DEPAR TMENT OF AGRICULT URE

Pacific Southwest 

Research Station

General Technical 

Report

PSW-GTR-246

September 2013

United States Department of Agriculture 

Forest Service

Proceedings of the International 

Workshop on Monitoring Forest 

Degradation in Southeast Asia

November 13-14, 2012 – Bangkok, Thailand
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Editors

Leif A. Mortenson is forest inventory specialist, US Department of Agriculture Forest Service, Pacific Northwest 

Research Station, 620 SW Main St. Suite 400, Portland, OR 97205 (email address: leifmortenson@fs.fed.us)

James J.Halperin is research forester, Center for International Forestry Research, Lusaka, Zambia (email: 

j.halperin@cgiar.org)

Patricia N. Manley is conservation and biodiversity supervisory scientist, US Department of Agriculture Forest 

Service, Pacific Southwest Research Station, 2480 Carson Road, Placerville, CA 95667 (email: 

pmanley@fs.fed.us)

Rick L. Turner is ecologist, US Department of Agriculture Forest Service, Tongass National Forest, 8510 

Mendenhall Loop Road, Juneau, AK 99801 (email:rlturner@fs.fed.us) 

Papers are provided by the authors in camera-ready form for printing. Authors are responsible for the content and 

accuracy. Opinions expressed may not necessarily reflect the position of the U.S. Department of Agriculture.

This GTR and all associated materials, including country level reports for Cambodia, Laos, and Vietnam, can be 

accessed at “http://www.leafasia.org/events/degradation-regional-workshop.”

Cover photo: Houaphanh Province, Lao PDR by Leif Mortenson.
i 

Proceedings of the International

Workshop on Monitoring Forest

Degradation in Southeast Asia

November 13-14, 2012±Bangkok, Thailand 

Leif A. Mortenson, James J. Halperin, Patricia N. Manley, 

and Rick L. Turner

Editors

U.S. Department of Agriculture, Forest Service

Pacific Southwest Research Station

Albany, CA

General Technical Report PSW-GTR-246

September 2013
ii

Conference Sponsors

USDA Forest Service International Programs (USFS-IP)

USA International Development (USAID) – Asia

Lowering Emissions in Asia’s Forests (LEAF)

We would like to acknowledge all of the preliminary work that went into crafting this 

workshop. There were numerous reviews of the agenda to ensure that the resulting 

workshop would truly meet the original goals for the Scope of Work. The workshop 

Steering Committee of David Ganz, Sandra Brown, and James Halperin is due special 

acknowledgement. Additionally, much effort was put into selecting and bringing together 

the appropriate experts from around the region and the world to present and participate in 

the workshop. The tireless coordination efforts of Ms. Elle Rattiya deserve a very large 

thank you. Thanks also go out to Peter Stephen, Jamie Halperin, Leif Mortenson, and Pat 

Manley for facilitating small group sessions, to Leif Mortenson and Nguyen Hanh Quyen 

for tirelessly taking notes during the entire workshop, and to all the participants for their 

active engagement that made this workshop a success. Our reviewers for this GTR, 

Thomas Brandeis, Beth Lebow and Gabriel Eickhoff contributed extremely valuable 

suggestions for improving the document 
iii

Abstract

Mortenson, Leif $.; Halperin, James J.; Manley, Patricia N.; Turner, Rich L. 2013 

Proceedings of the international workshop on monitoring forest degradation in Southeast 

Asia. Gen. Tech. Rep. PSW-GTR-246. Albany, CA: U.S. Department of Agriculture, 

Forest Service, Pacific Southwest Research Station. 56 p. 

The international workshop on monitoring forest degradation in Southeast Asia provided 

a forum for discussion of the technical, social and political challenges and successes that 

have occurred during recent work in sub-national forest degradation monitoring. The 

2012 workshop, held in Bangkok, Thailand, followed recent US Forest Service/LEAF 

(USAID’s Lowering Emissions in Asia’s Forests) forest degradation monitoring options 

assessments that took place in Cambodia, Lao PDR and Viet Nam. Forest degradation 

can play a significant role in decreasing forest carbon, and therefore should be included 

in forest carbon monitoring for purposes of greenhouse gas inventories and participation 

in prospective carbon markets. Yet despite this, accurate monitoring methodologies are 

not widely available, and pertinent definitions and drivers are not clearly defined. The 

workshop allowed for a comprehensive update of techniques being used in case studies 

worldwide, in addition to the implications of forest degradation definitions and thresholds 

that lack consensus. Topics discussed included drivers at varying levels, remote sensing 

techniques and approaches, ground based field measurements, uncertainties and design 

considerations, integration of monitoring techniques, and regional themes and next steps.

Key words: forest degradation monitoring, Southeast Asia, climate change, carbon. 
iv

CIFOR Center for International Forestry Research

DBH diameter at breast height

FAO Food and Agriculture Organization

FIPD Forest Inventory and Planning Division

FRA 2015 Global Forest Resources Assessment 2015

GHG Greenhouse Gas

GIS Geographic Information Systems

GLAS Geoscience Laser Altimeter System

GOFC GOLD Global Observation of Forest and Land Cover Dynamics

GPS Global Positioning System

IPCC Intergovernmental Panel on Climate Change

I-REDD+ (the European Union’s) Impacts of REDD+ project

IUCN International Union for Conservation of Nature

LiDAR Light Detection And Ranging

LEAF Lowering Emissions in Asia's Forests

MODIS Moderate Resolution Imaging Spectroradiometer

REDD+ Reducing Emissions from Deforestation and forest Degradation

RL Reference Level

SNV Netherlands Development Organization

SPOT 5 Systeme Pout l’Observation de la Terre Satellite

UNFCCC United Nations Framework Convention on Climate Change

USAID/RDMA United States Agency for International Development / Regional DevelopmentMission for Asia

USFS United States Forest Service

USFS-IP United States Forest Service-International Programs
v

Contents

1 Introduction

James Halperin and David Ganz

Institutional Context, Drivers and Detectability 

in REDD+ Implementation

5 Section Summary 

Patricia Manley

10 Definitions in the Context of the United Nations Framework

Convention on Climate Change

Sandra Brown

11 Degradation Activities, Drivers, and Emissions: US Forest Service LEAF

Country Assessments

Patricia Manley, Leif Mortenson, James Halperin, and Rick Turner

Remote Sensing

13 Section Summary 

Belinda Arunarwati Margono

16 Detecting and Monitoring Deforestation and Forest Degradation: Issues

and Obstacles for Southeast Asia

Douglas Muchoney and Sharon Hamann

17 Measuring Global Canopy Reduction: A Forest Degradation Proxy

for FRA2015

Kenneth MacDicken and Erik Lindquist

18 Remote Sensing Application Challenges in the Mekong Region

Jeffrey Himel

19 Assessing Forest Degradation in Guyana with GeoEye, Quickbird

and Landsat

Bobby Braswell, Steve Hagen, William Salas, Michael Palace, Sandra Brown, 

Filipe Casarim, and Nancy Harris

20 Mapping Deforestation and Forest Degradation Using Landsat Time

Series: a Case of Sumatra—Indonesia

Belinda Arunarwati Margono

Ground-Based Field Measurements

22 Section Summary 

Nophea Sasaki

25 Carbon and Biodiversity Loss Due to Forest Degradation—

a Cambodian Case Study

Nophea Sasaki, Kimsun Chheng, and Nobuya Mizoue
vi

26 Local Involvement in Measuring and Governing Carbon Stocks in 

China, Vietnam, Indonesia, and Laos

Michael Køie Poulsen

27 Methods for Monitoring Emissions and Removals from Forest Harvesting 

for Timber and Fuelwood: Lessons from Guyana 

Sandra Brown

Uncertainty and Design Considerations

29 Section Summary 

Stephen Hagen

32 Uncertainty Issues in Forest Monitoring: All You Wanted to Know 

About Uncertainties and Never Dared to Ask 

Michael Köhl, Charles Scott, and Daniel Plugge

33 Application of a Monte Carlo Framework with Bootstrapping for 

	 Quantification of Uncertainty in Baseline Map of Carbon Emissions

from Deforestation in Tropical Regions

William Salas and Steve Hagen

Integration of Monitoring Techniques

35 Section Summary:

Yoshiyuki Kiyono and Rick Turner

38 Practicalities of Methodologies in Monitoring Forest Degradation in 

the Tropics

Yoshiyuki Kiyono 

39 Forest Degradation Sub-National Assessments: Monitoring Options for

Cambodia, Lao PDR, and Vietnam

Rick Turner, James Halperin, Patricia Manley, and Leif Mortenson

Detection of Forest Degradation Drivers

41 Summary of Small Group Discussions 

Patricia Manley

Monitoring Objectives and Thresholds

46 Summary of Small Group Discussions 

Patricia Manley

Regional Themes and Next Steps

50 Summary of Small Group Discussions 

Rick Turner

53 Conclusions

James Halperin and David Ganz

56 Literature Cited
1 

Introduction 

James Halperin1 and David Ganz2 

Globally, approximately two-thirds of the world’s forests are considered degraded, but 

practical, cost-effective tools for monitoring forest quality remain elusive. Techniques 

for monitoring deforestation and changes to forest carbon stocks are widespread and well 

published. However, techniques for monitoring forest degradation are relatively untested 

in developing countries despite their inclusion in the United Nations Framework 

Convention on Climate Change (UNFCCC) negotiations on Reducing Emissions from 

Deforestation and Forest Degradation (REDD+). The lack of a forest degradation 

definition, challenges in forest monitoring methodologies, access to emerging 

technologies and knowledge, and the development of appropriate sampling frameworks 

all further complicate forest degradation monitoring.

The United States Agency for International Development/Regional Development 

Mission for Asia (USAID/RDMA)-funded Lowering Emissions in Asia’s Forests (LEAF) 

program is designed to assist partner countries in Southeast Asia reduce negative impacts 

of unsustainable forest use on Greenhouse Gas (GHG) emissions. Two of LEAF’s main 

objectives include building technical capacity for monitoring changes in forest carbon 

stocks and demonstrating innovation in sustainable land management. By collaborating 

directly with key departments and agencies in partner countries, LEAF aims to help its 

institutional partners reach their goals of reducing GHG emissions through improving 

both forest management and forest monitoring systems needed to reliably and 

transparently account for GHG emissions reductions in the forest sector. 

LEAF is partnering with the United States Forest Service-International Programs (USFSIP) to provide critical technical assistance in identifying and developing forest monitoring 

methodologies that can estimate GHG emissions, with a special emphasis on forest 

degradation. The key objectives of this partnership include: 

• Assessing forest degradation drivers and monitoring options at the sub-national 

level in Lao PDR, Vietnam, and Cambodia; 

• Convening a forest monitoring experts workshop to discuss lessons learned from 

the sub-national assessments and operational aspects of various forest degradation 

monitoring approaches, highlighting potentially successful approaches given 

existing drivers; and

• Communicating results of these activities to develop forest degradation 

monitoring demonstration programs and strengthen capacity in partner countries. 

___________________________ 

1

Center for International Forestry Research, Lusaka, Zambia 

2

LEAF Asia, Liberty Square, Suite 2002, 287 Silom Road, Bangrak, Bangkok, 10500 

Thailand

Corresponding author: j.halperin@cgiar.org 
2 

The goal of this proceedings document is to bring together the ideas and lessons 

learned from the forest monitoring experts’ workshop. By exploring remote sensing tools, 

modeling, and field-based monitoring approaches, the workshop findings promote 

integrating feasible monitoring methods into national and sub-national forest monitoring 

systems, to detect how much degradation is taking place. Explicitly addressing 

degradation can reduce associated carbon emissions and protect largely intact forests 

before they suffer from degradation. 

The experts who attended the forest monitoring experts’ workshop represented a wide 

range of specialties and experience from national, regional, and global perspectives. 

Specific objectives of the workshop included both understanding implications of 

definitions in context of operationalizing forest monitoring degradation; and assessing

case studies, and current and emerging best practices to detect and monitor forest 

degradation. Key questions from the workshop highlighted linkages between forest 

degradation drivers, monitoring methods and the challenges and opportunities associated

with relevant monitoring methodologies. 

The two-day workshop was formatted in order to take advantage of the range and 

depth of experience of the workshop participants. Morning presentations were followed 

by focus group discussions to thoroughly address questions such as: 

• Which drivers and degradation sources can be detected; 

• Which methods are most effective at detection and, 

• What challenges and opportunities are involved in detecting degradation? 

This proceedings report presents abstracts from presentations, followed by an indepth analysis of conclusions from topic areas in remote sensing, ground based field 

measurements, uncertainty analysis, and integration of data sources. Key findings from 

the USFS sub-national assessments at LEAF intervention areas form a central component 

to link forest degradation drivers and monitoring options. This work thus builds upon 

country-level degradation assessments in Vietnam, Laos, and Cambodia, and is part of 

LEAF’s commitment to building institutional technical capacity across the region for 

monitoring changes in forest carbon stocks and demonstrating innovations in sustainable 

land management. The focus group discussion summaries integrate combined knowledge 

from the participant experts with the lessons learned from the LEAF/USFS assessments 

in order to provide next steps for advancing forest degradation monitoring demonstration 

activities. 

