibilityofperformingcoordinated,large
scalesatelliteobservationsand2. totestandcomparetheuseofvariousobservations,models,toolsandmethodologiesinordertoprovide
options,adviceandguidelinestoCountrieswillingto
implementnationalsystems.

RemoteSensingfromSpaceGEOSSCoordinateAccesstoSatelliteData(withCEOS)TERRALANDSATIRS

RESOURCESAT
ALOS
SAC-C
SPOT

CBERS
©GEOSecretariat
Remote Sensing Technology Center of Japan

Access to EO satellites• Various kind of satellite data available so far• Continuously develop appropriate applications.

MOS

LANDSATJERS-1ENVISAT

ADEOS

THEOS
ALOS
ADEOS-II

IKONOSTRMM
QuickBird

WorldView-2
AQUA

Remote Sensing Technology Center of Japan

Earth Observation Satellite Operation

JAXA Earth Observation Center 䠄 EOC䠖 Hatoyama town 䠅Remote Sensing Technology Center of Japan

EOS Data Applications

ForestCoast

Agriculture

DisasterOthersRemote Sensing Technology Center of Japan

FCTNetworkof“NationalDemonstrators”
11NDCountries

From2009•
Brazil•Guyana•Mexico•Indonesia(Kalimantan)•
Australia(Tasmania)•
Cameroon•
Tanzania

FromJune2010
FromJune2011•
Colombia•
Nepal•
DRCongo•
Peru,and•
addingSumatratoIndonesia

•Australia(Tasmania)•Brazil•Cameroon•Colombia•DRCongo•Guyana•Indonesia(Sumatra,
Kalimantan)•Mexico•Nepal•Peru•Tanzania.

ALOS Launched: Jan. 2006
PRISM:PanchromaticRemotesensingInstrument
forStereoMappingRes.2.5m,Swath35Km(triplet),70Km(Nadir)
AVNIR2:AdvancedVisibleandNearInfraredRadiometertype2
Res.10m,4bands,Swath70KmPALSAR:PhasedArraytypeLbandSyntheticApertureRadarRes.10m(fine),Swath:70Km
Res.100m(ScanSar),Swath:250350Km

PALSAR
8.9m

2.9m

AVNIR2
PRISM

SolarArrayPaddle 22m

Velocity

Nadir(Forest)Illegal logging Monitoring,Brazil¾ The Brazilian Institute of Environment (IBAMA) and the Federal
Police Department (DPF) had a strong interest in PALSAR data toprotect forest from illegal logging.
¾ JICA launched 3 year project called “Utilization of ALOS images tosupport protection of the Brazilian Amazon forest and combat
against illegal deforestation”.
¾ RESTEC
transfers
techniques of
PALSAR as well
as RS and GIS
software.
¾ IBAMA makes
color composite
images fromPALSAR
ScanSAR strip
data and overlay
on Google Earth.

©METI, JAXA©RESTEC included ©JAXA, METI
Background ©Cnes/Spot Image, ©2011 GeoEye and ©2010 Google
Remote Sensing Technology Center of Japan

Forest/Non-Forest (FNF)
Indonesia, Sumatra, Liau
PALSARForest/NonForest
PALSARRGB
2009
2009
ṇゎ⋡䠖81.8%

䖃:HH䠈䖃:HV䠈䖃:HH/HV䖃:Forest䠈䖃:NonForest䠈䖃:Water13

Remote Sensing Technology Center of Japan

Land Cover (LC)Indonesia, Sumatra, Liau
Natural forest
Natural mangrove forest
Natural re-growth
AcaciaOil Palm
RubberCoconut
Open area
Other
Water

14
Remote Sensing Technology Center of Japan

Sumatra, Indonesia (GEO-FCT ND)
FCTproductsinSumatra,Indonesia
1) SlopeCorrectedMosaic(HH)2)SlopeCorrectedMosaic(HV)

Remote Sensing Technology Center of JapanPALSAR Global Mosaic (PGM)

1. PALSARGlobalMosaic(PGM)are10/25mresolutionseamlessorthorectified
PALSARmosaicdatasetscoveringwholegloballandarea.
2. PGMiscomposedbythe‘TileofImages’byeachlat/longdegreeandpolarization
(HH/HV).
3. ProcessedbyRESTECunderthecontractofJAXA
Remote Sensing Technology Center of Japan

GEOSS-AP Symposium
1st,2nd,3rd and5th GEOSSAsiaPacificSymposiums(includingFCT
session)wereheldinJapan.

17
Remote Sensing Technology Center of Japan

Remote Sensing Technology Center of Japan5thGEOSSAPWG3
ForestCarbonTracking
•
•
•
•
•
•
•
•
•
•
•
•

Co– ChairYoshiki Yamagata,NationalInstituteforEnvironmental
Studies,JapanCo– ChairMiriamBaltuck,CommonwealthScientificandIndustrial
ResearchOrganisation,Australia
Ake Rosenquist,soloEO,Japan
Hamdan Omar,ForestResearchInstituteofMalaysia,Malaysia
Orbita Roswintiarti,IndonesianNationalInstituteofAeronauticsand
Space,Indonesia
NguyenPhy Hung,ForestInventoryandPlanningInstitute,Vietnam
ChandraShekhar Jha,IndianSpaceResearchOrganisation,India
MasanobuShimada,JapanAerospaceExplorationAgendy,Japan
Kenlo Nasahara,UniversityofTsukuba,Japan
TamotsuSato,ForestryandForestProductsResearchInstitute,Japan
MitsuruOsaki,HokkaidoUniversity,Japan
NobukoSaigusa,NationalInstituteforEnvironmentalStudies,Japan

Remote Sensing Technology Center of JapanCO2emissionfromdeforestation,1990s

Hugeemissionsfromtropics
NIES(Itoet.al) 20

FCTNetworkof“NationalDemonstrators”
11NDCountries

From2009•
Brazil•Guyana•Mexico•Indonesia(Kalimantan)•
Australia(Tasmania)•
Cameroon•
Tanzania

FromJune2010
FromJune2011•
Colombia•
Nepal•
DRCongo•
Peru,and•
addingSumatratoIndonesia

•Australia(Tasmania)•Brazil•Cameroon•Colombia•DRCongo•Guyana•Indonesia(Sumatra,
Kalimantan)•Mexico•Nepal•Peru•Tanzania.

GEOFCTandGEOCFP
GEO&APN:CapacityBuildingofALOSsatellitedatato
supportMappingandMonitoringDeforestationand
DegradationinIndonesia(20112012)¾ GEO’sCallForProposal (CFP)wasreleasedbyGEO
secretariatinFebruary2009.
¾ FirstfacetofacemeetinginBaliasasidemeetingofthe4th
GEOSSAPSymposiuminMarch2010.
¾ SecondfacttofacemeetinginJapaninMarch2010.
¾ RESTEC,BPPTandMinistryofForestrysubmittedtheFull
Proposal toGEOsecretariatinApril2010.
¾ TheprojectwasacceptedbyGEOsecretariatinOctober
2010.

22
Remote Sensing Technology Center of Japan

GEOFCTSideMeeting#4GEOSSAPSymposiuminBali,Indonesia

23

CapacityBuildingofALOSsatellitedatatosupportMappingand
MonitoringDeforestationandDegradationinIndonesia
Proposal Overview (Objectives)¾ Todevelopnewchangedetectionmappingandmonitoring
methodsforforestandpeatlandinIndonesiausingALOS/PALSAR
satellitedata¾ Toassesstheapplicabilityofthenewmethods,intermsof
reliability,credibility,andconsistencytoimprovecapacity,
capability,andqualityofmappingandmonitoringsystemfor
deforestationanddegradationonforestandpeatland.
¾ Toincorporatethenewlydevelopedmethodologystarttooperate
withinbothNationalandLocallevel ofdecisionmakingprocess24
Remote Sensing Technology Center of Japan

GEO-CFP Capacity BuildingAcapacitybuildingprojectproposedbyIndonesiaBPPTand
MinistryofForestrywithRESTECwasacceptedbyGEOCFP(Call
ForProposals)andAPN(AsiaPacificNetworkforGlobalChangeResearch).2weekstrainingwasheldinTokyoinMay2012.

25
Remote Sensing Technology Center of Japan

Capacity Building• Carrying out various programs from fundamental to advanced
based on both Japanese and International programs
VietnamBhutanResearch on GLOF in the
Bhutan Himalayas *4

Demonstration Project of theEnvironmental Disaster Prevention
System in Mekong Delta Region

VietnamResearch and Development Center
for Space Technology in Hoa Lac

ThailandLand Management &Urban Planning

Philippines
Updating 1/50,000
scale Map by ALOS

Sri Lanka
World Food Programme
ProjectIndonesia
Forest Resource
Management
ProjectIndonesia
ALOS Application & Verification ProjectIndonesia
JICA

*1

JETRO

World Bank

*2

JAXA

*3

Development of Volcano Activities
Monitoring System Using JapaneseSatellite Images (ALOS)

MexicoTraining for Forest
Management with ALOS
Bolivia
Training for Andean
Glacier Monitoring with
ALOS
BrazilTraining for Amazon
Deforestation Monitoringwith ALOS

Other Funds*1 JICA (Japan International Cooperation Agency䠅

*3 JAXA (Japan Aerospace Exploration Agency)*2 JETRO (Japan External Trade Organization)*4

Project of JICA and JST (Japan Science and Technology Agency)

BrazilIllegal Deforestation Monitoringwith ALOS PALSAR

5

Remote Sensing Technology Center of Japan

ContributiontofivecoreareasofGFOI(GFOIWorkPlanfor20122013)
1.CoordinationofSatelliteDataSupply
2.CapacityBuildingandtheNationalDemonstrators(FCTandGFOI)
3.MethodandGuidelinesDocumentation
4.ResearchandDevelopmentPlan
5.GFOIGovernance,Administration and
Coordination

Remote Sensing Technology Center of Japan

ContributiontoIntergovernmentalPanelonClimateChange(IPCC)z InordertocontributetothepreparationoftheIPCC5thAssessmentReport(AR5),whichwillbesubmittedtotheIPCCPlenaryin2014,
theMinistryofEducation,Culture,Sports,ScienceandTechnology(MEXT)
hasstreamlinedasupportingandcooperatingframeworkfortheIPCC
WorkingGroup1(WG1).
z

Itspurposeistopromotecoordinationandcooperationamongresearch
institutionsandresearchersinJapanconductingresearchesonphysicalscienceaspectsofclimatechangerelatedtotheIPCCWG1.

zForitsimplementation,theRESTEClaunchedanationalsupportofficeof
IPCCWG1andhasbeenpromotingactivitiesofsupportandcooperation
described.
Remote Sensing Technology Center of Japan

NationalSupportOfficesforIPCCAR5
Working GroupϨ

WorkingGroupϩ

WorkingGroupϪ

National Liaison Committee

MEXT
JMA

MOE

METI

N’lSupportOfficeforWG1

N’lSupportOfficeforWG2

N’lSupportOfficeforWG3(RESTEC*)(GEF**)(GISPRI***)㻺㼍㼠㼕㼛㼚㼍㼘㻌㻱㼤㼑㼏㼡㼠㼕㼢㼑㻌㻯㼛㼙㼙㼕㼠㼠㼑㼑㻌㼒㼛㼞㻌㼃㻳䊠㻺㼍㼠㼕㼛㼚㼍㼘㻌㻱㼤㼑㼏㼡㼠㼕㼢㼑㻌㻯㼛㼙㼙㼕㼠㼠㼑㼑㻌㼒㼛㼞㻌㼃㻳䊡㻺㼍㼠㼕㼛㼚㼍㼘㻌㻱㼤㼑㼏㼡㼠㼕㼢㼑㻌㻯㼛㼙㼙㼕㼠㼠㼑㼑㻌㼒㼛㼞㻌㼃㻳䊢*:RemoteSensingTechnology
Center**:GlobalenvironmentalForum***:GlobalIndustrialandSocial
ProgressResearchInstituteJAXA’s Earth Observation Satellites
andForest Mapping
23-25 October 2012

Masanobu Tsuji
Earth Observation Research Center (EORC)
Japan Aerospace Exploration Agency (JAXA)IPCC Expert Meeting: Role of Remote Sensing in Forest and National GHG Inventories

Japanese Main Activities of Earth Observation
GEOSS 10 years implementation plan㽲Disasters㽳
HealthDisaster prevention and mitigation
Monitoring flood, earthquake, crustal
deformation, forest, biomass, etc.

㽴Energy㽵㽶㽷Weather
MTSAT (JMA)㽸
Eco-systems
㽹
Agriculture

Greenhouse gas and carbon flux

ClimateWater㽺
Bio-diversity

Climate change and water cycle
Precipitation, cloud, aerosol, SST, snow, ice,etc.

1

Mid-Term Plan
TargetSensors

Disaster/
ResourceSAR

JFY 2005

2006

2007

2008

2009

2010

2011

2012

2013

2015

ALOS

Optical

2017

2018

ALOS-3 䋨Optical䋩

PRISM, AVNIR2 (Optical)

Precipitation Radar

2016

ALOS-2 (SAR)

PALSAR㩷2014

GPM/DPR

TRMM/PR㩷

Microwave
Radiometer

ClimateChange/
Water
Cycle

GCOM-W/ AMSR2G

Aqua/AMSR-E

Optical Sensor
㩷㩷
Cloud Radar

GlobalWarming

GCOM-C/ SGLI

EarthCARE/CPR

ETS-VIII

Mobile Communication

Communication

WidebandInternetworking㩷㩷

Nextgeneration

WINDS
DRTS

Data Relay

Navigation

GOSAT-2

GOSAT

Spectrometer

DRTS Follow-on
QZSS

Quasi-zenith 㩷
Operation

Development

Research㪉㩷GOSAT (Greenhouse Gases Observing Satellite)
Jan.

2010

Jan.

Apr.

Apr.

Jul.

Jul.

Oct.

2011

Oct.

TANSO-FTS(Fourier Transform
Spectrometer)

TANSO-CAI(Cloud andAerosol Imager)

the highest spectrometer
resolution in the world㩷㩷

Global Distribution Map of CO2
slight annual increase in any seasonsz Measure global distribution of GHGs, andunderstand how their emission is reducedz The world’s only satellite for monitoring
CO2 and methane㪊㩷TRMM/PR, Aqua/AMSR-E: Japan-US joint project
TRMM/PR (launch: 1997)

3-D structure of rainfall ofSuper Typhoon KROSA observed by PR¾Japan provides PR (Precipitation
Radar) and launch vehicle, andUS provides four observation
instruments.
¾ Understanding tropical rainfall
structure using radar instruments
Aqua/AMSR-E (launch: 2002)

01Z 5 Oct. 2007

El Niño Watch¾Japan provides AMSR-E, andUS provides four observation
instruments.

Monitoring area

¾ Understanding water circulation
mechanism

Global Rainfall Map in Near Real TimeTyphoon MORAKOT (09W): Aug. 5 – 10, 2009 (Big impact in Chinese Taipei)• Global rainfall map merging TRMM, AMSR-E and othersatellite information• Available 4-hour after observation, hourly update• 0.1-degree latitude/longitude grid
http://sharaku.eorc.jaxa.jp/GSMaP/Global Change Observation Mission (GCOM)
 Long-term observation (over 10 years) for global climate change and water cycle.
 Two satellite series;
9 GCOM-W : Microwave observation using AMSR2 (AMSR-E follow on)
for observing water circulation(water vapor, precipitation, soil moisture, sea surface temp., wind speed, etc)

9 GCOM-C : Optical multi-channel observation using SGLI (GLI follow on)
for radiation budget and carbon cycle(aerosol, clouds, ocean color, vegetation, snow ice, etc)
GCOM-C1(CLIMATE)

AMSR2
AMSR

GCOM-W1-W1(WATER)ER)
or SHIZUKU”
UKU”

SGLI

Launched: 18 May 2012
(Regular observation operationfrom 10 August 2012)

Target Launch: JFY 2015

SensorAdvanced Microwave ScanningRadiometer 2 (AMSR2)

SensorSecond generation GLobal Imager(SGLI)

Design Life

5 years

Design Life

5 years

6

The current status of GCOM-W1 “SHIZUKU”
 Initial CAL/VAL has started in August, after satellite commissioning L1 Products: released in Jan 2013, L2 Products: released in May 2013
at “GCOM-W1 Data Providing Service” https://gcom-w1.jaxa.jp

16th Sep 2012SHIZUKU/AMSR2

Sea ice extent in the Arctic Ocean

Typhoon No.11 “HAIKUI” on 7 August

Sea ice extent in the Arctic Ocean has become
the smallest (3.49 million square kilometers) on
this September in observation history,comparing with the past record marked in 2007. 㩷“SHIZUKU” image identified the eye of thetyphoon more clearly than “HIMAWARI”.
㪎㩷GCOM Geophysical Parameters
AtmosphereLand
land coverW
Water &
EnergyCycle

land
surface
temp.

cloud
properties
aerosol
properties

water
vapor
cloud
liquidwater

primary
productionsoil
moisturesnow ice
distribution

precipitation

snow
depth
snow
surfaceproperties

sea icesea
surface
wind
speed

PAR

concentrationsnow
surfacetemp.

Cryosphereabovegroundbiomass

ocean
color
sea
surface
temp.

Ocean

C

Carbon Cycle &Radiation Budget㪏㩷International Cooperation Projects
Global Precipitation Measurement (GPM)/
Dual-frequency Precipitation Radar (DPR)Global Precipitation Observation with high accuracy
and frequency at three-hour intervals with the GPM
Core Satellite and eight Constellation Satellites.
 JAXA provided NASA with the Dual-frequency
Precipitation Radar (DPR) for Core Satellite on March.
 DPR is the world’s only precipitation radar which canobserve 3D structure of rainfall. The simultaneousdual-frequency observation can detect even weak
rainfall and snowfall.

Core
Satellite

Constellation
Satellites

EarthCARE/Cloud Profiling Radar (CPR䋩 EarthCARE will observe 3D structure of clouds andaerosols, and reduce errors in climate change andweather forecast , by Japan-Europe cooperation.
 JAXA provides Cloud Profiling Radar (CPR) to ESA.
 CPR is the world’s first W-band Doppler radar(94GHz) aboard a satellite. We can understand thevertical structure of clouds, as well as the ascending
and descending movement of clouds.

Cloud Profiling
Radar (CPR)㪐㩷ALOS seriesz Wide-swath and high-resolution data of ALOS series will contribute to public safety, land
management, assurance of food/resource/energy, solution of global environment issues
z Promoting public-private partnership, since its data has commercial value
2011

2006

JFY2013

JFY2015

ALOS-2 (SAR)
ALOS-3 (Optical )ALOS(Optical & SAR)

• Wide-swath (Max 490km),
High-resolution (1-3m)• L-band SAR (PALSAR-2)
ALOS(Optical & SAR)

ALOS-2 (SAR) (Launch: JFY2013)(2006䌾2011)z Panchromatic imager with wide-swath(50km) and high-resolution (0.8m)
z obtain global 3D geospatial information

ALOS-3 (Optical) (Launch: JFY2015)

㪈㪇㩷

Monitor the Forest in Amazon (illegal logging)©JAXA,METI

1995 (JERS-1)

2007 (ALOS)• Within seven days after the data acquisitions, JAXA provides the quickly
processed SAR images to IBAMA.
• The data are being utilized for the illegal deforestation monitoring.
㪈㪈㩷㪧㪘㪣㪪㪘㪩㩷㪈㪇㫄㩷㪞㫃㫆㪹㪸㫃㩷㪝㫆㫉㪼㫊㫋㪆㪥㫆㫅㪄㪝㫆㫉㪼㫊㫋㩷㪤㪸㫇㩷㪉㪇㪇㪐㩷©JAXA,METI analyzed by JAXA㪈㪉㩷PALSAR 25-m yearly global mosaic
Year 2010

Riau

Red: HH
Green: HV
Blue: HH/HV(c) JAXA/METILandsat-7 ETM+
(2010/06/18)(c) USGS, NASAPALSAR mosaic(2010)(c) JAXA/METI

R: band-4, G: band-3, B: band-2(false-color)

R: HH, G: HV, B: HH/HVRGB composite ofmulti-year
PALSAR mosaic
HV polarization
2008

Clear-cut in 2008
Clear-cut in 2009

2010

2009

Clear-cut in 2010(c) JAXA/METIPALSAR-based forest loss map in Riau, Indonesia

Forest/Non-Forest classification by Random Tree method

Accuracy on FNF: 89.51%

Reference map(WWF)Class
Unclassified
ForestNon-Forest
Non-Forest(Water)

Col

Result of classification(Random Tree method)

Trends of forest area in Riau, Indonesia

Landsat
PALSAR• Data of 1985, 1990, 2000, and 2007 are obtained from the WWF Indonesia land cover data (Uryu et al., 2008; Uryu et al., 2010).
• Data of 1996 are obtained from the report by Forest watch Indonesia/Global forest watch (2002).

Activities of JAXA / EORC
Earth Observation Satellites

Level 1 processing

Data Relay
Test Satellite

Users
Domestic/
Foreign
Partners

Radiometric/Geometriccorrection

Distribution

GroundStations

Map projection

Processing
High level processing

Receiving

Value-added products
Mission(Sensor)data

Government, Local
authorities, Universities,
Research institutes,
Private enterprises

ArchivingNon-commercial/Commercial
products

Calibration/Validation
Applications Research
Accuracy improvement
Processing algorithm/
technique development(C) METI/JAXA, analyzed by JAXAAll Raw data
Certain ofprocessed data

Total 644TB
as of 2008 end

Collaboration with groundbased research andmodeling research

Earth Observation Satellites
can contribute toForest and GHG Inventoriesof this beautiful planet
This photo is taken by KAGUYA, JAXA’s Lunar Explorer㪉㪇㩷

Thank you for your attention!

㪉㪈㩷The role of RS/GIS applications for National
Forest Monitoring Systems in the context of
REDD+
Dr.IngeJONCKHEEREUNREDDTeam(FAOHQRome,Italy)
INPE/FUNCATETeam(SaoJosedosCampos,Brasil)
IPCCExpertMeeting,Hayama,JAPAN23October 2012

UN-REDD Programme• SupportscountriesbenefitfromREDD+(UNFCCC)– NationalREDD+StrategiesandReadiness• Establishedin2008byFAO,UNDP&UNEP– ResponsetoUNFrameworkConventionClimateChange(UNFCCC)Bali
ActionPlan2007• OffersUNJointProgram:DeliveringasOneUN• AgreeddeliveryplatformwithForestCarbonPartnership(FCPF)andForestInvestmentProgramme (FIP)• Currentcontributions:US$about70million(withoutpledges)fromdonorsNorway,Denmark,SpainandJapan

6 UN-REDD Work Areas

REDD+Strategies

MRVandMonitoring
REDD+Governance
StakeholderEngagement
MultipleBenefitsofforests/REDD+
TransparentEquitableAccountable
ManagementofREDD+Payments
REDD+asCatalystofGreenEconomy

National Programmes (NP)www.un-redd.org

Lead Implementation Role
MRV in the NP: FAO
Measurement(M),reporting(R),verification(V)(MRV)– Cornerstoneforcarbonmonitoring• nationalcommunicationstoUNFCCC• startingpointforR(E)Lestablishment• supportsnational/subnationalimplementationof
incentivesystems– Supporttopolicyformulationandfeedback• knowledgeofdriversofchange• informationonmultiplebenefits

MRV: Measurement

The IPCC’s methodological approach to calculate anthropogenic GHG emissions by sources and removals
by sinks related to forest land.

MRV: Measurement of area change (AD) andforest carbon stock changes (EF)ThisinformationprovidesthebasistocompileaGHGinventory1)Activitydata(AD)– Area/forestcoverchangedata(hectaresperyear)– Achievedusingasatellitelandrepresentationsystem(SLRS)– SofarmainlybasedonLandsat,MODIS,upcomingRapidEye,
DMCii insomecasesaswellasothercommercial(VHR)data2)Emissionfactors(EF)– Forestcarbonchange– Assessmentofbiomass,carbonstocksandemissionfactors– Dataareobtainedfromnationalforestinventory(NFI)!Upcoming:databaseandsoftwarebasedonallometric
relationshipsgatheredworldwidetocalculateEFsandbiomass,
InitiativeofFAO,UniversityofTuscia andCIRAD(tobelaunchedinDoha)(L.Picard,L.SaintAndré,R.Valentini andM.Henry)3)GHGInventory(GHGI)– GHGassessmenttodeterminenationalmitigationperformance– BasedonthedatacollectedfromtheNFIandSLRS– UNFCCCtemplatesavailable

ACTIVITYDATA
SatelliteLand
RepresentationSystem

2005

x
EMISSIONFACTORSNationalForest
Inventory=
GHGInventory

REDD+ under the UNFCCC:The Cancun Agreements (COP 16)•

Decides thatPartiesshouldfollowaphased approachtoREDD+:
1. Readiness
2. Resultsbaseddemonstrationactivities
3. Positiveincentiveforverifiedperformance•

DevelopingcountryPartiesarerequested todevelop:– Robustandtransparentnationalforest
monitoringsystems forthemonitoringand
reportingofREDD+activities(Monitoring&MRV)– AsystemforprovidinginformationonREDD+
safeguards

Information, Monitoring and
MRV Development through the 3
REDD+ Phases
REDD+PHASESCapacitybuilding&development

Year2

 Positive
incentivefor
verified
performance Implementationof
P&Msand
demonstration
activities Readiness DevelopmentofP&Ms

Year1

PhaseIII

PhaseII

PhaseI

Year3Year...

Year...

Year...

REDD+Safeguards
InformationSystem

REDD+Safeguards
InformationSystem

MonitoringSystem

MonitoringSystem

SLRS

SLRS:
AD

NFI:
EF

GHGI:CO2e

MRVSystem

Monitoring Systems•

ToassesswhetherREDD+isresultinginnetpositive
outcomes,i.e.resultsbased•

InPhase2ofREDD+– Tomonitortheoutcomesofdemonstration
activities

Phase 3Phase 2•InPhase3ofREDD+– Tomonitortheoutcomesofnationalpoliciesand
measuresonallthenationalterritory•

Technicalrequirements– SatelliteLandMonitoringSystem(operational
remotesensing)– WebGISinterface(fortransparency,openaccess)Year...

Monitoring systems : the endTosustainablymanageforestatnationallevelTofulfilltherequirementoftheREDD+Decision(1/CP.16)(establishanoperationalNFMSandmovetowardsthephase2,phasedapproach)TodemonstratetotheinternationalcommunitythatthecountryismonitoringitsREDD+activitiesinatransparentandverifiableway

To securefundsfortheimplementationofnationalREDD+andforestpoliciesTomonitorthenationalforestresourcesandtheimpactsofnationalforestpolicies

National forest monitoring systems
REDD+
MONITORINGSYSTEM
REMOTESENSINGWEBINTERFACE SatellitedatatomonitorREDD+
activitiesatsubnational(demonstration)andnationallevel Disseminatedoverinternet
throughawebGISinterface

COMMUNITYMONITORINGACTIVITYDATA
SatelliteLand
RepresentationSystem

2005

 Measurementsofareachange
(ActivityData)

Forest monitoring system : Brazil• PRODES – AmazonDeforestation
MonitoringProject(AnnualDeforestationAssessment)• DETER – Nearrealtime
DeforestationDetectionwithMODIS(SupportforLawEnforcementfor
DeforestationControl)• DEGRAD – AmazonDegradation
MonitoringProject

http://www.dpi.inpe.br/prodesdigital/prodes.php• DETEX  Selectiveloggingactivities• TerraClass  Landusemonitoring
ofdeforestedarea(2008)

FAO-INPE collaboration••

Developmentofprototypesofnationalforestmonitoringsystemsfor/with
developingcountries:DRC,PNG,ParaguayUpcomingin20122013:Tanzania,Zambia,MongoliaandVietNam
Buildonexistingnationalforestmonitoringexperiencesandalgorithms•

Twocomponents:•

Combinationofopensourcedatabase,userinterface,toolsandalgorithms
adaptedaccordingtocountryneedsFreeofchargeandsupportedbyanalysisandprogrammingteamsinBrazil
(INPE)andFAOHQ
Linkageofinformationfromothertechnicalpartnersandcontributorsfor
analysisandverification.
Newoutcomestobepresentedatthe18th COPinDoha,Qatar(2012).

•••

TerraX platform
NationalforestmonitoringportalObjectives
1. Enablethedevelopingcountriescountries tofollowallthe
actionsrelatedtotheimplementationofitsnationalREDD+
policiesandmeasuresusingRSdata;
2. BuildaplatformtoobtainregularinformationontheirREDD+
results;
3. Actionsshouldberelated,directlyorindirectly,tothenational
REDD+strategiesandmayalsoincludeactionsunrelatedto
carbonassessment,e.g.forestlawenforcement;
4. SupporttheREDD+phasedapproachunderParagraph73
1/CP.16;

Start-up Phase
Democratic Republic of Congo(DRC)

RépubliqueDémocratiqueduCongo

How• DevelopmentofaDRCREDD+walltowallNationalForest
MonitoringSystembasedon(freely)availablesatellitedata;Æ Withoutreinventingthewheel!
– CapacitybuildingandknowledgeandtechnologytransferÆ training;– Drawon(freely)availablesatellitedataandexistingmappingandchange
detectiontechnologies;– IncludetoolsdevelopedandappliedbyFAOandINPE;– LinkedtotheREDD+nationalregistry– Buildonconcreteandexistingcollaborationsincountry(DIAF,OSFAC,
OFAC,etc.)andactionstostrengthenDRC’stechnicalcapacitiestomonitor
theirforestland.

Mission impossible ?
• Cost:$100kforbetain2011,$450kin2012• Time:6monthsforbeta,18monthstobe
operational;• Laboratory:8computers;• (Wo)Manpower:5technicians;• Data: mostlyfreelyavailablesatellitedata;• Operational:selfsustainingsystem;• Phasedprocess.

TerraCongo• StartupPhase:developmentcarriedoutatthepremisesofFAO
UNREDD;• GuidedbytheDRCGovernment,FAOandINPE/FUNCATEis
guidingtheDRCSystemdevelopmentandoperationalisationofthesystematanationallevel,ensuringthatnecessary
adaptationsaremadetoreflectnationalcircumstances;• TheDRCNationalForestMonitoringsystemislinkedwiththe
INPEplatformTerraAmazon(renamedTerraCongofortheDRC),
whichcombinesGIS,imageprocessing,databasemanagementanddataaccessfunctionalities;• Inasecondphase(2012),thesystemwillbeputinplaceinthe
DRC.

PRODES methodology
Outputs:
Yearly deforestation map

Application of linear mixing model

SOIL

SHADOW

GREEN VEGETATION

Multidata approachDMC
CCD/CBERSLANDSATLANDSATCBERSSISPRODESState boundaries

DRC Conceptual Model
INPE

T0Forest
Map

T0Forest
Mask

T1
Deforestation

T0NonForestMask

T1
Reforestation

Project 1

Project 2

TerraCongo workshop
Multi-user edition

TerraCongo Workshop
Calibration of interpretation

Technician 3

Technician 1

Technician 2

Technician 4

Technician 5

TerraCongo Workshop Results

Processing chain:
FRA RSS,FAO-FIN,UN-REDD
FAOFRARSSFAOFIN

UNREDD

LC/LUsamplesites
checkedbyregionalexperts

OpenForis toolkit

EditioninTA

Preprocessing
Segmentation
Supervisedclassification

TerraCongo linked to dissemination portal
Mass/batchexportoftime
series andprecomputed
statistics

Dissemination
PortalMass/batchimportoftime
seriesExtensions

GeoServer
Input
Data

GeoWebCache

GeoNetwork

Dissemination
Database

OutputData

Analysis
SystemTerraCongo

Output
Data

Dissemination
SystemGeoBatch
AnalysisDatabase

Ingestion,preprocessing,
statistic computation,dataandmetadata publishing

DRC National Forest monitoring portal
http://rdc-snsf.com• Allowsanyusertointeractwiththesystem
throughawebinterface;• Visualisecountrydata;• Downloadstatisticsondeforestationandtheother4REDD+activities;• Visualiseinformationonloggingconcessions,
protectedareasetc;• LookatexistingREDD+projects;• Allowusers(aimistoinvolvelocal
communitiesasmuchaspossible)toprovide
feedbackonareasofdeforestation,etc.

Milestones and way forward• FAOHQTeaminplace,allpartnersonboard:
INPE/FUNCATE,GeoSolutions,linkwithECJRC• Firstprototypeoftheportal(15/9/2011)• TrainingofincountrypeopleinBelem&Rome(sept2011)• Finalreleaseofthesoftware(1/12/2011)• BetaversionpresentationCOP17,Durban(dec 2011)• Algorithmadaptationandcountrytailoring(2012)• PresentationoffirstincountryresultsinCOP18,Doha

Way forward• Integrationofexistingdatapreprocessingandchange
detectionalgorithmsfordifferentecosystems• Approachof‘modules’whichallowsthecountriesto
pickandchosedependentonthecountryneeds(databulkdownloading,preprocessing(geometric/radiometric),cloudmasking,change
detection,statistics,mapping)• Allopensourceapplicationsaremorethanwelcome!
• Safeguards’system

Other Web-GIS dissemination portals• WebportaldevelopedatFAO– HQ(GeoSolutions
support)• Allowallenduserstofollowandhaveopenaccessto
availableforestdata,updatedfrequentlyto
representnationalforestconditions• LaunchedinDRC,PNGandParaguayrdcsnsf.org
pngnfms.org
paraguaysmf.org

Information on REDD+ Safeguards•

PartiesmustdevelopasystemforprovidinginformationonhowREDD+safeguardsare
beingaddressedandrespectedthroughouttheimplementationofREDD+activities•

SomeoftheREDD+safeguards(Appendix1ofDecision1/CP.16)willrequirespatialand
monitoringinformation
a)
b)
c)
d)
e)
f)
g)

ConsistentwiththeobjectivesofNFPsandinternationalagreements
Transparentandeffectivenationalforestgovernancestructures
Respectfortheknowledgeandrightsofindigenouspeoplesandlocalcommunities;
Thefullandeffectiveparticipationofrelevantstakeholders
Consistentwiththeconservation ofnaturalforestsandbiologicaldiversity
Actionstoaddresstherisksofreversals(permanence)
Actionstoreducedisplacementofemissions(leakage)Dual functions of the national forest
monitoring system for REDD+

Fitting the Systems Together
NATIONALFORESTMONITORINGSYSTEM
REDD+SAFEGUARDSINFORMATIONSYSTEM

REDD+
MONITORINGSYSTEM

LOCALENGAGEMENTREMOTESENSINGCOMMUNICATIONWEBINTERFACEDOCUMENTATIONCOMMUNITYMONITORINGMRVSYSTEMMEASUREMENTREPORTING

VERIFICATION

Important data considerations• Forsustainable autonomousnationalsystems:
capacitybuildingandtechnologytransferis
neededincountry,especiallyinthefieldofradar/lidar,sofarmainlyeffortsinoptical
domain• Dataavailability/accessisalimitingfactorfor
workdoneincountry• ImportantistobuildlinkswithNationalForest
Inventories (NFIs)• ImportanttodistinguishR&Dfromoperational
applicationsreadytobeimplementedinNon
AnnexIcountries

What if…
…countries have no system?
• Thecountrywillbeunabletonationally
monitor theimplementationofitsREDD+
policiesandmeasuresandresultsbased
demonstrationactivitiesandactions;• FailingtoimplementspecificParagraphsof
Decisions4/CP.15and1/CP.16,meansthatthecountrywillnotbeinapositionto
demonstrateinternationally thatitis
effectivelyimplementingREDD+activitiesand
thusunabletoreceivefundingforREDD+
undertheUNFCCCinPhases2and3.

Thank you for your attention!
inge.jonckheere@fao.org
rdc-snsf.org
png-nfms.org
paraguay-smf.org

MINSITRY OF FORESTRY

Ruandha Agung Sugardiman

DG of Forestry Planning"IPCC Expert Meeting:Role of Remote Sensing in Forest and National GHG Inventories"
23 ‐ 25 October 2012, Hayama, JapanThe Cancun Agreements
FCCC/CP/2010/7/Add.1, paragraph 71
Decision 1/CP.16
The Cancun Agreements: Outcome of the work of the Ad Hoc Working
Group on Long-term Cooperative Action under the Convention• A national strategy or action plan;• A national forest reference emission level and/or forest reference level;• A robust and transparent national forest
monitoring system for the monitoring andreporting of REDD+ activities;• A system for providing information on REDD+
safeguards.
Report of the Conference of the Parties on its sixteenth session, held in Cancun from 29 November to 10 December 2010
Addendum Part Two: Action taken by the Conference of the Parties at its sixteenth session

Measurable, reportable and verifiable
SBSTA decision:• To establish robust and transparent national
forest monitoring systems [..] using a
combination of remote sensing andground‐based forest carbon inventory
approaches.
• Monitoring systems should provide estimates[..] suitable for review as agreed by the COP.

