Remote Sensing Method in
Implementing REDD+FRIM-FFPRI Research on Development of Carbon Monitoring
Methodology for REDD+ in Malaysia
Remote Sensing Component
Mohd Azahari Faidi, Hamdan Omar, Khali Aziz Hamzah, Dr. Rahman Kassim,
Gen Takao, Yasumasa Hirata

FRIM-FFPRI Training Workshop on REDD+ Research
Project in Peninsular Malaysia
10 February 2015
XCape Resort, Taman Negara Kuala Tahan, Pahang
ISO 9001 : 2008

www.frim.gov.myOutline
 Introduction
 Objectives
 Study area and data
 Methodology
 Remote sensing data processing Ground truth data collection
 Plotless sampling for biomass estimation
 Field survey for remote sensing data classificationwww.frim.gov.my Conclusion

Introduction
 REDD+ is a mechanism for providing financial rewards to countries
that reduce carbon emissions caused by deforestation and forestdegradation.

 Payments for avoided deforestation and forest degradation.
Implementation requires comparison of actual forest change rates
and the associated emissions, to a baseline.

 Remote sensing technology is expected to play a significant role as atransparent, replicable and long-term monitoring systems thatneeded for REDD+.

 This technology can be an essential data source, providing regular

www.frim.gov.mydata that contribute to estimations of state and trends, including
historical trends, of land use and carbon density in the landscapelevel.

Objectives
 The aims of this component, through some technical image
processing and sampling can be explained as follow:
 Identify an appropriate method for landuse classification
 Design an effective sampling scheme of the ground truth
for classification

 These objectives is being supported by some remote sensing

www.frim.gov.myground survey sampling, and images processing.

Study Area & Data
 Study area 4 scenes of Landsat Images (Path 126-127, Row 057-058)
over Peninsular Malaysia, covering Kelantan, Terengganu,Pahang, Johor, Melaka, Negeri Sembilan, Selangor and Perak.

 Data USGS Landsat images
 ~ 200 images taken in1990 – 2010
 Cloud ≤ 50%

 ~ 100 field surveys
 ~ 500 Google Earth interpretation

http://glovis.usgs.gov/

www.frim.gov.my Ground truths

Methodology
Methodology : Remote Sensing data processing Raw data Landsat TM & ETM can be download freely fromhttp://glovis.usgs.gov/ - (.tiff format)
 Landsat data that provided 30m spatial resolution and 7 spectral
bands is one of the most valuable datasets for studying landuse.

 Mostly covered by haze, cloud and cloud shadow especially intropical area.

 Zhe Zhu et al. 2012 has successfully develop the Fmask algorithm
www.frim.gov.myto detect cloud and cloud shadow from Landsat Imagery.

• Data processing flow
Data Acquisitions
USGS/GLCF
Existing Archive

Geo-references
Metadata

Atmospheric Correction
Radiometric Correction

Matlab R2012a

Cloud & cloud shadow detection

Projection Name:
Rectified Skew
Orthomorphic
Spheroid Name:
Modified Everest
Datum:
Kertau 1948
Zhe Zhu et al. 2012

Cloudless Images production
Image enhancement

Contrast table

Image Mosaic
Radiometric, Atmospheric, Georeferenced, and Cloudless LandsatImages

www.frim.gov.myERDAS 2011

Data Acquisitions
1990,1992,1996, 1998, 2000, 2002,2004, 2006, 2008, 2010www.frim.gov.my

Total Landsat image (200):- 126_057: 47- 126_058: 48- 127_057: 52- 127_058: 53

Geo-references image
Basically all the data from USGS & GLCF is already georeferenced to standard world projection:- Projection name: Universal Transverse Mercator (UTM)- Spheroid name: WGS 84- Datum name: WGS 84- Projection name: Rectified Skew
Orthomorphic (RSO)- Spheroid name: ModifiedEverest- Datum name: Kertau 1948
Raw Metadata

www.frim.gov.myStandard Peninsular Malaysia
projection:

Radiometric & Atmospheric correction
Using method that has been establish by Sergio
M.V. et al 2008
- Radiometric correction- Conversion DN to satellite radiances- Radiances to TOA reflectanceBand 1- Atmospheric correction- Dark Target Approach (DTA)- Lhaze from minimum radiances value

Band 7

Band 5

Band 4

Band 3www.frim.gov.myBand 2

Cloud and cloud shadow detection usingMatlab
Using Object-based cloudand cloud shadow
detection by Zhe Zhu et al.
2012:- Fmask algorithm- Potential cloud layer- Potential cloudshadow layer- Buffer surrounding
cloud and cloud shadow,
3 pixel (90m)
www.frim.gov.myWater
Land
Cloud
Cloud shadowLandsat TM image (126_057)