Forest degradation is a challenging concept to operationalize within forest monitoring 

programs. This holds true even in countries with long-standing forest monitoring 

experience. Spatial extent, occurrence, frequency, and intensity are all variables of forest 

degradation drivers that must be taken into account when developing forest degradation 

monitoring initiatives. LEAF and the US Forest Service hope that this document provides 

a solid base in addressing these issues to bring forest degradation monitoring into the 

mainstream.
3 

Participants in the International Workshop on Monitoring Forest 

Degradation in Southeast Asia held November 13-14, 2012 in 

Bangkok, Thailand. 

(Photo: Nicole Kravec) 
4 

Institutional Context, Driversand 

Detectability in REDD+ Implementation

Village protection forest of Namat Village, Viengxay District, Lao 

PDR.

(Photo: James Halperin)
5 

Section Summary

Patricia Manley1

REDD+ and Forest Degradation

REDD+ implementation requires two key steps: (1) establish a reference emission level 

or reference level (RL); and (2) monitor performance against the reference level (RL). A 

monitoring system must be able to monitor the performance of implementing action plans 

to reduce emissions. Degradation activities are of concern because it is unknown to what 

degree they are contributing to emissions, with potentially and in some cases likely 

significant contributions to emissions. For REDD+ the key feature is the need to estimate 

the net emissions from anthropogenic-caused (i.e., human-caused) changes in forests 

remaining forests. 

Forest degradation is brought about by many factors, or drivers, resulting in different 

carbon impacts. International experts tend to agree that simple classifications are most 

useful, with two categories of drivers - direct (or proximate) and indirect (or underlying) 

– being commonly used (Kissinger et al. 2012). Direct forest degradation drivers are 

activities that cause long-term (persisting from some undefined number of years) loss in 

forest biomass, and directly contribute to CO2 emissions. The duration of the impact in a 

given location can be a function of one or more activities, and a single event (pulse 

disturbance) or repeated use (chronic disturbance) over one or more decades. 

Indirect drivers are the underlying causes that result in the occurrence of direct 

drivers. Currently there are no international agreements through IPCC or the UNFCCC 

on how to classify forest degradation drivers. However, relevant to a REDD+ 

mechanism, there are several ways to classify the activities that contribute to degradation 

of forests. International experts tend to agree that simple classifications are most easily 

understood. In this context, we adopted the direct and indirect drivers system 

recommended by Kissinger et al. 2012. Direct forest degradation drivers are human 

activities that cause a long-term loss in forest carbon stocks in forests that remain forests. 

Characteristics of Degradation Activities

Degradation activities are dynamic across landscapes and over time. Individual activities 

are a function of the intersection of opportunity and need, and both of these factors are 

constantly changing in response to environmental and social pressures. Characteristics of 

individual activities include: occurrence (rare or common), spatial extent (limited or 

extensive), intensity (low or high impact per occurrence), temporal frequency (singular, 

recurring, chronic), and temporal persistence (short or extended recovery per occurrence). 

___________________________ 

1

US Forest Service, Pacific Southwest Research Station, 2480 Carson Road, Placerville, 

CA 95667 

Corresponding author: pmanley@fs.fed.us 
6 

Further, multiple degradation activities are often occurring in the same forested areas. A 

composite description of the spatial and temporal characteristics of individual 

degradation activities for a given landscape can provide a model for degradation across a 

given landscape. The model can then be studied to determine measurement approaches 

that are most effective and efficient. Similarly, available and proposed survey and 

monitoring designs can be evaluated relative to which degradation activities are expected 

to be detected and how reliably their impact may be captured. The characteristics of 

degradation activities across a given landscape (at any scale) can and will change in 

response to a variety of influences, including land use, resource use policies, and market 

forces. Characterizations and subsequent measurement and monitoring approaches need 

to be robust to changes in degradation activities over time. 

Prevalent Direct Drivers

Current inventories of GHG emissions do not effectively account for degradation because 

associated emissions are difficult to detect. Manley et al. (this volume) conducted an 

assessment of degradation activities and monitoring options at the sub-national scale 

within three countries in Southeast Asia: Viet Nam, Lao PDR, and Cambodia. They 

found that six primary degradation activities were present in all districts, and common in 

one or more of them: planned selective tree harvest, unplanned selective tree harvest, 

commercial fuelwood collection, customary fuelwood collection, shifting cultivation, and 

wildfire. Tree harvest was widespread in all three assessments, whereas the other 

activities varied in their extent among the districts. 

There are many challenges in defining, identifying, detecting, and quantifying losses 

or changes in carbon resulting from degradation. Some experts would argue that 

degradation is an inconsequential loss of carbon compared to deforestation and that it is 

too expensive and difficult to quantify. Others would argue that only those activities that 

result in substantial reductions in carbon density are worth pursuing, and they should be 

addressed individually. Yet others would argue that it is the degree of degradation in a 

given area that is of interest, not individual activities. Most would agree, however, that 

low levels of degradation – meaning minor losses in carbon density – are not the primary 

concern or target of degradation monitoring efforts. Workshop participants generally 

agreed that degradation was a significant source of carbon loss in many landscapes in 

Southeast Asia and neighboring countries, and that losses were likely to be substantial 

and needed to be quantified. 

Degradation Detection and Estimation

A fundamental objective in any sampling effort is the ability to reliably detect the subject 

of the sampling effort. It may seem like an overly simplistic objective, but in reality 

many biological and physical features in natural environments are difficult to detect. 

Detectability is the ability for an observer to directly or indirectly determine the presence 

of the feature when it is present. Spatial distribution, extent, and intensity all affect the 

ability of a given sampling effort to detect carbon loss. For example, rare, small, or 

cryptic species may be difficult for observers to detect within a sample plot in the forest, 

even when they are present. In the case of degradation, if the degradation is low 
7 

intensity (e.g., few small trees per hectare or small areas that are dispersed over a large 

landscape) it may be difficult to detect either by remote sensing (minimal reductions in 

canopy cover) or dispersed small (0.1 ha) sample plots. Landscape-wide data, such as 

remote sensing, provides a 100% sample, so detectability is a function of resolution of the 

imagery - high resolution will have greater detectability but is more expensive. Fieldsampling methods that use larger plots and/or a high density of plots have a greater 

likelihood of detecting rare occurrences across a landscape, but variability in occurrence 

of degraded conditions greatly increases sample size requirements, thereby increasing the 

cost of field sampling. 

Detection of degradation activities is also a function of temporal characteristics. If 

detectability is based on changes in canopy, it is likely to be highly ephemeral, or have a 

short half-life in that the effects quickly become difficult to detect while the impact is 

much longer lasting. For example, when a substantial proportion of the forest canopy is 

reduced through tree removal, gaps in the canopy are likely to recover quickly, but 

biomass will take much longer to recover. Multiple activities that occur on a periodic or 

chronic basis in the overlapping locations and varying over time present the most 

challenging scenario for detection and characterization of degradation, particularly 

detection of individual drivers. 

Estimating Carbon Loss and Emissions 

Fundamentally, there are two approaches to quantifying carbon losses and associated 

emissions: direct measure and estimation through statistical modeling. The stock-change 

method of accounting (compares two measurements of standing biomass taken at two or 

more points in time) relies more heavily on direct measure (although statistical modeling 

is still used to derive final estimates of emissions). The gain-loss method of accounting 

(comparison of gains derived through growth models and losses derived through 

estimation) requires information on the extent of degradation activities in order to model 

associated losses. In reality, reliable estimates can be used to enhance efficiency and 

accuracy in the use of either accounting method. 

Planned activities, namely commercial timber harvest operations (particularly in areas 

that are not highly vulnerable to post-harvest unplanned tree removal), represent the most 

consistent and predictable degradation activity. As such, the ability to model and then 

estimate biomass reductions is greatest for planned timber harvest activities. For a given 

landscape (e.g., district or province), planned timber harvest may be consistent enough to 

develop emission factor estimates of reductions in biomass associated with standard 

silvicultural prescriptions implemented in certain forest types. This library of biomass 

losses can be generated through developing and refining estimates using remote sensing 

and field measurements for certain activities in certain forest types. With such a library 

of statistically modeled estimates, one would only need to know the forest type, the 

treatment prescription, and have medium resolution remote sensing data to estimate 

biomass reductions. This could be a very efficient means of estimating changes in carbon 

density and associated emissions; however, it is a reliable approach primarily where there 

is a high level of consistency in management activities.
8 

The greatest extent of degradation activities arguably is unplanned degradation 

activities. These activities will have the greatest extent (occurring everywhere there is 

access) and they are likely to be highly variable over time and space in response to 

changing direct and indirect drivers, such as changes in local policies, subsidies, food 

availability and prices, and environmental quality. Thus, estimating changes in biomass 

and associated emissions is more challenging for this class of activities. It is possible for 

statistical estimation techniques to be used to either derive estimates or improve groundbased estimates of carbon loss. The risk of degradation may be more tractable to estimate 

than degradation intensity, with risk largely being a function of proximity, access, 

population density, and prosperity. 

Monitoring Challenges and Opportunities 

Clearly, no one monitoring approach will be most efficient and effective in all 

circumstances. Similarly, not all degradation activities can be, nor do they need to be, 

accounted for in a monitoring system. It seems most important to identify and achieve a 

given level of accuracy and precision in accounting for carbon loss and emissions, and to 

use whatever tools are available and effective in achieving that objective. In some cases, 

the greatest losses will be most reliably detected and characterized primarily by using 

direct field measurement. In other cases, high-resolution remote sensing data might be

available and changes in biomass are readily estimates using these data. In most cases, 

however, it will be a combination of multiple sources of data that will provide the most 

reliable estimates over time. 

The challenges include the following: 

• Degradation activities and their characteristics can be highly variable across 

landscapes – particularly unplanned or customary activities associated with 

domestic uses of wood - and can change rapidly in their intensity in response to 

indirect drivers. 

• The ecological consequences of forest carbon losses through degradation 

activities remains unexplored, but they are assumed to be a significant impact on 

forest quality. 

• Given the complexity of human behavior, combined with variability in 

environmental factors acting on forested ecosystems, the challenge of monitoring 

changes in forest carbon becomes a socio-economic monitoring and modeling 

exercise, as much as it is an ecological one. 

• Other than planned timber harvest activities, variability in degradation location 

and intensity makes it difficult to develop a standard set of emission factors 

associated with degradation even in a given location. 

• Lower intensity degradation can be difficult to detect with remote sensing and 

difficult to quantify reliably using field data. 

• Primary limitations of various data sources – moderate resolution remote sensing 

has low detectability, high resolution remote sensing comes at a higher cost and 

requires higher internal capacity, field-based data are only as reliable as trained 

field personnel and sample sizes afford - are likely to persist for 5-10 years into 
9 

the future, so we need to develop best practices and expectations based on 

available methods. 

• Debate exists as to under what circumstances degradation is to be included in 

reference levels for REDD+.

o Needs to constitute a significant contribution to emissions, but how one 

defines significant is open. 

o A degradation activity needs to be quantified to some degree before it can 

be determined to be significant. 

The opportunities that lie ahead are the following: 

• Broadly applicable tools can be developed that users can implement to estimate 

emissions associated with more standard, predictable activities, such as planned 

timber harvest. 

• Decision support tools are also being developed that can be used to derive 

reference levels for REDD+, and decision support tools in general are becoming 

more readily available and user friendly

• High resolution remote sensing continues to become increasingly available and 

affordable, but fast-paced changes in technology impact its repeatability, which 

may make it most useful for training and validation as opposed to as a primary 

data source for carbon status or change. 

• Small scale demonstration projects provide the opportunity to: 

1) Identify and validate the type and location of degradation activities that 

need to be quantified; 

2) Determine what detection and description methods best match the spatial 

and temporal characteristics of the primary degradation sources; 

3) Develop and refine modeling approaches that can maximize the efficiency 

of the monitoring system; and

4) Demonstrate how to link these data in a decision support framework to 

develop robust reference levels and emission estimates. 
10

Definitions in the Context of the United Nations 

Framework Convention on Climate Change

Sandra Brown1 

Abstract

In relation to REDD+ implementation, two key steps are needed: (1) establishing a 

reference emission level or reference level (RL) and (2) monitoring performance against 

the RL. The RL is based on historic emissions and this can serve as a key starting point 

for designing any system for future monitoring. A monitoring system must be able to 

monitor the performance of implementing action plans to reduce emissions. Thus there is 

a need to know what actions are causing emissions. So designing a system for 

establishing the RL is the first step---need to know where, by which drivers, over what 

time frame, and the magnitude of the emissions for each driver. With regard to 

degradation, the IPCC provides a framework for accounting for such emissions--this is 

the change in carbon stocks of forests remaining forests. A common IPCC definition of 

forest degradation is: “A direct, human-induced, long-term loss (persisting for X years or 

more) of at least Y% of forest carbon stocks since time T and not qualifying as 

deforestation”. Attempts are made to define the magnitude of X, Y and T, but I argue 

that this is not needed in detail—suffice it to say that for REDD+ the key feature is the 

need for estimating the net emissions from anthropogenic-caused (i.e. direct human 

caused) changes in forests remaining forests. Many drivers causing different carbon 

impacts bring about forest degradation, thus no one monitoring system will fit all causes. 