NFI COMPONENTS
1. FOREST RESOURCES ASSESSMENT (FRA)
2. FOREST RESOURCES MONITORING (FRM)
3. GEOGRPHIC INFORMATION SYSTEM (GIS)
4. USER’S INVOLVEMENT (USER)

Objectives:
1. To provide information on
location and area of forest
type and land covers.
2. To build and develop
national forest inventory
and monitoring system.
3. To predict stem (timber)volume, growth and yield of
forest stand based onforest type, species andgroup of species.

1. FDS (Field Data System)
2. DIAS (Digital Images Analysis System)
3. FRM (Forest Resources Monitoring)
4. GIS (Geographic Information System)
5. Information Services of NFIProvide information for
decision making process to
support policy formulation,planning, management andcontrolling on forestry sector
in national, regional andprovince level.

INPUT:OUTPUT:• SPATIAL AND ATRIBUTE DATA• ADMINISTRATIVE BOUNDARY• LAND COVER• CONCESSIONAIRE BOUNDARY• TSP/PSP GRID

• THEMATIC MAP• FOREST RESOURCES INFORMATION• SPATIAL ANALYSIS RESULTS

GIS

LAND COVER &LAND USEINPUT:STEM VOLUME ANDSRATIFICATIONRELATIONSHIPAUXILIARIESDATA• REMOTE SENSING DIGITAL• DATA FOR TRIALSDIAS

LIST OF CLUSTERGRIDS AND STRATAINPUT:• MASTER FILE OF FIELD DATA

Processing
application/
program
TSP/PSP Data

FDSDATA FOR TRIALSOUTPUT:OUTPUT:• IMAGERIES HARD COPY
• MOSAIC IMAGERIES• FOREST RESOURCES STATISTIC• POTENCY• TSP/PSP (NFI)• Balance of forest
resources• Remote sensing• Land cover map

Emission
FactorForestInventory

ForestMonitoring

Activities
Data

Data sharing& exchange

Spatial Data
Networking

Mapping

SpatialAnalysis• Clearance House of
Spatial Data
• WebGIS• GIS work
• Modeling

National Forest Monitoring System as main system for REDD+ MRV

M

R
ACTIVITY DATA

Satellite Land Representation
Systemx

EMISSION FACTOR
National Forest Inventory

V=

REDD+ GHG Inventory

LULUCF
Inventory

UNFCCCInventorycompilation
QA/QC

Emission
Inventory
Database

UNFCCC Secretariat
Roster of Experts

Following Good practices and Guidelines of the Intergovernmental
Panel on Climate Change (IPCC)

EMISSION
FACTOREMISSION
ESTIMATE=

ACTIVITY
DATA

CO

2
equivalentX

Modified from: Alberto Sandoval, Rosa Ramon –UNREDD general and MRV framework COP, 2010 Activity data:
Land cover change: Landsat 5, Landsat 7 ETM+
(1990, 1996, 2000, 2003, 2006, 2009, 2011)
 Emission/Removal Factor:
National Forest Inventory (NFI) Sample Plots• 1990‐1996 ( 2.735 cluster plots)• 1996‐2000 ( 1.145 cluster plots)• 2000‐2006 ( 485 cluster plots)• 2006‐2014 (>3.000 cluster plots)  Redesign NFI

IrelandTurkeySan Francisco
Washington D.C.

SabangEquadorBrazil•Archipelago  Intensity of Cloud Cover is very high(~10‐30%)

Landsat archives 1990‐2010, cloud cover <20%

Merauke.

PATH/ROWLANDSAT DATA

Remark: Landsat 7 ETM+ coverage for the whole Indonesia (217 scene)

INDONESIALAND COVER MAP
2009/2010

Remark: Landsat images of 2009/2010 were orthorectified, using SRTM and ground checked.

Cluster Plot (PSP/TSP) Distribution in
Grid 20 X 20 km

A Cluster contents
9 Tract

A tract Temporary Sample Plot
(TSP) contents 8 Sub Plot.
Measurement using sampling point(BAF 4)

Permanent Sample Plot (PSP)
Only on tract 5
size 100 m x 100 mwith 16 sub-plot.

2003
1998
2000

1999
1997
1996
2001
2005

1992
123 Ton C

1998
133 Ton C

Land Cover Map
2009/2010

HF/HD
HF/LD

LF/HD
LF/LD

Note:

Low ‐ HighForest ‐ Deforestation

1.760.000.000 ton CO2 e

REDD+
NATIONAL STRATEGY

Accumulation of all provinces/
regional (sub‐national) Action
Plan to reduce GHG emissions
INDONESIA
Land Cover Map
2009

553.000.000 ton CO2 e
688.000.000 ton CO2 e
92.000.000 ton CO2 e

60.000.000 ton CO2 e

313.000.000 ton CO2 e

58.000.000 ton CO2 e

Regional Sumatera:• REL is defined by central government• Regional Action Plan to reduce GHG emissions must be taken into
account Regional Development Plan and Province Spatial Planning

Result:1.Source carbon
2.Sink carbon

Ruandha Agung Sugardiman
DG of Forestry Planning, MoF"IPCC Expert Meeting:Role of Remote Sensing in Forest and National GHG Inventories“
23 ‐ 25 October 2012, Hayama, Japan

Remote sensing data-based forest
inventories and carbon stock reporting
IPCC Expert meeting on Role of Remote Sensing in Forest and National GHG
Inventories

October 23-25, 2012
Hayama, Japan

Brice Mora

Outline Technical capabilities of remote sensing sensors for thegeneration of (national) REDD+ information products
 Operational level of forest information products in REDD+ context
 Key synergy potentials for generating improved forest information
products• a Canadian example
 Monitoring of changes in forest area
• forest degradation
 Biomass map comparison (Uganda case) GOFC-GOLD REDD Sourcebook (update)

Source: Romijn, Herold, et al. 2012

Country national forest monitoring capacity gaps

Source: Romijn, Herold, et al. 2012

Technical capabilities of remote sensing sensors for thegeneration of (national) REDD+ information products
Source: De Sy et al., Herold, Achard, Asner, ...,
2012.
Synergies of multiple remote sensing data
sources for REDD+ monitoring.
In:
Current Opinion in Environmental Sustainability- black = very suitable- dark grey = suitable- light grey = contributing- white = limited to notechnical capabilities

Operational level of forest information products in
REDD+ context

Source: De Sy et al., 2012

Synergies of RS data: the Canadian example>25,000 km
min. swath: 500 m
min. 3 hits / m2
25 m cells with alist of attributes

LIDAR transectsColors: ecozones

1,000 km

Synergies of RS data: the Canadian example

Synergies of RS data: terrestrial Lidar

Animation available online:
http://www.youtube.com/watch?v=1y43jjt5wN0&feature=plcp

WU terrestrial Laser Scanning Research
http://www.grs.wur.nl/UK/Research/Remote+sensing+science/LiDAR/

Monitoring of changes in forest area

http://www.decc.gov.uk/assets/decc/11/tacklingclimate-change/international-climate-change/6316drivers-deforestation-report.pdf

Sources:
Kissinger et al., 2012
Hosonuma et al., 2012

Monitoring of changes in forest area

http://www.decc.gov.uk/assets/decc/11/tacklingclimate-change/international-climate-change/6316drivers-deforestation-report.pdf

Source: Kissinger et al., 2012

Monitoring of changes in forest area
Spatial distribution of deforestation areas and its follow-up land use
in Indonesia (Landsat data 2000-2009 period)

http://www.decc.gov.uk/assets/decc/11/tackling-climate-change/international-climatechange/6316-drivers-deforestation-report.pdf

Direct approaches to detect forest degradation
Highly Detectable

Detection limited &increasing data/effort

Detection very limited• Deforestation• Forest fragmentation• Recent slash-and-burn
agriculture• Major canopy fires• Major roads• Conversion to tree
monocultures• Hydroelectric dams
and other forms offlood disturbances• Large-scale mining• Selective logging• Forest surface fires• A range of edgeeffects• Old-slash-and-burn
agriculture• Small scale mining• Unpaved secondary
roads (6-20-m wide)• Selective thinning of
canopy trees• Harvesting of most
non-timber plants
products• Old-mechanized
selective logging• Narrow sub-canopy
roads (<6-m wide)• Understory thinningand clear cutting• Invasion of exotic
species(using Landsat-type observations)

Peres et al., (2006) TREE

Monitoring of changes in forest area
Landsat 1990

Landsat 2000

Monteiro, A., Cardoso, D., Conrado, D., Veríssimo, A., & Souza Jr., C. (2011). Boletim Transparência Manejo Florestal Estado do Pará (2009 e 2010) (p. 16).
Belém: Imazon.

Biomass maps comparison in Uganda
Spatial Data
Landsat
MODIS
National LC
Global LC
National LC
Global LC
Global LC

Biomass data
+ Field data *+ Field data *+ Field data
+ Biome Av.
+ Biome Av.
+ Field data
+ IPCC Tier 1

Reference
Avitabile et al., 2012
Baccini et al., 2008
NBS, 2003 (Reference map)
Gibbs & Brown, 2007
Drigo, 2006
Henry et al., 2010
Reusch & Gibbs, 2008(* = subset of NBS field data)

Total
TotalBiomass
Biomassof Uganda
of Uganda(Tg)
Avitabile et al., 2011 (CBM)

Biomass (1000 T)

1.
2.
3.
4.
5.
6.
7.

18

Biomass maps comparison in Uganda
Difference maps (!)

Avitabile et al., 2011 (CBM)

GOFC-GOLD REDD+ Sourcebook updates
Notable sourcebook updates:
 UNFCCC/REDD+ progress••negotiation / policy topics
new country experiences

 Revise technical sections based on user survey
 Usefulness and presentation• training materials (lectures etc.)• develop interactivity (web-page, wiki, links)

Sponsors of the Global Terrestrial Observing System

Sourcebook authors:
Olivier Arino, Gregory P. Asner, Luigi Boschetti, Barbara Braatz, Emilio Chiuvieco, Ivan Csiszar,
Bernardus de Jong, Michael Falkowski, Sandro Federici, Scott Goetz, Nancy Harris, Yasumasa Hirata,
Anja A. Hoffman, Hans Joosten, Chris Justice, Josef Kellndorfer, Stephen Kull, Werner Kurz, Eric
Lambin, Mike McCall, Suvi Monni, Rebecca Moore, Brice Mora, Erik Næsset, Ross Nelson, Marc
Paganini, Tim Pearson, Gary Richards, David Roy, Jeremy Russell-Smith, David Shoch, Florian Siegert,
Margaret Skutsch, Allan Spessa, Patrick Van Laake, Michael Wulder

Support for GOFC-GOLD REDD working group :Conclusions (1/2)
Remote sensing is an essential component of monitoring forests for REDD+.



Many suitable remote sensing sensors available but operational usefulness for
REDD+ is often constrained.
••

Coordinated international efforts of the remote sensing community and data
providers needed Landsat Data Continuity Mission / Sentinel-2.
Countries have confidence investments in building capacity for use of
remote sensing forest monitoring will provide long-term benefits under
REDD+.

Transition from remote sensing research to more operational generation of
information products on the national level requires additional efforts.

Research efforts needed to further develop
approaches for different national circumstances.



Remote sensing capacities do exist in developing countries. Further developmentof technological transfer and capacity development as part of South-South andregional cooperation needed.

andconsolidate

appropriate

Conclusions (2/2)
 Degradation monitoring:• less efficient than for deforestation: lower C-emissions per ha versus higher
costs & lower accuracies• significance of different degradation processes
 Biomass/carbon stock (change) data are essential:• a few products suitable for national use• largely uncertain in many developing countries
 Need for efficient, effective and equitable solutions integrating different data
sources for REDD+ MRV

 GOFC-GOLD REDD+ Sourcebook as a hub Next GOFC-GOLD Sourcebook version released for COP18

References

Asner, G. P., Knapp, D. E., Broadbent, E. N., Oliveira, P. J. C., Keller, M., & Silva, J. N. (2005). Selective
logging in the Brazilian Amazon. Science, 310, 480–482.
http://www.sciencemag.org/content/310/5747/480.full.pdf

Avitabile, V., Herold, M., Henry, M., & Schmullius, C. (2011). Mapping biomass with remote sensing: acomparison of methods for the case study of Uganda. Carbon balance and management, 6(1), 1–14.
http://www.cbmjournal.com/content/6/1/7



De Sy, V., Herold, M., Achard, F., Asner, G. P., Held, A., Kellndorfer, J., & Verbesselt, J. (2012). Synergies ofmultiple remote sensing data sources for REDD + monitoring. Current Opinion in Environmental Sustainability,
4, 1–11.
http://dx.doi.org/10.1016/j.cosust.2012.09.013

GOFC-GOLD. (2011). A sourcebook of methods and procedures for monitoring and reporting anthropogenic
greenhouse gas emissions and removals caused by deforestation, gains and losses of carbon stocks in forests
remaining forests, and forestation - Version COP17-1. Carbon (p. 209). Alberta, Canada.
http://www.gofcgold.wur.nl/reddHerold, M., Román-Cuesta, R. M., Mollicone, D., Hirata, Y., Van Laake, P., Asner, G. P., Souza, C., et al.
(2011). Options for monitoring and estimating historical carbon emissions from forest degradation in thecontext of REDD+. Carbon balance and management, 6(13), 1–7. doi:10.1186/1750-0680-6-13
http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=3233497&tool=pmcentrez&rendertype=abstract

Kissinger, G., Herold, M., & De Sy, V. (2012). Drivers of Deforestation and Forest Degradation: A Synthesis
Report for REDD+ Policymakers (p. 26). Vancouver, BC, Canada.
http://www.decc.gov.uk/assets/decc/11/tackling-climate-change/international-climate-change/6316-drivers-deforestationreport.pdf

Romijn, E., Herold, M., Kooistra, L., Murdiyarso, D., & Verchot, L. (2012). Assessing capacities of non-Annex I
countries for national forest monitoring in the context of REDD+. Environmental Science & Policy, 19-20, 33–
48.
http://www.sciencedirect.com/science/article/pii/S1462901112000202

Thank you
Brice Morabrice.mora@wur.nl

http://www.gofcgold.wur.nl/
Twitter: @GOFCGOLD_LC

Greenhouse Gas Inventories:
The Case of Land Use, Land Use Change and Forestry(LULUCF) Mapping in Eastern and Southern Africa

Erick Khamala
Remote Sensing Officer
Regional Centre for Mapping ofResources for Development (RCMRD)
P.O Box 632 – 00618, Nairobi, Kenya
Email: ekhamala@rcmrd.org
Website: www.rcmrd.org

Hayama, Japan
23 – 25 October 2012

IPCC Expert Meeting:Role of Remote Sensing in Forest and National GHG Inventories

Presentation Outline
 RCMRD: Who we are Land Use / Land Cover Mapping: Sampling the RCMRD Menu
 GHG Inventories: LULUCF Mapping in Eastern and Southern Africa Region
 Looking Ahead: Challenges and Opportunities

1. RCMRD: Who we are Based in Nairobi, Kenya
 An inter-governmentalorganization establishedin 1975 under the auspicesof the Organization of
African Unity (OAU), now
African Union (AU) and theUnited Nations Economic
Commission of Africa(UNECA)

About RCMRD (Cont…)

Key Services
 Resource Mapping using Geo-Information
technologies (GIS, Remote Sensing, GPS) Training in modern Geo-information
technologies and applications and inInformation and Communication
Technologies (ICTs)
 Advisory and consultancy services in ourareas of specialization
 Servicing and maintenance of surveying
and mapping equipment

Management Structure
Conference of Ministers
Ministers responsible for Lands / Environment

Governing Council
Permanent / Principle Secretaries responsible for Lands / Environment

Technical Committee
Surveyor Generals

Director General2. Land Use / Land Cover Mapping: Sampling the RCMRD Menu
 FAO Africover Project: LC mapped for10 countries; Burundi, DR Congo,
Egypt, Eritrea, Kenya, Rwanda, Somalia, Sudan, Tanzania and Uganda Land use / Land Cover Mapping and Change Detection of South
Kordofan, Sudan; undertaken under the Range Management Strategy Study andKhor Abu Habil Catchments Basins Planning and Water Development Study

 Mapping Gums and Allied Dryland Resources in Karamoja,
Uganda: A Presidential Initiative that helped alleviate poverty and conflict in Karamoja
 Rehad River and Barrage Pond Mapping, Sudan: A project that showed
the increasing pressure on water resources due to agricultural expansion

 Resource and Pastoral Mobility Mapping, Northern Kenya: A projectthat supported Drought Risk Reduction

 Land Degradation and Natural Habitat Conservation, GHA:
Implemented under the AMESD program Forest Restoration Strategy, Mau Forest, Kenya: A project that mapped
Forest Cover Change and provided a strategic approach for Forest Cover Restoration

 Climate Change Implications on Agriculture: The case of Baringo, Kenya

Rehad Driver and Barrage Pond Mapping, Sudan

rdth
331
48 stOctober1987
October2008
January1988
January2009

Cross-Section of the Canal at Kilo56

Illegal Breakage Points By Upstream Farmers

Mau Forest,
Kenya

Historical
AssessmentofDeforestation

2000
2008
1986
1973

ThepaththeMauhascome!

Total land to bereforested will
be as follows:
Phase 1: 53,376
Phase 2: 28,828
Phase 3: 10,635
TOTAL: 92,839(Area in Hectares)

Total land to bereforested will
be as follows:
Phase 1: 39,571
Phase 2: 53,269
TOTAL: 92,840(Area in Hectares)

Total land to bereforested will
be as follows:
Phase 1: 62,688
Phase 2: 25,711
Phase 3: 4,441
TOTAL: 92,840(Area in Hectares)

BudgetOptionsforEachOption..... implications to agriculture

Lake Baringo
Catchment, Kenya

1973
1986
2009
(Kenya’s Vision 2030) 2030

2050
2080
LEGEND
Bare
Anthropogenic Disturbances
CraterForestGrassland
Lake
Open to Closed Shrubs
Riverine Vegetation*If factors were to remain constant

Historical and Future Land Use / Land Cover Change Analysis

Understanding Future Climate Change Trends
and their implications to Agriculture

Current Potential
Pyrethrum
Growing Areas

Future Potential
Pyrethrum
Growing Area

LegendCurrently Agriculture: Future Pyrethrum
Currently Forest: Future Pyrethrum
Currently Open to Closed Shrubs:
Future Pyrethrum
Currently Riverine Vegetation:
Future Pyrethrum
Area in Km2

HARNESSING NATURAL RESOURCES TO
ALLEVIATE POVERTY AND CIVIL CONFLICT:
Developing the Gum Arabic and Allied Dryland Resources
Sub-Sector in Karamoja Region of Uganda

Republicof
UgandaAlternative
Karamoja
Regionc) Importanceof GumResources
arabic, aloe andin
otherallied dryland
resources
Gum arabic
 is used primarily in the food industry as a stabilizer such as in the soft drinks
industry (Coca Cola, etc)

Aloe
 Health products help rebuild human skin, hair and digestive health

Other dryland resources Saddlewood – cosmetic industry
 Shea tree – shea nut butter
 Amarula – Amarula wine

Natural “plantation” of Aloe

Resource Maps: Distribution and Density

Gum Arabic

AloeMeasuring Production

Aloe Yield Estimation
DISTRICT

Density Class

Area (ha)Population (stems)

Estimated Yield (MT)

700.008,295.98

1,451,797

72.59

Medium2,363.00

182,529.69

215,658,829

10,782.94

High4,600.00

35.00

120,750

6.04

190,825.67

217,231,376

10,861.57

700.00324.52

56,791

2.84

Medium2,367.00

140,622.87

166,427,167

8,321.36

High6,300.00

45,009.33

212,669,084

10,633.65

185,956.71

379,153,042

18,957.65

700.0014715.08

2,575,140

128.76

Medium

1,863.00

116690.95

108,697,620

5,434.88

High6,033.00

2352.80

10,645,832

532.29Subtotal

133,758.83

121,918,592

5,868.45

TOTAL

510,541.21

718,303,010

35,915.15

KotidoLow

Mean Density(ha)

SubtotalMorotoLow

Subtotal

Nakapiripirit

LowEstimated Yield for Aloe

20000
18000
16000
14000
12000
Estimated Yield (Metric
10000
Tonnes)

KotidoMoroto8000

Nakapiripirit

6000
4000
2000
0
Low

Medium

High

Total

Density

Total yield of Aloe in Karamoja Region = 35,915.15 metric tonnes

3. GHG Inventories:
LULUCF Mapping in Eastern & Southern Africa Region RCMRD is implementing TWO MAJOR PROGRAMS:o LULUCF Mapping for Kenya: Under the Forest Preservation
Program being implemented via the Japanese Grant Aid to
the Republic of Kenya.
Implementers: Pasco Corporation (Japan), RCMRD, Government of Kenya (Kenya
Forest Service and the Department of Resource Surveys and Remote Sensing)o Eastern and Southern Africa Land Cover Mapping for GHG
Inventories: Funded by USAID
Implementers: RCMRD, EPA (USA) Both programs commenced on 1st March 2012

Approach Multi-institutional:o North – South partnershipo Partnership with Government Agencies

 Use of Earth Observation Data:o Three epochs high resolution EO data (1990, 2000, 2010)
o Incorporates use of existing ancillary data and field data Capacity development:o On-the-job training of nationals in the mapping methodology

 Consultative process:o National stakeholder engagement

Methodology
 Satellite image processing:o Image quality assessmento Re-projectiono Ortho-rectification

 National consultationsoooTo agree on classification scheme and definition of classes
Collect existing ancillary data
Establish networks with countries

 Image classification:o Using 6 IPCC classes (Forest land, Cropland, Grassland, Wetland, Settlement, Other land)at details agreed with each countryo Preliminary image interpretation for each epoch (on-screen digitization / segmentation /supervised classification)o Ground truthing fieldworko Final image interpretationo Accuracy assessmento Change detectiono Statistics derivationo Cartographic design and draft final map compilationo Internal quality check at all stages of image classification

Methodology (Cont..)

 Draft final products preparation:
o
o
o
o
o

Three-epoch land use maps (1990, 2000, 2010)
Change maps (1990 – 2000, 2000 – 2010, 1990 – 2010)Land use and land use change statistics
Procedure manual
Project Report Stakeholder engagement:o National mapping agencies (quality control throughout the mapping process)o National stakeholders engagement on final products Final products preparation and submission:
o
o
o
o
o
o

Three-epoch land use maps (1990, 2000, 2010)
Change maps (1990 – 2000, 2000 – 2010, 1990 – 2010)Land use and land use change statistics
Procedure manual
Project data and metadata
Project Report

The Case of Kenya: using ALOS AVNIR2, DMC, Landsat
 Primary satellite images for 2010 LU interpretation: ALOS AVNIR2ALOS – Advanced Land Observing Satellite
AVNIR2 – Advanced Visible and Near Infrared Radiometer Type 2
Key characteristics of ALOS AVNIR2:
o 10m Spatial Resolution (at Nadir)o Multispectral (4 bands)o 70 Km Swath Width (at Nadir) Secondary satellite images for 2010 LU interpretation: DMC2 andLandsat TM

DMC – Drought Monitoring Constellation
Key characteristics of DMC2:
o 22m spatial resolutiono Multispectral (3 bands)o 650Km Swath Width Primary and secondary satellite images for 1990 and 2000 LU
interpretation: Landsat TM and ETMKey characteristics of Landsat:o 28.5m spatial resolutiono Multispectral (7 bands for TM and 8 bands for ETM)
o 185Km Swath Width

ALOS AVNIR2:
27th Jan. 2011

DMC2: 24th Feb. 2011

Landsat TM:
25th Feb. 1987

Mapping Procedure Ortho-rectification of images done by PASCO Corporation, Japan RCMRD began with 2010 LU interpretation:







Forest land was the first to be mapped via on-screen digitization using MADCAT software
Reason for using on-screen digitization was due to national definition of Forest Land: Minimum landarea be at least 0.5 Ha; Minimum tree crown cover (cc) be at least 15%; Minimum tree height be
at least 2mSettlements were also mapped via on-screen digitization using MADCAT software
Cropland, Grassland, Wetland, Other land were mapped via segmentation using MADCAT software
All 6 IPCC class layers were then merged into one shapefile for LU 2010 using ARCGIS 10 software

The LU 1990 and LU 2000 were mapped by editing the LU 2010 shapefile using MADCAT softwareChange detection was done in a vector environment using ArcGIS 10 software:
Change maps produced were: 1990 – 2000, 2000 – 2010, and 1990 - 2010
Potentially, a total of 36 LU change classes were to be derived for each change map

Was done using ArcGIS 10 at national scale and further split to 1:100,000 scale topos index grids
Image maps in false color were also produces based on the 1:100,000 scale topos index grids






Done on pilot basis.
ALOS PRISM used for GCP collection
Maps for Forest CC, Forest Type, Forest Legal Status and Agro-ecological Zones derived
Detailed forest inventory undertaken with support of derived MFE maps

 1990 and 2000 LU interpretation:
 Change detection mapping: Cartographic design and map compilation:

 Detailed Mapping of Mau Forest Ecosystem (MFE)TrendofKenyaLandUseCategories– 1990,2000and2010(AreainSq.Km)
450000.00

400000.00

350000.00

AreainSq.Km

300000.00

250000.00

200000.00

150000.00

100000.00

50000.00

0.00
1990_Area_Sq_Km

Forestland
44296.00

Cropland92433.00

2000_Area_Sq_Km

35399.00

96516.00

2010_Area_Sq_Km

41557.00

100661.00

Grassland
427577.00

Setlements
548.00Wetland17319.00

OtherLand
10045.00

426458.00

887.0017225.00

15758.00

423554.00

1237.00

14817.00

10435.00

Eastern and Southern Africa: using Landsat
The Case of Malawi
 Initially targeted to carry out mapping for 2000, 2005 and 2010 2005 Landsat data could not be used due to stripping problems
occasioned by the Scan Line Corrector (SLC) failure in Landsat 7
 Mapping now being done for 1990, 2000 and 2010
 Supervised classification being used to carry out the land use
mapping for the three epochs
 Change detection mapping not being done but relevant LU
statistics for each year (1990, 2000, 2010) to be submitted
to the ALU tool for GHG computations

False Colour Composit
Landsat TM, 2010

Natural Colour Composit
Landsat TM, 2010

Malawi LU Mapping

Classes Mapped

2010
2000
1990

1. Dense Forest
2. Moderate Forest
3. Sparse Forest
4. Closed Grassland
5. Open Grassland
6. Closed Shrubland
7. Open Shrubland
8. Perennial Crops
9. Annual Crops
10. Wetlands
11. Water Bodies
12. Settlements
13. Other Lands

4. Looking Ahead: Challenges and Opportunities
A. Challenges:
 Images dates did not exactly match the intended years
 Data gaps especially cloud cover - for Kenya it was easer tocircumvent the cloud problem using alternative images
 Mapping forest category was a challenge by virtue of the agreed
definitions (using both methods used for Kenya and Malawi)
 Expanded classes beyond the six IPCC classes (in the case of
Malawi’s 13 classes) possed an added challenge to the mapping Data availability (consistency of dates, data gaps – 2005, etc)

B. Opportunities:
 Synergy and capacity at national level developed Mapping methodology (documented in manual) developed Developed LU base maps will facilitate cheaper and faster future
LU mapping New satellites (Sentinel 2, Landsat 8, etc) should provide data
continuity
 Lessons learnt from the different methodological approaches Growing awareness/interest on the important of GHG inventorying

Integration of optical and SAR data forland cover classification
Hasi Bagan, Yoshiki Yamagata
CGER
National Institute for Environmental Studies, Japan

IPCC Expert Meeting: Role of Remote Sensing in Forest and National GHG Inventories
Hayama, Japan, 23-25 October 2012

Content
1. Integration of optical and SAR data for classification¾ Subspace classification methods¾ Evaluate the classification accuracy

2. Land cover classification and change analysis¾ Combination of remote sensing, GIS, and population census
data for urban growth analysis in Tokyo between 1972-2011¾ Land cover in Inner Mongolia and change analysis in the
Horqin sandy land from 1975 to 2007

1. Integration of optical and SAR data for classification¾ Subspace classification methods¾ Evaluate the classification accuracy

2. Land cover classification and change analysis¾ Combination of remote sensing, GIS, and population census
data for urban growth analysis in Tokyo between 1972-2011¾ Land cover in Inner Mongolia and change analysis in the
Horqin sandy land from 1975 to 2007

Subspace classification methods
We have developed Subspace classification
methods for multispectral, hyperspectral, andSAR data classification, which written in C++.
Subspace method:1) can achieve dimension reduction and classification
concurrently;
2) possesses high speed convergence.

Subspace classification method
Pixel:x㻝㻚 㻼㼑㼞㼒㼛㼞㼙㻌㻼㻯㻭㻌㼛㼚㻌㻿㼑㼘㼑㼏㼠㻌
㼟㼠㼡㼐㼥㼍㼚㼐㻌㼠㼑㼟㼠㻌㼟㼍㼙㼜㼘㼑㼟㻌
㼒㼛㼞㻌㼑㼍㼏㼔㻌㼏㼘㼍㼟㼟㼀㼞㼍㼕㼚㼕㼚㼓

㼑㼍㼏㼔㻌㼏㼘㼍㼟㼟㻌㼟㼑㼜㼍㼞㼍㼠㼑㼘㼥㻧㻞㻚 㻯㼍㼘㼏㼡㼘㼍㼠㼑㻌㻌㼙㼍㼖㼛㼞㻌㼑㼕㼓㼑㼚㼢㼍㼘㼡㼑㼟㻌㼠㼛㻌㼓㼑㼚㼑㼞㼍㼠㼑㻌㼏㼘㼍㼟㼟㻌㼟㼡㼎㼟㼜㼍㼏㼑㼟㻧㻟㻚 㻾㼛㼠㼍㼠㼑㻌㼟㼡㼎㼟㼜㼍㼏㼑㻌㼠㼛㻌㼞㼑㼐㼡㼏㼑㻌㼑㼞㼞㼛㼞㻧㻌㼓㼛㻌䐟㻚㻼㼞㼛㼖㼑㼏㼠㼕㼛㼚900

㻝㻚 㻯㼛㼙㼜㼡㼠㼑㻌㼠㼔㼑㻌CompareP(i) x㼜㼞㼛㼖㼑㼏㼠㼕㼛㼚㻌㼘㼑㼚㼓㼠㼔㼟㻌㼎㼑㼠㼣㼑㼑㼚㻌㼜㼕㼤㼑㼘㻌㼍㼚㼐㻌㼑㼍㼏㼔㻌㼟㼡㼎㼟㼜㼍㼏㼑㻧㻞㻚 㻼㼕㼤㼑㼘㻌㼤㻌㼕㼟㻌㼜㼘㼍㼏㼑㼐㻌㼕㼚㻌㼠㼔㼑㻌㼏㼘㼍㼟㼟㻌㼠㼔㼍㼠㻌 㻻㼡㼠㼜㼡㼠㼔㼍㼟㻌㼠㼔㼑㻌㼘㼍㼞㼓㼑㼟㼠㻌㼜㼞㼛㼖㼑㼏㼠㼕㼛㼚㻌㼘㼑㼚㼓㼠㼔㻚

SubspaceOA) Only 3 parameters are required to be set, and these can easily be
determined by an automatic procedure.
B) The computational speed is faster than SVM and similar to the MLC.
C) Could be a promising tool for this and future land cover classifications.
(Bagan. H.& Yamagata.Y., PE&RS, 76,(11), 2010)

1. Study area
Location of the study area

AVNIR-2(4, 3,1), 2009/04/13

2. Data sets
AVNIR-2 image acquired in 13 April 2009 with a 15-m
spatial resolution
PALSAR level 1.1 at L-band in quad-polarization mode
acquired in 7 April 2009
AVNIR-2

PALSAR

Spectral information

Layover

Scattering information

2. Data sets
1. AVNIR-2: 4 visible and near-infrared bands
2. PALSAR: 4 channels HH/HV/VH/VV Level 1.1 product
Convert to a normalized radar cross section (dB ), as follows:0
V 1.1
10.log10 I 2  Q 2  80
product

3. Coherency matrix T3: 9 independent elements from T3

T3

k k

*T

2
ª
SHH  SVV«
1«
SHH  SVV SHH  SVV
2««« 2 SHV SHH  SVV¬

SHH  SVV SHH  SVV
SHH  SVV

2

2 SHV SHH  SVV

2 SHH  SVV SHV º»»
2 SHH  SVV SHV »
»
2
4 SHV
»
¼

3 real diagonal elements and the real and imaginary parts of the 3
off-diagonal elements

3. Land cover classification
We designated 11 land-cover types in this experiment, based onfield survey, IKNOS, GeoEye-1, and Digital Map 2500 GIS data.

3. Land cover classification
we set up five data set groups designated G1–G5 to evaluate how
different source data sets contributed to classification accuracy.

Subspace, Support vector machine (SVM) , and Maximum likelihoodclassifier (MLC) methods were each applied to the five data set groups.

3. Land cover classification
Overall classification accuracy

Combining optical AVNIR-2 and PALSAR, achieved betterclassification results than the optical-only G1 data set.
Subspace method performed better than the SVM or MLC
method in high-dimensional data set classification.

3. Land cover classification
G1: 4BG3: 13B

G2: 8BG4: 14B

No data Urban Tree Water Paddy Wheat Paddy/f Lotus

BG5: 17N

Crop

Grass

Sparse Road

Conclusions and future work
Conclusions
SAR data and optical data provide complementary
information, and their combination leads to increasedclassification accuracy.
Fully polarimetric PALSAR data improve theaccuracy of the land cover classifications.
Subspace method is promising as a tool forclassification using optical+SAR images.
Future work
Add other polarimetric indicators to assess theclassification accuracy.
(Bagan. H., Kinoshita.T, & Yamagata.Y., IEEE TGRS, 50(4), 2012)

1. Integration of optical and SAR data for classification¾ Subspace classification methods¾ Evaluate the classification accuracy

2. Land cover classification and change analysis¾ Combination of remote sensing, GIS, and population census
data for urban growth analysis in Tokyo between 1972-2011¾ Land cover in Inner Mongolia and change analysis in the
Horqin sandy land from 1975 to 2007

Study area

Study area is composed of Tokyo, Chiba, Kanagawa, Saitama, and the part of Ibaraki.
Three Landsat scenes, namely the center, west, and east, cover the study area.

area䠖
15,930 km2
population䠖 37.6 million

The gray image is a DEM generated from SRTM.
Right lines refer to the major railway and metro lines.

Landsat MSS/TM/ETM+
We acquired Landsat images to interpret land-cover changes from 4
separate dates (nominally 1972, 1987, 2001, and 2011).

First, we designated 5 to 9 land-cover types in 1972, 1987,
2001 and 2011 Landsat imagery;
Then, we aggregated land-cover categories into 5 categories
to ensure uniform labeling and therefore facilitatecomparisons.

Land cover change vs. population growth

Total area of each of the land-cover categories
from 1972 to 2011.

Urban area increased rapidly,
and there was a marked
decrease in cropland.
Forest decreased between
1972 and 1987, after 1987
there was a steady increase offorest growth.
Population increased from25.2 million in 1970 to 37.6
million in 2010.
Rate of urban growth
exceeded the population
growth rate by a factor of 2.6

Population growth during 1970–2010 in 5-year increments.
(Bagan. H.& Yamagata.Y., Remote Sensing of Environment, 2012)

Change of urban/built-up in 1-km2 grid cells from 1972 to 2011

Urban expanded to the suburban, and decreased in the center of the city.

Change of cropland area in 1-km2 grid cells from 1972 to 2011

Decreases in cropland took place in either large and flat areas or in proximity to urban regions.

Change in population density between 1970 and 2005

Spatial distribution of population increase trends is similar to the urban expansion trends.

Correlations between the changes of land-cover categories(1972–2011) and population density change (1970–2005)

Urban

Crop

Forest

Grass

Water

Urban

1

Crop

-0.76911

Forest0.0213-0.3915

1Grass-0.1904-0.0445-0.4515

1

Water-0.1025-0.1306-0.1153

0.0431

1

Population

0.5902-0.4778-0.0066-0.08900.0213Population

1(Bagan. H.& Yamagata.Y., Remote Sensing of Environment, 2012)

Scatter plot of urban changes versus cropland changes
1

Crop change 1972 - 2011 (km2 )

0.5

0

-0.5
y = -0.8752x - 0.0058
r = -0.7691
-1
-1

-0.5

0

0.5

1

Urban change 1972-2011 (km2 )Strong, negative linear relationship between urban/built-up
change and cropland change.

