ISBN：978-4-905304-97-5

Reducing Emissions from Deforestation andforest Degradation and the Role of Conservation,
Sustainable Management of Forests and Enhancement

COOKBOOK
ANNEX
Research Manual Vol. 5
Satellite Image Analysis for Area Estimation Using Remote
Sensing
HIDEKI SAITO

Research achievement of the 4th mid-term plan 19 (Forest Management Technology-16)

Preface
The basic concept of REDD-plus is to provide economic incentives such as funding or
credits to developing countries for REDD activities (reducing emissions from deforestationand forest degradation) and "plus" activities (reducing CO2 emissions and CO2 levels in theatmosphere by carbon sequestration). Thus, in order to estimate the changes in the amount of
FDUERQVWRUHGLQIRUHVWVPRQLWRULQJXVLQJVFLHQWL¿FDSSURDFKLVHVVHQWLDOThe REDD Research and Development Center, Forestry and Forest Products ResearchInstitute compiled “REDD-plus Cookbook” in 2012; an easy-to-understand technical manual
which provide basic knowledge and techniques required for REDD-plus with the main focus on
the forest carbon monitoring methods. Knowledge and techniques required for REDD-plus arecompiled in units called “Recipe” in this REDD-plus Cookbook. This REDD-plus Cookbook isintended for the policy makers working for the introduction of REDD-plus and the practitioners
and experts working on the REDD-plus activities.
As REDD-plus Cookbook focuses on basic knowledge and techniques required for REDDplus, the experts working on the REDD-plus activities will need further detailed information in
WKH¿HOG7KXVZHFRPSLOHG³5(''SOXV&RRNERRN$QQH[´WRSURYLGHWKHH[SHUWVZLWKPRUH
GHWDLOHGDQGSUDFWLFDOLQIRUPDWLRQLQHDFK³5HFLSH´,QWKLVPDQXDOVSHFL¿FPHDVXULQJPHWKRGVare explained. Also, this manual is intended to be used as a textbook for building capacity. In
order to have a better technological understanding on these topics, it is recommended to readthis manual in conjunction with REDD-plus Cookbook.
We hope this manual will contribute to the promotion of REDD-plus in many parts of theworld.
March, 2019
The REDD Research and Development Center
Forestry and Forest Products Research InstituteContents
1 Outline
1.1 Purpose of REDD-plus Cookbook Annex

11

1.2 How to use this survey manual

1

1.3 Recipes covered by REDD-plus Cookbook

1

2 What is necessary for satellite image analysis

2

2.1 Computers

2

2.2 Display

2

2.3 Internet connection

2

2.4 Software2

2.5 Programming environment

3

3 Acquiring satellite images
4 Consideration of atmospheric correction, cloud removal, and seasonality

48

4.1 Steps for atmospheric correction

8

4.2 Cloud masking

13

4.3 Seasonality

14

 'H¿QLWLRQRIWKHFODVV

6 Ground truth15

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16 2EMHFWEDVHGFODVVL¿FDWLRQ

178 Accuracy assessment

18

References

18

REDD-plus COOKBOOK ANNEX

1 Outline■ 1.1 Purpose of REDD-plus Cookbook Annex
The “REDD-plus Cookbook,” published by the Forestry and Forest Products Research Institutein 2012, is a technical manual that simply explains the basic knowledge and techniques for
REDD+ with a focus on forest carbon monitoring. This satellite image analysis manual explains
the analytical method to estimate forest areas using remote sensing technique. Forest areas
derived from remote sensing techniques will be used to estimate forest carbon stock, as indicatedby the REDD-plus Cookbook.
This manual was prepared for foresters who estimate forest areas using satellite data in practice. We hope that it also provides useful information to students studying forestry and forest
ecology, and organizations, such as NGOs, that are interested in REDD+.