Shadow and cloud shadow layer

www.frim.gov.myCloudless, geocode, radiometric and atmospheric corrected image

Cloud and cloud shadow detection using Fmask algorithm

Cloud and cloud shadow

Cloud and cloud shadow

LandWater

Note: 3 pixel buffer
surrounding cloudand cloud shadow

www.frim.gov.myLandWater

Mosaic Image

Note: No data (cloud andshadow detected on allscenes)

www.frim.gov.mySelection Criteria:
1. Percentage of cloud and cloud
shadow coverage
2. Image quality (haze, brightness,etc.,)
3. Date of data acquisition (priorityto the latest imagery)www.frim.gov.myRadiometric, Atmospheric, Geo-referenced,
and Cloudless Landsat Images

Methodology : Ground truth data for biomass estimation

 Using Prism method for forest inventory activity
All FRs w/ Biomass Plotsshould be used for GT, too.
Some Biomass Plots would
be measured for GT, too, forthe calibration experiment.

Some FRs w/o Biomass
Plots should be also used for
GT for sufficient GT Plots inwider area.

Forest Reserve
Forest Reserve
GT Plots for non-forest land
covers should be carefully
allocated around thetargeted FRs and/or alongroads between FRs.

Forest Reserve

LegendForest Reserve

Conceptual diagram for Ground Truthing allocationin relation to Biomass Plotswww.frim.gov.myGT Plot (forest)
GT Plot (forest) on aBiomass Plot
GT Plot (non-forest)
Biomass Plot

Main activities during forest survey

1. Measurement of the positioning
data of the plots by using GPS

2. Observation of the forest type
3. Identification of the dominantspecies

4. Measurement of the total basal
area by applying Prism method

5. Measurement of DBH and totalheightconditionwww.frim.gov.my6. Taking photographs of the forest

Procedure of the survey
1. Measurement of the positioning data of the plots by using GPS: Position at the center of the plot (using GPS) Slope direction and inclination (using Clinometers)

2. Observation of the forest type

3. Identification of the tree species
 Dominant tree species (local name if possible)
 “Dipterocarp” or “Non Dipterocarp” (at least)

www.frim.gov.my①. Mangrove forest②. Peat swamp forest③. Low land forest④. Hill forest4. Measurement of the tree:
 Counts trees by Bitterlich/Prism method
5. Measurement of DBH
 Measure DBH (at the height of 1.3m)※ All trees that have been counted in Bitterlich/Prism method.

6. Taking photographs of the forest condition
 Photos toward 4 directions (N-E-S-W) and Vertically above
 Photos from a proper position where the plot can be

www.frim.gov.myoverlookedAdvantages of plotless sampling The cost is lower
 Data collection is faster Easily planned
 It gives better estimates of the mean than unrestricted random

Vertex transponder

Equipments for field survey

www.frim.gov.myGPSDbh Tape Prism

Ground truths data for remote sensing classification

 Ground truth is a term used in remote sensing; it refers toinformation collected on location.

 Ground truth allows image data to be related to realfeatures and materials on the ground.

 The collection of ground-truth data enables calibration of

www.frim.gov.myremote-sensing data, and aids in the interpretation andanalysis of what is being sensed.

Ground truths data for remote sensing classification
There are many methods to get the ground truth data forremote sensing classification. For example,
 Field work activity
 Interpretation from higher resolution images (aerialphoto, Google earth images)
 From current reference data(forest map, vegetation
map, etc.)

 The field work for ground truth data collection requires

www.frim.gov.myrepresentative samples, and photos should tell the story of
the selected landuseCollecting ground truth data from Google Earth Since field survey is time consuming and needs muchbudget, GE can be considered another complementary
product for remote sensing analysis,

 The high spatial resolution images released from GE, as afree and open data source, have provided great supports forthe traditional land use mapping.

 Can be treated as ancillary data to collect the training or

www.frim.gov.mytesting samples for land use classification and validation orused as a visualization tool for land use maps.

Collecting ground truth data from Google Earth
There are several clues can be use to help for interpret
images in GE:

 Shape Size Pattern Tone and Colorwww.frim.gov.my Shadow

CONCLUSION

www.frim.gov.my

This project component
explore some technicalimage processing andsampling that can help ininterpretation and analysisof remote sensing data thatcan help for implementing
REDD+.

www.frim.gov.my

THANK YOU