The correlation between the urban/built-up changes for
1972–2011 and population density changes for 1970–2005
Population change 1970 - 2005 (thousand)

15

7.5

0

-7.5

y = 6.6383x - 0.1942
r = 0.5902

-15
-1

-0.5

0

0.5

1

Urban change 1972-2011 (km2 )Urban/built-up change has a significant positive correlation withthe population density change.

1. Integration of optical and SAR data for classification¾ Subspace classification methods¾ Evaluate the classification accuracy

2. Land cover classification and change analysis¾ Combination of remote sensing, GIS, and population census
data for urban growth analysis in Tokyo between 1972-2011¾ Land cover in Inner Mongolia and change analysis in the
Horqin sandy land from 1975 to 2007

Inner Mongolia Autonomous Region

The Horqin Sandy Land is in the arid
and semi-arid southeastern part of the
Inner Mongolian Autonomous Regionin China.

1975

1987

1999

2007

Water Tree

Crop

Sandy Sparse Grass Urban Bare Wetland(Bagan, H. et al., IEEE JSTARS., 2010)

Land cover changes in Inner Mongolia from 1975 to 2007 using Landsat
Major changes areconversion of grass
and forest to
cropland fields,
rapid urban growth,and water area
decreased heavily
over the last three
decades.
1975

1987
1999
Water Tree

Crop

Sandy Sparse Grass Urban Bare Wetland

Bagan, H. et al., IEEE JSTARS., 20102007

In reaction to land degradation events, the central
government initiated the Sloping Land Conversion
Program (also known as Grain for Green) in 1999.
Inner Mongolia is implementation of the ecologicalmigration policy:
the families of local herders and farmers weremoved from desertified steppe and settled in newvillages which mainly located along river courses.

Today…….?

Thank you!

Minato Mirai, Yokohama.

Height: Lidar; RGB: airborne image

BRAZILIAN MINISTRY OF SCIENCE,TECHNOLOGY AND INNOVATIONNATIONAL INSTITUTE FOR SPACE RESEARCH

AmazonforestbiomassestimationusingXandPbandSARdata

CARLOSALBERTOPIRES DECASTROFILHO– MSc
CORINA DACOSTAFREITAS PhD
SIDNEI JOÃOSIQUEIRASANT´ANNA PhD

BRAZILIAN NATIONAL INSTITUTE FOR SPACE RESEARCH

Motivation
9 Carbon accounting is needed to support the objectives of international
agreement to mitigate global climate change. Need for forest biomass
estimation for carbon policy development;
9 Traditional forest inventories that rely on fieldwork require high costs,
specially in the tropics, where forest extends over large geographic areas
with difficult terrain;
9 Remote sensing is an important tool for tropical forest measurements;
9 ... “Brazil has 53% of the world´s REDDs (Reduce Emissions for
Deforestation and Degradation) potential” (FUJIHARA, 2010, apud
BONATELLI, 2010).

BRAZILIAN NATIONAL INSTITUTE FOR SPACE RESEARCH

Objective
Present the preliminary results using CADAF
forest inventories together with X and P band
SAR data to model the biomass stock in different
regions of the Amazon Forest.

BRAZILIAN NATIONAL INSTITUTE FOR SPACE RESEARCH

AmazonRegion

BRAZILIAN NATIONAL INSTITUTE FOR SPACE RESEARCH

AmazonRegionITALY
SLOVAKIABOSNIAFRANCEPORTUGALGERMANY
AUSTRIABELGIUMCZECHREPUBLICSPAIN

ENGLANDSWITZERLAND

NETHERLANDS

5 000 000 Km2

BRAZILIAN NATIONAL INSTITUTE FOR SPACE RESEARCH

AmazonRegion

BRAZILIAN NATIONAL INSTITUTE FOR SPACE RESEARCH

AmazonRegion

Canopyh:30to50m

GroundBRAZILIAN NATIONAL INSTITUTE FOR SPACE RESEARCHTechnicalProblem
OPTICALWITHOUT INFORMATIONINTERACTION WITH CANOPY

BRAZILIAN NATIONAL INSTITUTE FOR SPACE RESEARCHTechnicalProblem
OPTICALWITHOUT INFORMATIONINTERACTION WITH CANOPYBRAZILIAN NATIONAL INSTITUTE FOR SPACE RESEARCH

CloudCoverinBrazilianAmazon

Annual probability of obtaining a Landsat
TM scene with 10% or less cloud cover.

MonthlyprobabilitiesofobtainingaLandsatTMscenewith30%orlesscloudcover. Asner (2001).

BRAZILIAN NATIONAL INSTITUTE FOR SPACE RESEARCH

AmazonRegion

70’s mapping
80’s & 90’s mapping

BRAZILIAN NATIONAL INSTITUTE FOR SPACE RESEARCHAMAZONCARTOGRAPHYPROJECT
9 “Amazon Radiography”: SAR (Synthetic Aperture Radar) data
9 CENSIPAM (Operations and Management Center of the Amazonian
Protection System)
9 Use of PolInSAR X and P bands
9 2.9 x 0.9m spatial mapping resolution  5.0 x 5.0m spatial product resolution
9 1st stage: Map approximately 1,150,000 km2
9 Process over 10 PB of data

BRAZILIAN NATIONAL INSTITUTE FOR SPACE RESEARCH

CartographicBlank

1.150.000km2

ForestNon-Forest area
Forest area
Area of Project

BRAZILIAN NATIONAL INSTITUTE FOR SPACE RESEARCHTechnicalSolution
PBandXBandCANOPYGROUND

BRAZILIAN NATIONAL INSTITUTE FOR SPACE RESEARCHTechnicalSolution
PBandXBandDSM

DTM
CANOPYGROUNDBRAZILIAN NATIONAL INSTITUTE FOR SPACE RESEARCH

X-band interferometry(Digital Surface Model – DSM)

P-band Interferometry(Digital Terrain Model – DTM)

BRAZILIAN NATIONAL INSTITUTE FOR SPACE RESEARCH

DSM

Digital (Interferometric) Height Model
Hint

DTM

Hint = DSM – DTM
Hint is related to trees height
related to Biomass

BRAZILIAN NATIONAL INSTITUTE FOR SPACE RESEARCH

PHASESOF“AMAZONRADIOGRAPHY”PROJECT•FieldSurveyData•ProcessingofSARdata•ExtractionofCartographicFeatures

BRAZILIAN NATIONAL INSTITUTE FOR SPACE RESEARCHBRAZILIAN NATIONAL INSTITUTE FOR SPACE RESEARCH

RADARPRESIGNSOLUTIONSIDEALSOLUTION 1

SOLUTION 2

BRAZILIAN NATIONAL INSTITUTE FOR SPACE RESEARCHTOPOGRAPHICMAP(1:50.000)

BRAZILIAN NATIONAL INSTITUTE FOR SPACE RESEARCHTOPOGRAPHICMAP(1:50.000)

BRAZILIAN NATIONAL INSTITUTE FOR SPACE RESEARCHTOPOGRAPHICMAP(1:50.000)

BRAZILIAN NATIONAL INSTITUTE FOR SPACE RESEARCH

CarbonStockBRAZILIAN NATIONAL INSTITUTE FOR SPACE RESEARCH

ProjectStatus

Cartography (20%)

Processing (38%)

Field Survey Data (100%)BRAZILIAN NATIONAL INSTITUTE FOR SPACE RESEARCH

CarbonDynamicsofAmazonianForestProject
9 proposedbytheGovernmentofBrazilandtheForestryandForestProductResearchInstituteinJapan:agreementwithJICAin2009
9 AimsatbuildingafoundationonconservingAmazonianforesttocontributetothecountermeasuresagainstdeforestationandglobalclimatechange,includingREDD
activities
9 INPA(BrazilianNationalInstituteforAmazonResearch):205newcontinuousforest
inventories(CFI)inaccordancewithIPCCguidelineof2006
9 plotsof125x20minSãoGabrieldaCachoeiraandUnini:DBH(>10cm)
9 LIMAetal.Allometricmodelsforestimatingabove andbelowgroundbiomassin
AmazonianforestatSãoGabrieldaCachoeiraintheupperRioNegro,Brazil.Forest
EcologyandManagement,227(2012)163172.

BRAZILIAN NATIONAL INSTITUTE FOR SPACE RESEARCH

DataReceived
Cam1AFi,0.126509321853519,67.0155487954617,29ago109:05:07
Cam1AIn,0.127532836049795,67.0159766077996,29ago108:11:25
Cam1BFi,0.12435257434845,67.0145147200674,29ago109:53:56
Cam1BIn,0.125418165698648,67.0149656664581,29ago109:32:50……

Plotarea:

125m

 Coordinatetransformationfrom
Lat/LontoUTMfromcenterpoints

BRAZILIAN NATIONAL INSTITUTE FOR SPACE RESEARCH

DataReceived
Cam1AFi,0.126509321853519,67.0155487954617,29ago109:05:07
Cam1AIn,0.127532836049795,67.0159766077996,29ago108:11:25
Cam1BFi,0.12435257434845,67.0145147200674,29ago109:53:56
Cam1BIn,0.125418165698648,67.0149656664581,29ago109:32:50……

Plotarea:

10m- Acquisition of the four coordinatesof the polygon.

- Construction of the plot´spolygonBRAZILIAN NATIONAL INSTITUTE FOR SPACE RESEARCH

BiomassPredictionModelsUsingRADARData
9 PolSARhighfrequencyCandXbandssaturateat40t/ha

9 PolSARlowfrequencyLandPbandssaturateatratesabove100t/ha
(BEAUDOINetal.,1994;POPEetal.,1994;IMHOFF,1995;KASISCHKEetal.,1997;
SAATCHIeMOGHADDAM,2000;ASKNEetal.,2003;SANTOSetal.,2003;e
SAATCHIetal.,2007)
9 PolInSARdatacanachieveratesover300t/ha(NEEFFetal.,2005;GAMA,2007;TREUHAFTetal.,2009;WILLIAMSetal.,2009;
NIetal.,2010)

BRAZILIAN NATIONAL INSTITUTE FOR SPACE RESEARCH

StateofArtRADARBiomassPredictionModelsfor
BrazilianAmazonForest
9 NEEFFetal.(2005)
9Tapajósregion:CenteroftheAmazonforest
9 44plotsrangingfrom5.0(SS)to350.0(PF)t/ha
9 r2 =0.84

AGBiomass

44 ,965  13,87 H int  10 ,566V 0pHH

9 SAMBATTIetal.(2012)
9Paragominasregion:EastofAmazonforest
9 42plotsrangingfrom0.042(SS)to385.077(PF)t/ha
9 r2 =0.83

log10 AGBiomass 6.68  6.94 log10 H int  0.03( pHV  pVV )BRAZILIAN NATIONAL INSTITUTE FOR SPACE RESEARCH

StudyArea

BRAZILIAN NATIONAL INSTITUTE FOR SPACE RESEARCH

StudyArea

São Gabriel da Cachoeira

Rio Unini

BRAZILIAN NATIONAL INSTITUTE FOR SPACE RESEARCH

StudyArea
9 RioUniniwatershed
9 Isolatedsite
9 densetropicalforest
X
areasofcontactbetweencampinarana(grassland)andevergreenforest
9 60plotsontheradarprocessingblockof6263w0102s

BRAZILIAN NATIONAL INSTITUTE FOR SPACE RESEARCH

StudyArea
9 SãoGabrieldaCachoeiracity
surroundings
9 originalhomogeneousprimaryforestinterceptedwithcampinarana
9 29plotsontheradarprocessingblockof
6768w0001s

BRAZILIAN NATIONAL INSTITUTE FOR SPACE RESEARCH

Google Earth (2010)

BRAZILIAN NATIONAL INSTITUTE FOR SPACE RESEARCH

Google Earth (2010)

BRAZILIAN NATIONAL INSTITUTE FOR SPACE RESEARCH

Google Earth (2010)

BRAZILIAN NATIONAL INSTITUTE FOR SPACE RESEARCH

Google Earth (2010)

BRAZILIAN NATIONAL INSTITUTE FOR SPACE RESEARCH

Google Earth (2010)

DSM (InSAR X)

Amplitude image Phh

DTM (InSAR P)

BRAZILIAN NATIONAL INSTITUTE FOR SPACE RESEARCH

MethodforthePreliminaryResults
9 featureextractionusingtextureandtopography:118independentvariables
9 Pearsoncorrelationmethodandforwardstepwisemultipleregressionmethodtoselectupto20features
9 bestsubsetsearchmethodtoanalyzethemultipleregressionmodelsusingthepreviouslyselectedfeatures
9 thehigherdeterminationcoefficientwaschosenafteranalyzingthecorrelation
betweenthevariables:nostatisticalsignificantcorrelationwasaccepted
9 graphswerebuilttoanalyzeeachvariable
9 detailedresidualandoutlieranalyzesarestilltobedone

BRAZILIAN NATIONAL INSTITUTE FOR SPACE RESEARCH

PreliminaryResultsandAnalysis– SãoGabrieldaCachoeira
9 29plotsrangingfrom169t/hato643t/ha
9 log10AGBiomass =18.48+1.33(log10Hint)+0.003(XhhMean)+8.62(PhvEntropy)
9 r2=0.69
2.9

9 largerrangeofAGBiomass values
logAGBiomass

9 significantcorrelationbetween
AGBiomass andHint

2.8
2.7
2.6
2.5
2.4

y=1.5468x+0.4787
R²=0.5631

2.3
2.2
1.15

1.2

1.25

1.3
1.35
logHint

1.4

1.45

1.5

BRAZILIAN NATIONAL INSTITUTE FOR SPACE RESEARCH

PreliminaryResultsandAnalysis– Unini
9 60plotsrangingfrom280t/hato668t/ha
9log10AGBiomass =1.86+0.64(log10Hint)+0.02(PvvCorrelation)– 0.21(PhvPvv)
9 r2=0.41
9 shorterrangeofAGBiomass values

2.9

9 poorcorrelationbetween
AGBiomass andHint

2.7

logAGBiomass

2.8

2.6
2.5
2.4

y=0.9288x+1.3633
R²=0.11

2.3
2.2
1.32

1.34

1.36

1.38
1.4
LogHint

1.42

1.44

1.46

BRAZILIAN NATIONAL INSTITUTE FOR SPACE RESEARCH

Conclusion
9 The mapping of the Amazon Forest has a strategic dimension
and it will provide a deeper understanding of the region, including
geospatial data of biomass and carbon stocks.
9Different regions of the Amazon forest must be first analyzed
separately, creating different models.
9The interferometric height has been proven as a significant
variable on the estimation of biomass.
9 Possibility for LIDAR data to calibrate the interferometric height
9 Further analyzes will be held.

BRAZILIAN NATIONAL INSTITUTE FOR SPACE RESEARCH

Thankyou!

pires@dpi.inpe.brBRAZILIAN NATIONAL INSTITUTE FOR SPACE RESEARCH

Orthoimage

OrthoimageMap

DTM

DSM

ForestStratificationData

VectorGeoinformation

BRAZILIAN NATIONAL INSTITUTE FOR SPACE RESEARCH

DoctoralThesis– MainStages
Featureextraction

FeatureSelection

FieldWork:
CFIandBiomass Measure by INPA
inCADAFPROJECT

Prediction Models  KDD
Models Evaluation

Featureclass Discretization

MapConstruction

Data features needed:- Idt and x/y of individual tree inside the plot;- Idt and Lat/Lon of the plot;- Accuracy of plot positioning;- date of inventory;- DBH;- height ;- family, genus and specie;- other notes (condition of the individual, type of forest).

BRAZILIAN NATIONAL INSTITUTE FOR SPACE RESEARCH

PreliminaryResults– SãoGabrieldaCachoeira

2.9
2.8
2.7
2.6
2.5
2.4
2.3
2.2

y=1.5468x+0.4787
R²=0.5631
1.15

logAGBiomass

logAGBiomass

logAGBiomass

9 Graphs

1.25

2.9
2.8
2.7
2.6
2.5
2.4
2.3
2.2

1.35
logHint

1.45

1.55

2.9
2.8
2.7
2.6
2.5
2.4
2.3
2.2
2.186

y=16.815x 34.298
R²=0.2388
2.191
2.196
3x3_PHV_En

2.201

y=0.0027x+2.2799
R²=0.0748
80

100

120
5x5_XHH_Me

140

160

BRAZILIAN NATIONAL INSTITUTE FOR SPACE RESEARCH

logAGBiomass

9 Graphs
2.9
2.8
2.7
2.6
2.5
2.4
2.3
2.2

y=0.9288x+1.3633
R²=0.11
1.3

1.35

2.9
2.8
2.7
2.6
2.5
2.4
2.3
2.2

logAGBiomass
0.3logAGBiomass

PreliminaryResults– Unini

0.2

0.1

1.4
LogHint

1.45

1.5

y=0.3255x+2.6396
R²=0.1482
0
0.1
PHVPVV

0.2

0.3

0.4

10

2.9
2.8
2.7
2.6
2.5
2.4
2.3
2.2

y=0.0291x+2.7789
R²=0.2918
8

6
4
5x5_PVV_Cor

20

IPCC Expert Meeting
Role of Remote Sensing in Forest and National GHG Inventories
Hayama, Japan
23 – 25 October 2012

Analysis of the Normalized Differential
Vegetation Index (NDVI) for the Detectionof Forest Degradation Coverage in MéxicoEngineer Carmen Meneses Tovar
Sub manager of Remote Sensing
National Forest Inventory and Geomatic Department
National Forest Commission - Mexico
cmeneses@conafor.gob.mx / meneses_carmen@hotmail.com

Superficie
BosqueForest de
Coverin Mexico
Other wooded Forest 20147303.57 ha

Forest66465860.23 ha

Objetive• Show the yearly trend of the forest coverage changes in the country• Find relations between forest coverage features and the Normalized
Differential Vegetation Index (NDVI) estimated from satellite imagery• Detect forest degradation throw NDVI changes.
2006
2012

Study area:
México 1.956.612 km2
Sensor: MODIS 250 m

Definitions: Understanding the Mexican
Vegetation condition"Primary vegetation" is defined as vegetation that preserves, in large part, its condition ofdensity, coverage, and number of species, from its original, primary, ecosystem andfrom that represented in the cartography of Use of the Ground and Vegetation fromINEGI at a scale of 1:250,000. (INEGI, 2004)
TEMPERATE FOREST
9 Conifers (Pino)
9 Oak (Quercus)
9 Sacred fir (Abies u Oyamel)
9 Cupressus (Cedro)
9 Douglas fir (Ayarin)
9 Mixed: Pine-oak and Oak-pine
9 Mesophilus

TROPICAL FOREST
9 High (perennial - deciduous)
9 Medium (perennial - deciduous)
9 Low (perennial - deciduous)
ARID AND SEMIARID AND OTHER

9 Chaparral
9 Mesquite
9 Shrubs
9 Wetland, mangroves, palm"Secondary vegetation" is defined as the vegetation present where it has substituted
totally or partially for the original (primary) vegetation as a result of some changes in
the use of the ground or because of natural causes or where there is evidence ofrecovery of the vegetation community in some of the sucesional stages of vegetation (“trees”, “bushes or shrubs” and “herbaceous)”. (INEGI, 2004)

Definition
Deforestation or Loss of forest: permanent change in the forest coverage to land use.
 The loss of primary vegetation (e.g. Conifers forest→ Urban)
 The loss of primary vegetation with secondary trees vegetation (Conifers forest /VSA →Crop)
Degradation or alteration of the forest condition: indicates a change or degradation in the
coverage without necessarily a loss from its original condition, but a negative change to a
structure that diminishes its capacity to generate service and products and can be considereda loss of biodiversity or a decrease in biomass.
 primary vegetation that changes to secondary vegetation
 secondary vegetation that converts to inferior states for example from trees to bushes ortress to herbals
Recovery process: is the natural expansion of forest into areas where the land had been in use.

Input Data: MODIS satelite imagery
Dic. 2000
Sep. 2005

NDVI 53 monthly composites (32 days)←

Dry Period→| Transition | ← Wet Period→ |Transition

Fuente: imágenes del sensor MODIS procesadas por el “Institute for Advanced Computer Studies” de la
Universidad de Maryland, USA

Plant cell components and their reflectance behavior

NDVI

B 2  B1B 2  B1

[1] Servilla, M. S., Multispectral Remote Sensing and Agriculture, Photon Research Associates Inc. Albuquerque N.M. 1-4, 1998

Fields subplots of National Forest and soils Inventories (NF&SI)

Classification of field observation of NFI between 2004
and 2007 by Vegetation community.
Vegetation community

National Statistics and Geography
Institute key

Numberof sites

Holm oak forest

Holm oak and holm oak-pine

Pine forest

Pine, fir, juniper, cypress
Juniperand pine-holm
predominance of pine

Desert and dune

Microphyllous desert scrub land

199

Mangrove

Rhizophora spp.

980

Shrub land

Various types of scrub land

10 945

Mesophile forest

Very moist montane forest

1 526

Rangeland

Natural rangeland and through presence of
sodium and chalk235

Highandmedium- High- and medium-altitude rain forest
altitude rain forest(deciduous or evergreen)

16 976Tule vegetation

Lowland rainevergreen)
Thyphus spp.

Without plant cover

Without plant cover

Lowland rain forest

forest20 139oakwith(deciduous

6 276

or

190
1 229

Behavior of NDVI for different Vegetation communities

Yearly NDVI behavior by vegetation type240

220
selvas altas
bosque mesofilo

200

manglarbosque pino
180

bosque quercus
selvas bajas
matorral160

pastizales
desiertos

140

120
1

4

7

6 470

10 13 16 19 22 25 28 31 34 37 40 43 46 49 52

We applied statistic control of process methodology in every
vegetated community taking in a count a level of 3 σ.
We found in forest and tropical forest that points below of the range
have more standing dead or stumps than trees.

NDVI - Undisturbed plot

Date
10/12/2005
08/06/2009cmm
DN
DIAM crown
13.25
3.47
13.13
2.77

N° Trees
160
139

%
Cover
89.5
51

m
hight
11.37
9.54

Stumps5
1

NDVI - PLOT 54508 (BQ)
250
200

NDVI

150

152

143

133

120
107

100
50
2005

2006

2007

2008

2009

Dry season (Year)NDVI - Disturbed plot

cm
DN
11.82
0

date
N° Trees
09/08/2005 192
17/04/2009 0m
DIAM Crown
2.51
0

%
Cover
60.1
0

m
Hight
8.98
0

Stump
0
0

200

NDVI

178
180
160

179

147
141

140

129

128
120

100
80
2005

2006

2007

2008

Dry season (Year)

2009

2010

Oak Forest con VSA

Deciduous Tropical Forest

250

250

200

150

150
NDVI

NDVI

200

100

100

50

50

37 Plots remeasured

229 Plots remeasured
0

0
2005

2006

2007

2008

2009

2005

2006

2007

2008

2009

10 Plots re measured

Dry season (Year)Dry season (Year)

Conniferus

Sacred Fir Forest

250

250

200

150

150
NDVI

NDVI

200

100

100

50

50

172 Plots re measured

10 Plots re measured

0

0
2005

2006

2007

2008

Dry season (Year)

2009

2005

2006

2007

2008

2009

Dry season (Year)

Resultado del Comparativo entre el NDVI de la época secade los años 2011 y 2010 years

Some plot of the remeasured cycle withdegradation indicate:
5 Re measured plot inside degradated areas

2 plot Damage by pest or insect 36 of 44 trees

2 plot Damage by fire 100 %Damage by deforestation 1 subplot

Input Data: National Forest Inventory (NFI)
2004 – 2007 and updating INF 2009 - 2014
Variables:
Trees: 39
Seedling and bushes strata: 23
Herbaceous strata: 20
Soil: 10
General and ecological information: 80

Plot
SubPlot
Trees ( DN ≥7.5 cm) and stump
Trees ( DN ≤ 7.5 cm) and shrubs
Numbers of Field gang
Number Digital Pictures of Field validation
Number of link tables in Data BaseSITIO 21m2 Herbáceo

45.14mRUMBO N 0o
Az = 0o12.56m2 (r=2m)
Repoblado(cualitativo ycuantitativo)

SITIO 1

RUMBO SW 60o
Az = 240o

400m2 (r=11.28m)
Arbolado y
submuestra(cuantitativo)

RUMBO SE 60o
Az = 120o

SITIO 4SITIO 3

Other Variables: Seedling Data (Sub Plot 12.56 m2)
Sub Sample trees and Lichen and moss
COVERT (SUBPLOT 12.56 m2)
Covert %Vegetation
Tress
ShrubsHerbaceous
Total (0 300%)
Frequency and age of seedling (Subplot 12.56 m2)
Height (m)0.25 - 1.50
1.51 - 2.75
> 2.75Age Frequency
Age
Frequency Age
Frequency

Genus

1
No.
árbol

2
Diámetro
basal (cm)Class
1. Bracken
2. Orquids
3. Moss
4. Liquen
5. Cactus
6. Bromelias
7. Others

Scare <
15%

3
Azimut *4
Distancia *
(m)

5
Edad
(años)

6
No deanillos en
2.5cm

Vigor

7
Longitud 10
anillos (mm)Diversity of Epiphyte
Trunk
Branches
Abundant Plentiful > Scare < Abundant 15 Plentiful
15 - 40%
40%
15%
- 40%
> 40%

Damage8 Grosordecorteza(mm)

Damage %Use

9Distribución de productos(Número de trozas**)
1

2

3

4

5

6

7

8

Estimation of Wood Volume m3 /hafrom dasometric values.
The first approximation of the woody volume is calculate assuming a cone equation.
woody volume {m3} V = π · r2 · h
3h= Total height (1.3 ≥ h ≤ 35 m)
r = DN/ 2 {m2}DN=diameter at breast height (7.5 ≥ h ≤ 100 cm)
In this equation do not taking in account: standing dead, stump, branches or specie

Estimation of Aerial Biomass Ton/ha
Quality control of NFI data:- 1,267,446 records in the original database- 37,334 records excluded from the biomass analysis
Causa de la exclusión del individuo

Criterio
1
2
3
4
5
6
7

Descripción
Cociente entre altura total y DN mayor a 2
Sin información de DN
Sin información de altura
AB del árbol muy grandeΣAB árboles del conglomerado muy grande
Sin nombre científico de especie, nombre comúno género
Condición de tocón (con o sin marca)
Total general

Condición del arbolado
Árbol
Tocón
Total
muerto encon
Tocón sin
general
pie
marca
marca
Árbol vivo
464
41
2
507
2804
140
4063
18905
25912
69
198
59
198
524
9
2
11
2703
8
2711
7612
6049

8001

11
4133

46
19151

Fuente Ben de John et al.

7612
57
37334

Biomass data: Local allometric equation

In México:• 2,900 species in the NFI• 120 species with >2000 records thathave with biomass-volume equations(70% of total records)

27 genuses (e.g. Pinus, Quercus, Ficus,
Piper)
176 species have eq. (some species withmore than one eq. E.g. Pinus
pseudostrobus )Fuente Ben de John et al.

Relation between NDVI and Biomass
Total Biomass [Ton / ha] Vs. NDVI

250

Total Biomass ton / haBAy = 4E-11x5.6919
R² = 0.8334

200

y = 4E-08x4.3318
R² = 0.6384

150

BS
SAQ

y = 2E-10x5.347

BM

R² = 0.7741

SMQ

y = 3E-08x4.2473
R² = 0.4882

100

SBQ

BQP

BPQ
SMS

SAP

BP
SBS

BQ
50

SBC
BJ

SMC

SBK
0

70
Primary Forest

90

110
Forest with Secondary
vegetation Tress

130

150

170
Forest wtih Secundary
vegetation herbaceous

190

Discussion
Limitation about images:
9
9
9
9
9
9

Anisotropic illumination considerations
Atmospheric effect considerations
Presence of clouds and cloud shadows.
Noise from the reflectance of vegetated ground
Effect of treatment of the data or of saturation on NDVI
Phenological aspects of the vegetation.

Limitation by Biomass estimation
9
9
9
9
9
9
9
9

seedling
Fallen leaves
Fuel
StumpsStanding deadBranches
Trees bellow 7.5 cm DHB
There is not equation for arid and semiarid areas, wetland or jungles.

Oportunity areas in degradation
indicators
Plot level volume increment based on re-measured sites
9 In 2008 about 2,000 plots re-measured (e.g. DBH, Height)
9 In 2009 there will be 4,500 plots re-measured

We can explore others data base information.
9
9
9
9
9
9
9
9

Liquen and moss
Number of species
Damage condition
Soil information
Fuel.
CovertStump
Standing dead

Forest stand dynamics
9 Growth rate
9 Mortality rate
9 Harvesting ratePublishedwww.fao.org/forestry/fra
www.fao.org/forestry/unasylva

http://infoteca.semarnat.gob.mx/website/geointegrador/mviewer/viewer.htm
http://148.223.105.188:2222/gif/snif_portal/index.php?option=com_content&task=view&id=61&Itemid=98

www.fao.org/forestry/unasylva

Thanks for your attention

Using SPOT-5 satellite multispectral
imagery and LiDAR data to monitor
forest carbon stocks and stock change
in New Zealand
Deborah Burgess, NZ Ministry for the Environment

Outline• New Zealand’s national circumstances• Mapping forest land and land-use change using satellite
remote sensing• Quantifying carbon stock using ground-based forest plotmeasurements and LiDAR metrics

New Zealand’s National
Circumstances• All forest lands are managed• 8 million hectares of natural forest• 2 million hectares of planted forest• No pre-existing national spatial forest
inventory• Signatory to UNFCCC and Kyoto
Protocol• Historical difficulties obtaining
access to forests

Forest definition• New Zealand has chosen the following parameters:• Tree height at maturity of 5 metres• Minimum crown cover of 30%• Minimum area of 1 hectare• Minimum width of 30 metres

Mapping approach• Wall-to-wall mapping based on satellite imagery at
three mapping dates: 1990, 2008, 2012
• Constrained acquisition parameters:• Sun angle > 45 degrees• Incidence angle < 20 degrees• < 10 % cloud cover• Imagery converted tostandardised reflectance

SPOT-5 scenes
acquired Oct 2011 toMarch 2012

Standardising spectral reflectance

Perspective view of regular image mosaic

Standardised reflectance mosaic

Standardising spectral reflectance

Vertical view of regular image mosaic

Standardised reflectance mosaic

Mapping Pre-1990 and Post-1989 forest
areas
Landsat-4 1990

1990 plant date confirmed when
forest entered into NZ Emissions
Trading Scheme (green polygon)

SPOT-2 1996

Landsat-7 2001

SPOT-2 2008

Identifying deforestation
Oblique aerial photography is used toobtain evidence of land-use change

SPOT 5 21/11/2007

Bay of Plenty 1,857,506 5,730,852 NZTMSPOT 5 19/1/2011

Quantifying carbon stock- ground plots• Pre-1990 forest sampled on an 8-km random origin grid– 191 plots• Post-1989 forest sampled on a 4-km grid within
8-km grid – 246 plots• Circular plots 0.06 ha in size measured
to estimate:• above-ground biomass• below-ground biomass• dead wood
• litterDouble sampling approach using
LiDAR• Based on an 8 km x 1 km grid forpre-1990 planted forest• Incomplete transects only throughforests containing an 8-km grid
ground plot
• 191 ground plots and 686 LiDAR onlyplots
• LiDAR data acquired in early 2010

Pre-1990 planted forest sampling in theCentral North Island of New Zealand

Airborne scanning LiDAR
LiDAR
Image left:•

LiDAR transect between points in white rectangle

Image below:•

Ground surface model and 5 m wide point cloud•First return density 3.46 points per m2

Pre-1990 planted forest sampling in theCentral North Island of New ZealandPercentage variance explained by various
Total C versus LiDAR metric multiple
regression models

Regression model for predicting total carbon in
pre-1990 planted forest using LiDAR metrics

Reduced uncertainty in total
carbon estimate
Estimates of total carbon (t/ha) and 95% confidence
interval in Pre-1990 planted forests at 2010
Ground-based estimate

Regression estimate

100.52 ± 14.17

99.07 ± 11.03• 95% confidence interval reduced from 14.1% to 11%• Would require a further 122 ground plots to achieve asimilar improvement without LiDAR plots• Similar approach adopted for Post-1989 forest
inventory where key LiDAR metric was P30ht

Lessons learnt• LiDAR is a cost effective way to reduce uncertainty incarbon stock estimates• LIDAR must be used in conjunction with ground sampling
due to variations caused by season and silviculture• Ideally phase 1 LiDAR sample should sample all forests
regardless of whether they contain a ground plot
• Image reflectance standardisation in important in steep
terrain to ensure accurate classification of forest extent• Deforestation cannot be reliably distinguished fromharvesting in 10m resolution multispectral satellite imagery

References
Mapping:
Ministry for the Environment, 2012. Land-Use and Carbon Analysis System: Satellite
imagery interpretation guide for land-use classes (2nd edition). Wellington: Ministry for
the Environment. http://www.mfe.govt.nz/publications/climate/satellite-imageryinterpretation-guide/index.html
Shepherd, JD and Dymond, JR, 2003. Correcting satellite imagery for the variance ofreflectance and illumination with topography, Int. J. Remote Sensing, 2003, vol. 24,
No. 17, pp. 3503-3514.

References (continued)
LiDAR:
Beets, PN, Brandon, AM, Goulding, CJ, Kimberley, MO, Paul, TSH and Searles, N,
2011. The inventory of carbon stock in New Zealand’s post-1989 planted forest forreporting under the Kyoto protocol, Forest Ecology and Management, vol. 262, pp.
1119-1130.

Beets, PN, Brandon, AM, Goulding, CJ, Kimberley, MO, Paul, TSH and Searles, N,
2012. The national inventory of carbon stock in New Zealand’s pre-1990 planted forest
using a LiDAR incomplete-transect approach, Forest Ecology and Management, vol.
280, pp. 187-197.

Stephens, PR, Kimberley, MO, Beets, PN, Paul, TSH, Searles, N, Bell, A, Brack, C
and Broadley, J, 2012. Airborne scanning LiDAR in a double sampling forest carbon
inventory, Remote Sensing of Environment, vol. 117, pp. 348-357.

㪝㫆㫉㪼㫊㫋㩷㪛㪼㪾㫉㪸㪻㪸㫋㫀㫆㫅㩷㪑㩷㫋㪿㪼㩷㪛㪜㪞㪩㪘㪛㩷㫇㫉㫆㫁㪼㪺㫋㩷㪸㫋㩷㪠㪥㪧㪜㪧㫉㪼㫊㪼㫅㫋㪼㪻㩷㪹㫐㩷㪑㩷㪫㪿㪼㫃㫄㪸㩷㪢㫉㫌㪾㪪㫃㫀㪻㪼㫊㩷㪹㫐㩷㪑㩷㪛㪸㫃㫋㫆㫅㩷㪭㪸㫃㪼㫉㫀㪸㫅㫆㩷㩿㪠㪥㪧㪜㪀•Linear Mixing Model• soil image• shadow image• green vegetation image㪣㫀㫅㪼㪸㫉㩷㪤㫀㫏㫋㫌㫉㪼㩷㪤㫆㪻㪼㫃㩷㪄 㩿㪪㪿㫀㫄㪸㪹㫌㫂㫌㫉㫆㩷㩽㩷㪪㫄㫀㫋㪿㪃㩷㪈㪐㪐㪈㪀Yearly Deforestation in the Brazilian Amazonia
35000

Km2/year

30000
25000
20000
15000
10000
5000
0
88 89 90 91 92 93 94 95 96 97 98 99 00 01 02 03 04 05 06 07 0809 10 11(a)(b)(b)(d)Year

Use of airborne and satellite LiDAR for estimating forest
carbon stock and its changes

Yasumasa Hirata
Forestry and Forest Products Research Institute, Japan

Forestry and Forest Products Research InstituteCalculation of GHG emission
from LULUCF sector

Danilo Mollicone, FAO

Ground-based
inventory

Remote sensing
Decision of classification classes
Plotless sampling ineach class

Plot survey ineach class

Image classification

Allometry

Verification

Calculation ofcarbon stock

Estimation of forest area by forest typeand disturbance degree

Estimation of mean carbon stock byforest type and disturbance degree

7RWDOFDUERQVWRFN Ȉ )RUHVWDUHDi x mean carbon stocki)

Monitoring of carbon stock changes by deforestationand forest degradation using remote sensing

Field survey for estimating carbon stock

Forestry and Forest Products Research InstituteTwo types of data from field survey• For area estimation– Training data for image classification– Verification data for the result of classification• For estimation of carbon stock per unit area as
emission factor– Tree census data

Forestry and Forest Products Research InstituteProblems in field survey• Ownership• Accessibility and road condition• Weather• Topography (steep slope, stream, etc)
• Dangerous animals, insects, and plants• Mining• Illegal logger

Forestry and Forest Produ
P
ProductsdResearch InstitutePossibility of LiDAR measurement• Satellite LiDARĺTraining data and verification data acquisition• Airborne LiDAR– Estimation of carbon stock by forest type anddegradation degree• It is difficult to estimate area and its changes
from LiDAR measurement.