■ 1.2 How to use this survey manual
The data analysis methods presented here are standard methods for REDD+. When estimating forest area based on satellite image analysis, results would be different depend on source
data, acquisition date, and analysis method. Reliable forest area estimates must select theappropriate data, acquisition period, and analytical method. The accuracy, precision, and bias
of maps must be evaluated thoroughly. Therefore, before performing satellite image analysis, it
is recommended to read this manual and understand all processes.
&ODVVL¿FDWLRQVDQGDFFXUDF\DVVHVVPHQWRIVDWHOOLWHLPDJHVIRUDUHDHVWLPDWHVXVLQJUHPRWH
sensing, have different default parameters and operational methods, depending on the software used. It is important to carefully read manuals and tutorials of software before starting ananalysis.

■ 1.3 Recipes covered by REDD-plus Cookbook
Recipes covered by REDD-plus Cookbook are shown in [ ]. Please refer to REDD-plus
Cookbook.
REDD-plus Cookbook can be downloaded from the sites below.
http://redd.ffpri.affrc.go.jp/pub_db/publications/cookbook/index_en.html

1Vol.5

Satellite Image Analysis for Area Estimation Using Remote Sensing

2 What is necessary for satellite image analysis■ 2.1 Computers
7RSHUIRUPLPDJHDQG*,6DQDO\VLVFRPSXWHUVZLWKDGYDQFHGVSHFL¿FDWLRQVDUHQHFHVVDU\For example, ERDAS Imagine is a commonly used image analysis software and recommends
a 64-bit CPU that can perform multicore processing and uses memory of 8 GB or more. The
software requires 4 GB of disc space. Depending on the mapping project, several TB of space
DUHQHFHVVDU\IRUVWRULQJGDWDLQWHUPHGLDWH¿OHVDQGUHVXOWVZKLFKUHTXLUHVIDVWGLVFDFFHVVThe operating system should be 64-bit (Windows10 Pro 64-bit, etc.). Image analysis software is
generally capable of 3D display using OpenGL, etc. Software that can use GPU for operations
are available. To maximize these functions, a high-performance graphics card is necessary.

■ 2.2 Display
A large display is necessary for image analysis. Dual display is convenient for comparisonof images and analysis while looking at reference images. A high-performance graphics card isnecessary for dual display output.

■ 2.3 Internet connection
Landsat images, commonly used for REDD+ projects, are usually downloaded from theUnited States Geological Survey (USGS) homepage. Each Landsat satellite image is 800-1000
MB, which requires a high-speed internet connection. Recently, Google Earth has been used as
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essential. In addition, to access help for analytical methods, software manuals, user community
pages, and free tools require an internet connection.

■ 2.4 Software
Image analysis software includes ERDAS Imagine, PCI, ENVI, TNTmips, etc., which are allavailable for a price. As for open source software, there is GRASS, which is distributed with
the GIS software QGIS as a set. Representative GIS software, ArcGIS, has plug-ins, such as
Spatial Analyst, and some level of processing is possible. Specialized software such as the
REMHFWEDVHGFODVVL¿FDWLRQVRIWZDUHH&RJQLWLRQDUHDOVRDYDLODEOHIRUIRUHVWFRYHUPDSSLQJ

2

REDD-plus COOKBOOK ANNEX■ 2.5 Programming environment
Recent image analysis software has high-quality GUI, and a programming environment is
not essential. However, scripts that download multiple Landsat satellite images at once, or free
image analysis tools, such as cloud removal, are available on the internet. To use these tools,
programming skills, such as C and Python, are necessary.

3Vol.5

Satellite Image Analysis for Area Estimation Using Remote Sensing

3 Acquiring satellite images [Recipe-T05]
Various types of data from satellite images are used for forest area estimates. Free online
Landsat images are generally used. Because forest area estimates for REDD+ are restricted
costs on the monitoring. Here, we present the steps to download Landsat images from the
USGS Earth Explorer (https://earthexplorer.usgs.gov/). To use this site, one must register asa user with the USGS. A tutorial with more details on the download steps can be found here:
https://earthexplorer.usgs.gov/documents/helptutorial.pdf.

Figure 1. Earth Explorer Search Criteria screen
Earth Explorer, USGS (https://earthexplorer.usgs.gov/)
Landsat image scenes are indicated by a path determined by the orbit, and row determinedby the latitude at the center of the scene. First, input the path and row of the observation target(Figure 1).
1: Input the path.
2: Input the row.
3: Input the start date for acquisition of search target images.
4: Input the end data for acquisition of search target images.
5: Click on “Show” to display the observation target site.