And the emissions for all causes of degradation may not need to be included in a 

monitoring system—it will depend on their magnitude relative to other emission sources. 

An overview of a decision support tool was presented that provides guidance on how to 

decide which degrading activities to include.

___________________________ 

1

Winrock International, 621 N Kent St., Suite 1200, Arlington, VA 22209 

Corresponding author: SBrown@winrock.org 
11

Degradation Activities, Drivers, and Emissions: US 

Forest Service LEAF Country Assessments

Patricia Manley1, Leif Mortenson2, James Halperin3, and Rick Turner4

Abstract

Degradation is emerging as a common outcome of forest activities, and associated 

greenhouse gas (GHG) emissions have the potential to be significant. Understanding the 

activities and drivers of degradation is central to the ability to effectively measure, 

monitor, and mitigate associated emissions. Current inventories of GHG emissions do 

not effectively account for degradation because emissions are difficult to detect. We 

conducted an assessment of degradation activities and monitoring options at the subnational scale within three countries in Southeast Asia: Viet Nam, Cambodia and Lao 

PDR. Visual surveys were conducted in the first half of 2012 across districts (Con 

Cuong, Aural, and Viengxay and Xamtai, respectively), which were ~200,000 ha in size. 

We found six primary degradation activities that were present in all districts, and 

common in one or more of them: planned selective tree harvest, unplanned selective tree 

harvest, commercial fuelwood collection, customary fuelwood collection, shifting 

cultivation, and wildfire. Timber harvest was widespread in all three assessments, 

whereas the other activities varied in their extent among the districts. Shifting cultivation 

was quite commonly occurring in Lao PDR, wildfire was a concern in all districts, but 

was prevalent in Cambodia. Fuelwood collection occurred in all districts, but was 

widespread and intensive in some locations in Viet Nam. The character of degradation 

activities affects their ability to be adequately measured and monitored with available 

methods. Characteristics affecting the ability of measurement methods to detect and 

adequately describe (i.e., accuracy and precision) include: occurrence (rare or common), 

spatial extent (limited or extensive), intensity (low or high impact), and temporal 

persistence (shifting, recurring, or chronic). A composite description of the spatial and 

temporal characteristics of individual degradation activities for a given landscape 

provides a blueprint for designing measurement approaches that are effective and 

efficient. Similarly, proposed survey and monitoring designs can be evaluated relative to 

which degradation activities are expected to be detected and how well their impact will 

be captured. The characteristics of degradation activities across a given landscape (at any 

scale) can and will change in response to a variety of influences, including land use, 

resource use policies, and market forces. Characterizations and subsequent measurement

and monitoring approaches need to be robust to changes in degradation activities over 

time. 

___________________________ 

1

US Forest Service, Pacific Southwest Research Station, 2480 Carson Road, Placerville, 

CA 95667 

2

US Forest Service, Pacific Northwest Research Station, 620 SW Main St. Suite 

400, Portland, OR 97205 

3

Center for International Forestry Research, Lusaka, Zambia 4

US Forest Service, Tongass National Forest, 8510 Mendenhall Loop Road, Juneau AK, 

99801 

Corresponding author: pmanley@fs.fed.us 
12

Remote Sensing

2010 Land cover map of Con Cuong District, Nghe An Province, 

north-central Viet Nam. 

(Map produced by LEAF/SNV-Viet Nam, 2012)
13

Section Summary

Belinda Arunarwati Margono1, 2

Remote sensing is an important data source for monitoring the change of forest cover, in 

terms of both total removal of forest cover (deforestation), and change of canopy cover, 

structure and forest ecosystem services that result in forest degradation. In the context of 

Intergovernmental Panel on Climate Change (IPCC), forest degradation monitoring 

requires information regarding the degree of forest disturbance that results in a reduction 

in carbon stock within a specific time interval. Currently, remote sensing data does not 

directly provide readily available information on how much carbon has been released 

from a disturbed forest. In this regard, the integration between remote sensing data and 

field/ground based measurement is key. While many types of remote sensing can detect 

total loss of forest cover (deforestation), it is more challenging but still feasible for 

remote sensing data to detect changes in the extent of remaining forests that have been 

disturbed. Advanced techniques for analyzing remote sensing data are able to roughly 

estimate the area of disturbance; however it should be equipped with sufficient field 

verification. Field data is needed to provide estimates about how much biomass or carbon 

has been removed per unit area of the disturbance. 

 An agreement on the main direct drivers of forest degradation is of vital need for use 

of remote sensing data. Consensus on the main direct drivers will lead to a general 

understanding of the scale and intensity of disturbances that can be detected by the 

selected remote sensing data. In general, forest degradation is the effect of forest logging 

activities, either selective planned logging or illegal logging. Currently, remote sensing 

can detect forest degradation through indicators such as canopy disturbance and 

infrastructure development (forest roads, logging roads logging decks, etc.). However, 

the ability of remote sensing data to detect the above indicators depends on the spatial 

and spectral resolution. Higher resolution data (e.g., LiDAR) provides more detailed 

information but also has higher costs for data acquisition, data processing, and data 

management. Lower resolution data (e.g., Landsat) provides a less sensitive measure of 

forest change, but is more readily obtained and repeated. The availability and 

repeatability of remote sensing data has implications in the tropics for obtaining cloudfree images and detecting, degradation with rapid post-disturbance canopy recovery. 

These strengths and weaknesses should be taken into consideration to select appropriate 

remote sensing data for the application. 

Setting the objectives of monitoring is a key for setting an efficient system; and a 

general understanding of forest degradation drivers will assist in determining these 

objectives. Once monitoring objectives are determined, it is possible to assess monitoring 

options based on desired precision and financial considerations. In terms of financial 

considerations, very high spatial and spectral remote sensing data combined with 

___________________________ 

1

Department of Geographical Sciences, University of Maryland, College Park, MD 

20742 

2

Ministry of Forestry of Indonesia, Jakarta 10270, Indonesia 

Corresponding author: bmargono@umd.edu 
14

advanced processing techniques should only be introduced for specific areas of interest 

and may not be favorable for wall to wall mapping at a national scale. Further, the 

availability of required hardware, software and capacity human resources are also 

important considerations. 

Main challenges

General challenges of using remote sensing data for monitoring forest degradation in the 

tropics: 

• Persistent clouds and haze in tropical areas lead to difficulties in acquiring cloud-free 

remote sensing data needed to fulfill monitoring objectives.

• Data continuity is a big challenge in establishing a credible forest resource monitoring 

system. A sufficient monitoring system must be supported by the use of remote 

sensing data that ensure data continuity and availability. 

• Data analysis repeatability is another consideration. The techniques for analyzing the 

data must be implementable, replicable and must allow for the use of alternative data 

instead of selected remote sensing data when necessary.

• Steep terrain and forest seasonality in certain areas require specific methodologies 

(e.g., more image processing procedures and more field verification).

• Determining at what point high-resolution remote sensing data sets and advanced 

techniques need to be applied, and how to link those results with wall-to-wall 

interpreted maps derived from lower resolution data sources. 

• A comprehensive package of human resource technical requirements, hardware and 

software is very important for developing a credible monitoring system, thus 

continued capacity building/strengthening are required to get optimal results.

More specific challenges:

• Clearly defining the causes of forest degradation in the area of interest is paramount. 

Lack of understanding about the local causes of forest degradation can result in 

activities that may not contribute to the monitoring objective.

• Dynamic change of forest cover as a result of fast crown recovery after disturbance in 

the tropics is another key issue. Appropriate spatio-temporal data sets need to be used 

to capture dynamic forest cover changes.

• Complex forest structure and composition in tropical forests are sometimes difficult 

to analyze with remote sensing data; for example vegetation indices, such as Leaf 

Area Index, will saturate after a value of 4-6 m2

/m2 in tropical forests.

• Mismatched time of data acquisition and field data collection is a classic problem, 

especially in developing countries where budgets for field surveys are often 

inadequate. It is important to complete and process ground based field measurements 

that temporally coincide with remotely sensed data acquisition. Mismatches in time 

between the field measurements and remote sensing data sets lead to errors in 

analysis. 

• Ability to detect single/individual tree removal by selective logging is another key 

issue, and likely requires high spatial resolution data. For this, there is a need to 

obtain imagery that is spatially and spectrally able to detect individual tree crowns, 

and temporally close in time to the logging activity.
15

Promising opportunities/approaches 

• Roughly define the main driver(s) of forest degradation in the area of interest and 

select appropriate indicators that can be used for deriving information from remote 

sensing data (e.g., change in canopy cover).

• In general, select basic/simple classes for forest extent (e.g. dividing area of ‘forest 

remaining forest’) into two main classes, such as primary undisturbed forest and 

degraded forest. Segregating different degrees of degradation can be accomplished 

when appropriate high-resolution spatial data and sufficient field validation are 

available.

• Develop and apply a low cost policy to provide high spatial resolution data set, 

including simple procedures for data pre-processing. 

• Develop and apply methods using high spatial resolution data and advanced 

techniques (e.g. the method applied for Guyana) for analyzing a specific area of 

interest. This is necessary to test the method (or other possible methods) in different 

areas, especially areas with steep terrain and deciduous forest types.

• Apply timely field/ground data measurement in order to support forest inventory 

programs and validate remote sensing results. The field measurements should focus 

on providing information on forest disturbance and potential carbon released. 

• Hybrid approach of using a per-pixel classification method and a GIS-based 

fragmentation method to map degraded forest (e.g. the method applied for SumateraIndonesia) can be applied to developing national wall-to-wall coverages.
16

Detecting and Monitoring Deforestation and Forest 

Degradation: Issues and Obstacles for Southeast Asia

Douglas Muchoney1 and Sharon Hamann1 

Abstract

Forest degradation can be defined as the loss of forest volume, biomass and/or forest 

productivity caused by natural or human influences. Achieving Reduced Emissions from 

Deforestation and Forest Degradation (REDD+) requires that deforestation and 

degradation can be efficiently, reliably, and cost-effectively detected and quantified, 

often where ground and aerial surveying is problematic. Therefore, remote sensing 

approaches, coupled with field and/or aerial data, offer possible solutions and 

efficiencies. While synoptic coverage using high-resolution data such as Landsat can 

provide estimates of deforestation and degradation, very high-resolution satellite data can 

be used to calibrate and validate these estimates in a multi-stage sampling approach. This 

research coupled Landsat and very high-resolution satellite optical data to determine 

whether Landsat could quantify deforestation and degradation in montane and lowland 

forests of Cambodia. Initial results are that Landsat can detect deforestation, and also the 

“halo” effect of degradation from high-grading in areas adjacent to deforested areas. 

Issues include high-resolution data that have different spatial, temporal and radiometric 

characteristics, and were not necessarily acquired at the same time as the Landsat data. 

The ability to detect deforestation and degradation is also a function of when an event is 

imaged the level and type of disturbance/degradation, and the type and age of the forest. 

___________________________ 

1

U.S. Geological Survey, Climate and Land-Use Change, 519 National Center, Reston, 

VA 20192 USA 

Corresponding author: dmuchoney@usgs.gov 
17

Measuring Global Canopy Reduction: A Forest 

Degradation Proxy for FRA2015

Kenneth MacDicken1 and Erik Lindquist1 

Abstract

Global interest in forest degradation is widespread – but is fraught with widely differing 

views. Forest degradation by one definition may be sustainable forest management by 

another. The Global Forest Resources Assessment, conducted by FAO every five years, 

is working to find an approach to a global estimation of forest area that can address these 

differing views. This approach is based on the use of MODIS VCS satellite data to 

evaluate significant forest canopy reduction over a ten year time span. Significance in 

this case will be calculated as a threshold (most likely 20%) reduction in canopy area at 

the pixel scale. The reduction in forest canopy area may or may not be seen as 

degradation – depending on the values of the data user. 

This analysis will be completed during 2013 for use in FRA 2015 and will provide 

summaries of reduced forest area globally, regionally and by ecological zone. Country 

representatives will be provided with data from the initial analysis done by FAO so that 

they can review and revise as needed. A tier system will be used as part of FRA 2015 to 

describe the overall level of each variable – including the reduced forest canopy 

area. While not a report of forest degradation directly, it is intended to be a proxy that 

can provide at least some indication of the area of forest in which human activities have 

resulted in reduced canopy density forest. 

___________________________ 

1

FAO Forest Resources Assessment, Vialedelle Terme di Caracalla 15, 00100 Rome, 

Italy

Corresponding author: Kenneth.MacDicken@fao.org 
18

Remote Sensing Application Challenges

in the Mekong Region

Jeffrey Himel1

Abstract

Forest degradation is not just one of the cornerstones of “REDD+”, it is a critical element 

for Lao PDR and other countries where the primary driver of forest carbon loss is 

selective logging and small-scale conversion of forest for agriculture rather than 

deforestation. Unless we can reliably and accurately quantify the area of degradation 

using remote sensing technology, REDD+ will not be viable. This is complicated by the 

need to separate degradation from other changes within forests that have effects similar to 

degradation, such as seasonal change in deciduous trees (e.g., dropping leaves). 