Forestry and Forest Products Research InstituteAbout ICESat/GLAS• Launched on 12 Jan 2003
• Mission period from 2003 to2009• Footprint size is 65 m
• Interval of footprint is 170 m• Emitted 1064 qnd 532 nm
laser pulses

Forestry and Forest Products Research Institute

Satellite LiDAR measurement of 3-D structure of forest

Footprints of ICESat/GLAS in Cambodia

Waveform of LiDAR data in a mature forest•

Maximum tree height derived from
LiDAR data was about 43 m.

•

Intensity has a peak around 23mheight and it means canopy layer.

Intensity

Maximum tree height

Digital bins (0.1ns)

Forestry and Forest Products Research InstituteWaveform of LiDAR data in a degraded forest•

Maximum tree height derived from
LiDAR data was about 42 m.

•

Intensity was relayively weakthrough all layers.

Intensity

Maximum tree height

Digital bins (0.1ns)
Forestry and Forest Products Research InstituteWaveform of LiDAR data in a rubber plantation

Intensity

Maximum tree height•

Maximum tree height derived from
LiDAR data was about 23 m.

•

Intensity has a peak around 15m
height and it means canopy layer.

Digital bins (0.1ns)
Forestry and Forest Products Research InstituteConcluding remarks for satellite LiDAR• Stand structure can be
estimated from waveform ofsatellite LiDAR data .
• Leangth of waveform almost
indicated maximum tree height.
• Peak position of waveform
indicated the height of canopy
layer.
• The height and position of peakof waveform indicated thedegree of forest degradation.
• Next satellite LiDAR…?
– Spec of ICESat-2……
– i-LOVE on ISS in 1918?
Forestry and Forest Products Research Institute

Forest measurement by ALS
㪞㪧㪪
㪞㪧㪪
㪠㪤㪬
㪠㪤㪬

• Digital surface model(DSM) is created from first
return.

㪠㫅㫋㪼㫅㫊㫀㫋㫐㪦㫅㪼㩷㫊㪿㫆㫋㩷㫆㪽㩷㫃㪸㫊㪼㫉㩷

first return• Digital elevation model(DEM) is created from last
return.

㪫㫀㫄㪼㪫㫀㫄㪼• Digital canopy heightmodel (DCHM)=DSM –DEM㪝㫀㫉㫊㫋㩷㫉㪼㫋㫌㫉㫅last return
ᧁ

㪫㫀㫄㪼

ᧁ

㪣㪸㫊㫋㩷㫉㪼㫋㫌㫉㫅• Other information(waveform, penetration
rate, etc.) is also used toestimate stand condition.

Forestry and Forest Products Research InstituteWhat can be measured using ALS?
Crown area

• Direct measurement– Tree height– Crown area

Crown
depth

– Crown depth• Estimated parameter– DBH

height– Volume– (Carbon stock)

DBH

Forestry and Forest Products Research Institute80

Extraction and estimation

Cryptomeria
japonica

R2=0.71• Local maximum filter is applied to
DCHM to extract individual tree-tops.
• Watershed method or valley-following
method is applied to DCHM to extract
individual crowns.

DBH䋨cm䋩

60

40

20

0
0

10

20

80

R2=0.82

30

40

50

Chamaecyparis
obtusa⢷㜞⋥ᓘ䋨cm䋩

60

40

20

0

Extraction of tree height, crown area and tree number from DCHM 0

Hirata et al. (2009)

10

20

30

40

Crown area(m2)Forestry and Forest Products Research Institute50

Tree number and height from DCHM
Extraction of individual trees
thinning
operation

number ofstanding
trees

number of
extractive
trees

extractive
rate (%)

heavy(0.3 ha)

142

136

95.8

light
(0.4 ha)

245

212

86.5

no
(0.3 ha)

270

203

75.2

Total
(1.0 ha)657

551

H ground = 1.009 * H ALS – 0.59
r2 = 0.85

H ground (m)

40

0

0

40

83.9

H ALS(m)Tree height from DCHM
Hirata (2005)

Forestry and Forest Products Research InstituteSampling densitycrownfootprint

2 – 3 points/m2

25 points/m2

In the case of low sampling density, some treetops are not involved by any footprints.

In the case of high sampling density, each treetop is involved by one of footprints.

Hirata (2005)

Forestry and Forest Products Research InstituteSampling density

sampling density = 22.53

sampling density = 11.28

sampling density = 5.64

sampling density = 2.82

sampling density = 1.41

sampling density = 0.71

sampling density = 0.35

DCM derived fromdifferent
sampling density

sampling density = 5.64

sampling density = 22.53

sampling density = 11.28

sampling density = 2.82

sampling density = 1.41

As sampling density runs lower,
extractive tree-tops decrease.

sampling density = 0.71

sampling density = 0.35

Stnand parameter by ALS

Hirata et al. (2008)

Forestry and Forest Products Research InstituteStand density from DCHM
Stand density from the field survey (trees/ha)

4000

3000

2000

y = 1.0728x + 127.72
2
R = 0.9188

1000

N = 20

0
0

1000

2000

3000

4000

Stand density from the LiDAR data (trees/ha)

Hirata et al. (2008)

Forestry and Forest Products Research InstituteMean tree height from DCHM
Mean tree height from the field survey (m)

40

30

20

y = 0.8439x + 1.9284
2
R = 0.9893

10

N = 20

0
0

10

20

30

40

Mean tree height from the LiDAR data (m)Hirata et al. (2008)

Forestry and Forest Products Research InstituteStand volume by ALS

Stand volume from the field survey (m 3 )

1200

1000
800

600
y = 1.2152x - 9.5895
2
R = 0.8634

400

N = 20

200

0
0

200

400

600

800

1000

1200

Stand volume from the LiDAR data (m 3 )Hirata et al. (2008)

Forestry and Forest Products Research InstitutePenetration of laser through canopy

Forestry and Forest Products Research Institute

Evaluation of different levels of thinning

Hirata et al. (2009)

Forestry and Forest Products Research InstituteRelative frequency distributions of the heights
above the ground where laser pulses reached
Heavy Thinning

Light Thinning

20

15

10

5

25

Height above the ground (m㪀Height above the ground (m㪀25Height above the ground (m㪀No Thinning

25

20

15

10

5

0

0
0

20

40

60

20

15

10

5

0
0

Percentage (%)

20

40

60

0

Percentage (%)

Hirata et al. (2009)20

40

Percentage (%)

Forestry and Forest Products Research InstituteThinning and penetration rate

Penetration rate of laser pulses (%)

100

80

60

40

20

0
0

20

40

60

80

100

Ratio of removed trees for basal area (%)

Hirata et al. (2009)

Forestry and Forest Products Research Institute60

Total basal area of remaining trees
and penetration rate
Penetration rate of laser pulses (%)

100

80

60

40

20

0
0

20

40

60

80

100

2

Total basal area of remaining trees (m /ha )Hirata et al. (2009)

Forestry and Forest Products Research Institute

Aerial survey in tropical forest• High spec survey by aircraft㧦⸘᷹ኻ⽎▸࿐㧔⸘MO㧕

LIDAR/Camera Pod

GPS AntennaImplementation of LiDAR measurement• Survey implementation in January, 2012 in Cambodia
and in October, 2012 in Malaysia (Sabah)– 10cm resolution aerial photos– 25-50 cm LiDAR height models

Aerial photo & canopy heightdegraded forest

Canopy Height
0

10

20

30

40 m

Aerial photo & canopy heightdegraded forest

Canopy Height
0

10

20

30

40 m

Aerial photo & canopy height
DEF & DDF patches

Canopy Height
0

10

20

30

40 m

Aerial photo & canopy height
Deciduous forest

Canopy Height
0

10

20

30

40 m

Aerial photo & canopy height
Conserved forest

Canopy Height
0

10

20

30

40 m

Aerial photo & canopy height
Conserved forest

Canopy Height
0

10

20

30

Concluding remarks for airborne LiDAR• Airborne LiDAR is applicable to estimate forest standparameters accurately, therefore it can estimate forest
carbon stock by forest type.
• Cost should be investigated. Ground-based inventory is
not also cheap.
– 500-1000 US$ per 30m*30m plot
– Car, specialists (species identification, instrument handling),
villagers– Up to access, road condition, weather, forest condition, distancefrom accommodation ….

• LiDAR measurement is strongly influenced by weather
and regulation of a country (permission).

40 m

Thank you for your attention!

hirat09@affrc.go.jp

Detecting Forest Degradationby using Gram-Schmidt
transformation
IPCC Expert Meeting:Role of Remote Sensing in Forest and National
GHG Inventories.
October 23-25. 2012 Jokosuka, Hayama, Japan
Carlos Bahamondez
Forest researcher
Instituto Forestal
Sede Valdivia
Chile

Methodology degradation
 Three main issues are needed:

An operational model to forest degradation
 A remote sensing based algorithm fordetecting forest degradation.
 A model for the degradation -drivers
relationshipDegradation in Chile

Drivers for Degradation in Chile comes fromrural population poverty condition.
Basically, the main driven forces are
represented as:––

The presence of a large and illegal informal
fuelwood market. (demand 8 million
m3/year)An important group of small landowners withfew chances to get liquidity from their small
properties.

How does forest degradation works?

Main activity
is grazing(Cattle)Forest(seen as $$$)

Unsustainable
Cutting practices
Starting from
Understory
vegetation

Landuse change

Small properties
Requires more
Agricultural lands

Degradation unsolved issues
Lack of country definition or a clear global one ( FAO 2005)
provokingDegradation in forest involves several scales and scopes:






Problem in disseminating
Make the problem invisible
No problem No action

Biodiversity
CarbonOther emergent properties from Forests

Spatially involves at least:A stand level
A landscape level

The Methodology: integrating forest standlevel knowledge and landscape monitoringby using RS techniques
Multitemporal
Imagery (MTI)(90-00)
Sellecting
degraded, normal,
bare areas and wet
Forests on
MTI
Modeling forest
By using NFI
data

Validating
results
Generating
new ad hocaxes from
Imagery Data
(get off data
not ortogonal)

Mapping
Potential
Degradedspot

I. Stand level modeling forest using an operational
approach to degradation (FAO WP Nº158, 2010)
Using the Stock chart to discriminate degraded –non degraded

Stock Chart
Forest type
Roble-Rauli-Coihue
Subtype: Roble-Rauli-Coihue y Roble

High levelof competition

Management area

Degraded

Lower
ThresholdvalueII. At landscape level Method : Collecting samples from MTI
 Technique :Linear transformation (Gram-

Schmidt)
 Material: Landsat 1990 (TM) -2001
 AOI Región de la Araucania

Roble-Rauli-Coihue

Ex. Principal Components(ETM+)~0,9 MM ha

Ex.Tasseled Cap
Brightness vs Greeness+Vegetation

Forests

Projections and Gram-Schmidt
ortonormalizing

Projecting onto an space explain the proportion of one vector overthe subspace. The larger the angle xs the less influence in the subspace.
Inner product or cosine(xs)x’xSin n e r p ro d u c t

1.5
1
0.5
0
0

20

40

60

80

100

xs

sn  xn 

x n , sn 1
sn 1

2

sn 1  ........... 

x n , s2
s2

2

s2 

x n , s1
s1

2

s1

C1
C2

C1
C2

Trainning data patterns:

C1
C2
C1
C2

C1
C2

.
.
.
Cn

.
.
.
Cn

.
.
.
Cn

2.s1~Brightness

1.s2~Greenness

C1
C2
.
.
.
Cn

.
.
.
Cn

.
.
.
Cn

1990

2001

C1
C2
C1
C2
C1
C2

3. degraded s4
.
.
.
Cn

.
.
.
.
.
Cn
.
Cn

Generating Brightness, Greeness, Wetness andDegradation axes••

Centering vector the darkest one in the AOIBrightness Forests <20% canopy cover.

From MTI, collecting the ten most representative vectors for this category and thencalculate the mean value vector.
•

Greenness dense plain forests >60% canopy coverFrom MTI, collecting the ten most representative vectors for this category but also thesemust be the most distant vector from Brightness and then calculate the mean value
vector.
•Wet Forests >60% canopy cover high humidity environment

Very difficult for collecting, no heuristics for selecting, at the end the five most distantvector to Brightness, and Greeness was applied.
•

C1
C2

Degraded forests according to definition
Collecting at most vectors as possible from MTI given the NFI information available, anumber of 14 was averaged. No distant requirement was applied.

.
.
.
Cn

C1
C2

.
.
.
Cn

Results: Covariance Matrix Inspection
Brightness
open
forestGreenessplain
forestWetness
forestDegraded

Proportionofvariation

Brightness
open
forest1.419,48

940,6523,95

187,2330,129

Greenessplain
forest940,652.611,56

156,3525,53

55,431

Wetness
forest23,95

156,35

78,36

14,41

1,663

187,23

25,53

14,41

147,60

3,133

Total

90,356

Degraded

Results: Coming back from space,checking correlations
120

25

100

20

G(m2/ha)

CCF%

80
60

15

10

40
5

20
0

0

0

10

20

30

40

50

0

10

Degradation value

Degradation

20

30

Degra dation value

Basal Área(m2/ha)Crown
Competition
Fraction %

-0,72

-0,63

40

50

Validation(only 12 independents samples (red dots))
Validation sample
60

50

G(m2/ha)

40

30

20
10

0
0

20

40

60

80

100

De gra da tion va lue

Mapping potential degraded spots
Agreements: 69%

Conclusions ….this study….
The applied methods showed an acceptableperformance in detecting degraded forests.
 The method are far for being automate
 Given that is time consuming and highly interactive
and demanding The axis Wetness is not easy to determine.
 The amount of suitable samples are difficult to find forboth training and validation (its weakness andstrength).
 The method is intuitive which it is good.
 It is compulsory to check the potential degraded maps
in field.
P(d42/ m, I43)  144
P(42
m /44
d43) {P(d )Ongoing tests….. 1444
444
posteriori
Evidence
Pr ior Modeling the degradations drivers could be an

important input to improve degraded maps
originated by this method.
 A Bayesian model is being tested, where
potential degraded map is the evidence comingfrom field truth records.
 The drivers model (ANN or Logistic) become
the priorThanks for your attention!!
cbahamon@infor.cl

Using Remote Sensing Measurements Constrain Terrestrial
Biosphere Model Predictions
Paul R. Moorcroft
Alex Antonarakis

Harvard University8

Trend in atmospheric CO2
concentrations

Trends in global temperatures overthe past 160 years (WMO 2010)

Predicted temperature changes over the coming century (AR4 2007)

Terrestrial biosphere model (DVGM) predictions for the long-term response ofterrestrial ecosystems to climate change;;&%%%% !&!&
%%%%
2
29 7 7 > 3 
2977>3

- there is a large degree
ee ofof uncertainty
uncerrtainty inin thethe predictedpredicted response
response of theohuman-induced climate
climate change.
chhange.
terrestrial biosphere tohuman-induced- because of feedbacks
ks ontonto
to the atmosphere, the response of the biospher
biosphere
re to
e of
of thethe largest
larggest sourcessoourc
ces off unc
certaintty ffor
or tthehe a
mo u nchanges in climate is one
uncertainty
amountoccur
of climate change thatt will occur.

$$%&$"%#$ "%
2-! "&'"! "%2 %332<&%%%% !&3




!%0!"*$! 4 9

   !)&%&"")$%


-%&"+%








!)&%&"")$%



 2  3



 

4

-$%&"%
    
-$%&"%

Sources of error in terrestrial biosphere model simulations
Process Error: inaccuracies the in model’s equations and parameter values:
minimize by constrain model’s predictions against key observables:- hourly, monthly & yearly carbon fluxes- hourly water fluxes (ET)- above-ground vegetation dynamics (growth & mortality)
Forcing Error: - inaccuracies in meteorological data used in the simulation:
minimize by forcing model with observed climate data.
Initialization Error - errors in the model’s state variables at the beginning of
the simulation: minimize by initializing with observed ecosystem structure
and composition.

(Moorcroft et al. 2001, Moorcroft 2006, Medvigy et al. 2009

e.g. Calibration and Testing of the ED2 terrestrial biosphere modelat Harvard Forest (42oN, -72oW)

ED2 structured biosphere model∂n (i) (z,a,t)∂∂= − [ g(z,a,t)n (i) (z,a,t)] − n (i) (z,a,t)
∂t
∂z
∂a
(i)
−μ(z,a,t)n (z,a,t)∂ p(a,t)=∂t−∂p(a,t) − λ (a,t) p(a,t)∂aCanopy Composition(abundance of different
plant functional types)

Ground-Based
Inventory
Measurements
Canopy Structure(tree size class
distribution)

ED2
BIOSPHERE
MODEL

Ecosystem
CarbonFluxesLong-term
Vegetation
Dynamics

ED2 Biosphere Model Predictions for Harvard Forest: 10-yr simulations(1992-2001)= optimization period

Net Carbon Fluxes (NEP)

AGG model

Initial model

2

4

6

HET model0

NEP (tC ha1 y1)

Observations

1994

1996

1998

2000

2002

Medvigy & Moorcroft (2012)

Regional decadal-scale evaluation- stand composition & harvesting rates: US Forest Service& Quebec forest inventory 1985 - 1995

- climate drivers : ECMWF 6-hour reanalysis dataset- no change in any of the model parametersSpatial patterns of above-ground biomass growth:
Observed

Orig

HF-OPT

Predictions by plant
functional type:
Early-successional hardwoods

Mid-successional hardwoods:

Late-successional hardwoods:

Northern pines:

Late-successional conifers:

Antonarakis et al. in prep.

Using Remote Sensing Observations of Forest Structure & Composition
to Constrain Terrestrial Biosphere Model Simulations
In many areas of the world there are limited ground-based
measurements that can be used to constrain terrestrial biosphere models.
 can we use remote sensing measurements to provide information on
above-ground ecosystem state? [Initialization Error]

Hyperspectral
Imagery

CanopyChemistry and/or Spectral
mixtureanalysis

Airborne Lidar
Measurementsof Forest
Structure

Radiative
Transfer
Model

CanopyComposition(abundance ofdifferent plant
functional types)

ED2
BIOSPHERE
MODEL

Canopy Structure(tree size class
distribution)

Ecosystem
CarbonFluxesLong-term
Vegetation
Dynamics

Antonarakis et al. in prep.

Plant Functional Type Composition from Hyperspectral Remote Sensing Data

ED2 PFTs
LPH
site

HEM
site

EMS
site

Hardwoods:- early-successional (birch)- mid-successional: (red oak &red maple)- late-successional: (beech,
sugar maple)
Conifers:- northern pines (white & red pine)- late-successional conifer(hemlock)Antonarakis et al. in prep.

Composition via Multiple End-Member Spectral Mixture Analysis

Identigy areas
dominated by asingle plant
functional typeDefine Endmember pixels 

?7E
=7E
;7E
-G7/@@>=,F7/7=89
AG7/?799;

7E

7E 97E;7E=7E?7E877E

?7E
=7E
;7E

-G8/7:=>,F7/7:??
AG7/?;>8@

97E
7E

7E 97E;7E=7E?7E877E



?7E

=7E
;7E
97E
7E 97E;7E=7E?7E877E


877E
?7E

=7E
;7E
97E
7E

7E 97E;7E=7E?7E877E

  

-G7/;<??,F7/7=@=
AG7/7@;7@

=7E
;7E
97E
7E

-G7/=:9;,F7/7<;
AG7/<:===



                

7E

877E



877E -G7/@;>:,17/7><:AG7/=;@;?
?7E

Plant Functional
Typeclassification 

877E

                  
877E

97E

MultipleEndmenber
Spectral
MixtureAnalysis
Algorithm      

Hyperspectral
Imagery(AVIRIS)7E 97E;7E=7E?7E877E


877E
?7E

-G7/89>;,F7/7=;
AG7/77>99

=7E
;7E
97E
7E

7E 97E;7E=7E?7E877E


Antonarakis et al. in prep.
Northern Pines Conifers
Early-Successional

2.9 km

LPH site
HEM site

0.8

0.8

0.7

0.7

0.6

0.6
0.5

0.4

0.4

0.3

0.3

0.2

0.2

0.1

0.1

2.2 km

2.2 kmLate-Successional
Hardwoods
Later Successional Hardwoods
0.9

0.9

0.8

0.8

0.8

0.7

0.7

0.7

0.6

0.6

0.6

0.5

2.9 km

0.9

0.5

2.2 km

0.9

Mid-Successional
Hardwoods
Mid Successional Hardwoods

2.9 km

2.9 km

Early-Successional
Hardwoods
Early Successional Hardwoods

0.9

0.5

EMS site

Late Successional Conifers
Late-Successional
Conifers

1

2.9 kmResults: Harvard Forest
Plant Functional TypeClassification

0.5

0.4

0.4

0.4

0.3

0.3

0.3

0.2

0.2

0.2

0.1

0.1

0.1

2.2 km

2.2 kmAntonarakis et al. in prep.

Lidar-derived estimates of canopy structure
Unimodal Canopy

Bimodal Canopy

LVIS waveform
Lidar (20m
footprint)

##$"%
%' &"%$* +##$"%
%' &"%$* +Results: Composition and Structure at 3 Harvard Forest sites
OBSERVED

EMS

LPH

HEM

REMOTE SENSING ESTIMATE

Seasonal Patterns of Carbon Fluxes (2003-2009)
Net Ecosystem Productivity (NEP)

Gross Primary Productivity (GPP)

EMS

LPH

HEM

Yearly Carbon Fluxes (2003-2009)
Net Ecosystem Productivity (NEP) Gross Primary Productivity (GPP)

EMS

LPH

HEM

Conclusions- Lidar in conjunction with hyperspectral remote sensing measurements canbe used to provide consistent spatially-extensive measurements of finescale above-ground ecosystem composition and structure.
- These measurements pave the way for far more accurate regional- andglobal-scale terrestrial biosphere model simulations that are critical for
predicting current ecosystem function and how ecosystems will change inthe future/
-There is an ongoing convergence between the needs of the terrestrial
monitoring and modeling communities.
Key underlying questions:
What is the current state of the land-surface?
And how is it changing as a result of human activities?
To adequately answer these questions, both communities require:- accurate measurements of ecosystem state.
- accurate measurement of changes in ecosystem state.

Future Directions:
Using repeated remotely sensed imaging to constrain process error (vegetation
dynamics)

Radar (Pol-InSAR) measurements of vegetation structure:Acknowledgements
Lab: David Medvigy, Naomi Levine, Ke Zhang, Marcos Longo, Tom Powell,Alex Antonarakis, Shirley Dong
Collaborators: Andes Amazon Initiative Consortium
Steve Wofsy, Bill Munger, David Foster, D. Hollinger, Andrew Richardson,
Xiaoyang Zhang, Mark Friedl.
References:
Moorcroft et al. 2001. Ecological Monographs 74:557-586.
Moorcroft 2006. Trends in Ecology and Evolution 21:400-407
Medvigy et al. (2009) JGR Biogeosciences 114: G01002
Medvigy et al. (2010) Proceedings of the National Academy of Sciences
107:8275-8280.

Funding:

Department of Energy
National Aeronautics and Space Administration
National Science Foundation
Moore Foundation98

Obtaining reliable fine-scale estimates of biomass
using ground-based methods

John Raison, Keryn Paul and Stephen Roxburgh
CSIRO Ecosystem Sciences
Canberra, Australia(John.Raison@csiro.au)

CSIRO.

GHG accounting in the land sector• Special challenges of large and heterogeneous areas, long(multi-decadal) time-scales• Complex drivers of net GHG emissions – bio-physicalenvironment, and range of managed and natural
disturbances• Need to estimate change against a variable background• Remote-sensing in combination with ground observations
and models to estimate emissions/removals are needed

CSIRO.

Spatial variability

CSIRO.

Components of Net GHG balanceLand areas with different
vegetation/management/disturbance have different GHG
balance due to:• Change in C stocks in biomass (above –ground & roots)
and dead OM• Change in soil C and non- CO2 GHG emissions• Fire emissions - mostly CO2 , but also non- CO2 GHG
gases
CSIRO.

Biomass carbon change (~ 50% C)• Removal of biomass due to harvest, LUC,
and fire• Replacement - the pattern of biomass
accumulation over time in regenerating orplanted forests – ‘C accumulation curves’for different forest types

CSIRO.

CSIRO.

Stand biomass carbon

Forest biomass Carbon accumulation (and loss)
curves can inform removals and emissions factors

Time
CSIRO.

Weighing large eucalypt trunk

CSIRO.

Broad approaches to estimating
forest biomass• Use ‘default’ values from IPCC or other studies – Very
uncertain at local scales!
• Develop site-level equations to estimate biomass from
simpler tree or stand measures. Try to ‘generalize’ these• Weigh whole stand biomass, and use to check estimation
methods such as equations – rarely done
• Try to develop a model that can be used for spatial
estimation of biomass (Aust. NCAS)CSIRO.

Key uncertainties in estimating forest biomass• Sampling design• Forest inventory• Development of equations to convert inventory
measures to biomass• Application of equations across species andsites• Scaling from trees to plots to the landscape
CSIRO.

Insights from a major study of forest biomass• Data on plantings up to 30 years of age from ~ 1500
sites across Australia• Allometric equations for > 100 species• Total site survey and associated destructive harvest of
total above- and below-ground biomass on sample
plots in 13 contrasting plantings. Data used to test thereliability of alternative methods to estimate thedirectly measured biomass• Monte Carlo simulations to estimate the componentsof uncertainty of biomass estimates
CSIRO.

Effect of sampling intensity (# trees) on the coefficientof variation (CV) of sampled basal area (~ biomass)

2.5

y = 1.1346x-0.48
CV of BA estimate

CV of BA estimate

2.0

1.5

1.0

Tube stocky = 1.135x-0.48

Direct seeded

y = 2.265x-0.50

Curve Y
Series4
Curve YPower (Tube stock)

0.5

0.0
0

20

40

60

80

-100
100

100

300

500

# Trees sampled

CSIRO.

700

900

1100

1300

1500

Effect of sample number on average percentage error
in allometrics (absolute difference in biomass derived
from the estimated allometric cf the 'true' allometric)
across 18 tree species.

Average%Errorinallometrics

30

25
y=41.735x0.473
R²=0.99

20

15

10

5

0
0

5

10

15

20

25

30

Numberoftreesharvested

CSIRO.

Variation in allometrics of eucalypt trees caused by
species and site factors
500

TropicalTemperate, high rainfall (>500 mm)
Temperate, low rainfall (<500 mm)

450

Abovegroundbiomass(kgtree1)

400
350
300
250
200
150
100
50
0
0

2

4

6

8

10

12

14

16

18

20

Stemdiameter(DBH,cm)
CSIRO.

22

24

26

28

30

Errors in estimates of above-ground Biomass (t) ofmixed tropical lowland Dipterocarp forest caused byusing different allometrics (Basuki et al, FEM, 2009)• Measured average tree biomass (122 sample trees,
diameter 6- 200 cm) 2.28 t• Estimated using site-specific allometrics

2.28 – 2.46

• Estimated using other tropical allometrics- Brown 3.83 (+ 68%)
- Chave 4.18 (+83%)- Ketterings 1.24 (- 46%)CSIRO.

Eucalyptus viminalis – field observations
250
Observeddata

Treemass(kg)

200

150

100

50

0
0

10

20

Treediameter(cm)
CSIRO.

30

40

Fitted allometric equation
7
6

Fittedmodel
95%ConfidenceInterval
95%PredictionInterval

5

Ln(Treemass(kg))

432
y=2.22x 2.54
R²=0.91

1
0
1

1.5

2

2.5

3

3.5

4

1
2

Ln(Treediameter(cm))
CSIRO.

Untransformed data – problem of large trees!
500
450

Fittedmodel
95%ConfidenceInterval

400

95%PredictionInterval

Treemass(kg)

350
300
250
200
150
100
50
0
0

10

20

Treediameter(cm)
CSIRO.

30

40

Large trees are very important!

• Large trees dominate stand biomass• Large trees are often poorly represented whendeveloping allometric equations• Serious errors arise when estimating biomass forlarge trees that are outside the size range used todevelop allometric equations

CSIRO.

Effect of estimation method on the coefficient ofvariation in estimates of above-ground biomass.

0
N<100;Nositeandspeciesbasedallometrics(346)
N<100;Somesiteandspeciesbasedallometrics(777)
N=100200;Nositeandspeciesbasedallometrics(190)
N=100200;Somesiteandspeciesbasedallometrics(73)N>200;Nositeandspeciesbasedallometrics(28)N>200;Somesiteandspeciesbasedallometrics(37)Precisionsampled;Fullsitebasedallometrics(8)Precisionsampled;Directwholeplotharvesting(13)CSIRO.

%Coefficientofvariationofabovegroundbiomass
5
10
15
20
25

30

35

Estimating root biomass• Destructive sampling in contrasting forests isneeded to establish a basis for prediction at otherlocations• Root:Shoot ratios (e.g. IPCC defaults) are often
used to estimate root mass from above-groundbiomass for different ‘classes’ of forest• Forest type, age, and site conditions markedly
affect root:shoot ratios
CSIRO.

Variation in Root:Shoot ratio with proportion of trees
and site productivity in young plantings
1.0

Roottoshootratio(R:S)

0.9
0.8
0.7
0.6
0.5
0.81

0.4

0.62

0.3

0.51

0.51
0.2
0.1
0.0

CSIRO.

0.40
0.28

Effect of Root:Shoot ratio on % of total biomass thatis below-ground• If R:S ratio is 0.2, 17% of biomass is belowground• If R:S ratio is 0.5, 33% of biomass is belowground• If R:S ratio is 0.8, 44% of biomass is belowground
CSIRO.

Direct measurement of root biomass

CSIRO.

Conclusions• Reliable estimation of biomass at fine scales is required for arange of uses, including calibration of remotely- sensed imagery• Considerable care and effort is required to gain reliable estimatesof biomass at fine scales, particularly in complex tropical forests.
Application of inappropriate, or generalized allometrics can
produce very uncertain biomass estimates• Estimates of the biomass of single large trees (or small patches oftrees) will be highly uncertain even when using site-specificallometric equations. Biomass estimates over larger areas (> 1ha?) may result in more reliable calibration of remotely-sensed
imagery• Root are an important biomass component (~ 20-50 % of total
biomass) – uncertainties are high
CSIRO.

Estimating tropical forest biomass
with multisource remote sensing
data: predictions between regions
Mark Cutler(m.e.j.cutler@dundee.ac.uk)
Anand Vetrivel (UoD), Prof. Giles Foody & Dr Doreen Boyd (Univ. of
Nottingham)

A little history……
1. Estimate tropical forest biomass at one site
(Danum Valley, Malaysia)

A little history……
Estimate tropical forest biomass at one site (Danum
Valley, Malaysia)– Multispectral (Landsat Thematic Mapper (TM)) used
to estimate AGB by deriving a relationship between
field measured AGB and remotely sensed response– ANN performed better than vegetation indices (r =0.8, n = 12)• Question: could this relationship be applied to estimate
biomass at other sites?
Foody, G.M., Cutler, M.E.J., McMorrow, J., Pelz, D., Tangki, H., Boyd, D.S. and Douglas, I., (2001),
Mapping the biomass of Bornean tropical forest. Global Ecology and Biogeography, 10 (4), 379-387.

Study sites

Danum Valley,
Malaysia

Primary & logged
lowland dipterocarp
forestManuas, Brazil

Biological Dynamics
of Forest Fragments
Project (BDFFP) sites

Khunkong Thailand

Plantation / tropicaldeciduous forest

ANN resultsSite(s) network applied to:Site usedin training

ThailandBrazilMalaysia

Combined

Thailand

0.71

-0.13

0.12

-0.21

Brazil

-0.2

0.84

-0.3

0.16

Malaysia

0.21

0.59

0.83

0.24

• Poor generalisation when applied to other sites i.e.
very limited ‘transferability’ of relationships• Worked better when ANNs trained with data fromall sites AND a label telling the network from whichsite the data were from (r=0.48, significant at 95%)
Foody, G.M., Boyd, D.S. and Cutler, M.E.J., 2003, Predictive relations of tropical forest biomass fromLandsat TM data and their transferability between regions. Remote Sensing of Environment, 85, 463-474.

Lowland Dipterocarp forest
at Danum Valley, Sabah,
Malaysia

Forest disturbance in the Khunkong catchment, N.E. Thailand

Why combine SAR with multispectral
data?
• SAR can be used to estimate AGB directly, but
saturates at relatively low biomass• SAR texture can provide information relating
to forest structure and geometry• Replace country label with SAR texture to givean independent observation of forest
structure/functional type?

Cutler, M.E.J., et al., 2012 Estimating tropical forest biomass with a combination of SAR image texture and Landsat TM data: anassessment of predictions between regions. ISPRS J. of Photogrammetry & Remote Sensing, 70, 66-77.

Ground & Remotely Sensed Data
Landsat TM

JERS-1

July /
27 (27)
August 1993
& 95

Landsat 4
July 1992

December
1995

Thailand

December1997

65 (40)Landsat 5
January 1997

March 1997

Malaysia

Nov. & Dec.
1996

52 (27)Landsat 5
March 1997

December
1996

Site

Field Survey

BrazilNo. ofplots(used)• All data pre-processed in same way– Landsat TM: radiometric, atmospheric & topographic
corrections applied– JERS-1: radiometric calibration & topographic
normalisation

SAR Image Texture Measures• SAR image texture relates to forest structure andgeometric properties, which can be correlatedwith AGB or used to discriminate between forest
types– Grey Level Co-occurrence Matrix (GLCM)• Based upon spatial distribution of grey level pairs of pixels• Can derive texture measures including entropy, variance,
dissimilarity, homogeneity, correlation, second moment andenergy (Haralick, 1979)– Wavelet (discrete wavelet frame transform)• Decomposes image into four frequency component• Approximation (low frequency), Details (high frequency) invertical, horizontal & diagonal directions(A) Raw backscatter; Texture measures derived from JERS-1: (B) energy, (C)homogeneity, (D) contrast, (E) second moment, and (F) contrast

AGB estimation• AGB estimated using ANNs trained under four
separate training ‘scenarios’:
1. ANNs trained and tested with samples from samesite only (SAR texture measures only)
2. ANNs trained with data from a single site and used
to estimate AGB at other two sites (SAR texture
measures only)
3. ANNs trained and tested with samples drawn fromall three sites (SAR texture measures only)
4. As scenario 3, but trained using a combination ofSAR texture and multispectral data

• Method– ANNs trained with:• 8 GLCM texture measures– Variable window sizes (3x3 – 13x13)• 3 levels of Coiflet wavelet decomposition– Four frequency component images• All 6 non-thermal Landsat TM bands (scenario 4)– Brazil (18 training samples; 9 testing), Malaysia(34;18), Thailand (42;21)– MLP, GRNN & RBF networks tested– Correlation between ANN predicted and field AGB
used to select best performing network• Scenario 1 – trained & tested on same site
– Strong correlations noted:• Brazil: MLP, 13x13 window, 6 texture measures; r=0.84• Malaysia: RBF, 7x7 window, 7 texture measures; r=0.79• Thailand: MLP, 7x7 window, 5 texture measures; r=0.83– GLCM texture consistently produced stronger
relationships than wavelet texture BUT lack ofconsistency in network type, window size andtexture measure combinations used

• Scenario 2 – trained with one site and applied
to other sites– Networks trained with data from Brazil able toestimate AGB in Malaysia (r=0.74) and vice versa– Poor ‘transferability’ to Thailand– Again, lack of consistency in network type andcombination of texture measures for strongest
performing networks

• Scenario 3 – trained & tested with SAR texture from allsites– r=0.53 (significant at 95% confidence level)– Compares to r=0.49 when Landsat TM data only used toestimate same AGB (Foody et al., 2003)– Underestimation of biomass in high-biomass plots• Scenario 4 – trained & tested with SAR & Landsat TM
data from all sites– r=0.77 (sig. at 99% confidence level)- addition of SAR texture leads to stronger correlations andability to estimate AGB at all three sites• Fishers r-to-z transformation suggests increase in correlation
coefficient not significant BUT small samples numbers (34,31)

Scenario 4: training and testing with SAR GLCM-texture & Landsat TM data from all sites

Predicted Biomass (t ha-1)

700

600

500

400

300

200

100

0
0

100

200

300

Observed Biomass (t ha-1)

400

500

600

700

Observations• Multispectral and SAR data independently exhibit
strong correlations when trained & tested withdata from same site
• Limited ability to ‘transfer’ relationship derived at
one site, to other sites• Strongest results when trained and tested withdata from all sites, and when SAR texture andmultispectral data combined• Network type & texture features included instrongest networks not consistent

Factors limiting transferability• Allometry and ground data

Uncertainty in AGB estimation

Uncertainty in allometry
700

600

AGB (t/ha)

500

400

300

200

100

0
1

6

11

16

21

26

31

36

Plot Number
Brown (1991) t/ha

Basuki (2009) t/ha

41

46

51

• Brazil - max.
difference of
239 t ha-1 (60%)• Malaysia – up to
260 t ha-1 (50%)• Thailand – up to
74 t ha-1 (27%)

Uncertainty in allometry• AGB values seen to vary particularly for highbiomass / large tree plots• ANNs estimates showed similar r values toprevious AGB estimates• Clearly implications for other direct methods!