Lao PDR has developed a good quality data and experience base through the efforts of 

the Forest Inventory and Planning Division (FIPD) with the support of a range of 

coordinated donors. FIPD has acquired and processed wall-to-wall satellite imagery 

covering Lao PDR for 1995 and 2000 using LandSAT, 2005 using SPOT 5 and 2010 

using RapidEye. FIPD analysts are finalizing the Benchmark and Historical Forest Cover 

Maps for these time periods. Lessons learned through this work highlight the importance 

of first principles: image pre-processing, image analysis, and accuracy assessment. 

Investment in careful and quality pre-processing of 5to 10m-resolution satellite imagery 

like RapidEye provides the best “bang for the buck”. This pre-processing includes orthorectification to 1:25,000 scale, and proper haze removal. Object-based image processing 

provides an excellent starting point for analysis, from which we can work backwards and 

train staff to become more consistent and progressively improve the accuracy of their

work. Full annual or semi-annual country coverage at 5m is now feasible technically and 

cost-wise and so should be a top priority for donors. Wall-to-wall RapidEye coverage of 

Lao PDR for 2010 cost a total of US$225,000. 

___________________________ 

1

Aruna Technology Ltd, 417 Sisowath Blvd., Sangkat Chaktomuk, Phnom Penh, 

Cambodia 

Corresponding author: jeffrey.himel@arunatechnology.com 
19

Assessing Forest Degradation in Guyana with GeoEye, 

Quickbird and Landsat

Bobby Braswell1, Steve Hagen1, William Salas1, Michael Palace2, Sandra 

Brown3, Felipe Casarim3, and Nancy Harris3 

Abstract

Forest degradation is defined as a change in forest quality and condition (e.g. reduction in 

biomass), while deforestation is a change in forest area. This pilot study evaluated several 

image processing approaches to map degradation and estimate carbon removals from 

logging. From the Joint Concept Note on REDD+ cooperation between Guyana and 

Norway carbon loss as indirect effect of new infrastructure is addressed as follows: “The 

establishment of new infrastructure in forest areas often contributes to forest carbon loss 

outside the areas directly affected by construction. Unless a larger or smaller area or 

greenhouse gas emission impact can be documented through remote sensing or field 

observations, the area within a distance extending 500 meters from the new infrastructure 

(incl. mining sites, roads, pipelines, reservoirs) shall be accounted with a 50% annual 

carbon loss through forest degradation.” Our premise is that if we can detect logging at 

various levels of intensity equivalent to less than 50% removal, then we can apply this 

approach to examine degradation surrounding new infrastructure. For this preliminary 

degradation analysis, we examined two Landsat 5 TM images and two high-resolution 

scenes from GeoEye/Quickbird from central Guyana. Field data on logging concession 

(maximum allowable cut) and logging impacts of forest gaps were used to test a suite of 

image processing approaches. Our goal was to examine areas known to be recently 

logged (i.e. degraded) and quantify the signal in the remote sensing data from this 

activity. From this analysis, we present results comparing pre-logging imagery to 

imagery acquired approximately post-logging. 

___________________________ 

1

Applied Geosolutions, 87 Packers Falls Rd., Durham, NH 03924 

2

Institute for the Study of Earth, Oceans and Space, Complex System Research Center, 

Morse Hall, University of New Hampshire, 39 College Road, Durham, NH 03824 

3

Winrock International, 621 N Kent St., Suite 1200, Arlington, VA 22209 

Corresponding author: wsalas@appliedgeosolutions.com 
20

Mapping Deforestation and Forest Degradation Using 

Landsat Time Series: a Case of Sumatra—Indonesia 

Belinda Arunarwati Margono1, 2

Abstract

Indonesia experiences the second highest rate of deforestation among tropical countries 

(FAO 2005, 2010). Consequently, timely and accurate forest data are required to combat 

deforestation and forest degradation in support of climate change mitigation and 

biodiversity conservation policy initiatives. Remote sensing is considered as a significant 

data source for forest monitoring purposes, and has been widely used for monitoring 

deforestation and forest degradation. However, varying definitions have been an obstacle 

for optimizing the use of remote sensing for a reliable monitoring system, in addition to 

the problem of cloud cover in Indonesia. In this work, we demonstrate an applicable 

definition and methods that enable using remote sensing data sets for forest monitoring. 

We examined Sumatra as a case study, as Sumatra Island stands out due to intensive 

forest clearing that has resulted in the conversion of 70% of the island’s forested area 

through 2010. We present here a hybrid approach to quantifying the extent and change of 

primary forest in terms of primary intact and primary degraded classes using per-pixel 

supervised classification mapping followed by a GIS-based fragmentation analysis. Loss 

of Sumatra’s primary intact and primary degraded forests was estimated to provide 

suitable information for the objectives of the United Nations Framework on Climate 

Change (UNFCCC) and the Reducing Emissions from Deforestation and Forest 

Degradation (REDD and REDD+) program. Results quantified 7.54Mha of primary 

forest loss in Sumatra during the last two decades (1990-2010). An additional 2.31Mha of 

primary forest was degraded. Of the 7.54Mha cleared, 7.25Mha was in a degraded state 

when cleared, and 0.28Mha was in a primary state. The Geoscience Laser Altimeter 

System (GLAS) data set was employed to evaluate results. GLAS-derived tree canopy 

height indicated a significant structural difference between primary intact and primary 

degraded forests (mean height 28m + 8.7m and 19m + 8.2m, respectively). Results 

demonstrate a method for quantifying primary forest cover stand-replacement disturbance 

and degradation that can be replicated across the tropics in support of REDD+ initiatives. 

Furthermore, we can incorporate the results with the biomass assessment derived from 

GLAS data to illustrate the biomass by type in Sumatra.

___________________________ 

1

Department of Geographical Sciences, University of Maryland, College Park, MD 

20742 

2

Ministry of Forestry of Indonesia, Jakarta 10270, Indonesia 

Corresponding author: bmargono@umd.edu 
21

Ground-Based Field Measurements

Outlining the location of plots along L-shaped transects in Long Kem 

village cluster, Xamtai District, Houaphanh Province, Lao PDR.

(Photo: Leif Mortenson) 
22

Section Summary

Nophea Sasaki1

Although deforestation has been the main focus of international debate in REDD+, forest 

degradation could emit even more carbon emissions because forest degradation can take 

place in any accessible forest. Accounting for emission factors requires the use of stockchange or gain-loss approach depending on the forests in questions. Ground based field 

measurements are a critical basis for both approaches. Carbon stocks in logged forests 

could vary highly depending to some extent on logging intensity and collateral damages. 

Obtaining area estimates, or activity data, of degraded forest requires remote sensing 

but without clear definition of “forest degradation”, it may not be possible using 

traditional remote sensing. Change detection techniques, along with information on 

logging planning and operations, become important to derive the activity data. The 

challenge is that information on degraded forest caused by unplanned logging is difficult 

to obtain because such logging is not easy to detect at a landscape or regional scale. At 

local scales, for example in a REDD+ project site, unplanned logging could however be 

tracked.

Monitoring forest degradation, and related carbon emissions and biodiversity loss, 

requires understanding of how trees were selectively harvested. Experience in tropical 

forest management suggests that in many instances, forest degradation is caused by 

unsustainable felling of commercially valuable timber species for immediate profits. This 

was found to be true in northeastern Cambodia, which was documented in a case study by 

Sasaki et al. (this volume).

• Unplanned selective logging for timber is a major driver of forest degradation as 

commercially valuable timber species are likely to be harvested. For example, in 

Southeast Asia highly expensive timber from trees such as Dalbergia 

cochinchinensis can be sold by the kilogram. In this case, any individuals with a 

chainsaw can fell trees and transport their timber for sale, even without a road 

network. Unplanned logging makes it difficult to monitor the degradation because 

location of the felled trees is not known. 

• With planned selective logging, often all marketable trees (i.e., with diameter 

greater than 30 cm) are harvested. The harvested area is still forest by current 

definitions but between 40 and 50% of pre-harvest carbon stocks (Sasaki et al. 

this volume) could be lost. Resulting net carbon emissions will depend on the 

intensity of extraction methods, post harvest wood residue, post harvest growth 

rates, and amount of carbon in harvested wood products. 

• Monitoring forest degradation at landscape or regional scale with affordable costs 

needs tools and resources that can relate forest canopy loss to biomass loss from 

individual trees or, more likely, a group of tree species according to their plant 

functional types with remote sensing technology. There is a strong need for 

improved allometric relationships to relate forest canopy parameters to biomass.

_ 

__________________________ 

1 United Graduate School of Applied Informatics, Harborland Center Building 22F, 1-3-3 

Hiagashikawasaki-cho, Chuo-ku, Kobe, 650-0044, Japan 

Corresponding author: nopsasaki@gmail.com 
23

Financing incentives require political commitment from developing countries and all 

logging companies should have a minimum number of certified foresters to carry out 

logging planning and operations. With the aid of GPS and GIS technology, a transparent 

platform for tracking local activities could help monitor forest degradation. 

 Full accounting for carbon emissions from tropical forest degradation needs an 

understanding of two important variables, namely the activity data and emission factors 

relevant to the forests in question. As an example, Winrock International has proposed 

two methods for estimating net carbon emissions for forest harvesting practices in 

Guyana.

• Method 1: a standard approach of using medium resolution imagery to monitor 

the expansion of logging infrastructure into non-logged areas to obtain activity 

data combined with ground plots and the stock-change method for emission 

factors. Emission factors are the difference between C of unlogged and logged 

forests. However, the condition of logged forests is likely to be highly variable 

depending on forest type, logging intensity, location, growth rate, and the postharvesting human activities. These conditions will increase uncertainty of pre- and 

post-harvest carbon stock estimates.

• Method 2: a combination of data sources such as timber extraction rates, 

management plans, and very high-resolution imagery for activity data combined 

with ground measurements in active logging gaps/concessions and the gain-loss 

method for emission factors. Estimating logging gaps in active timber concessions 

could be done using change detection technique and the loss in live biomass 

caused all harvesting activities, such as felling, damage to the residual stand, and 

creation of skid trails, log landings, and roads are then linked to the unit of timber 

removed (e.g. cubic meters).

• The carbon stock emissions from skid trail creation can be obtained from ground 

measurements by mapping and measuring the area of a sample of skid rails using 

GPS and measuring the damaged trees along the trails—such data can be linked to 

timber extraction rates.

 Costs are another concern in forest carbon monitoring. The European Union’s project 

on the Impacts of Reducing Emissions from Deforestation and Forest Degradation and 

Enhancing Carbon Stocks (I-REDD+) work package 4 focuses on community based 

monitoring. Community based monitoring offers great potential for lowering costs while 

providing direct tree measurement. Direct tree measurements on the ground are necessary 

as part of the ground truthing activities to check the correctness of the remote sensing 

based activity data. Direct tree measurements are also paramount for developing biomass 

estimates in either stock-change or gain-loss methodologies. 

• I-REDD+’s study sought to evaluate the accuracy and cost effectiveness of 

monitoring performed by local communities versus professional foresters in four 

study sites in China, Vietnam, Indonesia and Laos. Community members were 

trained in establishing sample plots, measuring trees and using simple field 

protocols. 

• A total of 289 sample plots were established between 2011 and 2012 and both 

local communities and professional foresters were involved in measuring trees in 

all these plots. Comparison of the carbon stock estimates between the two groups 
24

show that there was no significant difference except in forest types with very high 

biomass. Although the initial costs of training community members were high, 

community’s involvement in forest monitoring can significantly reduce the costs 

on salaries, transportation, accommodation and other costs compared to that when 

professional foresters were hired. 

• In addition, the involvement of communities in monitoring can directly be linked 

to effective benefit sharing from REDD+ and improvement of forest governance. 

Accordingly, if carbon stocks are measured locally, communities are likely to put 

more efforts on managing their forested areas for carbon and biodiversity 

conservation. 

Main challenges and opportunities

• Monitoring forest degradation requires the development of a tool that can relate 

the loss of canopy cover of individual trees or group of individual trees to carbon 

stocks. By so doing, one can monitor carbon and biodiversity loss at the landscape 

or regional scale.

• Unmanaged harvesting for fuel wood and charcoal production is very difficult to 

monitor with precision. Tree stump surveys are one way to detect these activities 

and estimate associated carbon stock losses.

• In managed areas, it is possible to use a stock-change approach. However, even 

with proper stratification by year of harvest and extraction rate, intensive 

sampling may be needed to obtain reasonable precision of carbon emission 

estimates. Intensive fieldwork is also needed to establish the models for loss-gain

approaches. Although once the models are established they can be used costeffectively.

• Accounting for carbon storage in harvested wood products needs a life cycle 

assessment of the products in questions. This is necessary because carbon 

emissions from wood products depend very much on turnover rates and these 

rates depend on how the products are used. 

• Accounting for carbon in post-harvest wood residue will require post-harvest 

surveys to estimate the ‘dead wood’ carbon pool, as well as knowledge of decay 

rates. Otherwise, wood residue could be considered a “committed emission”. 