Factors limiting transferability• Allometry and ground data
• Lack of consistency in texture measures included in best
performing networks– Recently implemented a Genetic Algorithm to select optimal
texture features for each site

• Lack of ground data

– Coupling r.s. data with ecosystem simulation models under test

• Data processing– Atmos. Correction, co-location of plots, co-registration of SARmultispectral data etc.
– Disparity between JERS-1 & Landsat TM data collection (Brazil)– Radiometric correction of SAR• Now looking at topographic correction / speckle suppression andeffects on derived texture measures

Conclusions• Both multispectral data and SAR texture on their own
showed strong relationships with tropical forest AGB at
single sites• Accuracy of predictive relationships declined whenapplied to different sites– lack of consistency in optimum input texture measures,type of network & window size
– Stronger relationships when applied to forests of similar‘type’, suggests relationships may transfer between regions
to forests of similar functional type

• Should we be aiming for universally applicable
relationship in the tropics or a ‘bank’ of relationships
segmented by forest type, structure etc.

Acknowledgements
We are grateful to all involved with these projects for their assistanceand input, particularly members of the EU-funded INDFORSUS project
and NERC TIGER project.
The JERS-1 data were acquired with financial assistance from theCarnegie Trust for the Universities of Scotland.
This research benefited from involvement in the Royal Society’s
Southeast Asia Rainforest Research ProgrammeLarge-area Carbon
Stock Estimation“Combine & assign approach”
Average biomass values are
assigned to land cover strata
Resulting maps are semicontinuous depictions ofspatial variation in carbon
stocks.
Recently, advances have been
made in “Direct Remote
Sensing” of biomass.

Gibbs et al, 2007
Goetz et al. 2009, CBMComposite of MODIS Reflectance Images

Mosaic of best quality
reflectance data

MODIS processing and compositing
Removes artifacts associated with variable viewgeometry
Atmospherically corrected and cloud screened
Artifacts due to residual clouds and shadows arepresent
Compositing over time successfully removes artifacts(Schaaf et al. 2002 RSE & Ju et al. 2010 RSE)

Aboveground biomass from MODIS calibrated with Inventory Data

The model explained 82% of the variance in above-groundbiomass density, with an RMS error of 50.5 Mg/ha(data available for download at www.whrc.org/Africa)

This is a Significant Improvement over GIS / LC-based Maps

Baccini et al, 2008

Gibbs et al, 2007
Goetz et al. 2009, CBM

Vegetation structure from Lidar
Lidarmetricshavebeenusedextensivelytocharacterizevegetation
structureandtoestimateAGBiomass

70m
Lidarshowsnosignofsaturationevenatveryhighbiomassdensity
LbandRadarsaturates~100Mg/haPbandmayextendthisrange(to200?Mg/ha)

The advantage of Lidar for AGB estimates

Zolkos et al. in press, Rem. Sens. Env.

The Geoscience Laser Altimeter System (GLAS)

The figure shows 30% of the
GLAS L2A (year 2003) shotsafter screening procedures (1.3
million observations)
Screening for cloud attenuation,
topographic slope (SRTM),
waveform noise thresholds, etc.
30-40% remain after screening.

Co-located Field Measurements

Biomass = 205 (t/ha)

Biomass = 78 (t/ha)

Biomass = 30 (t/ha)

North

West

East

South

40 meters

Pan-Tropical Biomass Map• Best quality MODIS mosaic• Multiple years• cloud free

• Screened GLAS metrics

MODIS 500 m composite
year 2007-2008• Series of metrics (tree height, height of median energy, etc.)• 2 –stage model
Field observations ~ GLAS metrics
GLAS-based estimates ~ MODISGLAS year 2007

PantropicalVegetationCarbonStocks

Baccinietal.2012NatureClimateChange

Recent maps of Pantropical Carbon Stocks(both partly based on linking satellite lidar and field data)

TotalCinAbovegroundbiomass=229Pg
TotalCinAbovegroundbiomass=193Pg

Mg/haHarris et al. 2012 Science“A difference of this magnitude in tropicaldeforestation emission estimates between two
state-of-the-art analyses is also cause forconcern in climate policy circles”

A case of apples and oranges“Emissions from deforestation in the Baccini et al. study (0.96 PgCyr-1)
are 19% higher than in the Harris et al study (0.81 PgCyr-1), rather than
180% higher.

Concluding thoughts• Full wave-form Lidar emerges as a critical tool for biomass
mapping• Pan-tropical direct mapping of carbon stocks is now a reality• Despite methodological differences stock estimates reported
from direct mapping are relatively consistent at the national
scale, and converging. The same is true of estimates of pantropical emissions from deforestation.
• Carbon density maps come with uncertainty estimates, but
not necessarily at the grid cell-scale.
• There is considerable room for improvement in the error
quantification – but ultimately ground data might be as limiting
than models relating them to remotely sensed observations.

Thank you!

Thisisthecapabilitywewouldliketohave..

Changesinheightand
biomassoverLaSelvatropicalforest

Goetz,S.J.,&Dubayah,R.O.(2011)
CarbonManagement2(3):231244

㪞㫃㫆㪹㪸㫃㩷㪸㫅㪻㩷㪩㪼㪾㫀㫆㫅㪸㫃㩷㪚㪦㪉㩷㪝㫃㫌㫏㩷㪜㫊㫋㫀㫄㪸㫋㫀㫆㫅㩷㪬㫊㫀㫅㪾㩷㪘㫋㫄㫆㫊㫇㪿㪼㫉㫀㪺㩷㪚㪦㪉㩷㪛㪸㫋㪸㩷㪦㪹㫋㪸㫀㫅㪼㪻㩷㪹㫐㩷㪞㪦㪪㪘㪫㪃㩷㪞㪦㪪㪘㪫㪄㪉㪃㩷㪸㫅㪻㩷㪦㫋㪿㪼㫉㩷㪝㫌㫋㫌㫉㪼㩷㪪㪸㫋㪼㫃㫃㫀㫋㪼㫊㪫㫊㫌㫅㪼㫆㩷㪤㪸㫋㫊㫌㫅㪸㪾㪸㪞㪦㪪㪘㪫㪄㪉㩷㪧㫉㫆㫁㪼㪺㫋㩷㪧㫉㪼㫇㪸㫉㪸㫋㫀㫆㫅㩷㪫㪼㪸㫄㪥㪸㫋㫀㫆㫅㪸㫃㩷㪠㫅㫊㫋㫀㫋㫌㫋㪼㩷㪽㫆㫉㩷㪜㫅㫍㫀㫉㫆㫅㫄㪼㫅㫋㪸㫃㩷㪪㫋㫌㪻㫀㪼㫊㩷㩿㪥㪠㪜㪪㪀㪃㩷㪡㪸㫇㪸㫅㪧㫉㪼㫊㪼㫅㫋㪼㪻㩷㪸㫋㩷㪠㪧㪚㪚㩷㪜㫏㫇㪼㫉㫋㩷㪤㪼㪼㫋㫀㫅㪾㪑㩷㪩㫆㫃㪼㩷㫆㪽㩷㪩㪼㫄㫆㫋㪼㩷㪪㪼㫅㫊㫀㫅㪾㩷㩿㪩㪪㪀㩷㫀㫅㩷㪝㫆㫉㪼㫊㫋㩷㪸㫅㪻㩷㪥㪸㫋㫀㫆㫅㪸㫃㩷㪞㪟㪞㩷㪜㫄㫀㫊㫊㫀㫆㫅㩷㪠㫅㫍㪼㫅㫋㫆㫉㫀㪼㫊㪃㩷㪦㪺㫋㫆㪹㪼㫉㩷㪉㪊㪄㪉㪌㪃㩷㪉㪇㪈㪉㪃㩷㪟㪸㫐㪸㫄㪸㪃㩷㪡㪸㫇㪸㫅㩷㪫㫆㪻㪸㫐㵭㫊㩷㪧㫉㪼㫊㪼㫅㫋㪸㫋㫀㫆㫅y 㪠㫅㫋㫉㫆㪻㫌㪺㫋㫀㫆㫅y 㪪㪸㫋㪼㫃㫃㫀㫋㪼㩷㫉㪼㫄㫆㫋㪼㩷㫊㪼㫅㫊㫀㫅㪾㩷㫆㪽㩷㪾㫉㪼㪼㫅㪿㫆㫌㫊㪼㩷㪾㪸㫊㪼㫊㩿㪞㪟㪞㫊㪀y 㪫㫆㫇㪄㪻㫆㫎㫅㩷㪽㫃㫌㫏㩷㪼㫊㫋㫀㫄㪸㫋㫀㫆㫅㩷㫌㫊㫀㫅㪾㩷㫊㪸㫋㪼㫃㫃㫀㫋㪼㩷㪞㪟㪞㩷㪻㪸㫋㪸y 㪉㪇㪈㪇㩷㪬㫋㫉㪼㪺㪿㫋㩷㫄㪼㪼㫋㫀㫅㪾y 㵰㪜㫏㫇㪼㫉㫋㩷㪤㪼㪼㫋㫀㫅㪾㩷㫆㫅㩷㪬㫅㪺㪼㫉㫋㪸㫀㫅㫋㫐㩷㪸㫅㪻㩷㪭㪸㫃㫀㪻㪸㫋㫀㫆㫅㩷㫆㪽㩷㪜㫄㫀㫊㫊㫀㫆㫅㩷㪠㫅㫍㪼㫅㫋㫆㫉㫀㪼㫊㵱㩷㩿㪤㪸㫉㪺㪿㩷㪉㪊㪄㪉㪌㪃㩷㪉㪇㪈㪇㪀y 㪉㪇㪈㪇㩷㵨 㪉㪇㪈㪉㩷㪧㫉㫆㪾㫉㪼㫊㫊y 㪞㪦㪪㪘㪫㩷㪚㪦㪉㩷㪺㫆㫅㪺㪼㫅㫋㫉㪸㫋㫀㫆㫅㩷㫇㫉㫆㪻㫌㪺㫋㫊㩷㪽㫉㫆㫄㩷㪡㪸㫇㪸㫅㪃㩷㪬㪪㪃㩷㪸㫅㪻㩷㪜㫌㫉㫆㫇㪼y 㪞㪦㪪㪘㪫㩷㪽㫃㫌㫏㩷㫀㫅㫋㪼㫉㪺㫆㫄㫇㪸㫉㫀㫊㫆㫅㫊y 㪝㫌㫋㫌㫉㪼㩷㪧㫉㫆㫊㫇㪼㪺㫋㫊y 㪥㪼㫎㩷㫊㪸㫋㪼㫃㫃㫀㫋㪼㫊㩷㪽㫆㪺㫌㫊㫀㫅㪾㩷㫆㫅㩷㫉㪼㪾㫀㫆㫅㪸㫃㩷㪽㫃㫌㫏㩷㪼㫊㫋㫀㫄㪸㫋㫀㫆㫅㪫㫊㫌㫅㪼㫆㩷㪤㪸㫋㫊㫌㫅㪸㪾㪸㪃㩷㪥㪸㫋㫀㫆㫅㪸㫃㩷㪠㫅㫊㫋㫀㫋㫌㫋㪼㩷㪽㫆㫉㩷㪜㫅㫍㫀㫉㫆㫅㫄㪼㫅㫋㪸㫃㩷㪪㫋㫌㪻㫀㪼㫊㪃㩷㪡㪸㫇㪸㫅㪧㫉㪼㫊㪼㫅㫋㪼㪻㩷㪸㫋㩷㪧㫉㪼㫊㪼㫅㫋㪼㪻㩷㪸㫋㩷㪠㪧㪚㪚㩷㪜㫏㫇㪼㫉㫋㩷㪤㪼㪼㫋㫀㫅㪾㪑㩷㪩㫆㫃㪼㩷㫆㪽㩷㪩㪼㫄㫆㫋㪼㩷㪪㪼㫅㫊㫀㫅㪾㩷㩿㪩㪪㪀㩷㫀㫅㩷㪝㫆㫉㪼㫊㫋㩷㪸㫅㪻㩷㪥㪸㫋㫀㫆㫅㪸㫃㩷㪞㪟㪞㩷㪜㫄㫀㫊㫊㫀㫆㫅㩷㪠㫅㫍㪼㫅㫋㫆㫉㫀㪼㫊㪃㩷㪦㪺㫋㫆㪹㪼㫉㩷㪉㪊㪄㪉㪌㪃㩷㪉㪇㪈㪉㪃㩷㪟㪸㫐㪸㫄㪸㪃㩷㪡㪸㫇㪸㫅㩷㪉㪟㫆㫎㩷㪻㫆㩷㫊㪸㫋㪼㫃㫃㫀㫋㪼㫊㩷㫆㪹㫊㪼㫉㫍㪼㩷㪺㪸㫉㪹㫆㫅㩷㪻㫀㫆㫏㫀㪻㪼㩷㫀㫅㩷㫋㪿㪼㩷㪸㫋㫄㫆㫊㫇㪿㪼㫉㪼㪖y 㪫㪿㪼㩷㫊㫌㫅㫃㫀㪾㪿㫋㩷㪾㫆㪼㫊㩷㪻㫆㫎㫅㫎㪸㫉㪻㩷㫋㪿㫉㫆㫌㪾㪿㩷㫋㪿㪼㩷㪜㪸㫉㫋㪿㵭㫊㩷㪸㫋㫄㫆㫊㫇㪿㪼㫉㪼㪃㩷㫉㪼㪽㫃㪼㪺㫋㫊㩷㪸㫋㩷㫋㪿㪼㩷㫊㫌㫉㪽㪸㪺㪼㪃㩷㫋㪿㪼㫅㩷㪾㫆㪼㫊㩷㫌㫇㫎㪸㫉㪻㩷㫋㪿㫉㫆㫌㪾㪿㩷㫋㪿㪼㩷㪸㫋㫄㫆㫊㫇㪿㪼㫉㪼㪃㩷㪸㫅㪻㩷㫉㪼㪸㪺㪿㪼㫊㩷㫋㫆㩷㫊㪸㫋㪼㫃㫃㫀㫋㪼㫊㪅y 㪚㪦㪉㩷㫄㫆㫃㪼㪺㫌㫃㪼㫊㩷㫀㫅㩷㫋㪿㪼㩷㪸㫋㫄㫆㫊㫇㪿㪼㫉㪼㩷㪸㪹㫊㫆㫉㪹㩷㫋㪿㪼㩷㫊㫌㫅㫃㫀㪾㪿㫋㩷㪸㫋㩷㫊㫇㪼㪺㫀㪽㫀㪺㩷㫎㪸㫍㪼㫃㪼㫅㪾㫋㪿㫊㪅y 㪫㪿㪼㩷㪸㪹㫊㫆㫉㫇㫋㫀㫆㫅㩷㫊㫋㫉㪼㫅㪾㫋㪿㩷㫀㫊㩷㪸㩷㪽㫌㫅㪺㫋㫀㫆㫅㩷㫆㪽㩷㫋㪿㪼㩷㫅㫌㫄㪹㪼㫉㩷㫆㪽㩷㪚㪦㪉㩷㫄㫆㫃㪼㪺㫌㫃㪼㫊㩷㫀㫅㩷㫋㪿㪼㩷㫃㫀㪾㪿㫋㩷㫇㪸㫋㪿㪅y 㪙㫐㩷㪸㫅㪸㫃㫐㫑㫀㫅㪾㩷㫋㪿㪼㩷㪸㪹㫊㫆㫉㫇㫋㫀㫆㫅㩷㪽㪼㪸㫋㫌㫉㪼㫊㪃㩷㫋㪿㪼㩷㩿㪺㫆㫃㫌㫄㫅㪀㩷㪸㫄㫆㫌㫅㫋㩷㫆㪽㩷㪚㪦㪉㩷㫀㫅㩷㫋㪿㪼㩷㪸㫋㫄㫆㫊㫇㪿㪼㫉㪼㩷㪺㪸㫅㩷㪹㪼㩷㪼㫊㫋㫀㫄㪸㫋㪼㪻㪅y 㪮㪼㩷㫅㪼㪼㪻㩷㪺㫃㫆㫌㪻㪆㪸㪼㫉㫆㫊㫆㫃㩷㪽㫉㪼㪼㩷㪿㫀㪾㪿㩷㫊㫀㪾㫅㪸㫃㪄㫋㫆㪄㫅㫆㫀㫊㪼㩷㪿㫋㫋㫇㪑㪆㪆㫆㪺㫆㪅㫁㫇㫃㪅㫅㪸㫊㪸㪅㪾㫆㫍㪆㫀㫄㪸㪾㪼㫊㪆㫆㪺㫆㪆㪦㪚㪦㪶㪺㫆㫃㫌㫄㫅㪅㫁㫇㪾㫉㪸㫋㫀㫆㩷㪻㪸㫋㪸㪅㪫㫊㫌㫅㪼㫆㩷㪤㪸㫋㫊㫌㫅㪸㪾㪸㪃㩷㪥㪸㫋㫀㫆㫅㪸㫃㩷㪠㫅㫊㫋㫀㫋㫌㫋㪼㩷㪽㫆㫉㩷㪜㫅㫍㫀㫉㫆㫅㫄㪼㫅㫋㪸㫃㩷㪪㫋㫌㪻㫀㪼㫊㪃㩷㪡㪸㫇㪸㫅㪧㫉㪼㫊㪼㫅㫋㪼㪻㩷㪸㫋㩷㪧㫉㪼㫊㪼㫅㫋㪼㪻㩷㪸㫋㩷㪠㪧㪚㪚㩷㪜㫏㫇㪼㫉㫋㩷㪤㪼㪼㫋㫀㫅㪾㪑㩷㪩㫆㫃㪼㩷㫆㪽㩷㪩㪼㫄㫆㫋㪼㩷㪪㪼㫅㫊㫀㫅㪾㩷㩿㪩㪪㪀㩷㫀㫅㩷㪝㫆㫉㪼㫊㫋㩷㪸㫅㪻㩷㪥㪸㫋㫀㫆㫅㪸㫃㩷㪞㪟㪞㩷㪜㫄㫀㫊㫊㫀㫆㫅㩷㪠㫅㫍㪼㫅㫋㫆㫉㫀㪼㫊㪃㩷㪦㪺㫋㫆㪹㪼㫉㩷㪉㪊㪄㪉㪌㪃㩷㪉㪇㪈㪉㪃㩷㪟㪸㫐㪸㫄㪸㪃㩷㪡㪸㫇㪸㫅㩷㪊㪭㫀㫊㫀㪹㫃㪼㩷㫋㫆㩷㪪㪿㫆㫉㫋㫎㪸㫍㪼㩷㪠㫅㪽㫉㪸㫉㪼㪻㩷㪩㪸㪻㫀㪸㫅㪺㪼㩷㪪㫇㪼㪺㫋㫉㫌㫄㩷㪦㪹㫋㪸㫀㫅㪼㪻㩷㪹㫐㩷㪪㪸㫋㪼㫃㫃㫀㫋㪼㫊㪫㫊㫌㫅㪼㫆㩷㪤㪸㫋㫊㫌㫅㪸㪾㪸㪃㩷㪥㪸㫋㫀㫆㫅㪸㫃㩷㪠㫅㫊㫋㫀㫋㫌㫋㪼㩷㪽㫆㫉㩷㪜㫅㫍㫀㫉㫆㫅㫄㪼㫅㫋㪸㫃㩷㪪㫋㫌㪻㫀㪼㫊㪃㩷㪡㪸㫇㪸㫅㪧㫉㪼㫊㪼㫅㫋㪼㪻㩷㪸㫋㩷㪧㫉㪼㫊㪼㫅㫋㪼㪻㩷㪸㫋㩷㪠㪧㪚㪚㩷㪜㫏㫇㪼㫉㫋㩷㪤㪼㪼㫋㫀㫅㪾㪑㩷㪩㫆㫃㪼㩷㫆㪽㩷㪩㪼㫄㫆㫋㪼㩷㪪㪼㫅㫊㫀㫅㪾㩷㩿㪩㪪㪀㩷㫀㫅㩷㪝㫆㫉㪼㫊㫋㩷㪸㫅㪻㩷㪥㪸㫋㫀㫆㫅㪸㫃㩷㪞㪟㪞㩷㪜㫄㫀㫊㫊㫀㫆㫅㩷㪠㫅㫍㪼㫅㫋㫆㫉㫀㪼㫊㪃㩷㪦㪺㫋㫆㪹㪼㫉㩷㪉㪊㪄㪉㪌㪃㩷㪉㪇㪈㪉㪃㩷㪟㪸㫐㪸㫄㪸㪃㩷㪡㪸㫇㪸㫅㩷㪋㪠㫅㫍㪼㫉㫊㪼㩷㪤㫆㪻㪼㫃㫃㫀㫅㪾㩷㪽㫆㫉㩷㪝㫃㫌㫏㩷㪜㫊㫋㫀㫄㪸㫋㫀㫆㫅㩷㪫㫊㫌㫅㪼㫆㩷㪤㪸㫋㫊㫌㫅㪸㪾㪸㪃㩷㪥㪸㫋㫀㫆㫅㪸㫃㩷㪠㫅㫊㫋㫀㫋㫌㫋㪼㩷㪽㫆㫉㩷㪜㫅㫍㫀㫉㫆㫅㫄㪼㫅㫋㪸㫃㩷㪪㫋㫌㪻㫀㪼㫊㪃㩷㪡㪸㫇㪸㫅㪧㫉㪼㫊㪼㫅㫋㪼㪻㩷㪸㫋㩷㪧㫉㪼㫊㪼㫅㫋㪼㪻㩷㪸㫋㩷㪠㪧㪚㪚㩷㪜㫏㫇㪼㫉㫋㩷㪤㪼㪼㫋㫀㫅㪾㪑㩷㪩㫆㫃㪼㩷㫆㪽㩷㪩㪼㫄㫆㫋㪼㩷㪪㪼㫅㫊㫀㫅㪾㩷㩿㪩㪪㪀㩷㫀㫅㩷㪝㫆㫉㪼㫊㫋㩷㪸㫅㪻㩷㪥㪸㫋㫀㫆㫅㪸㫃㩷㪞㪟㪞㩷㪜㫄㫀㫊㫊㫀㫆㫅㩷㪠㫅㫍㪼㫅㫋㫆㫉㫀㪼㫊㪃㩷㪦㪺㫋㫆㪹㪼㫉㩷㪉㪊㪄㪉㪌㪃㩷㪉㪇㪈㪉㪃㩷㪟㪸㫐㪸㫄㪸㪃㩷㪡㪸㫇㪸㫅㩷

㪞㪦㪪㪘㪫

㪌

㪞㫉㪼㪼㫅㪿㫆㫌㫊㪼㩷㪞㪸㫊㪼㫊㩷㪦㪹㫊㪼㫉㫍㫀㫅㪾㩷㪪㪸㫋㪼㫃㫃㫋㪼㩿㪞㪦㪪㪘㪫㪀

㫟㪡㪘㪯㪘㪦㪹㫁㪼㪺㫋㫀㫍㪼㫊㩷㫆㪽㩷㪞㪦㪪㪘㪫y 㪫㫆㩷㫆㪹㫋㪸㫀㫅㩷㫋㪿㪼㩷㪾㫃㫆㪹㪸㫃㩷㪻㫀㫊㫋㫉㫀㪹㫌㫋㫀㫆㫅㫊㩷㫆㪽㩷㪞㪟㪞㩷㪺㫆㫅㪺㪼㫅㫋㫉㪸㫋㫀㫆㫅㫊㩷㩿㪚㪦㪉㩷㪸㫅㪻㩷㪚㪟㪋㪀㩷㪸㫅㪻㩷㫋㪿㪼㫀㫉㩷㫋㪼㫄㫇㫆㫉㪸㫃㩷㫍㪸㫉㫀㪸㫋㫀㫆㫅㫊㪅y 㪫㫆㩷㫍㫀㫊㫌㪸㫃㫀㫑㪼㩷㪺㪿㪸㫅㪾㫀㫅㪾㩷㪾㫃㫆㪹㪸㫃㩷㪞㪟㪞㩷㪻㫀㫊㫋㫉㫀㪹㫌㫋㫀㫆㫅㫊y 㪫㫆㩷㪽㫀㫃㫃㩷㫋㪿㪼㩷㪹㫃㪸㫅㫂㫊㩷㫀㫅㩷㫋㪿㪼㩷㫅㪼㫋㫎㫆㫉㫂㩷㫆㪽㩷㪾㫉㫆㫌㫅㪻㩷㫄㫆㫅㫀㫋㫆㫉㫀㫅㪾㩷㫊㫋㪸㫋㫀㫆㫅㫊y 㪫㫆㩷㫀㫄㫇㫉㫆㫍㪼㩷㪸㪺㪺㫌㫉㪸㪺㫐㩷㫆㪽㩷㫋㪿㪼㩷㪺㪸㫉㪹㫆㫅㩷㪽㫃㫌㫏㩷㩿㫊㫆㫌㫉㪺㪼㫊㩷㪸㫅㪻㩷㫊㫀㫅㫂㫊㪀㩷㪼㫊㫋㫀㫄㪸㫋㫀㫆㫅㩷㫆㫅㩷㪸㩷㫊㫌㪹㪄㪺㫆㫅㫋㫀㫅㪼㫅㫋㪸㫃㩷㫊㪺㪸㫃㪼㪅y 㪫㫆㩷㪻㪼㫍㪼㫃㫆㫇㩷㫋㪼㪺㪿㫅㫆㫃㫆㪾㫀㪼㫊㩷㪽㫆㫉㩷㪽㫌㫋㫌㫉㪼㩷㪞㪟㪞㩷㫆㪹㫊㪼㫉㫍㫀㫅㪾㩷㫊㪸㫋㪼㫃㫃㫀㫋㪼㫊㪫㫊㫌㫅㪼㫆㩷㪤㪸㫋㫊㫌㫅㪸㪾㪸㪃㩷㪥㪸㫋㫀㫆㫅㪸㫃㩷㪠㫅㫊㫋㫀㫋㫌㫋㪼㩷㪽㫆㫉㩷㪜㫅㫍㫀㫉㫆㫅㫄㪼㫅㫋㪸㫃㩷㪪㫋㫌㪻㫀㪼㫊㪃㩷㪡㪸㫇㪸㫅㪧㫉㪼㫊㪼㫅㫋㪼㪻㩷㪸㫋㩷㪧㫉㪼㫊㪼㫅㫋㪼㪻㩷㪸㫋㩷㪠㪧㪚㪚㩷㪜㫏㫇㪼㫉㫋㩷㪤㪼㪼㫋㫀㫅㪾㪑㩷㪩㫆㫃㪼㩷㫆㪽㩷㪩㪼㫄㫆㫋㪼㩷㪪㪼㫅㫊㫀㫅㪾㩷㩿㪩㪪㪀㩷㫀㫅㩷㪝㫆㫉㪼㫊㫋㩷㪸㫅㪻㩷㪥㪸㫋㫀㫆㫅㪸㫃㩷㪞㪟㪞㩷㪜㫄㫀㫊㫊㫀㫆㫅㩷㪠㫅㫍㪼㫅㫋㫆㫉㫀㪼㫊㪃㩷㪦㪺㫋㫆㪹㪼㫉㩷㪉㪊㪄㪉㪌㪃㩷㪉㪇㪈㪉㪃㩷㪟㪸㫐㪸㫄㪸㪃㩷㪡㪸㫇㪸㫅㩷

㪏

Size

Main
body

3.7 m x 1.8 m x 2.0 m
㧔Wing Span 13.7m㧕

Mass

Total

1750kg

Power

Total

3.8 kW (EOL)Life Time
Orbit

Launch5 years
sun synchronous orbit
Local time

13:00+/-0:15

Altitude

666km

Inclination

98deg

Repeat3 days (44 revol.)Vehicle

H-IIA

Schedule

Jan. 23, 2009GOSAT andTANSO onboard GOSATTANSO=Thermal And Near infrared
Sensor for carbon Observation

TANSO-FTS(Fourier Transform
Spectrometer)-SWIR reflected on
the earth’s surface-TIR radiated fromthe ground and theatmosphere

TANSO-CAI(Cloud and Aerosol
Imager)
UV : 0.38 Pm
V: 0.67 Pm
NIR : 0.87 Pm
SWIR : 1.6 Pm㫟㪡㪘㪯㪘㪧㫉㫆㫍㫀㪻㪼㪻㩷㪹㫐㩷㪤㪅㩷㪥㪸㫂㪸㫁㫀㫄㪸㩷㩿㪡㪘㪯㪘㪀㪚㫌㫉㫉㪼㫅㫋㩷㪞㪦㪪㪘㪫㩷㪝㪫㪪㪦㪹㫊㪼㫉㫍㪸㫋㫀㫆㫅㩷㪧㪸㫋㫋㪼㫉㫅㪧㫉㫆㫍㫀㪻㪼㪻㩷㪹㫐㩷㪤㪅㩷㪥㪸㫂㪸㫁㫀㫄㪸㩷㩿㪡㪘㪯㪘㪀㪫㫊㫌㫅㪼㫆㩷㪤㪸㫋㫊㫌㫅㪸㪾㪸㪃㩷㪥㪸㫋㫀㫆㫅㪸㫃㩷㪠㫅㫊㫋㫀㫋㫌㫋㪼㩷㪽㫆㫉㩷㪜㫅㫍㫀㫉㫆㫅㫄㪼㫅㫋㪸㫃㩷㪪㫋㫌㪻㫀㪼㫊㪃㩷㪡㪸㫇㪸㫅㪧㫉㪼㫊㪼㫅㫋㪼㪻㩷㪸㫋㩷㪧㫉㪼㫊㪼㫅㫋㪼㪻㩷㪸㫋㩷㪠㪧㪚㪚㩷㪜㫏㫇㪼㫉㫋㩷㪤㪼㪼㫋㫀㫅㪾㪑㩷㪩㫆㫃㪼㩷㫆㪽㩷㪩㪼㫄㫆㫋㪼㩷㪪㪼㫅㫊㫀㫅㪾㩷㩿㪩㪪㪀㩷㫀㫅㩷㪝㫆㫉㪼㫊㫋㩷㪸㫅㪻㩷㪥㪸㫋㫀㫆㫅㪸㫃㩷㪞㪟㪞㩷㪜㫄㫀㫊㫊㫀㫆㫅㩷㪠㫅㫍㪼㫅㫋㫆㫉㫀㪼㫊㪃㩷㪦㪺㫋㫆㪹㪼㫉㩷㪉㪊㪄㪉㪌㪃㩷㪉㪇㪈㪉㪃㩷㪟㪸㫐㪸㫄㪸㪃㩷㪡㪸㫇㪸㫅㩷㪈㪇㪉㪇㪈㪇㩷㪬㫋㫉㪼㪺㪿㫋㩷㪤㪼㪼㫋㫀㫅㪾㪉㪇㪈㪇㩷㪬㫋㫉㪼㪺㪿㫋㩷㪤㪼㪼㫋㫀㫅㪾㪜㫏㪼㪺㫌㫋㫀㫍㪼㩷㪪㫌㫄㫄㪸㫉㫐㪫㪿㪼㫉㪼㩷㫎㪸㫊㩷㪺㫆㫅㫊㪼㫅㫊㫌㫊㩷㫋㪿㪸㫋㪃㩷㫎㪿㫀㫃㪼㩷㫉㪼㫄㫆㫋㪼㩷㫊㪼㫅㫊㫀㫅㪾㪃㩷㪸㫄㪹㫀㪼㫅㫋㩷㫄㪼㪸㫊㫌㫉㪼㫄㪼㫅㫋㩷㪸㫅㪻㩷㫀㫅㫍㪼㫉㫊㪼㩷㫄㫆㪻㪼㫃㫃㫀㫅㪾㩷㫋㪼㪺㪿㫅㫀㫈㫌㪼㫊㩷㪿㪸㫍㪼㩷㪹㪼㪼㫅㩷㫊㫌㪺㪺㪼㫊㫊㪽㫌㫃㫃㫐㩷㪻㪼㫄㫆㫅㫊㫋㫉㪸㫋㪼㪻㩷㫋㪿㪼㫐㩷㪸㫉㪼㩷㪺㫌㫉㫉㪼㫅㫋㫃㫐㩷㫅㫆㫋㩷㫊㫌㪽㪽㫀㪺㫀㪼㫅㫋㫃㫐㩷㪻㪼㫍㪼㫃㫆㫇㪼㪻㩷㫋㫆㩷㫇㫉㫆㫍㫀㪻㪼㩷㪺㫆㫄㫇㫉㪼㪿㪼㫅㫊㫀㫍㪼㩷㫍㪼㫉㫀㪽㫀㪺㪸㫋㫀㫆㫅㩷㪸㫋㩷㫋㪿㪼㩷㫉㪼㫈㫌㫀㫉㪼㪻㩷㪸㪺㪺㫌㫉㪸㪺㫐㪃㩷㫄㫌㪺㪿㩷㫀㫊㩷㫋㫆㩷㪹㪼㩷㪾㪸㫀㫅㪼㪻㩷㪽㫉㫆㫄㩷㫎㫆㫉㫂㫀㫅㪾㩷㫋㫆㪾㪼㫋㪿㪼㫉㪃㩷㫋㫆㩷㫀㫄㫇㫉㫆㫍㪼㩷㫍㪼㫉㫀㪽㫀㪺㪸㫋㫀㫆㫅㩷㫋㪼㪺㪿㫅㫀㫈㫌㪼㫊㩷㪸㫊㩷㫎㪼㫃㫃㩷㪸㫊㩷㪾㪸㫀㫅㩷㪹㪼㫋㫋㪼㫉㩷㫌㫅㪻㪼㫉㫊㫋㪸㫅㪻㫀㫅㪾㩷㫆㪽㩷㫀㫅㫍㪼㫅㫋㫆㫉㫐㩷㪼㫊㫋㫀㫄㪸㫋㪼㫊㪃㩷㪸㫅㪻㩷㫆㪽㩷㫅㪸㫋㫌㫉㪸㫃㩷㪼㫄㫀㫊㫊㫀㫆㫅㫊㩷㪸㫅㪻㩷㫉㪼㫄㫆㫍㪸㫃㫊㪅㪫㫊㫌㫅㪼㫆㩷㪤㪸㫋㫊㫌㫅㪸㪾㪸㪃㩷㪥㪸㫋㫀㫆㫅㪸㫃㩷㪠㫅㫊㫋㫀㫋㫌㫋㪼㩷㪽㫆㫉㩷㪜㫅㫍㫀㫉㫆㫅㫄㪼㫅㫋㪸㫃㩷㪪㫋㫌㪻㫀㪼㫊㪃㩷㪡㪸㫇㪸㫅㪧㫉㪼㫊㪼㫅㫋㪼㪻㩷㪸㫋㩷㪧㫉㪼㫊㪼㫅㫋㪼㪻㩷㪸㫋㩷㪠㪧㪚㪚㩷㪜㫏㫇㪼㫉㫋㩷㪤㪼㪼㫋㫀㫅㪾㪑㩷㪩㫆㫃㪼㩷㫆㪽㩷㪩㪼㫄㫆㫋㪼㩷㪪㪼㫅㫊㫀㫅㪾㩷㩿㪩㪪㪀㩷㫀㫅㩷㪝㫆㫉㪼㫊㫋㩷㪸㫅㪻㩷㪥㪸㫋㫀㫆㫅㪸㫃㩷㪞㪟㪞㩷㪜㫄㫀㫊㫊㫀㫆㫅㩷㪠㫅㫍㪼㫅㫋㫆㫉㫀㪼㫊㪃㩷㪦㪺㫋㫆㪹㪼㫉㩷㪉㪊㪄㪉㪌㪃㩷㪉㪇㪈㪉㪃㩷㪟㪸㫐㪸㫄㪸㪃㩷㪡㪸㫇㪸㫅㩷㪈㪉㪉㪇㪈㪇㩷㵨 㪉㪇㪈㪉㩷㪧㫉㫆㪾㫉㪼㫊㫊㪞㪦㪪㪘㪫㩷㪫㫀㫄㪼㫃㫀㫅㪼㩷y 㪡㪸㫅㫌㪸㫉㫐㪃㩷㪉㪇㪇㪐㪞㪦㪪㪘㪫㩷㪣㪸㫌㫅㪺㪿㩷㩿㪣㫀㪽㪼㫋㫀㫄㪼㩷㪔㩷㪌㩷㫐㪼㪸㫉㫊㪀y 㪝㪼㪹㫉㫌㪸㫉㫐㩷㪉㪇㪈㪇㪧㫌㪹㫃㫀㪺㩷㫉㪼㫃㪼㪸㫊㪼㩷㫆㪽㩷㪥㪠㪜㪪㩷㪞㪦㪪㪘㪫㩷㪣㪼㫍㪼㫃㩷㪉㩷㫇㫉㫆㪻㫌㪺㫋㩿㪯㪚㪦㪉㪃㩷㪯㪚㪟㪋㩷㪑㩷㪚㫆㫃㫌㫄㫅㪄㪸㫍㪼㫉㪸㪾㪼㩷㪺㫆㫅㪺㪼㫅㫋㫉㪸㫋㫀㫆㫅㪀y 㪡㫌㫅㪼㩷㪉㪇㪈㪉㪧㫌㪹㫃㫀㪺㩷㫉㪼㫃㪼㪸㫊㪼㩷㫆㪽㩷㫅㪼㫎㩷㪥㪠㪜㪪㩷㪞㪦㪪㪘㪫㩷㪣㪼㫍㪼㫃㩷㪉㫇㫉㫆㪻㫌㪺㫋
㵶㩷㪚㪦㪉㩷㪙㫀㪸㫊㩷㻃㩷㪈㪅㪉㩷㫇㫇㫄㪃㩷㪪㫋㪻㪅㩷㪛㪼㫍㪅㩷㻃㩷㪉㪅㪇㩷㫇㫇㫄㪸㫋㩷㫊㪼㫃㪼㪺㫋㪼㪻㩷㫍㪸㫃㫀㪻㪸㫋㫀㫆㫅㩷㫊㫀㫋㪼㫊y 㪡㫌㫅㪼㩷㪉㪇㪈㪉㪠㪮㪞㪞㪤㪪㪄㪏
㵶㩷㪝㫃㫌㫏㩷㪼㫊㫋㫀㫄㪸㫋㪼㩷㫀㫅㫋㪼㫉㪺㫆㫄㫇㪸㫉㫀㫊㫆㫅㪄 㪝㫀㫍㪼㩷㪺㫆㫅㪺㪼㫅㫋㫉㪸㫋㫀㫆㫅㩷㪻㪸㫋㪸㩷㫊㪼㫋㫊㪄 㪝㫀㫍㪼㩷㪽㫃㫌㫏㩷㪼㫊㫋㫀㫄㪸㫋㫀㫆㫅㩷㫊㪺㪿㪼㫄㪼㫊y 㪪㪼㫇㫋㪼㫄㪹㪼㫉㩷㪉㪇㪈㪉㪞㪦㪪㪘㪫㩷㪣㪼㫍㪼㫃㩷㪋㩷㫇㫉㫆㪻㫌㪺㫋㩷㩿㪚㪦㪉㩷㪽㫃㫌㫏㩷㪽㫉㫆㫄㩷㪍㪋㩷㫉㪼㪾㫀㫆㫅㫊㪀㩷㫉㪼㫃㪼㪸㫊㪼㩷㫋㫆㩷㪩㪘㩷㫉㪼㫊㪼㪸㫉㪺㪿㪼㫉㫊㪩㪘㩷㪑㩷㪞㪦㪪㪘㪫㩷㫉㪼㫊㪼㪸㫉㪺㪿㩷㪸㫅㫅㫆㫌㪺㪼㫄㪼㫅㫋㪫㫊㫌㫅㪼㫆㩷㪤㪸㫋㫊㫌㫅㪸㪾㪸㪃㩷㪥㪸㫋㫀㫆㫅㪸㫃㩷㪠㫅㫊㫋㫀㫋㫌㫋㪼㩷㪽㫆㫉㩷㪜㫅㫍㫀㫉㫆㫅㫄㪼㫅㫋㪸㫃㩷㪪㫋㫌㪻㫀㪼㫊㪃㩷㪡㪸㫇㪸㫅㪧㫉㪼㫊㪼㫅㫋㪼㪻㩷㪸㫋㩷㪧㫉㪼㫊㪼㫅㫋㪼㪻㩷㪸㫋㩷㪠㪧㪚㪚㩷㪜㫏㫇㪼㫉㫋㩷㪤㪼㪼㫋㫀㫅㪾㪑㩷㪩㫆㫃㪼㩷㫆㪽㩷㪩㪼㫄㫆㫋㪼㩷㪪㪼㫅㫊㫀㫅㪾㩷㩿㪩㪪㪀㩷㫀㫅㩷㪝㫆㫉㪼㫊㫋㩷㪸㫅㪻㩷㪥㪸㫋㫀㫆㫅㪸㫃㩷㪞㪟㪞㩷㪜㫄㫀㫊㫊㫀㫆㫅㩷㪠㫅㫍㪼㫅㫋㫆㫉㫀㪼㫊㪃㩷㪦㪺㫋㫆㪹㪼㫉㩷㪉㪊㪄㪉㪌㪃㩷㪉㪇㪈㪉㪃㩷㪟㪸㫐㪸㫄㪸㪃㩷㪡㪸㫇㪸㫅㩷㪈㪋㪞㪦㪪㪘㪫㩷㪯㪚㪦㪉㩷㩿㪡㫌㫃㫐㩷㪉㪇㪈㪉㪀㪥㫆㩷㪪㫌㫅㪾㫃㫀㫅㫋㪛㪸㫋㪸㪥㫆㩷㪺㫃㫆㫌㪻㪆㪸㪼㫉㫆㫊㫆㫃㪽㫉㪼㪼㩷㪻㪸㫋㪸㪫㫊㫌㫅㪼㫆㩷㪤㪸㫋㫊㫌㫅㪸㪾㪸㪃㩷㪥㪸㫋㫀㫆㫅㪸㫃㩷㪠㫅㫊㫋㫀㫋㫌㫋㪼㩷㪽㫆㫉㩷㪜㫅㫍㫀㫉㫆㫅㫄㪼㫅㫋㪸㫃㩷㪪㫋㫌㪻㫀㪼㫊㪃㩷㪡㪸㫇㪸㫅㪧㫉㪼㫊㪼㫅㫋㪼㪻㩷㪸㫋㩷㪧㫉㪼㫊㪼㫅㫋㪼㪻㩷㪸㫋㩷㪠㪧㪚㪚㩷㪜㫏㫇㪼㫉㫋㩷㪤㪼㪼㫋㫀㫅㪾㪑㩷㪩㫆㫃㪼㩷㫆㪽㩷㪩㪼㫄㫆㫋㪼㩷㪪㪼㫅㫊㫀㫅㪾㩷㩿㪩㪪㪀㩷㫀㫅㩷㪝㫆㫉㪼㫊㫋㩷㪸㫅㪻㩷㪥㪸㫋㫀㫆㫅㪸㫃㩷㪞㪟㪞㩷㪜㫄㫀㫊㫊㫀㫆㫅㩷㪠㫅㫍㪼㫅㫋㫆㫉㫀㪼㫊㪃㩷㪦㪺㫋㫆㪹㪼㫉㩷㪉㪊㪄㪉㪌㪃㩷㪉㪇㪈㪉㪃㩷㪟㪸㫐㪸㫄㪸㪃㩷㪡㪸㫇㪸㫅㩷㪈㪌㪞㪦㪪㪘㪫㩷㪯㪚㪦㪉㩷㩿㪡㫌㫃㫐㩷㪉㪇㪇㪐㪀㪫㫊㫌㫅㪼㫆㩷㪤㪸㫋㫊㫌㫅㪸㪾㪸㪃㩷㪥㪸㫋㫀㫆㫅㪸㫃㩷㪠㫅㫊㫋㫀㫋㫌㫋㪼㩷㪽㫆㫉㩷㪜㫅㫍㫀㫉㫆㫅㫄㪼㫅㫋㪸㫃㩷㪪㫋㫌㪻㫀㪼㫊㪃㩷㪡㪸㫇㪸㫅㪧㫉㪼㫊㪼㫅㫋㪼㪻㩷㪸㫋㩷㪧㫉㪼㫊㪼㫅㫋㪼㪻㩷㪸㫋㩷㪠㪧㪚㪚㩷㪜㫏㫇㪼㫉㫋㩷㪤㪼㪼㫋㫀㫅㪾㪑㩷㪩㫆㫃㪼㩷㫆㪽㩷㪩㪼㫄㫆㫋㪼㩷㪪㪼㫅㫊㫀㫅㪾㩷㩿㪩㪪㪀㩷㫀㫅㩷㪝㫆㫉㪼㫊㫋㩷㪸㫅㪻㩷㪥㪸㫋㫀㫆㫅㪸㫃㩷㪞㪟㪞㩷㪜㫄㫀㫊㫊㫀㫆㫅㩷㪠㫅㫍㪼㫅㫋㫆㫉㫀㪼㫊㪃㩷㪦㪺㫋㫆㪹㪼㫉㩷㪉㪊㪄㪉㪌㪃㩷㪉㪇㪈㪉㪃㩷㪟㪸㫐㪸㫄㪸㪃㩷㪡㪸㫇㪸㫅㩷㪈㪍㪞㪦㪪㪘㪫㩷㪯㪚㪦㪉㩷㩿㪡㪸㫅㫌㪸㫉㫐㩷㪆㩷㪡㫌㫃㫐㩷㪉㪇㪈㪉㪀㪡㪸㫅㫌㪸㫉㫐㩷㪉㪇㪈㪉㪡㫌㫃㫐㩷㪉㪇㪈㪉㪫㪿㪼㫊㪼㩷㪞㪦㪪㪘㪫㩷㫇㫆㫀㫅㫋㩷㫆㫉㩷㪾㫉㫀㪻㩷㪻㪸㫋㪸㩷㪸㫉㪼㩷㫌㫊㪼㪻㩷㫋㫆㩷㪺㫆㫅㫋㫉㫆㫃㪆㪸㪻㫁㫌㫊㫋㪆㫄㫆㪻㫀㪽㫐㩷㪼㫏㫀㫊㫋㫀㫅㪾㩷㫊㫌㫉㪽㪸㪺㪼㩷㪽㫃㫌㫏㩷㫄㫆㪻㪼㫃㫊㩷㫀㫅㩷㫋㪿㪼㩷㫀㫅㫍㪼㫉㫊㫀㫆㫅㩷㪸㫅㪸㫃㫐㫊㫀㫊㪅㪫㫊㫌㫅㪼㫆㩷㪤㪸㫋㫊㫌㫅㪸㪾㪸㪃㩷㪥㪸㫋㫀㫆㫅㪸㫃㩷㪠㫅㫊㫋㫀㫋㫌㫋㪼㩷㪽㫆㫉㩷㪜㫅㫍㫀㫉㫆㫅㫄㪼㫅㫋㪸㫃㩷㪪㫋㫌㪻㫀㪼㫊㪃㩷㪡㪸㫇㪸㫅㪧㫉㪼㫊㪼㫅㫋㪼㪻㩷㪸㫋㩷㪧㫉㪼㫊㪼㫅㫋㪼㪻㩷㪸㫋㩷㪠㪧㪚㪚㩷㪜㫏㫇㪼㫉㫋㩷㪤㪼㪼㫋㫀㫅㪾㪑㩷㪩㫆㫃㪼㩷㫆㪽㩷㪩㪼㫄㫆㫋㪼㩷㪪㪼㫅㫊㫀㫅㪾㩷㩿㪩㪪㪀㩷㫀㫅㩷㪝㫆㫉㪼㫊㫋㩷㪸㫅㪻㩷㪥㪸㫋㫀㫆㫅㪸㫃㩷㪞㪟㪞㩷㪜㫄㫀㫊㫊㫀㫆㫅㩷㪠㫅㫍㪼㫅㫋㫆㫉㫀㪼㫊㪃㩷㪦㪺㫋㫆㪹㪼㫉㩷㪉㪊㪄㪉㪌㪃㩷㪉㪇㪈㪉㪃㩷㪟㪸㫐㪸㫄㪸㪃㩷㪡㪸㫇㪸㫅㩷㪈㪎

64-region’s㪍㪋㪄㫉㪼㪾㫀㫆㫅㵭㫊㩷㪚㪦㪉㩷㪽㫃㫌㫏㪼㫊㩷㪼㫊㫋㫀㫄㪸㫋㪼㪻㩷㪽㫉㫆㫄㩷㪹㫆㫋㪿
CO2 fluxes estimated from both
ground-based and
GOSAT data㪾㫉㫆㫌㫅㪻㪄㪹㪸㫊㪼㪻㩷㪸㫅㪻㩷㪞㪦㪪㪘㪫㩷㪻㪸㫋㪸
2009/07

2009/102010/01

2010/04land㪫㫊㫌㫅㪼㫆㩷㪤㪸㫋㫊㫌㫅㪸㪾㪸㪃㩷㪥㪸㫋㫀㫆㫅㪸㫃㩷㪠㫅㫊㫋㫀㫋㫌㫋㪼㩷㪽㫆㫉㩷㪜㫅㫍㫀㫉㫆㫅㫄㪼㫅㫋㪸㫃㩷㪪㫋㫌㪻㫀㪼㫊㪃㩷㪡㪸㫇㪸㫅ocea㪤㪸㫂㫊㫐㫌㫋㫆㫍㩷㪼㫋㩷㪸㫃㪅㩷㪠㪮㪞㪞㪤㪪㪄㪏㪧㫉㪼㫊㪼㫅㫋㪼㪻㩷㪸㫋㩷㪧㫉㪼㫊㪼㫅㫋㪼㪻㩷㪸㫋㩷㪠㪧㪚㪚㩷㪜㫏㫇㪼㫉㫋㩷㪤㪼㪼㫋㫀㫅㪾㪑㩷㪩㫆㫃㪼㩷㫆㪽㩷㪩㪼㫄㫆㫋㪼㩷㪪㪼㫅㫊㫀㫅㪾㩷㩿㪩㪪㪀㩷㫀㫅㩷㪝㫆㫉㪼㫊㫋㩷㪸㫅㪻㩷㪥㪸㫋㫀㫆㫅㪸㫃㩷㪞㪟㪞㩷㪜㫄㫀㫊㫊㫀㫆㫅㩷䇭sinksourcen㪠㫅㫍㪼㫅㫋㫆㫉㫀㪼㫊㪃㩷㪦㪺㫋㫆㪹㪼㫉㩷㪉㪊㪄㪉㪌㪃㩷㪉㪇㪈㪉㪃㩷㪟㪸㫐㪸㫄㪸㪃㩷㪡㪸㫇㪸㫅㩷㪈㪏㪫㫀㫄㪼㩷㪪㪼㫉㫀㪼㫊㩷㫆㪽㩷㪤㫆㫅㫋㪿㫃㫐㩷㪚㪦㪉㩷㪝㫃㫌㫏㪫㫉㫆㫇㫀㪺㪸㫃㩷㪘㪽㫉㫀㪺㪸㩷㪪㪜㩷㪸㫅㪻㩷㪫㪼㫄㫇㩷㪥㩷㪘㫄㪼㫉㫀㪺㪸㩷㪥㪜Green䋺 A priori flux and its uncertainty䇮 Red䋺 Flux and its uncertainty estimatedfrom the ground-based data only,䇭 Blue䋺 Flux and its uncertainty estimated from both
the ground-based and GOSAT data䇮 Grey: GOSAT only
Bottom Grey bar䋺 Uncertainty reduction rate in percent (right axis)㪤㪸㫂㫊㫐㫌㫋㫆㫍㩷㪼㫋㩷㪸㫃㪅㩷㪠㪮㪞㪞㪤㪪㪄㪏㪫㫊㫌㫅㪼㫆㩷㪤㪸㫋㫊㫌㫅㪸㪾㪸㪃㩷㪥㪸㫋㫀㫆㫅㪸㫃㩷㪠㫅㫊㫋㫀㫋㫌㫋㪼㩷㪽㫆㫉㩷㪜㫅㫍㫀㫉㫆㫅㫄㪼㫅㫋㪸㫃㩷㪪㫋㫌㪻㫀㪼㫊㪃㩷㪡㪸㫇㪸㫅㪧㫉㪼㫊㪼㫅㫋㪼㪻㩷㪸㫋㩷㪧㫉㪼㫊㪼㫅㫋㪼㪻㩷㪸㫋㩷㪠㪧㪚㪚㩷㪜㫏㫇㪼㫉㫋㩷㪤㪼㪼㫋㫀㫅㪾㪑㩷㪩㫆㫃㪼㩷㫆㪽㩷㪩㪼㫄㫆㫋㪼㩷㪪㪼㫅㫊㫀㫅㪾㩷㩿㪩㪪㪀㩷㫀㫅㩷㪝㫆㫉㪼㫊㫋㩷㪸㫅㪻㩷㪥㪸㫋㫀㫆㫅㪸㫃㩷㪞㪟㪞㩷㪜㫄㫀㫊㫊㫀㫆㫅㩷㪈㪐㪠㫅㫍㪼㫅㫋㫆㫉㫀㪼㫊㪃㩷㪦㪺㫋㫆㪹㪼㫉㩷㪉㪊㪄㪉㪌㪃㩷㪉㪇㪈㪉㪃㩷㪟㪸㫐㪸㫄㪸㪃㩷㪡㪸㫇㪸㫅㩷㪝㫆㫉㫎㪸㫉㪻㩷㪤㫆㪻㪼㫃㫀㫅㪾㩷㫆㪽㩷㫋㪿㪼㩷㪚㪦㪉㩷㫀㫅㩷㫋㪿㪼㩷㪸㫋㫄㫆㫊㫇㪿㪼㫉㪼㩷㫎㫀㫋㪿㩷㪸㩷㫊㪼㫋㩷㫆㪽㩷㫊㫌㫉㪽㪸㪺㪼㩷㪽㫃㫌㫏㪼㫊
Anthropogenic

GOSAT SWIR L2
data andground-based
observations

Combines DMSP nightlights, BPannual country level reports,
CARMA LPS database, EDGAR
shipping, and AER2k upper air
data
Oda and Maksyutov(2010 )Andres et al. (2011)

Terrestrial biosphere NEE

JMA JCDAS
meteorologicalanalysis data

AtmosphericTransport Model
NIES-08.1i
Belikov et al.
(2011)

Surface CO2
flux models andinventories

Simulated by VISIT model
optimized to fit Globalview
seasonality, IIASA biomass
maps and Osnabruk NPP
database, GFED fire
Saito et al. (2010)
van der Werf et al. (2010)

Ocean-atmosphere exchange
Shipborne pCO2
observations by LDEO
dataset are optimized with anoffline ocean transport modelusing 4D-var data
assiimilation system
Valsala and Maksyutov. (2010)㪤㪸㫂㫊㫐㫌㫋㫆㫍㩷㪼㫋㩷㪸㫃㪅㩷㪠㪮㪞㪞㪤㪪㪄㪏㪫㫊㫌㫅㪼㫆㩷㪤㪸㫋㫊㫌㫅㪸㪾㪸㪃㩷㪥㪸㫋㫀㫆㫅㪸㫃㩷㪠㫅㫊㫋㫀㫋㫌㫋㪼㩷㪽㫆㫉㩷㪜㫅㫍㫀㫉㫆㫅㫄㪼㫅㫋㪸㫃㩷㪪㫋㫌㪻㫀㪼㫊㪃㩷㪡㪸㫇㪸㫅㪧㫉㪼㫊㪼㫅㫋㪼㪻㩷㪸㫋㩷㪧㫉㪼㫊㪼㫅㫋㪼㪻㩷㪸㫋㩷㪠㪧㪚㪚㩷㪜㫏㫇㪼㫉㫋㩷㪤㪼㪼㫋㫀㫅㪾㪑㩷㪩㫆㫃㪼㩷㫆㪽㩷㪩㪼㫄㫆㫋㪼㩷㪪㪼㫅㫊㫀㫅㪾㩷㩿㪩㪪㪀㩷㫀㫅㩷㪝㫆㫉㪼㫊㫋㩷㪸㫅㪻㩷㪥㪸㫋㫀㫆㫅㪸㫃㩷㪞㪟㪞㩷㪜㫄㫀㫊㫊㫀㫆㫅㩷㪉㪇㪠㫅㫍㪼㫅㫋㫆㫉㫀㪼㫊㪃㩷㪦㪺㫋㫆㪹㪼㫉㩷㪉㪊㪄㪉㪌㪃㩷㪉㪇㪈㪉㪃㩷㪟㪸㫐㪸㫄㪸㪃㩷㪡㪸㫇㪸㫅㩷㪨㫌㪼㫊㫋㫀㫆㫅㫊y 㪮㪿㪸㫋㩷㪺㪸㫌㫊㪼㫊㩷㪻㫀㪽㪽㪼㫉㪼㫅㪺㪼㫊㩷㪸㫄㫆㫅㪾㵰㪾㫉㫆㫌㫅㪻㪄㪹㪸㫊㪼㪻㩷㪻㪸㫋㪸㩷㫆㫅㫃㫐㵱㩷㪽㫃㫌㫏㪃㩷㵰㪞㪦㪪㪘㪫㩷㫆㫅㫃㫐㵱㩷㪽㫃㫌㫏㪃㩷㪸㫅㪻㩷㵰㪾㫉㫆㫌㫅㪻㪄㪹㪸㫊㪼㪻㩷㪻㪸㫋㪸㩷㪸㫅㪻㩷㪞㪦㪪㪘㪫㵱㩷㪽㫃㫌㫏㩷㪖y 㪛㫆㪼㫊㩷㪞㪦㪪㪘㪫㩷㪹㫉㫀㫅㪾㩷㫅㪼㫎㩷㫀㫅㪽㫆㫉㫄㪸㫋㫀㫆㫅㩷㫋㫆㩷㫉㪼㪾㫀㫆㫅㫊㩷㫎㪿㪼㫉㪼㩷㪾㫉㫆㫌㫅㪻㪄㪹㪸㫊㪼㪻㩷㪻㪸㫋㪸㩷㪸㫉㪼㩷㫅㫆㫋㩷㪼㫅㫆㫌㪾㪿㪖y 㪛㫆㩷㪼㫉㫉㫆㫉㫊㩷㩷㫀㫅㩷㪞㪦㪪㪘㪫㩷㪻㪸㫋㪸㩷㪺㪸㫌㫊㪼㩷㫋㪿㪼㩷㪽㫃㫌㫏㩷㪻㫀㪽㪽㪼㫉㪼㫅㪺㪼㩷㪖y 㪙㪼㪽㫆㫉㪼㩷㪸㫅㫊㫎㪼㫉㫀㫅㪾㩷㫋㪿㪼㫊㪼㩷㫈㫌㪼㫊㫋㫀㫆㫅㫊㪃㩷㫎㪼㩷㫄㫌㫊㫋㩷㫌㫅㪻㪼㫉㫊㫋㪸㫅㪻㩷㫋㪿㪼㩷㪹㪼㪿㪸㫍㫀㫆㫌㫉㫊㩷㫆㪽㩷㪽㫃㫌㫏㩷㪼㫊㫋㫀㫄㪸㫋㫀㫆㫅㩷㫊㫐㫊㫋㪼㫄㫊㪅㪫㫊㫌㫅㪼㫆㩷㪤㪸㫋㫊㫌㫅㪸㪾㪸㪃㩷㪥㪸㫋㫀㫆㫅㪸㫃㩷㪠㫅㫊㫋㫀㫋㫌㫋㪼㩷㪽㫆㫉㩷㪜㫅㫍㫀㫉㫆㫅㫄㪼㫅㫋㪸㫃㩷㪪㫋㫌㪻㫀㪼㫊㪃㩷㪡㪸㫇㪸㫅㪧㫉㪼㫊㪼㫅㫋㪼㪻㩷㪸㫋㩷㪧㫉㪼㫊㪼㫅㫋㪼㪻㩷㪸㫋㩷㪠㪧㪚㪚㩷㪜㫏㫇㪼㫉㫋㩷㪤㪼㪼㫋㫀㫅㪾㪑㩷㪩㫆㫃㪼㩷㫆㪽㩷㪩㪼㫄㫆㫋㪼㩷㪪㪼㫅㫊㫀㫅㪾㩷㩿㪩㪪㪀㩷㫀㫅㩷㪝㫆㫉㪼㫊㫋㩷㪸㫅㪻㩷㪥㪸㫋㫀㫆㫅㪸㫃㩷㪞㪟㪞㩷㪜㫄㫀㫊㫊㫀㫆㫅㩷㪠㫅㫍㪼㫅㫋㫆㫉㫀㪼㫊㪃㩷㪦㪺㫋㫆㪹㪼㫉㩷㪉㪊㪄㪉㪌㪃㩷㪉㪇㪈㪉㪃㩷㪟㪸㫐㪸㫄㪸㪃㩷㪡㪸㫇㪸㫅㩷㪉㪈㪠㪮㪞㪞㪤㪪㪄㪏
㪏㫋㪿㩷㪠㫅㫋㪼㫉㫅㪸㫋㫀㫆㫅㪸㫃㩷㪮㫆㫉㫂㫊㪿㫆㫇㩷㫆㫅㩷㪞㫉㪼㪼㫅㪿㫆㫌㫊㪼㩷㪞㪸㫊㩷㪤㪼㪸㫊㫌㫉㪼㫄㪼㫅㫋㫊㩷㪽㫉㫆㫄㩷㪪㫇㪸㪺㪼㪧㪸㫊㪸㪻㪼㫅㪸㪃㩷㪚㪘㪃㩷㪬㫅㫀㫋㪼㪻㩷㪪㫋㪸㫋㪼㫊㪡㫌㫅㪼㩷㪈㪏㩷㪄 㪉㪇㪃㩷㪉㪇㪈㪉㪿㫋㫋㫇㫊㪑㪆㪆㫊㫀㫋㪼㫊㪅㪾㫆㫆㪾㫃㪼㪅㪺㫆㫄㪆㫊㫀㫋㪼㪆㫀㫎㪾㪾㫄㫊㪏㪆㪿㫆㫄㪼㪫㫊㫌㫅㪼㫆㩷㪤㪸㫋㫊㫌㫅㪸㪾㪸㪃㩷㪥㪸㫋㫀㫆㫅㪸㫃㩷㪠㫅㫊㫋㫀㫋㫌㫋㪼㩷㪽㫆㫉㩷㪜㫅㫍㫀㫉㫆㫅㫄㪼㫅㫋㪸㫃㩷㪪㫋㫌㪻㫀㪼㫊㪃㩷㪡㪸㫇㪸㫅㪧㫉㪼㫊㪼㫅㫋㪼㪻㩷㪸㫋㩷㪧㫉㪼㫊㪼㫅㫋㪼㪻㩷㪸㫋㩷㪠㪧㪚㪚㩷㪜㫏㫇㪼㫉㫋㩷㪤㪼㪼㫋㫀㫅㪾㪑㩷㪩㫆㫃㪼㩷㫆㪽㩷㪩㪼㫄㫆㫋㪼㩷㪪㪼㫅㫊㫀㫅㪾㩷㩿㪩㪪㪀㩷㫀㫅㩷㪝㫆㫉㪼㫊㫋㩷㪸㫅㪻㩷㪥㪸㫋㫀㫆㫅㪸㫃㩷㪞㪟㪞㩷㪜㫄㫀㫊㫊㫀㫆㫅㩷㪠㫅㫍㪼㫅㫋㫆㫉㫀㪼㫊㪃㩷㪦㪺㫋㫆㪹㪼㫉㩷㪉㪊㪄㪉㪌㪃㩷㪉㪇㪈㪉㪃㩷㪟㪸㫐㪸㫄㪸㪃㩷㪡㪸㫇㪸㫅㩷㪉㪉㪝㫃㫌㫏㩷㪜㫊㫋㫀㫄㪸㫋㫀㫆㫅㩷㪚㫆㫄㫇㪸㫉㫀㫊㫆㫅㩷㪪㫋㫌㪻㫀㪼㫊㩷㪸㫋㩷㪠㪮㪞㪞㪤㪪㪄㪏

㪫㫊㫌㫅㪼㫆㩷㪤㪸㫋㫊㫌㫅㪸㪾㪸㪃㩷㪥㪸㫋㫀㫆㫅㪸㫃㩷㪠㫅㫊㫋㫀㫋㫌㫋㪼㩷㪽㫆㫉㩷㪜㫅㫍㫀㫉㫆㫅㫄㪼㫅㫋㪸㫃㩷㪪㫋㫌㪻㫀㪼㫊㪃㩷㪡㪸㫇㪸㫅㪧㫉㪼㫊㪼㫅㫋㪼㪻㩷㪸㫋㩷㪧㫉㪼㫊㪼㫅㫋㪼㪻㩷㪸㫋㩷㪠㪧㪚㪚㩷㪜㫏㫇㪼㫉㫋㩷㪤㪼㪼㫋㫀㫅㪾㪑㩷㪩㫆㫃㪼㩷㫆㪽㩷㪩㪼㫄㫆㫋㪼㩷㪪㪼㫅㫊㫀㫅㪾㩷㩿㪩㪪㪀㩷㫀㫅㩷㪝㫆㫉㪼㫊㫋㩷㪸㫅㪻㩷㪥㪸㫋㫀㫆㫅㪸㫃㩷㪞㪟㪞㩷㪜㫄㫀㫊㫊㫀㫆㫅㩷㪠㫅㫍㪼㫅㫋㫆㫉㫀㪼㫊㪃㩷㪦㪺㫋㫆㪹㪼㫉㩷㪉㪊㪄㪉㪌㪃㩷㪉㪇㪈㪉㪃㩷㪟㪸㫐㪸㫄㪸㪃㩷㪡㪸㫇㪸㫅㩷㪉㪊㪈㩷㪆㩷㪈㪋㩷

㪚㫆㫅㫋㪼㫅㫋㩷㫆㪽㩷㫋㪿㫀㫊㩷㫋㪸㫃㫂㩷㪚㫆㫅㫋㪼㫅㫋㩷㫆㪽㩷㫋㪿㫀㫊㩷㫋㪸㫃㫂• 㪚㫌㫉㫉㪼㫅㫋㫃㫐㩷㫋㪿㪼㫉㪼㩷㪸㫉㪼㩷㪽㫀㫍㪼㩷㫀㫅㪻㪼㫇㪼㫅㪻㪼㫅㫋㫃㫐㪄㫉㪼㫋㫉㫀㪼㫍㪼㪻㩷㪞㪦㪪㪘㪫㩷㪯㪚㪦㪉㩷㪻㪸㫋㪸㫊㪼㫋㫊㪑㩷㪈㪅 㩷㪥㪠㪜㪪㩷㪪㪮 㪠㪩㩷㪣㪼㫍㪼㫃㩷㪉㩷㫍㪇㪉㪅㪁㪁㩷㪉㪅 㩷㪘㪚㪦㪪㩷㪙㪉㪅㪐㩷㪊㪅 㩷㪥㪠㪜㪪㩷㪧㪧㪛㪝㪄㪛㪦㪘㪪㩷㩷㪋㪅 㩷㪪㪩㪦㪥㪄㪢㪠㪫㩷㪩㪼㫄 㫆㪫㪼㪚㩷㪌㪅 㩷㪬㪅㩷㫆㪽㩷㪣㪼㫀㪺㪼㫊㫋㪼㫉㩷㩷㪝㪧㩷㩷㩷㪘㫃㫃㩷㫇㫉㫆㪻㫌㪺㫋㫊㩷㫌㫅㪻㪼㫉㩷㪺㫆㫅㫋㫀㫅㫌㪸㫃㩷㫀㫄㫇㫉㫆㫍㪼㫄 㪼㫅㫋㩷㩷• 㪜㫊㫋㫀㫄 㪸㫋㪼㪻㩷㫄 㫆㫅㫋㪿㫃㫐㩷㫉㪼㪾㫀㫆㫅㪸㫃㩷㪚㪦㪉㩷㪽㫃㫌㫏㪼㫊㩷㪽㫉㫆㫄㩷㫋㪿㪼㫊㪼㩷㪺㫌㫉㫉㪼㫅㫋㩷㪻㪸㫋㪸㫊㪼㫋㫊㩷㫌㫊㫀㫅㪾㩷㪸㩷㫊㫀㫅㪾㫃㪼㩷㫀㫅㫍㪼㫉㫊㪼㩷㫄 㫆㪻㪼㫃㫀㫅㪾㩷㫊㫐㫊㫋㪼㫄 㩷㪸㫅㪻㩷㪼㫍㪸㫃㫌㪸㫋㪼㪻㩷㫋㪿㪼㩷㫊㫇㫉㪼㪸㪻㩷㩷㪯 㩷㪧㫉㫆㪻㫌㪺㫋㫊㩷㪝㫃㫌㫏㩷㪼㫊㫋㫀㫄㪸㫋㪼㫊㩷㩷 㪥㪠㪜㪪㩷㪣㪼㫍㪼㫃㩷㪉㩷㫍㪇㪉㩷㪚㪦㪉㩷㩷 㪘㪚㪦㪪㩷㪙㪉㪅㪐㩷㪪㫌㫉㪽㪸㪺㪼㩷㪦㪹㫊㪅㩷㩷㪥㪠㪜㪪㩷㪣㪼㫍㪼㫃㩷㪋㩷㩷 㪥㪠㪜㪪㩷㪧㪧㪛㪝㪄㪛㪦㪘㪪㩷㪪㫇㫉㪼㪸㪻㪖㩷
㪠㫅㫍㪼㫉㫊㪼㩷㫄 㫆㪻㪼㫃㫀㫅㪾 㩷㩷㪪㫐㫊㫋㪼㫄 㩷㩷㩷 㪪㪩㪦㪥㩷㪩㪼㫄 㫆㪫㪼㪚㩷㩷 㪬㪅㩷㫆㪽㩷㪣㪼㫀㪺㪼㫊㫋㪼㫉㩷㩷㩷• 㪤㫆㫋㫀㫍㪸㫋㫀㫆㫅㪑㩷㩷㪥㪼㪼㪻㩷㫋㫆㩷㫇㫉㫆㫍㫀㪻㪼㩷㫋㪿㪼㩷㫌㫊㪼㫉㫊㩷㫆㪽㩷㪞㪦㪪㪘㪫㪄㪹㪸㫊㪼㪻㩷㫊㫌㫉㪽㪸㪺㪼㩷㪚㪦㪉㩷㪽㫃㫌㫏㩷㪻㪸㫋㪸㩷㫎㫀㫋㪿㩷㪸㪻㪻㫀㫋㫀㫆㫅㪸㫃㩷㫀㫅㪽㫆㫉㫄 㪸㫋㫀㫆㫅㩷㫆㫅㩷㫋㪿㪼㩷㪻㪼㪾㫉㪼㪼㩷㫆㪽㩷㫌㫅㪺㪼㫉㫋㪸㫀㫅㫋㫐㩷㪸㫊㫊㫆㪺㫀㪸㫋㪼㪻㩷㫎㫀㫋㪿㩷㫋㪿㪼㩷㪽㫃㫌㫏㩷㪼㫊㫋㫀㫄 㪸㫋㪼㫊㩷㩷㩷㪫㫊㫌㫅㪼㫆㩷㪤㪸㫋㫊㫌㫅㪸㪾㪸㪃㩷㪥㪸㫋㫀㫆㫅㪸㫃㩷㪠㫅㫊㫋㫀㫋㫌㫋㪼㩷㪽㫆㫉㩷㪜㫅㫍㫀㫉㫆㫅㫄㪼㫅㫋㪸㫃㩷㪪㫋㫌㪻㫀㪼㫊㪃㩷㪡㪸㫇㪸㫅㩷㩷㩷 㩷㪸㫊㩷㫇㪸㫉㫋㩷㫆㪽㩷㪽㫃㫌㫏㩷㫀㫅㫋㪼㫉㪄㪺㫆㫄㫇㪸㫉㫀㫊㫆㫅㩷㪺㪸㫄 㫇㪸㫀㪾㫅㩷㫃㪼㪸㪻㩷㪹㫐㩷㪪㪅㩷㪟㫆㫌㫎㪼㫃㫀㫅㪾㩷㩿㫋㪿㫀㫊㩷㪧㪤㪀㩷㪧㫉㪼㫊㪼㫅㫋㪼㪻㩷㪸㫋㩷㪧㫉㪼㫊㪼㫅㫋㪼㪻㩷㪸㫋㩷㪠㪧㪚㪚㩷㪜㫏㫇㪼㫉㫋㩷㪤㪼㪼㫋㫀㫅㪾㪑㩷㪩㫆㫃㪼㩷㫆㪽㩷㪩㪼㫄㫆㫋㪼㩷㪪㪼㫅㫊㫀㫅㪾㩷㩿㪩㪪㪀㩷㫀㫅㩷㪝㫆㫉㪼㫊㫋㩷㪸㫅㪻㩷㪥㪸㫋㫀㫆㫅㪸㫃㩷㪞㪟㪞㩷㪜㫄㫀㫊㫊㫀㫆㫅㩷㪫㪸㫂㪸㪾㫀㩷㪼㫋㩷㪸㫃㪃㩷㪠㪮㪞㪞㪤㪪㪄㪏㪠㫅㫍㪼㫅㫋㫆㫉㫀㪼㫊㪃㩷㪦㪺㫋㫆㪹㪼㫉㩷㪉㪊㪄㪉㪌㪃㩷㪉㪇㪈㪉㪃㩷㪟㪸㫐㪸㫄㪸㪃㩷㪡㪸㫇㪸㫅㩷㪉㪋㪈㫐㫉㩷㫋㫀㫄㪼㩷㫊㪼㫉㫀㪼㫊㩷㫆㪽㩷㫄㫆㫅㫋㪿㫃㫐㩷㪽㫃㫌㫏㩷㪽㫆㫉㩷㫊㪼㫃㪼㪺㫋㪼㪻㩷㫉㪼㪾㫀㫆㫅㫊㪫㫊㫌㫅㪼㫆㩷㪤㪸㫋㫊㫌㫅㪸㪾㪸㪃㩷㪥㪸㫋㫀㫆㫅㪸㫃㩷㪠㫅㫊㫋㫀㫋㫌㫋㪼㩷㪽㫆㫉㩷㪜㫅㫍㫀㫉㫆㫅㫄㪼㫅㫋㪸㫃㩷㪪㫋㫌㪻㫀㪼㫊㪃㩷㪡㪸㫇㪸㫅㪧㫉㪼㫊㪼㫅㫋㪼㪻㩷㪸㫋㩷㪧㫉㪼㫊㪼㫅㫋㪼㪻㩷㪸㫋㩷㪠㪧㪚㪚㩷㪜㫏㫇㪼㫉㫋㩷㪤㪼㪼㫋㫀㫅㪾㪑㩷㪩㫆㫃㪼㩷㫆㪽㩷㪩㪼㫄㫆㫋㪼㩷㪪㪼㫅㫊㫀㫅㪾㩷㩿㪩㪪㪀㩷㫀㫅㩷㪝㫆㫉㪼㫊㫋㩷㪸㫅㪻㩷㪥㪸㫋㫀㫆㫅㪸㫃㩷㪞㪟㪞㩷㪜㫄㫀㫊㫊㫀㫆㫅㩷㪫㪸㫂㪸㪾㫀㩷㪼㫋㩷㪸㫃㪃㩷㪠㪮㪞㪞㪤㪪㪄㪏㪉㪌㪠㫅㫍㪼㫅㫋㫆㫉㫀㪼㫊㪃㩷㪦㪺㫋㫆㪹㪼㫉㩷㪉㪊㪄㪉㪌㪃㩷㪉㪇㪈㪉㪃㩷㪟㪸㫐㪸㫄㪸㪃㩷㪡㪸㫇㪸㫅㩷㪫㫊㫌㫅㪼㫆㩷㪤㪸㫋㫊㫌㫅㪸㪾㪸㪃㩷㪥㪸㫋㫀㫆㫅㪸㫃㩷㪠㫅㫊㫋㫀㫋㫌㫋㪼㩷㪽㫆㫉㩷㪜㫅㫍㫀㫉㫆㫅㫄㪼㫅㫋㪸㫃㩷㪪㫋㫌㪻㫀㪼㫊㪃㩷㪡㪸㫇㪸㫅㪧㫉㪼㫊㪼㫅㫋㪼㪻㩷㪸㫋㩷㪧㫉㪼㫊㪼㫅㫋㪼㪻㩷㪸㫋㩷㪠㪧㪚㪚㩷㪜㫏㫇㪼㫉㫋㩷㪤㪼㪼㫋㫀㫅㪾㪑㩷㪩㫆㫃㪼㩷㫆㪽㩷㪩㪼㫄㫆㫋㪼㩷㪪㪼㫅㫊㫀㫅㪾㩷㩿㪩㪪㪀㩷㫀㫅㩷㪝㫆㫉㪼㫊㫋㩷㪸㫅㪻㩷㪥㪸㫋㫀㫆㫅㪸㫃㩷㪞㪟㪞㩷㪜㫄㫀㫊㫊㫀㫆㫅㩷㪫㪸㫂㪸㪾㫀㩷㪼㫋㩷㪸㫃㪃㩷㪠㪮㪞㪞㪤㪪㪄㪏㪉㪍㪠㫅㫍㪼㫅㫋㫆㫉㫀㪼㫊㪃㩷㪦㪺㫋㫆㪹㪼㫉㩷㪉㪊㪄㪉㪌㪃㩷㪉㪇㪈㪉㪃㩷㪟㪸㫐㪸㫄㪸㪃㩷㪡㪸㫇㪸㫅㩷㪚㫌㫉㫉㪼㫅㫋㩷㪪㫌㪹㫄㫀㫊㫊㫀㫆㫅㫊

Current!Submissions!!
• 5!models:!NIES,!LSCE,!UoE,!SRON,!CAO^NIES!!