• Carbon removals during regrowth after logging (i.e., sequestration) need to be 

accounted for to estimate net emissions. Removals can be estimated by collecting 

data in a chronosequence of logging gaps. Accurate information on site history 

will be needed along with repeated measurements of permanent sample plots.

• Involvement of local people (communities) in forest monitoring activities could 

achieve significant cost reduction while increasing the sense of responsibility of 

local people for protecting their forests for carbon and related benefits. 
25

Carbon and Biodiversity Loss Due to Forest 

Degradation – a Cambodian Case Study

Nophea Sasaki1, Kimsun Chheng2, and Nobuya Mizoue3

Abstract

Tropical forests are diverse in terms of stand and age structures, commercial and 

biodiversity values of individually trees, and dependency of local communities. 

Monitoring forest degradation in the tropics remains a challenge despite increasing global 

interests in reducing carbon emissions from deforestation and forest degradation and 

safeguarding biodiversity and local benefits. Monitoring forest degradation and related 

carbon emissions require understanding of selective logging practice and its decision in 

felling the trees. Based on data from 179 sample plots across three provinces in 

Cambodia, this study discussed the process of forest degradation and related carbon 

emissions in selectively logged forests. Data were analyzed for forest health quality and 

timber grades according to DBH size class (10-19, 20-29, 30-39, 40-49, 50-59, and 60+ 

cm), as timber harvesting under unplanned logging is typically based on these variables. 

Mean tree density and aboveground carbon stocks were estimated at 334.3 trees/ha 

and 360.2 MgCO2

/ha, respectively. In terms of carbon stocks, 36.8%, 44.9%, and 18.3% 

are respectively in forest health quality A, B, and C, suggesting forest condition is still 

good. Large trees in quality A and B are likely to be harvested as they have high 

commercial values. If all large trees (DBH>40 cm) are harvested, 45.6% of carbon stocks 

are gone and will gradually emit CO2 depending on how the harvested wood is used. 

Carbon in branches, wood wastes onsite and offsite will be released immediately. Timber 

grades will also influence the decision making of the loggers because of the prices. Trees 

in luxury grade are more expensive and most of those tree species are classified as 

critically endangered by the IUCN. Despite progress in remote sensing technology, 

monitoring the loss of such valuable trees remains a challenge unless methods to link 

canopy cover with individual tree species are developed, and the relevant users are 

trained. 

___________________________ 

1 United Graduate School of Applied Informatics, Harborland Center Building 22F, 1-3-3 

Hiagashikawasaki-cho, Chuo-ku, Kobe, 650-0044, Japan

2 Forestry Administration, #40 PreahNorodom Blvd., Phsar Kandal 2, Khann Daun Penh, 

Phnom Penh, Cambodia 

3 Laboratory of Forest Planning, Faculty of Agriculture, Kyushu University, Fukuoka 

812-8581, Japan 

Corresponding author: nopsasaki@gmail.com 
26

Local Involvement in Measuring and Governing Carbon 

Stocks in China, Vietnam, Indonesia and Laos

Michael Køie Poulsen1 

Abstract

An important element of MRV is to ensure accurate measurements of carbon stocks. 

Measuring trees on the ground may be needed for ground truthing of remote sensing 

results. It can also provide more accurate carbon stock monitoring than remote sensing 

alone. Local involvement in measuring trees for monitoring of carbon stocks may be 

advantageous in several ways. Involving local communities in monitoring of biomass in 

REDD+ schemes may cut costs of ground truthing and data gathering on changing rates 

of forest degradation. Moreover, local involvement can encourage local ownership of 

REDD+ projects. Empowering communities to monitor forest biomass carbon stocks may 

also contribute to local livelihoods and forest biodiversity conservation. 

But how well does community-based monitoring compare with monitoring by 

professional foresters? Work Package 4 under the EU I-REDD+ project is examining 

monitoring of forest biomass executed by local communities and professional foresters 

and is evaluating the accuracy and cost-effectiveness of monitoring with local 

participation.

Local community members in four I-REDD+ study sites in China, Vietnam, 

Indonesia and Laos have been trained in establishing vegetation plots, measuring trees 

and using simple field protocols. Community members participated in the establishment 

of 289 vegetation plots. During 2011 and 2012, the level of forest carbon was measured 

by both local communities and professional foresters in all these plots. The estimated 

levels of carbon were essentially the same whether community members or professional 

foresters did the work. Thus, there should be no scientific obstacles to involving 

communities. This will also reduce transaction costs over time compared to having 

professional foresters undertake all the measurements. The initial costs of training 

community members may be high, but more can be saved over time on salaries, 

transportation, accommodation and other costs for professional foresters. 

The involvement of communities in monitoring is directly linked to how benefits 

from REDD+ are distributed. Where control rights are shared between government and 

local communities, Benefit Distribution Mechanisms will be more just than when natural 

forests are under top-down state governance. Communities who can see the connection 

between correct measurement of carbon and the benefits received are most likely to 

manage their forested areas appropriately for carbon conservation. Forests managed for 

carbon conservation are also likely to preserve forest biodiversity. 

The I-REDD+ project will continue its work in China, Indonesia, Lao PDR and Vietnam 

from 2011 to 2014. 

___________________________ 

1

Skindergade 23, 1159 Copenhagen K, Denmark 

Corresponding author: mkp@nordeco.dk 
27

Methods for Monitoring Emissions and Removals from 

Forest Harvesting for Timber and Fuelwood: Lessons 

from Guyana

Sandra Brown1 

Abstract

Two methodologies for estimating net emissions from forest harvesting practices (for 

timber and possibly fuel) are presented: (1) a standard approach of using medium 

resolution imagery to monitor the expansion of logging infrastructure into non-logged 

areas for activity data combined with ground plots and the stock-change method for 

emission factors; and (2) a combination of data sources (timber extraction rates, 

management plans, very high resolution imagery) for activity data combined with ground 

measurements in active logging gaps/concessions and the gain-loss method for emission 

factors. For methodology 1, the carbon stock of logged forests is likely to be extremely 

variable and it will likely be difficult to meet a reasonable precision level without 

stratification by year of harvest and timber extraction rates and by intensive ground 

sampling using plots. Although logging roads and log landings can be identified and 

their area obtained from the medium resolution imagery, skid trails, which also cause 

damage and emissions, cannot be unambiguously identified in the imagery. For 

methodology 2, the emissions are estimated directly in the gaps, using the concept of 

change detection, in active timber concessions—the loss in live biomass is caused by 

felling the trees and the collateral damage caused as the tree falls and the emissions are 

then linked to the unit of timber removed (e.g. cubic meters). The emissions from skid 

trails can be obtained from ground measurements by mapping and measuring the area of 

a sample of skid rails using GPS and measuring the damaged trees along the trails—such 

data can be linked to timber extraction rates. The emissions from infrastructure such as 

roads and landings can be obtained in a manner similar to methodology 1. Carbon 

removals during regrowth after logging (the gains) can be estimated by collecting data in 

a chronosequence of logging gaps. The steps described for methodology 2 were 

implemented for the period 2001 to 2010 in Guyana as a case study, and the results for 

the net annual emissions from timber extraction, including an uncertainty analysis, are 

presented.

___________________________ 

1

Winrock International, 621 N Kent St., Suite 1200, Arlington, VA 22209 

Corresponding author: SBrown@winrock.org 
28

Uncertainty and Design Considerations

Deciduous forest in the eastern area of Central Cardamom Protected 

Forest, Cambodia.

(Photo: James Halperin)
29

Section Summary

Stephen Hagen1 

Well planned sampling designs and robust approaches to estimating uncertainty are 

critical components of forest monitoring. The importance of uncertainty estimation 

increases as deforestation and degradation issues become more closely tied to financing 

incentives for reducing greenhouse gas emissions in the forest sector. Investors like to 

know risk and risk is tightly linked to uncertainty. Uncertainty assessment is also 

important for evaluating the implications of forest management actions, and it helps us 

identify and design future projects to reduce uncertainty. 

Design considerations

The conservative principle for accountable carbon credits allows credit for the estimated 

lower 95% confidence limit of carbon stock resulting from the project minus the expected 

baseline carbon stock. This serves as a financial incentive to reduce uncertainties where 

possible. Uncertainties in forest stock carbon estimates can be reduced by a) inventory 

planning and sampling design optimization; b) technological development; and c) 

biomass allometric models.

A rational decision about an optimal design can be made only by comparing the set of 

alternatives using objective selection criteria that combine information on survey cost and 

the achievable reliability of the results. Many choices exist for optimizing sampling 

design. For example, the selection of plot locations can be chosen by systematic 

sampling, simple random sampling or stratified random sampling. The optimal plot 

design depends on objectives, costs, and variability of the population’s attributes of 

interest. Inventory planning steps include 1) identifying information needs and priorities; 

2) assembling and evaluating existing data to answer the questions; 3) selecting the main 

monitoring components; and 4) setting the precision and cost requirements to compute 

the optimal sample size. During optimization, one should minimize the total inventory 

cost subject to fixed precision requirements (confidence interval and confidence level) for 

key attributes of interest. After this process, the objectives and priorities should be 

reevaluated and the process should be repeated. 

There are many technological approaches that can help reduce error and increase 

confidence. Some examples include, a) improving the link between remote sensing and 

ground sampling; b) matching the imagery pixel resolution with the field plot size; c) 

improving the temporal resolution of remote sensing data; and d) reducing locational 

error by using survey-grade GPS to improve the links between remote sensing imagery 

and ground sampling (but with increased cost). In terms of statistical methods, several 

___________________________ 

1

Applied Geosolutions, 87 Packers Falls Rd., Durham, NH 03924 

Corresponding author: shagen@appliedgeosolutions.com 
30

technological developments are potentially useful including a) using regression to link 

modeled imagery to plots; b) using double sampling for stratification or regression; c) 

using stratification to create homogenous strata with respect to key attributes and costs; 

d) allocating plots to strata based on stratum size, variance, and/or costs; and e) making 

all plots permanent initially, but remeasuring based on change strata.

Improvements to biomass allometric models may include a) developing regional 

models to help cover the range of species, growth forms, and wood densities; b) including 

large trees and buttressed trees in equations; c) using Randomized Branch Sampling on a 

subsample of trees as a means to correct for bias in the biomass models; and d) terrestrial 

laser scanning for individual stem volume (which shows promise, but may be challenging 

to apply in the tropics). 

Uncertainty

Approaches for quantifying uncertainty should be decided upon at the initiation of a 

project. Unfortunately, this is typically done, if at all, at the end of a project; after the 

final products have been created. 

Uncertainties are a composite of errors arising from observations and models. 

Different types of errors can be quantified by their precision, accuracy, or bias. Precision 

is the variation about the sample mean. Accuracy is the variation about the true mean, 

which includes both precision and bias. Bias occurs when the sample mean differs 

systematically from the true mean. Uncertainties can arise from multiple sources. For

instance, we will never know the true values of large forest population parameters, so we 

measure a subsample of all of the individuals. The inaccuracy of estimation of these 

parameters from sample results is termed the sampling error. The size of sampling errors 

can be controlled by the survey design and the size of the sample. Non-sampling errors 

come from all other sources, such as faulty application of definitions, classification 

errors, measurement errors, model application errors, calculation errors, and sampling 

frame errors. These errors must be addressed on a case-by-case basis, through training 

and other quality control measures. To calculate the error budgets of total survey error, 

one needs to systematically identify and define all potential sources of error, Assess 

importance of each error and develop cost functions for reducing them, and balance the 

risk of mistakes due to the uncertainties with the cost of reducing the magnitude of the 

uncertainties. 

The most common approach to combining uncertainties from multiple sources is to 

“sum by quadrature”. In this approach, given uncertainties from multiple sources, total 

uncertainty is calculated as the square root of the sum of the squared uncertainty from all 

individual sources. This approach requires assumptions about the data, such as having a 

normal distribution, that are often incorrect. 

A Monte Carlo framework with bootstrapping allows one to combine uncertainties 

from many sources and does not require assumptions about data distributions. It provides 

a means of handling non-linear models and data with complicating characteristics such as 

leptokurtosis (peakedness) and heteroscedasticity (non-constant variance). 

The basic steps of a Monte Carlo framework include a) fit a model to the existing 

data; b) calculate the model residuals; c) create a new realization of the existing data by 
31

adding a random draw of residuals back to the original model predictions; d) fit a new 

model to this new realization to estimate or predict; and e) repeat n times. This approach 

provides n estimates for each new data point and the distribution created by the n

estimates can be used to quantify uncertainty. This distribution of estimates can be used 

as input into another model or otherwise combined with other observations to link 

uncertainty from multiple sources. Estimates from this approach can be combined 

efficiently to allow for spatial aggregation of uncertainty estimates, which often require 

dubious assumptions when done analytically. The downside to this approach is that it is 

data and computationally intensive. 

Conclusion 

Inventory planning is an optimization problem. Sampling error can be minimized for a 

given cost or the cost can be minimized for a desired level of maximum acceptable error. 

Monitoring costs need to be smaller than potential financial benefits. It is critical, 

however, to identify and manage all sources of error from early in the project. The IPCC 

should consider revisiting the 95% confidence interval due the potentially high cost of a 

monitoring system to meet this requirement.

Several issues must be addressed during implementation of an uncertainty 

accounting. Complete accounting of uncertainty is important, but agreement is needed as 

to what the project boundaries are (i.e. which sources of uncertainty should be included). 

Collaborative effort is required for complete uncertainty accounting. Sharing models and 

data is required among project members.
32

Uncertainty Issues in Forest Monitoring: All You Wanted 

to Know About Uncertainties and Never Dared to Ask

Michael Köhl1, Charles Scott2, and Daniel Plugge3 

Abstract

Uncertainties are a composite of errors arising from observations and the appropriateness 

of models. An error budget approach can be used to identify and accumulate the sources 

of errors to estimate change in emissions between two points in time. Various forest 

monitoring approaches can be used to estimate the changes in emissions due to 

deforestation and forest degradation. Sample-based approaches often combine remotely 

sensed data with probabilistic field samples to develop design-based estimates. The 

survey designer must choose between a host of imagery sources, sampling designs, plot 

designs, allometric models of tree biomass, and estimators. Each choice has implications 

for uncertainty and cost. We describe three general areas for improvement: 1) inventory 

planning, sampling design optimization, 2) technological development focused on use of 

imagery, and 3) tree biomass estimation.

___________________________ 

1

Institute of World Forestry, University of Hamburg, Leuschnerstr.91, 21031 Hamburg, 

Germany

2

National Inventory & Monitoring Applications Center, USDA Forest Service, 11 

Campus Blvd., Suite 200 Newton Square, PA 19073 

3

Institute of World Forestry, von Thünen-Institute, Leuschnerst.91, 21031 Hamburg, 

Germany

Corresponding author: ctscott@fs.fed.us 
33

Application of a Monte Carlo Framework with 

Bootstrapping for Quantification of Uncertainty in 

Baseline Map of Carbon Emissions from Deforestation 

in Tropical Regions 

*William Salas1 and Steve Hagen1 

Abstract

This presentation will provide an overview of an approach for quantifying uncertainty in 

spatial estimates of carbon emission from land use change. We generate uncertainty 

bounds around our final emissions estimate using a randomized, Monte Carlo (MC)-style 

sampling technique. This approach allows us to combine uncertainty from different 

sources without making assumptions about the distribution of the underlying data. We 

incorporate uncertainty from the following components: Estimates of forest loss; 

Estimates of aboveground biomass; and Estimates of belowground biomass. In each 

scenario of the MC simulation, forested pixels (1-km) within each 18.5-km block (the 

scale of MODIS-derived deforestation data) are selected randomly until the total cleared 

area estimated within the block is reached. Carbon stock information for the cleared 

pixels is then used to calculate an emissions estimate associated with forest loss for that 

scenario. Iterating through scenarios for each block results in a distribution of emissions 

associated with the estimated level of forest loss. This distribution is then used to define 

uncertainty based on a set confidence level.

___________________________ 

*Based on Harris et al., Baseline Map of Carbon Emissions from Deforestation in 

Tropical Regions. Science, 2012; 336 (6088): 1573 DOI: 10.1126/science.1217962 

1

Applied Geosolutions, 87 Packers Falls Rd., Durham, NH 03924 

Corresponding author: wsalas@appliedgeosolutions.com 
34

Integration of Monitoring Techniques

Water buffalo skid trail in degraded forest, Con Cuong District, Nghe 

An Province, Viet Nam.

(Photo: Leif Mortenson)
35

Section Summary

Yoshiyuki Kiyono1 and Rick Turner2 

Techniques for monitoring deforestation and associated changes to forest carbon stocks 

are widespread and well published. In contrast, techniques for monitoring forest 

degradation are relatively untested in developing countries despite their inclusion in 

UNFCCC REDD+ negotiations. In the Mekong countries, forest degradation may 

contribute a substantial portion of the total carbon losses from forests. There is a critical 

need to assess approaches for monitoring forest degradation, particularly at the subnational level. One potential obstacle in assessing monitoring approaches for the region is 

the lack of consensus on the definition of forest degradation. In addition to a common 

definition, management objectives must be determined, i.e. what desired threshold of 

degradation needs to be detected. Monitoring approaches should recognize common 

themes among countries regarding drivers of degradation, while taking into account the 

unique circumstances of each country, especially in regard to capacity for 

operationalizing any recommended protocols. 

Monitoring Carbon Stock in Areas Subject to Shifting Agriculture 

Conversion of natural forest to agricultural land is one of the most influential land use 

changes on the loss of both carbon stock and biodiversity in ecosystems. However, 

forestland designated for agricultural use can sometimes continue to be classified as 

forest when the residual amount of tree canopy cover remaining meets the accepted 

definition of forested land. Vegetation change induced by shortening of the fallow period 

of the slash-and-burn (swidden) agricultural cycle may result in increased forest 

degradation or eventually deforestation if the fallow period is too short to allow 

vegetation to grow back enough to meet the definition of forest land. Forests in northern 

Lao PDR are subject to intensive slash-and-burn agriculture and have been degraded by 

shortening of the fallow period. For such forest degradation, a practical approach to 

monitor carbon stock change is to use the parameter of plant community age. Destructive 

sampling for allometric equations for bamboo was conducted and a model using plant 

community age to monitor forest degradation was developed in Lao PDR (Kiyono et al. 

2007). For cyclic land use that includes a tree-removal stage, chronosequential changes in 

carbon stock can be estimated by determining the ages and spatial distribution of cleared 

land. Long-term ecosystem carbon stock change under different land use patterns can be 

simulated (Inoue et al. 2010). To estimate carbon stock under a shifting agriculture 

chronosequence, an integration of both ground-based measurement and remote sensing 

data are needed. 

__________________________ 

1

Forestry and Forest Products Research Institute (FFPRI), Matsunosato 1, Tsukuba, 

Ibaraki 305-8687, Japan 

2

US Forest Service, Tongass National Forest, 8510 Mendenhall Loop Road, Juneau AK, 

99801 

Corresponding author: kiono@ffpri.affrc.go.jp 
36

Forest carbon stock can be calculated by multiplying forest area with averaged carbon 

stocks per land area for given land use. Kiyono et al. (2011) assessed non-destructive 

approaches for monitoring anthropogenic greenhouse gas (GHG) emissions from tropical 

dry-land forest under the influence of various forms of human intervention. A matrix was 

developed to rank the suitability of each approach, including costs, potential to obtaining 

data in a large land area, technical difficulties, and applicability to various anthropogenic 

activities, including degradation drivers such as reducing fallow period of slash-and-burn 

agriculture, logging, and fuel wood collection. This matrix is complex and 

comprehensive; however, the assessments of the various approaches need to be examined 

in greater detail. According to the matrix rankings, no reasonable remote-sensing 

methods exist at present to monitor carbon loss due to forest degradation in forests with 

high biomass. To enable practical and frequent monitoring of all types of forests, it is 

vital to devise a new methodology to detect changes in high-biomass forests.

Monitoring Forest Degradation at the Sub-National Level

The Lowering Emissions in Asia’s Forests (LEAF) program of the United States 

Agency for International Development/Regional Development Mission for Asia 

(USAID/RDMA) recently asked the United States Forest Service to assess options for 

monitoring forest degradation at sub-national levels in three Mekong countries: 

Cambodia, Lao PDR, and Vietnam. The three focus areas were the Central Cardamom 

Protection Forest in southwestern Cambodia, the Xamtai and Viengxay Districts in the 

Houaphanh province of northern Lao PDR, and the district of Con Cuong, in Nghe An 

Province of north-central Vietnam. The study areas included all forested lands regardless 

of ownership or land use designation. The assessments followed a similar conceptual 

framework, which includes: 1) define biomass references for monitoring in each forest 

strata of interest, 2) identify and assess the scale and intensity of forest degradation 

drivers, and 3) identify and assess monitoring approaches based on defined biomass 

change thresholds. Three main monitoring approaches, including ground-based field 

measurements, remotely sensed imagery, and predictive modeling, were assessed using a 

qualitative ranking system. The results were then used to develop an integrated 

monitoring system for each study area that combined applicable elements of all three 

approaches. Emphasis was placed on cost efficiency by using existing infrastructure and 

data sources as much as possible. For each recommended monitoring system, workflow 

diagrams were developed which illustrate the relationships between the various data 

sources and analysis methods. 

One issue that emerged during the forest degradation monitoring workshop discussion 

was: should a monitoring system be designed to efficiently utilize existing capacity only, 

or should capacity building be a part of the recommendations? The monitoring systems 

for all three study areas include reasonably attainable recommendations for building 

capacity necessary for implementing monitoring. However, capacity-building goals may 

need to be set higher, if increased capacity is needed in order to implement a robust 

monitoring system. As REDD+ programs are implemented, increased funding may 

become available to support forest degradation monitoring. Therefore, flexibility should 

be incorporated into recommended monitoring systems to provide the ability to adjust to 

changing institutional capacities. Monitoring systems should also be implemented in an 
37

iterative fashion with full consultation of relevant stakeholders, with the initial 

recommendations used as a starting point from which refined protocols and methods are 

developed according to local circumstances and conditions. 
38

Practicalities of Methodologies in Monitoring Forest 

Degradation in the Tropics 

Yoshiyuki Kiyono1 

Abstract

Conversion of natural forest to agricultural land is one of the most important forms of 

land-use change affecting both carbon stock and biodiversity. When the agricultural land 

contains trees, e.g. fallow-land forest of slash-and-burn agriculture, the conversion can be 

categorized into forest degradation when the forest definition covers such vegetation. One 

practical method to monitor carbon stock change is an approach using a parameter for 

plant community age. For cyclic land use that includes clear-cutting stage, from which 

chronosequential changes in carbon stock can be estimated by determining time and 

spatial-distribution of cleared land. Inoue et al. (2007) detected slash-and-burn fields 

using a time-series of Landsat images and a model containing the parameter for plant 

community age, and estimated chronosequential changes in carbon stock in fallow land in 

northern Laos. Kiyono et al. (2011) examined non-destructive methodologies for 

practicalities in monitoring anthropogenic greenhouse gas (GHG) emissions from tropical 

dry-land forest under the influence of various forms of human intervention. No 

reasonable remote sensing methods exist for monitoring at a large scale the amount of 

carbon loss by forest conversion and logging in forests with high-biomass. To enable 

practical and frequent monitoring of all types of forests impacted by humans, it is vital to 

devise a new methodology to detect changes in high-biomass forests.

___________________________ 

1

Forestry and Forest Products Research Institute (FFPRI), Matsunosato 1, Tsukuba, 

Ibaraki 305-8687, Japan 

Corresponding author: kiono@ffpri.affrc.go.jp 
39

Forest Degradation Sub-National Assessments:

Monitoring Options for Cambodia, Lao PDR, and 

Vietnam

Rick Turner1, James Halperin2, Patricia Manley3, and Leif Mortenson4 

Abstract

Techniques for monitoring deforestation and associated changes to forest carbon stocks 

are widespread and well published. In contrast, techniques for monitoring forest 

degradation are relatively untested in developing countries despite their inclusion in 

UNFCCC REDD+ negotiations. The Lowering Emissions in Asia’s Forests (LEAF) 

program of the United States Agency for International Development/Regional 

Development Mission for Asia (USAID/RDMA) is working to address issues and 

challenges regarding forest degradation monitoring. The United States Forest Service was 

asked by LEAF and its partner organizations to assess options for monitoring forest 

degradation at sub-national levels in three Mekong countries: Cambodia, Lao PDR, and 

Vietnam. The study areas included the Central Cardamom Protection Forest in 

southwestern Cambodia (401,000 Ha), the Xamtai and Viengxay Districts in the 

Houaphanh province of northern Lao PDR (541,000 Ha), and the district of Con Cuong, 

in Nghe An Province of north-central Vietnam (175,000 Ha). 

To assess monitoring options in the study areas, a conceptual framework was developed 

that includes three basic steps: 1) define biomass references for monitoring in each forest 

strata of interest, 2) identify and assess the scale and intensity of forest degradation 

drivers, and 3) identify and assess monitoring approaches based on defined biomass 

change thresholds. We evaluated three main approaches, including ground-based field 

measurements, remotely sensed imagery, and predictive modeling. Potential variants of 

each approach were assessed for each study area using a qualitative ranking system. The 

results were then used to develop an integrated monitoring system for each study area 

combining elements of all three approaches. Each monitoring system includes specific 

recommendations for integrating the three monitoring approaches that will likely meet 

the stated objectives. Each monitoring system must consider the important drivers of 

degradation in the study area, the operational circumstances for monitoring, and the 

expected capacity for implementation. A critical next step is implementing and testing the 

monitoring systems in the three study areas as proof-of-concept for potential application 

to other geographic areas in the region. 