㪫㫊㫌㫅㪼㫆㩷㪤㪸㫋㫊㫌㫅㪸㪾㪸㪃㩷㪥㪸㫋㫀㫆㫅㪸㫃㩷㪠㫅㫊㫋㫀㫋㫌㫋㪼㩷㪽㫆㫉㩷㪜㫅㫍㫀㫉㫆㫅㫄㪼㫅㫋㪸㫃㩷㪪㫋㫌㪻㫀㪼㫊㪃㩷㪡㪸㫇㪸㫅㪧㫉㪼㫊㪼㫅㫋㪼㪻㩷㪸㫋㩷㪧㫉㪼㫊㪼㫅㫋㪼㪻㩷㪸㫋㩷㪠㪧㪚㪚㩷㪜㫏㫇㪼㫉㫋㩷㪤㪼㪼㫋㫀㫅㪾㪑㩷㪩㫆㫃㪼㩷㫆㪽㩷㪩㪼㫄㫆㫋㪼㩷㪪㪼㫅㫊㫀㫅㪾㩷㩿㪩㪪㪀㩷㫀㫅㩷㪝㫆㫉㪼㫊㫋㩷㪸㫅㪻㩷㪥㪸㫋㫀㫆㫅㪸㫃㩷㪞㪟㪞㩷㪜㫄㫀㫊㫊㫀㫆㫅㩷
IWGGMS-8, 19-6-2012
GOSAT CO2 ICE㪟㫆㫌㫎㪼㫃㫀㫅㪾㩷㪼㫋㩷㪸㫃㪅㪃㩷㪠㪮㪞㪞㪤㪪㪄㪏㪉㪎㪠㫅㫍㪼㫅㫋㫆㫉㫀㪼㫊㪃㩷㪦㪺㫋㫆㪹㪼㫉㩷㪉㪊㪄㪉㪌㪃㩷㪉㪇㪈㪉㪃㩷㪟㪸㫐㪸㫄㪸㪃㩷㪡㪸㫇㪸㫅㩷㪠㫅㫋㪼㫉㪄㪺㫆㫄㫇㪸㫉㫀㫊㫆㫅㩷㫉㪼㫊㫌㫃㫋㫊㪑㩷㪝㫃㫌㫏㩷㫄㪸㫇㫊Inter^comparison!results:!Flux!maps!!
GOSAT^only!inversions,!June!2009!

NIES:!

:!SRON!

LSCE:!!

:!CAO!

㪫㫊㫌㫅㪼㫆㩷㪤㪸㫋㫊㫌㫅㪸㪾㪸㪃㩷㪥㪸㫋㫀㫆㫅㪸㫃㩷㪠㫅㫊㫋㫀㫋㫌㫋㪼㩷㪽㫆㫉㩷㪜㫅㫍㫀㫉㫆㫅㫄㪼㫅㫋㪸㫃㩷㪪㫋㫌㪻㫀㪼㫊㪃㩷㪡㪸㫇㪸㫅㪧㫉㪼㫊㪼㫅㫋㪼㪻㩷㪸㫋㩷㪧㫉㪼㫊㪼㫅㫋㪼㪻㩷㪸㫋㩷㪠㪧㪚㪚㩷㪜㫏㫇㪼㫉㫋㩷㪤㪼㪼㫋㫀㫅㪾㪑㩷㪩㫆㫃㪼㩷㫆㪽㩷㪩㪼㫄㫆㫋㪼㩷㪪㪼㫅㫊㫀㫅㪾㩷㩿㪩㪪㪀㩷㫀㫅㩷㪝㫆㫉㪼㫊㫋㩷㪸㫅㪻㩷㪥㪸㫋㫀㫆㫅㪸㫃㩷㪞㪟㪞㩷㪜㫄㫀㫊㫊㫀㫆㫅㩷
IWGGMS-8, 19-6-2012
GOSAT CO2 ICE㪟㫆㫌㫎㪼㫃㫀㫅㪾㩷㪼㫋㩷㪸㫃㪅㪃㩷㪠㪮㪞㪞㪤㪪㪄㪏㪉㪏㪠㫅㫍㪼㫅㫋㫆㫉㫀㪼㫊㪃㩷㪦㪺㫋㫆㪹㪼㫉㩷㪉㪊㪄㪉㪌㪃㩷㪉㪇㪈㪉㪃㩷㪟㪸㫐㪸㫄㪸㪃㩷㪡㪸㫇㪸㫅㩷㪫㫀㫄㪼㩷㫊㪼㫉㫀㪼㫊㩷㫆㪽㩷㫉㪼㪾㫀㫆㫅㪸㫃㩷㫀㫅㫋㪼㪾㫉㪸㫃㫊

Time!series!of!regional!integrals!
Global!land!
㪊㪇㩷㪞㫋㪚㪆㫐㫉

Northern!land!
㪄㪉㪇㩷㪞㫋㪚㪆㫐㫉㪄㪊㪇㩷㪞㫋㪚㪆㫐㫉㪄㪊㪇㩷㪞㫋㪚㪆㫐㫉㪈㪇㩷㪞㫋㪚㪆㫐㫉

Tropical!land!

Global!Ocean!
㪋㩷㪞㫋㪚㪆㫐㫉

㪄㪌㩷㪞㫋㪚㪆㫐㫉㪄㪏㩷㪞㫋㪚㪆㫐㫉㪫㫊㫌㫅㪼㫆㩷㪤㪸㫋㫊㫌㫅㪸㪾㪸㪃㩷㪥㪸㫋㫀㫆㫅㪸㫃㩷㪠㫅㫊㫋㫀㫋㫌㫋㪼㩷㪽㫆㫉㩷㪜㫅㫍㫀㫉㫆㫅㫄㪼㫅㫋㪸㫃㩷㪪㫋㫌㪻㫀㪼㫊㪃㩷㪡㪸㫇㪸㫅㪧㫉㪼㫊㪼㫅㫋㪼㪻㩷㪸㫋㩷㪧㫉㪼㫊㪼㫅㫋㪼㪻㩷㪸㫋㩷㪠㪧㪚㪚㩷㪜㫏㫇㪼㫉㫋㩷㪤㪼㪼㫋㫀㫅㪾㪑㩷㪩㫆㫃㪼㩷㫆㪽㩷㪩㪼㫄㫆㫋㪼㩷㪪㪼㫅㫊㫀㫅㪾㩷㩿㪩㪪㪀㩷㫀㫅㩷㪝㫆㫉㪼㫊㫋㩷㪸㫅㪻㩷㪥㪸㫋㫀㫆㫅㪸㫃㩷㪞㪟㪞㩷㪜㫄㫀㫊㫊㫀㫆㫅㩷
IWGGMS-8, 19-6-2012
GOSAT CO2 ICE㪟㫆㫌㫎㪼㫃㫀㫅㪾㩷㪼㫋㩷㪸㫃㪅㪃㩷㪠㪮㪞㪞㪤㪪㪄㪏㪉㪐㪠㫅㫍㪼㫅㫋㫆㫉㫀㪼㫊㪃㩷㪦㪺㫋㫆㪹㪼㫉㩷㪉㪊㪄㪉㪌㪃㩷㪉㪇㪈㪉㪃㩷㪟㪸㫐㪸㫄㪸㪃㩷㪡㪸㫇㪸㫅㩷㪚㫌㫉㫉㪼㫅㫋㩷㪪㫋㪸㫋㫌㫊㩷㫆㪽㩷㪪㪸㫋㪼㫃㫃㫀㫋㪼㩷㪝㫃㫌㫏㩷㪜㫊㫋㫀㫄㪸㫋㫀㫆㫅y 㪞㫃㫆㪹㪸㫃㩷㪚㪦㪉㩷㫅㪼㫋㩷㪽㫃㫌㫏㩷㪻㫀㫊㫋㫉㫀㪹㫌㫋㫀㫆㫅㫊㩷㪸㫉㪼㩷㫆㪹㫋㪸㫀㫅㪼㪻㩷㫆㫅㫃㫐㩷㪽㫉㫆㫄㩷㪞㪦㪪㪘㪫㩷㪻㪸㫋㪸㩷㪸㫊㩷㫎㪼㫃㫃㩷㪸㫊㩷㪽㫉㫆㫄㩷㪹㫆㫋㪿㩷㪾㫉㫆㫌㫅㪻㪄㪹㪸㫊㪼㪻㩷㪸㫅㪻㩷㪞㪦㪪㪘㪫㩷㪻㪸㫋㪸㪅y 㪛㫀㪽㪽㪼㫉㪼㫅㪺㪼㫊㩷㪹㪼㫋㫎㪼㪼㫅㩷㫊㪸㫋㪼㫃㫃㫀㫋㪼㩷㪽㫃㫌㫏㩷㪸㫅㪻㩷㪾㫉㫆㫌㫅㪻㪄㪹㪸㫊㪼㪻㩷㪽㫃㫌㫏㩷㪸㫉㪼㩷㪽㫆㫌㫅㪻㪅y 㪫㪿㪼㩷㪹㪼㪿㪸㫍㫀㫆㫉㫊㩷㫆㪽㩷㫊㪸㫋㪼㫃㫃㫀㫋㪼㩷㪽㫃㫌㫏㩷㪼㫊㫋㫀㫄㪸㫋㫀㫆㫅㩷㫊㫐㫊㫋㪼㫄㫊㩷㪸㫉㪼㩷㪹㪼㫀㫅㪾㩷㫀㫅㫍㪼㫊㫋㫀㪾㪸㫋㪼㪻㪅y 㪠㫅㫇㫌㫋㩷㪞㪟㪞㩷㪺㫆㫅㪺㪼㫅㫋㫉㪸㫋㫀㫆㫅㩷㪻㪸㫋㪸㩷㪻㪼㫇㪼㫅㪻㪼㫅㪺㫐y 㪠㫅㫍㪼㫉㫊㪼㩷㫄㫆㪻㪼㫃㫃㫀㫅㪾㩷㫊㪺㪿㪼㫄㪼㩷㪻㪼㫇㪼㫅㪻㪼㫅㪺㫐㪫㫊㫌㫅㪼㫆㩷㪤㪸㫋㫊㫌㫅㪸㪾㪸㪃㩷㪥㪸㫋㫀㫆㫅㪸㫃㩷㪠㫅㫊㫋㫀㫋㫌㫋㪼㩷㪽㫆㫉㩷㪜㫅㫍㫀㫉㫆㫅㫄㪼㫅㫋㪸㫃㩷㪪㫋㫌㪻㫀㪼㫊㪃㩷㪡㪸㫇㪸㫅㪧㫉㪼㫊㪼㫅㫋㪼㪻㩷㪸㫋㩷㪧㫉㪼㫊㪼㫅㫋㪼㪻㩷㪸㫋㩷㪠㪧㪚㪚㩷㪜㫏㫇㪼㫉㫋㩷㪤㪼㪼㫋㫀㫅㪾㪑㩷㪩㫆㫃㪼㩷㫆㪽㩷㪩㪼㫄㫆㫋㪼㩷㪪㪼㫅㫊㫀㫅㪾㩷㩿㪩㪪㪀㩷㫀㫅㩷㪝㫆㫉㪼㫊㫋㩷㪸㫅㪻㩷㪥㪸㫋㫀㫆㫅㪸㫃㩷㪞㪟㪞㩷㪜㫄㫀㫊㫊㫀㫆㫅㩷㪠㫅㫍㪼㫅㫋㫆㫉㫀㪼㫊㪃㩷㪦㪺㫋㫆㪹㪼㫉㩷㪉㪊㪄㪉㪌㪃㩷㪉㪇㪈㪉㪃㩷㪟㪸㫐㪸㫄㪸㪃㩷㪡㪸㫇㪸㫅㩷㪊㪇㪝㫌㫋㫌㫉㪼㩷㪧㫉㫆㫊㫇㪼㪺㫋㪮㫆㫉㫃㪻㩷㪞㪟㪞㩷㪦㪹㫊㪼㫉㫍㪸㫋㫀㫆㫅㩷㪪㪸㫋㪼㫃㫃㫀㫋㪼㫊㪁(Calender year)

Mission2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024

ESA
SCIAMACHY㪜㪥㪭㪠㪪㪘㪫

Japan
GOSAT

㪞㪦㪪㪘㪫

NASA
OCO2

㪦㪚㪦㪄㪉

Japan
GOSAT2㪞㪦㪪㪘㪫㪄㪉

ESA
CarbonSat

㪚㪸㫉㪹㫆㫅㪪㪸㫋㪮㪼㩷㪸㫉㪼㩷㪿㪼㫉㪼㪅㪜㪪㪘㩷㪜㪥㪭㪠㪪㪘㪫㩷㩿㪉㪇㪇㪉㪄㪉㪇㪈㪉㪀㪡㪸㫇㪸㫅㩷㪞㪦㪪㪘㪫㩷㩿㪉㪇㪇㪐㪄㪀㪞㫉㪼㪼㫅㪰㪼㫃㫃㫆㫎
㪧㫀㫅㫂㪛㪼㫊㫀㪾㫅㪼㪻㩷㫃㫀㪽㪼㩷㫋㫀㫄㪼㪜㫏㫋㪼㫅㪻㪼㪻㩷㫆㫇㪼㫉㪸㫋㫀㫆㫅㪜㫅㪻㩷㫆㪽㩷㫄㫀㫊㫊㫀㫆㫅㪥㪘㪪㪘㩷㪦㪚㪦㪄㪉㩷㩿㪉㪇㪈㪋㪄㪀㪜㪪㪘㩷㪚㪸㫉㪹㫆㫅㪪㪸㫋㩷㩿㪉㪇㪈㪐㪄㪀㪁㪪㪸㫋㪼㫃㫃㫀㫋㪼㫊㩷㫎㪿㫀㪺㪿㩷㫄㪼㪸㫊㫌㫉㪼㩷㪺㫆㫃㫌㫄㫅㩷㪚㪦㪉㩷㪸㫄㫆㫌㫅㫋㪅㪫㫊㫌㫅㪼㫆㩷㪤㪸㫋㫊㫌㫅㪸㪾㪸㪃㩷㪥㪸㫋㫀㫆㫅㪸㫃㩷㪠㫅㫊㫋㫀㫋㫌㫋㪼㩷㪽㫆㫉㩷㪜㫅㫍㫀㫉㫆㫅㫄㪼㫅㫋㪸㫃㩷㪪㫋㫌㪻㫀㪼㫊㪃㩷㪡㪸㫇㪸㫅㪧㫉㪼㫊㪼㫅㫋㪼㪻㩷㪸㫋㩷㪧㫉㪼㫊㪼㫅㫋㪼㪻㩷㪸㫋㩷㪠㪧㪚㪚㩷㪜㫏㫇㪼㫉㫋㩷㪤㪼㪼㫋㫀㫅㪾㪑㩷㪩㫆㫃㪼㩷㫆㪽㩷㪩㪼㫄㫆㫋㪼㩷㪪㪼㫅㫊㫀㫅㪾㩷㩿㪩㪪㪀㩷㫀㫅㩷㪝㫆㫉㪼㫊㫋㩷㪸㫅㪻㩷㪥㪸㫋㫀㫆㫅㪸㫃㩷㪞㪟㪞㩷㪜㫄㫀㫊㫊㫀㫆㫅㩷㪠㫅㫍㪼㫅㫋㫆㫉㫀㪼㫊㪃㩷㪦㪺㫋㫆㪹㪼㫉㩷㪉㪊㪄㪉㪌㪃㩷㪉㪇㪈㪉㪃㩷㪟㪸㫐㪸㫄㪸㪃㩷㪡㪸㫇㪸㫅㩷㪊㪉㪤㫀㫅㫀㫊㫋㫉㫐㩷㫆㪽㩷㫋㪿㪼㩷㪜㫅㫍㫀㫉㫆㫅㫄㪼㫅㫋㵭㫊㩷㪧㪼㫉㫊㫇㪼㪺㫋㫀㫍㪼㩷㫆㫅㩷㪝㫌㫋㫌㫉㪼㩷㪞㪦㪪㪘㪫㩷㪤㫀㫊㫊㫀㫆㫅㫊㩷㪸㫅㪻㩷㪠㫅㫋㪼㫉㫅㪸㫋㫀㫆㫅㪸㫃㩷㪚㫆㫆㫇㪼㫉㪸㫋㫀㫆㫅㩷㪧㫉㪼㫊㪼㫅㫋㪼㪻㩷㪸㫋㩷㪬㪥㩷㪩㫀㫆㪂㪉㪇㪫㫊㫌㫅㪼㫆㩷㪤㪸㫋㫊㫌㫅㪸㪾㪸㪃㩷㪥㪸㫋㫀㫆㫅㪸㫃㩷㪠㫅㫊㫋㫀㫋㫌㫋㪼㩷㪽㫆㫉㩷㪜㫅㫍㫀㫉㫆㫅㫄㪼㫅㫋㪸㫃㩷㪪㫋㫌㪻㫀㪼㫊㪃㩷㪡㪸㫇㪸㫅㪧㫉㪼㫊㪼㫅㫋㪼㪻㩷㪸㫋㩷㪧㫉㪼㫊㪼㫅㫋㪼㪻㩷㪸㫋㩷㪠㪧㪚㪚㩷㪜㫏㫇㪼㫉㫋㩷㪤㪼㪼㫋㫀㫅㪾㪑㩷㪩㫆㫃㪼㩷㫆㪽㩷㪩㪼㫄㫆㫋㪼㩷㪪㪼㫅㫊㫀㫅㪾㩷㩿㪩㪪㪀㩷㫀㫅㩷㪝㫆㫉㪼㫊㫋㩷㪸㫅㪻㩷㪥㪸㫋㫀㫆㫅㪸㫃㩷㪞㪟㪞㩷㪜㫄㫀㫊㫊㫀㫆㫅㩷㪠㫅㫍㪼㫅㫋㫆㫉㫀㪼㫊㪃㩷㪦㪺㫋㫆㪹㪼㫉㩷㪉㪊㪄㪉㪌㪃㩷㪉㪇㪈㪉㪃㩷㪟㪸㫐㪸㫄㪸㪃㩷㪡㪸㫇㪸㫅㩷㪊㪊㪞㪦㪪㪘㪫㪄㪉㩷㪚㪿㪸㫃㫃㪼㫅㪾㪼㫊㪛㪼㫋㪼㪺㫋㫀㫆㫅㩷㫆㪽㩷㫊㫄㪸㫃㫃㩷㪚㪦㪉㩷㫍㪸㫉㫀㪸㫋㫀㫆㫅㫊y 㪞㪦㪪㪘㪫㩷㪸㪺㪿㫀㪼㫍㪼㫊㩷㪸㩷㵰㫊㫀㫅㪾㫃㪼㩷㫊㪿㫆㫋㵱㩷㫇㫉㪼㪺㫀㫊㫀㫆㫅㩷㫆㪽㩷㻃㪉㩷㫇㫇㫄㪅y 㪞㪦㪪㪘㪫㪄㪉㩷㪞㫆㪸㫃㩷㪑㩷㪇㪅㪌㩷㫇㫇㫄㩷㫇㫉㪼㪺㫀㫊㫀㫆㫅㩷㫆㪽㩷㫄㫆㫅㫋㪿㫃㫐㩷㪺㫆㫃㫌㫄㫅㩷㪚㪦㪉㩷㪽㫆㫉㩷㪌㩷㪻㪼㪾㫉㪼㪼㩷㫄㪼㫊㪿㩷㩿㪌㪇㪇㫂㫄㩷㫏㩷㪌㪇㪇㩷㫂㫄㪀㩷㫋㫆㩷㫉㪼㫍㪼㪸㫃㩷㫉㪼㪾㫀㫆㫅㪸㫃㩷㪚㪦㪉㩷㪹㪼㪿㪸㫍㫀㫆㫌㫉㫊㩷㩿㪼㪅㪾㪅㩷㫀㫅㩷㫋㫉㫆㫇㫀㪺㪸㫃㩷㫉㪸㫀㫅㩷㪽㫆㫉㪼㫊㫋㫊㪀㪅㪋㩷㫇㫇㫄㩷
㫀㫅㩷㪉㪌㪇㪇㩷㫂㫄y 㪫㪿㫀㫊㩷㪾㫆㪸㫃㩷㫎㫀㫃㫃㩷㪹㪼㩷㪸㪺㪿㫀㪼㫍㪼㪻㩷㪹㫐㩷㪸㫍㪼㫉㪸㪾㫀㫅㪾㩷㫊㫀㫏㫋㪼㪼㫅㩷㵰㫊㫀㫅㪾㫃㪼㩷㫊㪿㫆㫋㵱㩷㪺㫆㫃㫌㫄㫅㩷㪚㪦㪉㩷㪻㪸㫋㪸㩷㫀㫅㩷㪸㩷㫄㫆㫅㫋㪿㪅y 㪫㪿㫀㫊㩷㪾㫆㪸㫃㩷㫉㪼㫈㫌㫀㫉㪼㫊㩷㫊㫀㪾㫅㫀㪾㫀㪺㪸㫅㫋㩷㩿㪊㩷㫋㫀㫄㪼㫊㩷㫆㫉㩷㫄㫆㫉㪼㪀㩷㫀㫅㪺㫉㪼㪸㫊㪼㩷㫆㪽㩷㫊㪸㫋㪼㫃㫃㫀㫋㪼㩷㪚㪦㪉㩷㪻㪸㫋㪸㩷㫀㫅㩷㪸㩷㫄㫆㫅㫋㪿㪅㩷㩷

㪚㪸㫉㪹㫆㫅㩷㪫㫉㪸㪺㫂㪼㫉㩷㪘㫇㫉㫀㫃㩷㪈㪋㪃㩷㪉㪇㪈㪇㪫㫊㫌㫅㪼㫆㩷㪤㪸㫋㫊㫌㫅㪸㪾㪸㪃㩷㪥㪸㫋㫀㫆㫅㪸㫃㩷㪠㫅㫊㫋㫀㫋㫌㫋㪼㩷㪽㫆㫉㩷㪜㫅㫍㫀㫉㫆㫅㫄㪼㫅㫋㪸㫃㩷㪪㫋㫌㪻㫀㪼㫊㪃㩷㪡㪸㫇㪸㫅㪧㫉㪼㫊㪼㫅㫋㪼㪻㩷㪸㫋㩷㪧㫉㪼㫊㪼㫅㫋㪼㪻㩷㪸㫋㩷㪠㪧㪚㪚㩷㪜㫏㫇㪼㫉㫋㩷㪤㪼㪼㫋㫀㫅㪾㪑㩷㪩㫆㫃㪼㩷㫆㪽㩷㪩㪼㫄㫆㫋㪼㩷㪪㪼㫅㫊㫀㫅㪾㩷㩿㪩㪪㪀㩷㫀㫅㩷㪝㫆㫉㪼㫊㫋㩷㪸㫅㪻㩷㪥㪸㫋㫀㫆㫅㪸㫃㩷㪞㪟㪞㩷㪜㫄㫀㫊㫊㫀㫆㫅㩷㪠㫅㫍㪼㫅㫋㫆㫉㫀㪼㫊㪃㩷㪦㪺㫋㫆㪹㪼㫉㩷㪉㪊㪄㪉㪌㪃㩷㪉㪇㪈㪉㪃㩷㪟㪸㫐㪸㫄㪸㪃㩷㪡㪸㫇㪸㫅㩷㪊㪋㪚㪸㫉㪹㫆㫅㪪㪸㫋㩷㵨 㪪㫇㪸㫋㫀㪸㫃㩷㫉㪼㫊㫆㫃㫌㫋㫀㫆㫅㩷㩽㩷㪺㫆㫍㪼㫉㪸㪾㪼

㪙㫆㫍㪼㫅㫊㫄㪸㫅㫅㩷㪼㫋㩷㪸㫃㪅㩷㩿㪉㪇㪈㪇㪀㪫㫊㫌㫅㪼㫆㩷㪤㪸㫋㫊㫌㫅㪸㪾㪸㪃㩷㪥㪸㫋㫀㫆㫅㪸㫃㩷㪠㫅㫊㫋㫀㫋㫌㫋㪼㩷㪽㫆㫉㩷㪜㫅㫍㫀㫉㫆㫅㫄㪼㫅㫋㪸㫃㩷㪪㫋㫌㪻㫀㪼㫊㪃㩷㪡㪸㫇㪸㫅㪧㫉㪼㫊㪼㫅㫋㪼㪻㩷㪸㫋㩷㪧㫉㪼㫊㪼㫅㫋㪼㪻㩷㪸㫋㩷㪠㪧㪚㪚㩷㪜㫏㫇㪼㫉㫋㩷㪤㪼㪼㫋㫀㫅㪾㪑㩷㪩㫆㫃㪼㩷㫆㪽㩷㪩㪼㫄㫆㫋㪼㩷㪪㪼㫅㫊㫀㫅㪾㩷㩿㪩㪪㪀㩷㫀㫅㩷㪝㫆㫉㪼㫊㫋㩷㪸㫅㪻㩷㪥㪸㫋㫀㫆㫅㪸㫃㩷㪞㪟㪞㩷㪜㫄㫀㫊㫊㫀㫆㫅㩷㪊㪌㪠㫅㫍㪼㫅㫋㫆㫉㫀㪼㫊㪃㩷㪦㪺㫋㫆㪹㪼㫉㩷㪉㪊㪄㪉㪌㪃㩷㪉㪇㪈㪉㪃㩷㪟㪸㫐㪸㫄㪸㪃㩷㪡㪸㫇㪸㫅㩷㪜㪪㪘㩷㪚㪸㫉㪹㫆㫅㪪㪸㫋㪑㩷㪯㪚㪦㪉㩷㪞㫉㪸㪻㫀㪼㫅㫋㩷㫅㪼㪸㫉㩷㪘㫅㫋㪿㫆㫉㫆㫇㫆㪾㪼㫅㫀㪺㩷㪪㫆㫌㫉㪺㪼㩷㪪㫀㫄㫌㫃㪸㫋㫀㫆㫅㩷㫆㪽㩷㫋㪿㪼㩷㪸㫋㫄㫆㫊㫇㪿㪼㫉㫀㪺㩷㪚㪦㪉㩷㪺㫆㫃㫌㫄㫅㩷㪼㫅㪿㪸㫅㪺㪼㫄㪼㫅㫋㩷㪻㫌㪼㩷㫋㫆㩷㪚㪦㪉㩷㪼㫄㫀㫊㫊㫀㫆㫅㩷㫆㪽㩷㪸㩷㫇㫆㫎㪼㫉㩷㫇㫃㪸㫅㫋㪅㩷㪫㪿㪼㩷㫎㫀㫅㪻㩷㫊㫇㪼㪼㪻㩷㫀㫊㩷㪈㩷㫄㪆㫊㪅㪫㪿㪼㩷㪸㫊㫊㫌㫄㪼㪻㩷㫇㫆㫎㪼㫉㩷㫇㫃㪸㫅㫋㩷㪼㫄㫀㫊㫊㫀㫆㫅㩷㫀㫊㩷㪈㪊㩷㪤㫋㪚㪦㪉㪆㫐㫉㪅㩿㪙㫆㫍㪼㫅㫊㫄㪸㫅㫅㩷㪼㫋㩷㪸㫃㪅㩷㩿㪉㪇㪈㪇㪀㪀㪬㫇㫋㫆㩷㪉㩼㩷㩿㻃㪏㩷㫇㫇㫄㪀㩷㪸㫅㫆㫄㪸㫃㫐㩷㫆㪽㩷㪺㫆㫃㫌㫄㫅㩷㪚㪦㪉㩷㫀㫅㩷㪈㪇㩷㵨 㪉㪇㩷㫂㫄㩷㫊㫇㪸㫋㫀㪸㫃㩷㫊㪺㪸㫃㪼㪅㪙㫆㫍㪼㫅㫊㫄㪸㫅㫅㩷㪼㫋㩷㪸㫃㪅㩷㩿㪉㪇㪈㪇㪀㪫㫊㫌㫅㪼㫆㩷㪤㪸㫋㫊㫌㫅㪸㪾㪸㪃㩷㪥㪸㫋㫀㫆㫅㪸㫃㩷㪠㫅㫊㫋㫀㫋㫌㫋㪼㩷㪽㫆㫉㩷㪜㫅㫍㫀㫉㫆㫅㫄㪼㫅㫋㪸㫃㩷㪪㫋㫌㪻㫀㪼㫊㪃㩷㪡㪸㫇㪸㫅㪧㫉㪼㫊㪼㫅㫋㪼㪻㩷㪸㫋㩷㪧㫉㪼㫊㪼㫅㫋㪼㪻㩷㪸㫋㩷㪠㪧㪚㪚㩷㪜㫏㫇㪼㫉㫋㩷㪤㪼㪼㫋㫀㫅㪾㪑㩷㪩㫆㫃㪼㩷㫆㪽㩷㪩㪼㫄㫆㫋㪼㩷㪪㪼㫅㫊㫀㫅㪾㩷㩿㪩㪪㪀㩷㫀㫅㩷㪝㫆㫉㪼㫊㫋㩷㪸㫅㪻㩷㪥㪸㫋㫀㫆㫅㪸㫃㩷㪞㪟㪞㩷㪜㫄㫀㫊㫊㫀㫆㫅㩷㪊㪍㪠㫅㫍㪼㫅㫋㫆㫉㫀㪼㫊㪃㩷㪦㪺㫋㫆㪹㪼㫉㩷㪉㪊㪄㪉㪌㪃㩷㪉㪇㪈㪉㪃㩷㪟㪸㫐㪸㫄㪸㪃㩷㪡㪸㫇㪸㫅㩷㪪㪸㫋㪼㫃㫃㫀㫋㪼㩷㪝㫃㫌㫏㩷㪸㫅㪻㩷㪠㫅㫍㪼㫅㫋㫆㫉㫐㪪㪸㫋㪼㫃㫃㫀㫋㪼㩷㪝㫃㫌㫏㩷㪸㫅㪻㩷㪠㫅㫍㪼㫅㫋㫆㫉㫐y 㪛㫀㪽㪽㪼㫉㪼㫅㪺㪼㩷㫆㪽㩷㫊㫇㪸㫋㫀㪸㫃㩷㫊㪺㪸㫃㪼y 㪪㪸㫋㪼㫃㫃㫀㫋㪼㩷㪑㩷㪍㪋㩷㫉㪼㪾㫀㫆㫅㫊㩷㫆㫉㩷㫊㪼㫍㪼㫉㪸㫃㩷㪻㪼㪾㫉㪼㪼㩷㪾㫉㫀㪻y 㪠㫅㫍㪼㫅㫋㫆㫉㫐㩷㪑㩷㫅㪸㫋㫀㫆㫅㪸㫃㪃㩷㫊㫌㪹㪄㫅㪸㫋㫀㫆㫅㪸㫃㪃㩷㪸㫅㪻㩷㩿㪩㪜㪛㪛㪂㪀㩷㫇㫉㫆㫁㪼㪺㫋㩷㫃㪼㫍㪼㫃㫊y 㪛㫀㪽㪽㪼㫉㪼㫅㪺㪼㩷㫆㪽㩷㫋㫀㫄㪼㩷㫊㪺㪸㫃㪼y 㪪㪸㫋㪼㫃㫃㫀㫋㪼㩷㪑㩷㫄㫆㫅㫋㪿㫃㫐㩷㪄 㪸㫅㫅㫌㪸㫃y 㪠㫅㫍㪼㫅㫋㫆㫉㫐㩷㪑㩷㪸㫅㫅㫌㪸㫃㪖y 㪛㫀㪽㪽㪼㫉㪼㫅㪺㪼㩷㫆㪽㩷㪺㫆㫄㫇㫆㫅㪼㫅㫋㫊㩷㫀㫅㪺㫃㫌㪻㪼㪻㩷㫀㫅㩷㵰㪽㫃㫌㫏㵱y 㪪㪸㫋㪼㫃㫃㫀㫋㪼㩷㪑㩷㪥㪼㫋㩷㪽㫃㫌㫏㩷㫀㫅㪺㫃㫌㪻㫀㫅㪾㩷㪽㫆㫊㫊㫀㫃㩷㪽㫌㪼㫃㩷㪸㫅㪻㩷㪽㫆㫉㪼㫊㫋㪆㫇㪼㪸㫋㩷㪽㫀㫉㪼㩷㪼㫄㫀㫊㫊㫀㫆㫅㫊y 㪠㫅㫍㪼㫅㫋㫆㫉㫐㩷㪑㩷y 㪛㫀㪽㪽㪼㫉㪼㫅㪺㪼㩷㫆㪽㩷㫇㫉㪼㪺㫀㫊㫀㫆㫅㫊㩷㩿㪼㫉㫉㫆㫉㩷㪹㪸㫉㫊㪀y 㪪㪸㫋㪼㫃㫃㫀㫋㪼㩷㪑㩷y 㪠㫅㫍㪼㫅㫋㫆㫉㫐㩷㪑㩷㪫㫊㫌㫅㪼㫆㩷㪤㪸㫋㫊㫌㫅㪸㪾㪸㪃㩷㪥㪸㫋㫀㫆㫅㪸㫃㩷㪠㫅㫊㫋㫀㫋㫌㫋㪼㩷㪽㫆㫉㩷㪜㫅㫍㫀㫉㫆㫅㫄㪼㫅㫋㪸㫃㩷㪪㫋㫌㪻㫀㪼㫊㪃㩷㪡㪸㫇㪸㫅㪧㫉㪼㫊㪼㫅㫋㪼㪻㩷㪸㫋㩷㪧㫉㪼㫊㪼㫅㫋㪼㪻㩷㪸㫋㩷㪠㪧㪚㪚㩷㪜㫏㫇㪼㫉㫋㩷㪤㪼㪼㫋㫀㫅㪾㪑㩷㪩㫆㫃㪼㩷㫆㪽㩷㪩㪼㫄㫆㫋㪼㩷㪪㪼㫅㫊㫀㫅㪾㩷㩿㪩㪪㪀㩷㫀㫅㩷㪝㫆㫉㪼㫊㫋㩷㪸㫅㪻㩷㪥㪸㫋㫀㫆㫅㪸㫃㩷㪞㪟㪞㩷㪜㫄㫀㫊㫊㫀㫆㫅㩷㪠㫅㫍㪼㫅㫋㫆㫉㫀㪼㫊㪃㩷㪦㪺㫋㫆㪹㪼㫉㩷㪉㪊㪄㪉㪌㪃㩷㪉㪇㪈㪉㪃㩷㪟㪸㫐㪸㫄㪸㪃㩷㪡㪸㫇㪸㫅㩷㪊㪏㪫㪿㪸㫅㫂㩷㫐㫆㫌㩷㪽㫆㫉㩷㫐㫆㫌㫉㩷㪸㫋㫋㪼㫅㫋㫀㫆㫅㪅㪫㫀㫄㪼㩷㪪㪼㫉㫀㪼㫊㩷㫆㪽㩷㪤㫆㫅㫋㪿㫃㫐㩷㪚㪦㪉㩷㪝㫃㫌㫏㪘㫄㪼㫉㫀㪺㪸㩷㪑㩷㪙㫆㫉㪼㪸㫃㪃㩷㪫㪼㫄㫇㪼㫉㪸㫋㪼㪃㩷㪫㫉㫆㫇㫀㪺㪸㫃㩷㪃㩷㪪㫆㫌㫋㪿㪫㫊㫌㫅㪼㫆㩷㪤㪸㫋㫊㫌㫅㪸㪾㪸㪃㩷㪥㪸㫋㫀㫆㫅㪸㫃㩷㪠㫅㫊㫋㫀㫋㫌㫋㪼㩷㪽㫆㫉㩷㪜㫅㫍㫀㫉㫆㫅㫄㪼㫅㫋㪸㫃㩷㪪㫋㫌㪻㫀㪼㫊㪃㩷㪡㪸㫇㪸㫅㪧㫉㪼㫊㪼㫅㫋㪼㪻㩷㪸㫋㩷㪧㫉㪼㫊㪼㫅㫋㪼㪻㩷㪸㫋㩷㪠㪧㪚㪚㩷㪜㫏㫇㪼㫉㫋㩷㪤㪼㪼㫋㫀㫅㪾㪑㩷㪩㫆㫃㪼㩷㫆㪽㩷㪩㪼㫄㫆㫋㪼㩷㪪㪼㫅㫊㫀㫅㪾㩷㩿㪩㪪㪀㩷㫀㫅㩷㪝㫆㫉㪼㫊㫋㩷㪸㫅㪻㩷㪥㪸㫋㫀㫆㫅㪸㫃㩷㪞㪟㪞㩷㪜㫄㫀㫊㫊㫀㫆㫅㩷㪤㪸㫂㫊㫐㫌㫋㫆㫍㩷㪼㫋㩷㪸㫃㪅㩷㪠㪮㪞㪞㪤㪪㪄㪏㪠㫅㫍㪼㫅㫋㫆㫉㫀㪼㫊㪃㩷㪦㪺㫋㫆㪹㪼㫉㩷㪉㪊㪄㪉㪌㪃㩷㪉㪇㪈㪉㪃㩷㪟㪸㫐㪸㫄㪸㪃㩷㪡㪸㫇㪸㫅㩷㪋㪇㪫㫀㫄㪼㩷㪪㪼㫉㫀㪼㫊㩷㫆㪽㩷㪤㫆㫅㫋㪿㫃㫐㩷㪚㪦㪉㩷㪝㫃㫌㫏㪫㫉㫆㫇㪅㩷㪘㪽㫉㫀㪺㪸㪃㩷㪪㪅㩷㪘㪽㫉㫀㪺㪸㪃㩷㪜㫌㫉㫆㫇㪼㪃㩷㪪㫀㫍㪼㫉㫀㪸㪫㫊㫌㫅㪼㫆㩷㪤㪸㫋㫊㫌㫅㪸㪾㪸㪃㩷㪥㪸㫋㫀㫆㫅㪸㫃㩷㪠㫅㫊㫋㫀㫋㫌㫋㪼㩷㪽㫆㫉㩷㪜㫅㫍㫀㫉㫆㫅㫄㪼㫅㫋㪸㫃㩷㪪㫋㫌㪻㫀㪼㫊㪃㩷㪡㪸㫇㪸㫅㪧㫉㪼㫊㪼㫅㫋㪼㪻㩷㪸㫋㩷㪧㫉㪼㫊㪼㫅㫋㪼㪻㩷㪸㫋㩷㪠㪧㪚㪚㩷㪜㫏㫇㪼㫉㫋㩷㪤㪼㪼㫋㫀㫅㪾㪑㩷㪩㫆㫃㪼㩷㫆㪽㩷㪩㪼㫄㫆㫋㪼㩷㪪㪼㫅㫊㫀㫅㪾㩷㩿㪩㪪㪀㩷㫀㫅㩷㪝㫆㫉㪼㫊㫋㩷㪸㫅㪻㩷㪥㪸㫋㫀㫆㫅㪸㫃㩷㪞㪟㪞㩷㪜㫄㫀㫊㫊㫀㫆㫅㩷㪤㪸㫂㫊㫐㫌㫋㫆㫍㩷㪼㫋㩷㪸㫃㪅㩷㪠㪮㪞㪞㪤㪪㪄㪏㪠㫅㫍㪼㫅㫋㫆㫉㫀㪼㫊㪃㩷㪦㪺㫋㫆㪹㪼㫉㩷㪉㪊㪄㪉㪌㪃㩷㪉㪇㪈㪉㪃㩷㪟㪸㫐㪸㫄㪸㪃㩷㪡㪸㫇㪸㫅㩷㪋㪈㪠㫅㫍㪼㫉㫊㪼㩷㪤㫆㪻㪼㫃㫀㫅㪾㩷㪪㪼㫋㫌㫇
64 Base Regions