___________________________ 

1

US Forest Service, Tongass National Forest, 8510 Mendenhall Loop Road, Juneau AK, 

99801 

2

Center for International Forestry Research, Lusaka, Zambia 3

US Forest Service, Pacific Southwest Research Station, 2480 Carson Road, Placerville, 

CA 95667 

4

US Forest Service, Pacific Northwest Research Station, 620 SW Main St. Suite 

400, Portland, OR 97205 

Corresponding author: rlturner@fs.fed.us
40

Detection of Forest Degradation Drivers 

Field evaluation, Con Cuong District, Nghe An Province, Viet Nam 

(Photo: Leif Mortenson)
41

Summary of Small Group Discussions 

Patricia Manley1

Workshop participants were asked to address sets of questions in small group 

discussions, which were subsequently brought to the entire group for discussion. The 

first set of questions was directed at identifying a set of degradation activities that could

be a primary focus for developing or refining methods and techniques for monitoring: 

• What drivers and degradation sources can be detected?

• What methods are most effective at detection?

• What are the challenges and opportunities in detecting degradation? 

Drivers

The definition and classification of drivers and degradation activities continues to present 

challenges. Currently there are no international agreements through IPCC or the 

UNFCCC on how to classify forest degradation drivers. However, relevant to a REDD+ 

mechanism, there are several ways to classify the activities that contribute to degradation 

of forests. Workshop participants differed in their opinions as to whether drivers were 

the correct focus, or whether the focus should simply be on human extraction of forest 

products that reduces forest carbon stocks. Despite these differences, all agreed that 

activities needed to be identified for monitoring degradation. 

 Traditional forest monitoring methods (remote sensing, ground-based field 

measurements) are most often used to assess changes in forest structure and quality. 

Understanding which direct drivers are present is the most important first step towards 

identifying possible monitoring methods. Participants of the workshop identified four 

primary and four secondary categories of drivers that are prevalent and substantial 

contributors to carbon loss in the forests of Southeast Asia:

• Primary

o Selective logging: commercial planned, commercial unplanned, and domestic 

(customary)

o Fuelwood gathering: commercial and domestic (customary)

o Shifting cultivation 

o Conversion to plantations (if using a definition of forest where plantation is 

not considered forest) 

• Secondary

o Human induced forest fire 

o Non-timber forest products (e.g., yellow vine) 

o Access

o Grazing 

___________________________ 

1

US Forest Service, Pacific Southwest Research Station, 2480 Carson Road, Placerville, 

CA 95667 

Corresponding author: pmanley@fs.fed.us 
42

There were a few differences in how the groups defined selective logging, with some 

separating single tree removal from multiple tree harvest, but generally single tree 

removal was synonymous with domestic or customary use. Individual trees are removed 

for a variety of applications, including personal use as housing material and for sale. The 

activities considered secondary were primarily a function of their prevalence, as opposed 

to the impact of individual occurrence on carbon. In landscapes in which these activities 

are prevalent, they could be considered primary. 

Field Methods 

The participants identified the primary field-based data sources that could detect and 

describe the primary degradation activities as the following:

• Forest management unit inventories

• Permanent sampling plots 

• Change detection inventory 

• Interviews/secondary data 

• Participatory monitoring

• Temporary sampling plots 

Although a diversity of opinions were expressed, the two groups that addressed field 

methods agreed that permanent plots and temporary plots both had important 

contributions to meeting monitoring information needs. Permanent plots provided 

measures of growth and accurate change detection for vigor, disease, and harvest. Cost 

was identified as the primary limiting factor for permanent plots – both the cost to install 

and the cost to relocate and remeasure in a precise manner. Temporary sampling plots 

have strengths and limitations that are complementary to permanent plots – faster to 

install for a given budget, additional plots can be measured, but they lack data on growth 

and have additional sources of error in estimates of change. Participatory monitoring was 

considered by most, but not all participants as a secondary source of data as opposed to a 

primary given the uncertainty associated with how to maintain and validate data quality. 

Remote-Sensing Methods 

Group participants identified a wide range of applications for remote sensing data in 

meeting forest carbon monitoring needs, with the primary focus being on selective 

logging. One of the contributions remote sensing can make to monitoring of selective 

logging was indirectly, through the detection of roads, skid trails, and landing areas. 

Degradation risk and predicted intensity of selective logging in association with these 

readily detected features is a unique contribution that remote sensing can make. Direct 

detections of selective logging, including individual tree removal, are more challenging, 

but can be estimated based on detected changes in canopy cover using combinations of 

high and moderate resolution imagery. At least one of the groups felt that remote 

sensing could offer reliable change detection, early warning information on shifts in use, 

detailed information on threatened areas, and frequent repeatability in areas that 

warranted it for whatever reason. Direct detections for plantations are accomplishable 
43

with low resolution imagery, and are possible with more advanced methodology for 

shifting cultivation and fire, but discussions were primarily limited to selective logging. 

 Ideally, remote sensing would be the primary source, if not a sole source, of data for 

monitoring forest degradation. It is objective, spatially consistent and comprehensive, 

repeatable with minimal error, and does not require capacity building across as large a 

population of technicians as is needed for ground based monitoring. In general terms, 

remote sensing faces many challenges in detecting degradation, including clouds, 

topography, access, cost, and capacity. Opportunities for overcoming these challenges lie 

in technological advances that offer higher resolution imagery at reduced costs to users 

and overall more accessible technology – a process that takes place over an uncertain 

time scale and over which most practitioners do not have control. In the meantime, 

practical challenges posed by existing technology need to be addressed. 

 Technical challenges associated with detecting selective logging fell into three 

categories: 1) visual interpretation; 2) object-based image classification; and 3) spectral 

un-mixing. Visual interpretation challenges included short-duration of detectability of 

canopy gaps in many cases, ability to obtain sufficient cloud-free images over a short 

period of time, steep topography, consistency among observers over time, voluminous 

data associated with higher resolution imagery, and scaling up. Object-based image 

classification can also be challenging, namely effectively setting up routines for 

segmentation. Spectral un-mixing challenges included obtaining ancillary data to 

correctly classify local land cover classes, consistency in technology and interpretation, 

and consistently interpreting conditions in steep terrain (i.e., topographic illumination 

allometry). 

 Finally, feasibility challenges were identified, primarily in association with high 

resolution imagery. They included availability, access (for LiDAR), frequency needed to 

detect ephemeral canopy gaps, narrow window of time for data collection in deciduous 

forests, cost to obtain imagery, cost to process imagery, local capacity to process and 

interpret data (people, knowledge and hardware), ability to scale data to provide useful 

interpretations at multiple scales (e.g., “scaling-up”), and the difficulty in linking 

observed changes to specific drivers. 

Conclusions 

Challenges in detecting degradation with remote-sensing were numerous, and generally 

indicated that remotes sensing plays a central role in detecting and monitoring primary 

sources of degradation; however it must be used in conjunction with field-based data 

sources, statistical modeling, and decision support tools in order to achieve an adequate 

level of detectability and a reliable characterization of forest carbon to address emissions. 

Similarly, field-based sampling, although technically simple, is labor and time intensive, 

making it inefficient as a singular approach to monitoring degradation. 

 Challenges in designing and implementing field sampling for monitoring carbon 

paralleled the strengths and weaknesses of the methods – the intersection of time, cost, 

and the data yielded and its value. The lack of a clear singular best approach seems to 

indicate that a multi-pronged approach will be most effective, one that is tailored to meet 

the needs of a given landscape, and one that can be adapted over time to be more efficient 
44

and effective as opportunities arise. The overarching challenge then becomes to 

determine at what spatial scales monitoring approaches need to be similar, and in what 

ways do they need to be consistent over time. For example, an optimal monitoring 

approach for a district in one part of a country may look very different than an optimal 

monitoring approach for a district in another part of the country as a function of capacity, 

terrain, land use, etc. However, in order to maximize the utility of the monitoring data to 

address a range of questions about forest conditions, biodiversity, ecosystem services, 

and drivers (i.e., to maximize co-benefits), as much consistency as possible is desirable at 

province, multi-province, ecoregional and national scales. Finding a balance in these 

objectives will be enhanced by a few key demonstration projects that highlight how to 

assess and select optimal monitoring designs. A two-pronged, phased approach to 

implementation (design, capacity building, data acquisition, and analysis/interpretation) 

that simultaneously targets the development of efficient and effective monitoring at local 

scales (e.g., districts or provinces) and the development of an effective national inventory 

will enhance the probability that solid, forward progress is made toward meeting the 

information needs of REDD+ and realizing multiple co-benefits.
45

Monitoring Objectives and Thresholds

Pu Mat National Park, Con Cuong District, Nghe An

Province, Viet Nam

(Photo: Leif Mortenson)
46

Summary of Small Group Discussions 

Patricia Manley1 

Workshop participants were asked to address sets of questions in small group 

discussions, which were subsequently brought to the entire group for discussion. The 

second set of questions was directed at identifying a set of degradation activities that 

could be a primary focus for developing or refining methods and techniques for 

monitoring: 

• What are realistic monitoring objectives and what are the thresholds needed to 

assess change? 

• What are the appropriate scales to target for monitoring?

• What challenges and opportunities exist for accomplishing these objectives? 

The objectives were defined in terms of measures of condition, and thresholds were 

defined as minimum magnitudes of change that monitoring should attempt to detect with 

reasonable statistical power and precision. General tenets for identifying realistic goals 

were developed by the groups. They were outlined for each of three topics. 

Capacity Building

• Work from where we are today, which varies by country 

• Build on existing institutions and expertise 

• Build on existing reporting structures 

• Need to build capacity at all scales – need to identify what capacity is needed at 

what scale

• Target building computing capacity (GIS, remote sensing) at national and 

provincial scale

• A phased approach is prudent for capacity building 

Setting Thresholds 

• Degradation, afforestation, deforestation rates are relevant contexts for thresholds 

• Assess relative change from existing baseline conditions 

• Target thresholds should be informed by costs versus benefits 

___________________________ 

1

US Forest Service, Pacific Southwest Research Station, 2480 Carson Road, Placerville, 

CA 95667. 

Corresponding author: pmanley@fs.fed.us 
47

• Test and refine target thresholds over time (e.g., through data analysis and 

modeling) 

• Reporting frequency should be clearly identified – no more than 5 year intervals 

• Shifting cultivation can have significant temporary effects on some measure that 

could mask smaller more permanent effects 

Implementation

• A phased approach is prudent for implementation 

• Use a “no regrets” approach to guide investments in implementation, meaning 

they will be useful no matter what else gets accomplished 

Specific objectives and thresholds identified by the discussion groups varied by the 

type of degradation activity. Targeted activities were those most readily monitored 

through readily available remote sensing or field data collection methods, and included: 

selective logging, fuel wood, and swiddening. Fire was identified as another activity for 

which thresholds could be identified, but the group did not have the opportunity to 

address fire in any detail. 

Selective logging was defined here as activities that resulted in wood products (e.g., 

lumber, poles for houses). Planned commercial logging typically targets larger trees with 

diameters > 50 cm. Thresholds for this activity minimally would detect reductions of 

trees >50 cm in diameter and reductions of >50% of the biomass per hectare. Frequency 

of resample was identified as a maximum of 30 years, assuming that reentry for 

commercial logging would not be more frequent than that. However, frequency of 

resample most likely would be a function of reentry rates and required reporting 

frequencies specific to the area. 

Unplanned logging represents forest uses for domestic or commercial uses. 

Unplanned logging can occur in areas that are also subject to planned commercial 

logging. Tree removal for customary uses typically targets smaller trees with < 50 cm 

diameters, limited primarily by equipment (e.g., machete, buffalo). Target thresholds for 

areas subject to unplanned selective logging would be loss of trees >30 cm in diameter 

and reductions of >30% of the biomass per hectare. Frequency of resample could be any 

feasible time period, given that tree removal in most areas is occurring throughout the 

year and every year. Of course, over time accessibility and need will vary, so some areas 

that were not subject to unplanned logging could begin to be logged. Given the dynamic 

nature of tree extraction activities, we recommend a minimum 5-year resample or a 

resample frequency coincident with other forest monitoring measurements for reporting 

purposes (whichever is shorter) to provide a comprehensive snapshot of conditions across 

contiguous landscape. 

Fuel wood removal commonly would occur in the same areas as unplanned logging, 

given that both are a function of access and need, and the same conditions are favorable 

to both activities. Therefore, we recommend that the thresholds set for unplanned 

logging be applied to any forested area with a reasonable risk of degradation by 

unplanned activities. Degradation risk can be determined by assessing the factors that 
48

directly and indirectly drive unplanned logging and intensive fuel wood collection, such 

as access, local population size, local poverty levels, etc. (see Chapter 1).

Swiddening (i.e., slash and burn shifting agriculture) involves a temporary deforested 

condition over variable sized areas for a variable number of years. Once the agricultural 

activity is stopped, the assumption is that the area will recover at the same pace as one 

would expect after a natural disturbance. The ability to detect swiddening will depend 

on the size of the clearing, the duration of use, and the density of areas cleared for 

swiddening at any point in time. Generally, medium resolution Landsat data can detect 

>1-hectare areas that are maintained for at least 3 years (providing a 4-5 year window of 

detection). The use of chronosequencing models can help to estimate change in 

biomass over short periods of time, when combined with accurate land use/land cover 

change maps. High resolution remote sensing data can enhance these estimates by 

detecting smaller clearings and refining models. Landscape attributes and village 

locations can be used to identify areas of high risk of degradation from swiddening, 

which in turn may influence the frequency and intensity of repeated measures. 

The challenges and opportunities for identifying monitoring objectives, and 

thresholds in particular, are many. The primary challenge and the most limiting factor is 

capacity. The group noted that starting with existing conditions, both ecologically and 

institutionally, is fundamental to making progress. In most countries, this means starting 

at basic levels of acquisition of equipment, data, and training. Investments in national 

and provincial monitoring data and data management/interpretation abilities are essential. 

The limitation of national and provincial-level investments is that they do not build 

capacity at the ground level, and as a result the benefits are mostly at those larger scales. 

Demonstration projects provide a complementary investment to larger-scale investments 

in that they take place in and involve local communities and staff. Local involvement is 

one important criterion to successfully determining forest conditions at a level of 

resolution and reliability that can be used for REDD+ monitoring, and that can help 

identify problems in a timely manner such that policies and other tools can be put in place 

to achieve the desired objectives of maintaining or increasing forest cover and forest 

quality. 
49

Regional Themes and Next Steps

Con Cuong District, Nghe An Province, Viet Nam.

(Photo: Leif Mortenson) 
50

Small Group Discussions and Conclusions

Rick Turner1 

At the conclusion of the workshop, a breakout group session discussed common themes 

that had emerged regarding forest degradation monitoring in the Southeast Asia region. 

The participants were also asked to list any important issues that may not have been 

sufficiently addressed during the workshop and that may require further discussion, and 

recommendations for next steps in moving forest degradation monitoring forward in the 

region were developed.

Regional Themes

One common theme that emerged during the workshop was that monitoring methods 

based on only one approach (e.g. remote sensing, ground-based measurements, predictive 

modeling) would likely be insufficient to provide robust estimates of biomass change due 

to degradation activities. Rather, an integrated monitoring system, combining elements of 

two or more approaches, would likely be more flexible in adapting to local and national 

circumstances and capacities. While regional consistency in monitoring methods is 

desirable, a single monitoring system approach may not fit all circumstances. The main 

drivers of forest degradation, as well as existing institutional capacities for implementing 

monitoring programs, can vary greatly among countries in the region. In addition, 

countries may apply different definitions for forest degradation, as well as different 

thresholds for detection. How degradation is defined will influence selection of methods 

and data sources for a particular monitoring system. Demonstration projects should be 

implemented in multiple countries and forest types to provide a comparison and proof-ofconcept of various approaches. 

Several needs were identified for implementing degradation monitoring in the region. 

Although a few REDD+ projects have been established in limited areas, there is a need to 

scale up to larger landscapes that are more relevant to estimating overall emissions and 

that would provide more efficient management, monitoring, and reporting. It is essential 

that current REDD+ projects include degradation monitoring in their monitoring 

programs to avoid omitting a potentially large source of emissions from their estimates. 

It was also recognized that institutional capacity for monitoring needs to be strengthened 

in all countries in the region in order to support a robust, long-term monitoring program. 

Some identified technical needs included the development of botanical skills of field 

measurement crews, more consistent forest vegetation classifications and accurate forest 

type maps, and the development of more reliable allometric biomass equations for 

common forest types. 

___________________________ 

1

US Forest Service, Tongass National Forest, 8510 Mendenhall Loop Road, Juneau AK, 

99801. 

Corresponding author: rlturner@fs.fed.us 
51

Issues for Further Discussion

Some important issues needing further collaboration include the need for a more precise 

definition of forest degradation. Without a well-defined concept of degradation, 

establishing meaningful monitoring objectives and thresholds will be problematic. There 

is a need to determine criteria to identify conditions under which swiddening and tree 

plantations may degrade the ability of forests to store carbon. Additionally, consensus on 

the definition of forest is needed to properly address the effects of swiddening and tree 

plantations on carbon monitoring. The significance of fire should also be more 

thoroughly assessed, including possible interactions with other degradation drivers such 

as selective logging and shifting agriculture. In addition to biomass loss, the gain from 

forest regrowth over time after disturbance must be assessed to more accurately estimate 

net carbon stock change in Greenhouse Gas reporting. Just as with deforestation, leakage 

may become an issue in forest degradation, as management to reduce degradation 

activities in one area can potentially shift those activities to other landscapes within a 

country, or across national boundaries. 

Much of the discussion on degradation monitoring methods focused on remote 

sensing and ground-based field measurements. Although predictive modeling was 

addressed as a third monitoring approach, more information is needed to understand how 

modeling can be combined with the other methods to develop a more robust monitoring 

system, especially in areas where obtaining remote sensing or ground-based data is 

difficult due to technical or capacity constraints. Much valuable information could be 

obtained from case studies in which predictive modeling is an integral part of the 

monitoring system. Quantitative comparisons of cost versus accuracy and precision 

among the various monitoring approaches would also be informative. 

The participants felt that more discussion should be devoted to developing the 

institutional framework and governance needed to support a degradation monitoring 

program. Although it is important to build technical capacity within countries to 

implement monitoring, efforts also need to be made to ensure that technical information 

is relayed to policy makers to facilitate better understanding and encourage institutional 

commitment of resources to monitoring.

Next Steps

A recommended next step in initiating forest degradation monitoring in the region is to 

implement demonstration projects that aim to estimate and monitor forest degradation as 

case studies, most appropriately at the sub-national level. These projects should integrate 

various monitoring approaches into a system that is responsive to local degradation 

drivers and appropriate to the local and national circumstances. Logical sites for initial 

implementation of degradation monitoring projects could be the sub-national focus areas 

assessed by LEAF in Cambodia (Central Cardamon Protection Forest), Laos (Houaphanh 

Province), and Vietnam (Nghe An Province). Implementing the three demonstration 

projects would help to refine model parameters and workflows, as well as provide data on 

the contribution of degradation to the total emissions profile. It may also be instructive to 

identify similar on-going projects in other regions as additional sources of information. 
52

Another important next step is increasing efforts to build technical and financial 

capacity for monitoring in the region. It is important to determine the minimum 

requirements for monitoring forest degradation. Potential donor countries and 

organizations will need to be involved in determining minimum requirements, as the 

purpose of monitoring is to ensure that credits issued for carbon stored in forests is 

verifiable. Recipient countries and organizations are in need of cost assessments for 

implementing and maintaining a monitoring program, including costs of various 

monitoring approaches relative to benefits, i.e. carbon credits. 

Facilitating technical transfer is important to ensure that the most current research is 

incorporated into monitoring systems and the consistency is maintained across the region 

as much as practicable. One recommended approach is developing regional instructional 

manuals, including ground-based field measurement, remote sensing imagery analysis, 

and predictive modeling methods. Another potential conduit for technology transfer 

could be technical assistance initiatives such as SilvaCarbon, a global USAID-funded 

program aimed to build knowledge and capacity in forest carbon monitoring. This 

workshop can also form the foundation for continued dialogue, networking, and data 

sharing. Future workshops could be planned to share lessons learned from 

implementation of monitoring projects. 
53

Conclusions 

James Halperin1 and David Ganz2 

Monitoring forest degradation is a complex process that needs to account for a wide 

variety of forest characteristics, human activities, and programmatic resources in order to 

achieve reliable results. This workshop sought to deepen understanding of monitoring 

forest degradation as it relates to these issues by: a) discussing implications from 

definitions related to operationalizing forest monitoring degradation; b) assessing case 

studies with current, and emerging, best practices to detect and monitor forest 

degradation; and, c) identifying ways forward for forest degradation monitoring 

demonstration activities.

Forest degradation definitions do matter. However, not all experts agree to what 

degree definitions need to be clearly established before implementing monitoring 

activities. For example, a monitoring program generally sets thresholds for detectability 

and statistical precision when it may be unknown if these attributes are achievable. The 

process of monitoring often reveals data and trends that were not previously known and 

refinement of definitions can occur through implementation. Consensus from the 

workshop does indicate that forest biomass and biomass change must be the key 

parameters of interest in monitoring forest degradation for REDD+ initiatives. 

Case studies that focus on forest degradation monitoring are limited, especially those 

which focus on Southeast Asia. Efforts to assess and estimate forest degradation must be 

responsive to the direct drivers that impact forest biomass. The main direct drivers of 

degradation are selective logging for timber, harvesting for fuel wood, and shifting 

cultivation. Selective logging has received the majority of attention, as evidenced in the 

case studies presented. Methods formulated to monitor forest biomass after selective 

logging range from remote sensing, ground based field measurements, to development of 

Emission Factors that standardize the impacts of biomass per unit area. 

Effective methods to detect forest degradation at adequate levels of precision and 

repeatability, and at scales of interest, remain elusive. Remote sensing methodologies are 

often hampered by persistent cloud cover, quick canopy cover re-growth, a lack of 

expertise in automated processing and/or a lack access to imagery with spatial resolution 

at scales relevant to degradation drivers. However, remotely sensed imagery is 

consistently available and covers wide areas that often cannot be accessed. Coupled with 

ground based field measurements to provide biomass estimates, the use and applicability 

of remotely sensed data to monitor forest degradation cannot be understated. 

While this workshop provided ample lessons from case studies that can help promote 

forest degradation monitoring initiatives, six gaps in current knowledge remain. First, 

modeling approaches that integrate both remotely sensed data and ground based field 

___________________________ 

1

Center for International Forestry Research, Lusaka, Zambia

2

LEAF Asia, Liberty Square, Suite 2002, 287 Silom Road, Bangrak, Bangkok, 10500 

Thailand

Corresponding author: j.halperin@cgiar.org
54

measurement data rarely have been tested in tropical forests, if at all (albeit commonly 

used in temperate forests). Second, fire as a driver of forest degradation is poorly 

understood. Tropical fire ecology is an undeveloped field of science and little is known of 

biomass dynamics as it relates to fire regimes. Third, planned selective harvesting for 

timber impacts forest biomass dynamics in unpredictable ways. Likewise, it is debatable 

if this type of harvesting can be considered degradation when compared to unplanned 

harvesting. Fourth, tree plantations may be considered forests in some countries, and 

agricultural crops in others. In this context, it is possible that conversion could be 

considered either degradation or deforestation. This raises an important question 

pertaining to biodiversity safeguards. Fifth, forest re-growth after disturbance needs 

attention in order to account for net changes in forest biomass dynamics. Lastly, forest 

degradation monitoring may be feasible and practical at some scales in some places while 

not others. However, monitoring results will ultimately need to nest within national 

frameworks of emissions reporting. There is a large gap in knowledge of nesting 

approaches to indicate how monitoring at sub-national scale can inform national 

monitoring and reporting initiatives, and vice-versa.

Several key messages emerged from this workshop with strong consensus. The first 

key message is that demonstration activities to develop forest degradation monitoring 

methods and tools need more widespread implementation. Demonstration activities 

should strive to simultaneously address two or more of the knowledge gaps as mentioned 

above. These activities should occur at scales relevant to national reporting requirements 

but also where sufficient resources can be directed to provide adequate lessons learned. 

Further, demonstration activities need to address a range of drivers, forest types and 

conditions, and socio-economic circumstances in order to compare approaches, input data 

sources, and costs- all in relation to acceptable levels of precision. Successful 

demonstrations of forest degradation monitoring are not easy to achieve. Yet they are 

necessary for providing clarity into future operationalization of forest degradation 

monitoring programs. 

Secondly, forest degradation monitoring programs must be sensitive to the 

degradation drivers that impact forest biomass. For example, selective logging for timber 

in tropical wet forest generally has a large impact on biomass in relatively small areas. 

Harvesting trees for fuel wood has a smaller impact on biomass across large areas. In this 

context, there may be no ‘one size fits all’ approach to methodology development. 

Remote sensing and ground based field measurement approaches for selective logging 

may not be appropriate for fuel wood harvesting, and vice versa. 

Thirdly, capacity building and strengthening within partner institutions and civil 

society organizations needs to incorporate long-term solutions that aim high. Coupling 

researchers with practitioners and land use managers provides an effective framework for 

integrating advanced methodologies into forest monitoring programs. Lessons learned are 

most likely to be meaningful when accomplished through experience combined with trial 

and error. Programs addressing the key questions of forest degradation monitoring will 

ultimately reduce costs of technology and methodology transfer, through 

institutionalizing advanced monitoring frameworks. 

Forest degradation monitoring requires a depth of knowledge and understanding in 

many different disciplines including remote sensing, forest biometrics and mensuration, 
55

sampling theory and strategy, ecology, and modeling. The interdisciplinary nature of 

forest degradation monitoring makes it a challenging professional endeavor and requires 

significant commitments in terms of time and resources. This workshop aimed high in 

bringing together experts for achieving consensus on forest monitoring definitions, 

thresholds, and methodologies. The achievements included a common definition of the 

main parameter of interest; consensus that integration of data sources is crucial to take 

advantage of multiple inputs, and that monitoring approaches must be demonstrated and 

compared at multiple sites through project initiatives before being operationalized. The 

workshop organizers hope that the insights and ideas brought forward by this diverse 

group of experts will be able to influence forest degradation monitoring design initiatives 

well into the future.
56

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