Inverse modeling scheme : Fixed-lag Kalman Smoother(Bruhwiler et. al., 2005 ACP)

Assimilation window: 3 month
Number of source regions: 64
Tracer transport model: NIES08.1i (Belikov et al., 2011
GMD)

Prior flux uncertainty

Prior Flux Uncertainty

Land : Std. dev. of VISIT model monthly NEE about past 30year mean
Ocean : Std. dev. of assimilated ocean flux about 2001-2009
mean and ocean flux climatology

Model-observation mismatch errors
GLOBALVIEW : RSD of observations vs smoothed curve
GOSAT L2: Std. dev. of XCO2 values found in a 5q× 5q gridin a month, with min uncertainty of 3 ppm

㪤㪸㫂㫊㫐㫌㫋㫆㫍㩷㪼㫋㩷㪸㫃㪅㩷㪠㪮㪞㪞㪤㪪㪄㪏㪫㫊㫌㫅㪼㫆㩷㪤㪸㫋㫊㫌㫅㪸㪾㪸㪃㩷㪥㪸㫋㫀㫆㫅㪸㫃㩷㪠㫅㫊㫋㫀㫋㫌㫋㪼㩷㪽㫆㫉㩷㪜㫅㫍㫀㫉㫆㫅㫄㪼㫅㫋㪸㫃㩷㪪㫋㫌㪻㫀㪼㫊㪃㩷㪡㪸㫇㪸㫅㪧㫉㪼㫊㪼㫅㫋㪼㪻㩷㪸㫋㩷㪧㫉㪼㫊㪼㫅㫋㪼㪻㩷㪸㫋㩷㪠㪧㪚㪚㩷㪜㫏㫇㪼㫉㫋㩷㪤㪼㪼㫋㫀㫅㪾㪑㩷㪩㫆㫃㪼㩷㫆㪽㩷㪩㪼㫄㫆㫋㪼㩷㪪㪼㫅㫊㫀㫅㪾㩷㩿㪩㪪㪀㩷㫀㫅㩷㪝㫆㫉㪼㫊㫋㩷㪸㫅㪻㩷㪥㪸㫋㫀㫆㫅㪸㫃㩷㪞㪟㪞㩷㪜㫄㫀㫊㫊㫀㫆㫅㩷㪋㪉㪠㫅㫍㪼㫅㫋㫆㫉㫀㪼㫊㪃㩷㪦㪺㫋㫆㪹㪼㫉㩷㪉㪊㪄㪉㪌㪃㩷㪉㪇㪈㪉㪃㩷㪟㪸㫐㪸㫄㪸㪃㩷㪡㪸㫇㪸㫅㩷㪧㫉㪼㫇㫉㫆㫊㪼㫊㫊㫀㫅㪾㩷㫆㪽㩷㪦㪹㫊㪼㫉㫍㪸㫋㫀㫆㫅㪸㫃㩷㪛㪸㫋㪸GOSAT Level 2 XCO2
Data version:ver. 2.0
Data period:
2009/06 – 2010/07 (14 months)
Offset optimized by inversion at first step (2009/6-8 time
window)

Gridded Monthly Averages (5˚×5˚)Data screening:- Prediction-Observation mismatch: > 3 ppm Rejected- Outliers rejected based on Gap-filled Ensemble Model
Climatology (GECM) Saito et. al., 2011 JGR

GLOBALVIEW 2011
The weekly analyses were averaged to monthly values.
Choice of data sites: Followed Law et al. (2003, TC3
paper) + all available aircraft sites (139 data records
total).

㪤㪸㫂㫊㫐㫌㫋㫆㫍㩷㪼㫋㩷㪸㫃㪅㩷㪠㪮㪞㪞㪤㪪㪄㪏㪫㫊㫌㫅㪼㫆㩷㪤㪸㫋㫊㫌㫅㪸㪾㪸㪃㩷㪥㪸㫋㫀㫆㫅㪸㫃㩷㪠㫅㫊㫋㫀㫋㫌㫋㪼㩷㪽㫆㫉㩷㪜㫅㫍㫀㫉㫆㫅㫄㪼㫅㫋㪸㫃㩷㪪㫋㫌㪻㫀㪼㫊㪃㩷㪡㪸㫇㪸㫅㪧㫉㪼㫊㪼㫅㫋㪼㪻㩷㪸㫋㩷㪧㫉㪼㫊㪼㫅㫋㪼㪻㩷㪸㫋㩷㪠㪧㪚㪚㩷㪜㫏㫇㪼㫉㫋㩷㪤㪼㪼㫋㫀㫅㪾㪑㩷㪩㫆㫃㪼㩷㫆㪽㩷㪩㪼㫄㫆㫋㪼㩷㪪㪼㫅㫊㫀㫅㪾㩷㩿㪩㪪㪀㩷㫀㫅㩷㪝㫆㫉㪼㫊㫋㩷㪸㫅㪻㩷㪥㪸㫋㫀㫆㫅㪸㫃㩷㪞㪟㪞㩷㪜㫄㫀㫊㫊㫀㫆㫅㩷㪋㪊㪠㫅㫍㪼㫅㫋㫆㫉㫀㪼㫊㪃㩷㪦㪺㫋㫆㪹㪼㫉㩷㪉㪊㪄㪉㪌㪃㩷㪉㪇㪈㪉㪃㩷㪟㪸㫐㪸㫄㪸㪃㩷㪡㪸㫇㪸㫅㩷㪭㪸㫃㫀㪻㪸㫋㫀㫆㫅㩷㫆㪽㩷㪞㪦㪪㪘㪫㩷㪫㪘㪥㪪㪦㩷㪝㪫㪪㩷㪪㪮㪠㪩㪯㪚㪦㪉㩷㪸㫅㪻㩷㪯㪚㪟㪋㩷㪧㫉㫆㪻㫌㪺㫋㫊y 㪫㪿㪼㩷㪞㪦㪪㪘㪫㩷㪣㪼㫍㪼㫃㩷㪉㩷㪯㪚㪦㪉㩷㪻㪸㫋㪸㩷㫉㪼㫋㫉㫀㪼㫍㪼㪻㩷㪽㫉㫆㫄㩷㫋㪿㪼㩷㪫㪘㪥㪪㪦㪄㪝㪫㪪㩷㪪㪮㪠㪩㩷㪣㪼㫍㪼㫃㩷㪈㪙㩷㪻㪸㫋㪸㩷㫎㪼㫉㪼㩷㫃㫆㫎㪼㫉㩷㫋㪿㪸㫅㩷㫋㪿㪼㩷㪫㪚㪚㪦㪥㩷㪻㪸㫋㪸㩷㪹㫐㩷㻃㩷㪇㪅㪊㩷㩼㩷㩿㪈㪅㪉㩷㫇㫇㫄㪀㩷㪸㫅㪻㩷㫋㪿㪼㩷㫊㫋㪸㫅㪻㪸㫉㪻㩷㪻㪼㫍㫀㪸㫋㫀㫆㫅㩷㫆㪽㩷㫋㪿㪼㩷㪣㪼㫍㪼㫃㩷㪉㩷㪯㪚㪦㪉㩷㪻㪸㫋㪸㩷㫎㪸㫊㩷㻃㩷㪇㪅㪌㩷㩼㩷㩿㪉㪅㪇㩷㫇㫇㫄㪀㪅y 㪫㪿㪼㩷㪞㪦㪪㪘㪫㩷㪣㪼㫍㪼㫃㩷㪉㩷㪯㪚㪟㪋㩷㪻㪸㫋㪸㩷㫎㪼㫉㪼㩷㫃㫆㫎㪼㫉㩷㫋㪿㪸㫅㩷㫋㪿㪼㩷㪫㪚㪚㪦㪥㩷㪻㪸㫋㪸㩷㪹㫐㩷㻃㩷㪇㪅㪋㩼㩷㩿㪎㩷㫇㫇㪹㪀㩷㪸㫅㪻㩷㫋㪿㪼㩷㫊㫋㪸㫅㪻㪸㫉㪻㩷㪻㪼㫍㫀㪸㫋㫀㫆㫅㩷㫆㪽㩷㫋㪿㪼㩷㪣㪼㫍㪼㫃㩷㪉㩷㪯㪚㪟㪋㩷㫎㪸㫊㩷㻃㩷㪇㪅㪎㩷㩼㩷㩿㪈㪉㩷㫇㫇㪹㪀㪅㪿㫋㫋㫇㫊㪑㪆㪆㪻㪸㫋㪸㪅㪾㫆㫊㪸㫋㪅㫅㫀㪼㫊㪅㪾㫆㪅㫁㫇㪆㪞㫆㫊㪸㫋㪮㪼㪹㪛㪻㫊㪆㫇㫉㫆㪻㫌㪺㫋㫆㫉㪻㪼㫉㪆㪻㫀㫊㫋㫉㫀㪹㫌㫋㫀㫆㫅㪆㫌㫊㪼㫉㪆㪭㪇㪉㪯㪯㪶㪝㪫㪪㪶㪣㪉㪶㪭㪸㫃㫀㪻㪸㫋㫀㫆㫅㪶㪛㫆㪺㫌㫄㪼㫅㫋㪶㪞㪬㪶㪼㫅㪅㫇㪻㪽㪫㫊㫌㫅㪼㫆㩷㪤㪸㫋㫊㫌㫅㪸㪾㪸㪃㩷㪥㪸㫋㫀㫆㫅㪸㫃㩷㪠㫅㫊㫋㫀㫋㫌㫋㪼㩷㪽㫆㫉㩷㪜㫅㫍㫀㫉㫆㫅㫄㪼㫅㫋㪸㫃㩷㪪㫋㫌㪻㫀㪼㫊㪃㩷㪡㪸㫇㪸㫅㪧㫉㪼㫊㪼㫅㫋㪼㪻㩷㪸㫋㩷㪧㫉㪼㫊㪼㫅㫋㪼㪻㩷㪸㫋㩷㪠㪧㪚㪚㩷㪜㫏㫇㪼㫉㫋㩷㪤㪼㪼㫋㫀㫅㪾㪑㩷㪩㫆㫃㪼㩷㫆㪽㩷㪩㪼㫄㫆㫋㪼㩷㪪㪼㫅㫊㫀㫅㪾㩷㩿㪩㪪㪀㩷㫀㫅㩷㪝㫆㫉㪼㫊㫋㩷㪸㫅㪻㩷㪥㪸㫋㫀㫆㫅㪸㫃㩷㪞㪟㪞㩷㪜㫄㫀㫊㫊㫀㫆㫅㩷㪠㫅㫍㪼㫅㫋㫆㫉㫀㪼㫊㪃㩷㪦㪺㫋㫆㪹㪼㫉㩷㪉㪊㪄㪉㪌㪃㩷㪉㪇㪈㪉㪃㩷㪟㪸㫐㪸㫄㪸㪃㩷㪡㪸㫇㪸㫅㩷㪋㪋High spatial resolution remotesensing improves forest carbon
stock estimation in dry forests
Amon Murwira (PhD)
Tawanda W. Gara
University of Zimbabwe
Department of Geography and Environmental Science
IPCC Expert Meeting: Role of Remote Sensing (RS) in Forest
And National GHG Inventories, Hayama, Japan, 23-25 October 2012

Introduction
Forests important in the global carbon
cycle Savannas constitute 30 % of world
vegetation and 50% of Africa’s vegetation
 Most work on forest carbon stocks done
outside of the dry savanna forests.
 It is important for science to narrow thisknowledge gap particularly in Africansavannas.
Objectives
We tested whether and to what extent
vegetation indices derived from highspatial resolution satellite imagery(GeoEye-1, WorldView-2) can predict
forest carbon stocks in dry forests
 We tested this in two Southern Africansavanna woodland types with contrasting
annual rainfall amounts
Study Sites

Mukuvisi (Geoeye-1: 23/04/2010)

Malipati (WorldView-2: 15/05/2011)

Study Site Characteristics
Characteristics

Mukuvisi

Malipati

Longitude

31°04 -31°06 E

31°20 -31°24 E

Latitude17°49 -17°51 S

22°01 -22°03 S

Altitude (m)1400-1500

300-600

Rainfall (mm)800-1 000

300-600

Rain season

Oct-April

Nov-March

Temperature range(0C)
Vegetation type

5-26

5-33

Dry miombo

MopaneSoil classification

Fersiallitic

Lithosols,Vertisols

Field Sampling
Images classified into woodland and othercover types
 Random samples generated in woodland 22 samples Mukuvisi
 38 samples Malipati
Field Measurements
At each random point, a north-oriented
plot measuring 30m x 30m (0.09ha) was
marked using a tape measure.
 Plots were oriented to the north using amagnetic compass.
 The plot size determined following
Rahman et al (2008) 30m x 30m plot sizerecommendation for primary forests.


Field Measurements
For tree >5cm DBH and 3m in height we
identified the tree species and measured
DBH (at 1.3m above ground surface) We also measured and total tree heightusing a the trigonometric principle withclinometer dataForest Carbon Estimation

AGB (t / ha )VOB u WD u BEF

Where VOB is wood volume over barkestimated from allometric equations,WD is wood density and BEF is biomass
expansion factor.
WD and BEF were determined following
Brown (1997).
 Finally, we multiplied the above-groundbiomass by a conversion ratio of 0.5 to obtain
forest carbon (IPCC, 2003)

Volume Equations

Site

Woodlandtype

SpeciesAllometric Equation

Reference

MukuvisimiomboGeneralized equation

V / ha6.18 * BA 0.86

(Frost, 1996)

Malipati

mopaneColophospermum mopane

V

0.0001065 * DBH 2 .471

Combretum apiculatumV(0.0132  0.00079DBH  0.0103H t )Others speciesV

DBH 2 / 4 * S * H t *S * fgross(Henry et al., 2011)2

2(Abbot et al., 1997)(ILUA, 2008)

Volume allometric equations


Allometric equations for volume usedbecause remote sensing indices andvolume equations had high RMSE >18%

Remote sensing
Radiometric and geometric correction
 Remotely sensed vegetation indicescalculated:
 Simple Ratio (SR) Normalised Difference Vegetation Index(NDVI)
 Soil Adjusted Vegetation Index (SAVI)Relating Field carbon data with RS
vegetation indices
Forest carbon data tested for spatial
autocorrelation (result no evidence of
spatial autocorrelation)
 Used regression analysis using 75% of data 25 % of data reserved as test data
 Root Mean Square Error (RMSE) used formodel validation
 Used best regression model to map carbon


Results

Amon Murwira (PhD)
Tawanda W. Gara
University of Zimbabwe
Department of Geography and Environmental ScienceRegression Model: Malipati (SRWorldView-2 )30

25

y = 7.48 * e(0.52*x)
R2 = 0.73
n = 28p < 0.05

20
1.8

2.0

2.2

2.4
SR

2.6

2.8

Regression Model Validation:
Malipati (WorldView-2)
y = 13.64 + 0.504*x

28
Predicted carbon (Mg h a -1)

Carbon (Mg ha-1)R 2 = 0.58
RMSE = 11.16%
p < 0.05

26

24

22
1:1 line

20

18
18

20

22
24
Measured carbon (Mg ha -1)

26

Regression Model: Mukuvisi (SRGeoeye-1 )70
R2 = 0.6
n = 17p < 0.05

60

50

40
2.3

2.8

3.3

3.8

SR

Regression Model Validation:
Mukuvisi (Geoeye-1)
68
Predicted carbon (Mg ha -1 )Carbon (Mg ha-1)

y = 16.15 * e(0.3917*x)

1:1 line

6358

y = 3.807 + 0.9114*x

53

R 2 = 0.40
RMSE = 10.08%
p > 0.05

48
48

53

58
Measured carbon (Mg ha -1)

63Forest Carbon for study sites(Descriptive statistics)
Mukuvisi

MalipatiMin

Max Mean SDMin

Max MeanSD

Carbon(Mg ha-1)

40

72

57

9

20

33

25

3

DBH (cm)

6

85

23

13

5

30

15

5

Tree density(

14

54

29

8

3

47

27

10

Mukuvisi Carbon estimate(Geoeye-1: 23/04/2010)

Malipati Carbon estimate(WorldView-2: 15/05/2011)

Discussion
RMSE of between 10-11 % are an
improvement from previous studies where
RMSE values were significantly higher.
 Most studies on AGB have been basedon medium to low spatial resolution
imagery and have yielded low R2
 The mean carbon density in this study
compare well to carbon density estimatesof 61 and 36 Mg ha-1 for dry forests (IPCC,
2003).


Discussion
Findings validate the utility of our models
in estimating and modelling forest carbon
in savanna landscapes of Southern Africa.

Way Forward
Important to test these relationships at
multiple sites to increase confidence Currently, we have selected 8 sites inSouthern Africa covering Mozambique,Zimbabwe and Zambia to further test
these models.
Thank you

Image for Mukuvisi provided by GeoEye as an educational grantBIOMASS DYNAMICS IN
MOZAMBICAN WOODLAND
Casey Ryan, Neha Joshi, Timothy Hill*, Emily Woollen, Iain McNicol, Ed
Mitchard, Gemma Cassells, Iain Woodhouse, Mat Williams, Anthony Bloom,
Becky Stedham and John Grace
University of Edinburgh, School of GeoSciences* Now at University of St Andrews
Casey Ryancasey.ryan@ed.ac.uk
Lecturer in Ecosystem Services & Global Change
School of GeoSciences, University of Edinburgh

CONTENTS
1. Miombo woodlands:
current land use change
dynamics
2. Measuring biomass
stocks using ALOSPALSAR & ground data
3. Quantifying
deforestation anddegradation (and theircauses)AFRICAN WOODLANDSWhite, 1983

AFRICAN WOODLANDSWhite, 1983

LUC EMISSIONS INWOODLAND AFRICA
Large area of low C density
woodlands (2.4 M km2)

Deforestation emissions by national landcover
Agric
2%

Arid
8%

Highly populated
LUC driven by small scale
agriculture, not global commodities
Degradation likely to be substantial(Ahrends et al 2010)

Forest21%

Woodland54%

Few data on forest carbon stocks
Forest/Agricmosaic1%

Losses from soil after deforestation?
0.3 PgC/yr ???

Grassland
14%

Based on data in FAO FRA 2010 and Mayaux’s (2004)
classiﬁcation of national land cover

Kenya

Co
ng
o

DR Congo

Malawi

NiasCabo Delgadosaa

Zimbabwe

Namibiamb
iq

ZambiaMo
za

Angolaue

Tanzania

Bo
ts
wa
n

G
ab
on

PEOPLE AND MIOMBO WOODLANDS

NampulaTete

Zambezia
South Africa

ManicaSofalazaGa

Inhambane

MaputoMarzoli (2007), FAO (2010)

Williams et al 2008

FOOD, FUEL..

... AND FIRE

Marondera, Zimbabwe
50 years of ﬁre exclusion

Ryan & Williams 2011; Furley et al 2008

Marondera, Zimbabwe 50 years of annual ﬁre

OVERVIEW OF OUR WORKIN CENTRAL MOZAMBIQUE
Ground based estimates of stocks and ﬂows of C
Mapping biomass change with ALOS PALSAR(2007-10)
Project scale
District/Province scale
Estimating the contributionsof activities that cause forest loss
REDD Baselines

RADAR IMAGERY FOR
ESTIMATING ΔBIOMASSALOS PALSAR

ALOS PALSAR L-band(23 cm) radar.
12.5 m ground
resolution
no cloud or grass
problems
instead, terrain andspeckle and soil
moisture
Water

data from 2007 - 2010;2014 onwards

Dry soil

Wet soil

RADAR IMAGE HEREBIOMASS-BACKSCATTER RELATIONSHIP
96 ground calibration andvalidation plots (0.2-3 ha)
Forest, woodland andcropland10 x images from2007-2010
Regression ~stable overtime
Temporal co-variance inbiases:2007-2010 ρ = 0.54
Mean R2 = 0.50
Validation (holdout) RMSE = 9.8 tC/ha Bias = 1.6 tC/ha

Ryan et al (2012) GCB

BIOMASS-BACKSCATTER RELATIONSHIP
96 ground calibration andvalidation plots (0.2-3 ha)
Forest, woodland andcropland10 x images from2007-2010
Regression ~stable overtime
Temporal co-variance inbiases:2007-2010 ρ = 0.54
Mean R2 = 0.50
Validation (holdout) RMSE = 9.8 tC/ha Bias = 1.6 tC/ha

Ryan et al (2012) GCB

BIOMASS-BACKSCATTER RELATIONSHIP
96 ground calibration andvalidation plots (0.2-3 ha)
Forest, woodland andcropland10 x images from2007-2010
Regression ~stable overtime
Temporal co-variance inbiases:2007-2010 ρ = 0.54
Mean R2 = 0.50
Validation (holdout) RMSE = 9.8 tC/ha Bias = 1.6 tC/ha

Ryan et al (2012) GCB

DEFORESTATION AND
DEGRADATIONOver all the study area
loss of 6.9±4.6 % of
AGB in three years

2007
TgC

2.13±0.12

2010

1.98±0.11 -0.15±0.10

But is this from deforestation or degradation?
(need a ‘forest’ threshold, e.g. 15 tC/ha)Errors from bootstraping i.e randomly sampling
1/2 the regression data, and recalculating stocks
and changes 30,000 times

DEGRADATION VS
DEFORESTATION
Deﬁnitions: forest is land > 15tC/ha *Deforestation is forest--> non-forest
20
007

2010

ForestNon-Forest

ForestForestDe/aggradation

Afforestation

NonForest

Deforestation

Non-Forest
De/aggradation* for discussion. How do we link this to existing deﬁnitions?

ΔTotal

Deforestation

Degradation

Fallow regrowth?

Afforestation

-200

-150

-100

-50

0

50

100

Biomass loss (GgC)
errorbars show 1 standard deviation from the 30,000 bootstraps

Ryan et al (2012) GCBKEY FINDINGS
Degradation loss ~= Deforestation loss
Degradation uncertain
Small clearances accreting over time - challenge formonitoring
Biomass PDF has info on land use change? (Hill et al
2012)BIOMASS STOCKS: COMPARISON

Comparison
over wholescene
1100 km2
NB: Dates
different
Red lines show 95% CI

Hill et al, in prep

LIMITATIONS OF THE
APPROACH
Topographic correction needs good DEM
Environmental effects need to becontrolled for (regularly updated grounddata)
Detecting gradual change remains
uncertain
Saturates at high biomass
Backscatter responds to stocking density,
basal area and biomass. Brolly et al(2012)
Availability of data

UNDERSTANDING THE
CAUSES OF FOREST LOSS

Moving from estimates of land cover change toland use change
Goal: to utilise these hi-res change maps to quantify
the causes of deforestation and degradation

Joshi and Ryan, in revision, 2012

200%
0%

200%
0%

• Create discrete events• Random sample(stratiﬁed by area, distfrom rd, and intensityof loss)• Visited n = 76
• Identify land use
activities

1abar = 500 m

1b

2a

2b

3a

3b

1a

bar = 500 m

1b    
   
       

2a

2b

3a

3b

   
          
        



 
 
 

      
          
        



 
 
 

      
          
        



 
 
 

   •Biomass loss rate: -2.8±2.0 %/yr, much higher than past estimates of forest area
loss (0.75 %/yr)•50:50 split small scale agriculture vs all other activities•50:50 deforestation vs degradation•Uncertainty on regrowth and ﬁre

KEY FINDINGS
StocksALOS PALSAR is a useful tool for biomass estimation in woodlands,
despite variations caused by e.g soil moisture
Needs extensive ground data. Terrain correction can be a challenge.
‘Plot&PALSAR’ estimates are quite different from current global data
sets. (How well do we understand the errors on both?)
Stock Changes (2007-10)
Can detect sig. changes over 3 years. Much higher biomass loss rates
than currently assumed forest area loss rate (x2-3):
Degradation ~= deforestation losses
EO data can be linked to ground-based observations of the activities
causing forest loss: e.g. small scale agriculture = ~50% of losses.

SOME ISSUES:
1. How can we use targeted, detailed studies
at a national scale? i.e
upscale deforestation : degradation
ratio?
Understand processes causing forest
loss (future scenarios / baselines)
2. Can we map larger areas accurately given
data scarcity?
3. Inventory design: Different requirementsfor plot-based studies vs EO cal / val
4. Forest deﬁnitions (sigh!). Unclear how tolink current deﬁnitions to carbon density.

Thanks!
casey.ryan@ed.ac.uk

EO
Ryan, C. M., T. Hill, E. Woollen, C. Ghee, E. Mitchard, G. Cassells, J. Grace, I. H. Woodhouse and M. Williams (2012). “Quantifying
small-scale deforestation and forest degradation in African woodlands using radar imagery.” Global Change Biology 18(1): 243-257.
Joshi, N. and C. M. Ryan (2012). “The causes of deforestation and degradation in African woodlands: a method for their quantitative
assessment and a case study from central Mozambique.” Journal of Applied Geography In review.
Hill, T., M. Williams and C. M. Ryan (2012). “Estimating and characterising above ground forest disturbances using radar..” In review.
Williams, M., T. C. Hill and C. M. Ryan (2012). “Using biomass distributions to determine probability and intensity of tropical forest
disturbance.” Plant Ecology and Diversity in press.

StocksWoollen, E., C. Ryan and M. Williams (2012). “Carbon Stocks in an African Woodland Landscape: Spatial Distributions and Scales of
Variation.” Ecosystems 15(5): 804-818.
Ryan, C. M., M. Williams and J. Grace (2011). “Above- and Belowground Carbon Stocks in a Miombo Woodland Landscape ofMozambique.” Biotropica 43(4): 423-432.
Ryan, C. M. and M. Williams (2011). “How does ﬁre intensity and frequency affect miombo woodland tree populations and biomass?”
Ecological Applications 21(1): 48-60.
Williams, M., C. M. Ryan, R. M. Rees, E. Sambane, J. Fernando and J. Grace (2008). “Carbon sequestration and biodiversity of regrowing miombo woodlands in Mozambique.” Forest Ecology and Management 254(2): 145-155.
Furley, P. A., R. M. Rees, C. M. Ryan and G. Saiz (2008). “Savanna burning and the assessment of long-term ﬁre experiments withparticular reference to Zimbabwe.” Progress in Physical Geography 32(6): 611-634.

2007

08-Aug-07
23-Sep-07

Accuracy assessment:

10-May-08
25-Jun-08
28-Jun-09

False positive rate:<0.01%28-Sep-09
16-May-10
01-Jul-10
01-Oct-10

IKONOS image from 24 June 2009

2009

2009

0

2010

0.5

1

Hit rate: 65% of pixels,
89% of events

2

Kilometers

Protected forest

New farm

SPATIAL DISTRIBUTIONS AND LAND USEa) Mucombezeb) Nhambita

0.06
0.05

Heavily
deforested

Frequency

0.04c) Sanctuaryd) Remote Park

0.03

0.035

0.03

0.025

0.03

0.025

0.025

Village

0.02

Fire protectedundisturbed

0.02

0.02
0.03

0.015

0.015
0.015

0.02

0.01

0.01
0.01

0.01
0

0.005

0

20

40

60

80

0

0.005

0.005

0

e) Umbalauwa

20

40

60

80

0

0

f) Vunduzi

0.03

0.035

Village
0.02

0.03

60

80

0.025

Newly
colonised

Town andhinterland

0.02

0.025

0.015

40

g) Gorongosa

0.04

0.025

20

0.015

0.02
0.01

0.015

0.01

0.01
0.005

0.005
0

2007
2010

0.005
0

20

40

60

80

0

0

20
40
60
AGB (tC/ha)

80

0

0

20

40

60

RESULTS
But:
Deforestation can’t happen on
already cleared land
Detection of deforestation may beeasier at high biomass
So:
Restrict to areas of “forest” (>20tC/
ha)compare observations to a pseudodata set where H0 is true

80

0

0

20

40

60

80

RESULTS
But:
Deforestation can’t happen on
already cleared land
Detection of deforestation may beeasier at high biomass
So:
Restrict to areas of “forest” (>20tC/
ha)compare observations to a pseudodata set where H0 is true

RESULTS
But:
Deforestation can’t happen on
already cleared land
Detection of deforestation may beeasier at high biomass
So:
Restrict to areas of “forest” (>20tC/
ha)compare observations to a pseudodata set where H0 is true

RESULTS
But:
Deforestation can’t happen on
already cleared land
Detection of deforestation may beeasier at high biomass
So:
Restrict to areas of “forest” (>20tC/
ha)compare observations to a pseudodata set where H0 is true

RESULTS
But:
Deforestation can’t happen on
already cleared land
Detection of deforestation may beeasier at high biomass
So:
Restrict to areas of “forest” (>20tC/
ha)compare observations to a pseudodata set where H0 is true
<1tC/ha difference between
deforested land and comparable
surounds

MAP OF DEGRADEDAREAS ANDRESULTS: GROUND DATARESULTS: GROUND DATA

Deforestation:
mainly agriculture

Degradation:
many causes

RESULTS

Deforested

Area
(km2)

ΔC
GgC ± SD73

-84.8±9.7% of total

35%
Afforested

4732±4.1

ForestDegradation

424-55.3±79.9

Non-Forest
Degradation

615-41.7±34.8

Total

1159

-149±10065%

100%