4

REDD-plus COOKBOOK ANNEX

Figure 2. Earth Explorer Search Tab for data sets
Earth Explorer, USGS (https://earthexplorer.usgs.gov/)
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5

Vol.5

Satellite Image Analysis for Area Estimation Using Remote Sensing

Figure 3. Search Tab for data sets
Earth Explorer, USGS (https://earthexplorer.usgs.gov/)
7: Select L8 OLI/TIR from data sets.
8: Click on “Results” (Figure 3).

Figure 4. Search results
Earth Explorer, USGS (https://earthexplorer.usgs.gov/)

6

REDD-plus COOKBOOK ANNEX

Search results are displayed (Figure 4).
9: Click on the download icon of the desired image.

Figure 5. Download options dialog box
Earth Explorer, USGS (https://earthexplorer.usgs.gov/)
Options of the data available for download are displayed (Figure 5). If you click on the download icon for Level1 GeoTIFF Data Product (data size is about 800-1000 MB), a dialog box for
WKHGRZQORDGGHVWLQDWLRQDQGWKH¿OHQDPHDSSHDUV&KRRVHWKHIROGHUDQGVHWWKH¿OHQDPH
click on “OK,” to start the download.
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Products, are processed only after an order is placed. When the processing is complete, there
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point, there is a Python script ready, and multiple images can be downloaded through batchprocessing using this script.

7Vol.5

Satellite Image Analysis for Area Estimation Using Remote Sensing

4 Consideration of atmospheric correction, cloud removal, and seasonality
Downloaded satellite data stores radiance values acquired by the sensor during satellite orbit.
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light and a scattered-light component from aerosol and water vapor in the atmosphere, these
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is called atmospheric correction and can be performed easily using the ENVI module FLAASH(Fast Line-of-sight Atmospheric Analysis of Hypercubes) or commercially available software
such as ATCOR for Imagine. As for 6S, scripts such as the GRASS module and Py6S (http://
www.py6s.rtwilson.com/) are available free of charge. However, to be able to execute this, a
programming environment and the skills required to use such an environment are necessary.
For atmospheric correction, the USGS website performs a higher-order process called/DQGVDW 6XUIDFH 5HÀHFWDQFH +LJKHU/HYHO 'DWD 3URGXFWV SURYLGLQJ GDWD WKDW KDYH DOUHDG\
EHHQFRQYHUWHGWRJURXQGVXUIDFHUHÀHFWDQFH,I\RXGRQRWRZQDWPRVSKHULFFRUUHFWLRQVRIWware or the skills to perform atmospheric correction, using such data may be an option.

■ 4.1 Steps for atmospheric correction
Let us explain atmospheric correction using ATCOR for Imagine 2014 incorporated into
ERDAS. First, data downloaded in “2” are read by ERDAS Imagine to perform layer stacking.
7KHQDSDUDPHWHU¿OHLVSUHSDUHGWRFRQYHUWGLJLWDOQXPEHUVWRUDGLDQFHYDOXHVWKLVLVFDOOHG
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example.
c1[mW/cm2 sr micron]

7

c0

1

-6.366048

0.001273

2-6.518911

0.0013043-6.007121

0.0012014-5.065543

0.001013

5-3.099860

0.000620

6-0.770907

0.0001547-0.259837

0.000052

7KH¿UVWURZLQWKH¿UVWFROXPQLVWKHEDQGQXPEHUVHFRQGWRHLJKWKURZVDUHFRQYHUVLRQparameters of each band. The conversion parameter is different for each scene.
During processing, the data acquisition date and time and the latitude-longitude at the center

8

REDD-plus COOKBOOK ANNEXof the scene become necessary. Thus, this information should be prepared.
DATE_ACQUIRED = 2014-03-10
SCENE_CENTER_TIME = 03:14:00.1527749Z
CENTER LAT: 13.00646
CENTER LON: 106.624715
7KLVGDWDLVVWRUHGLQWKH¿OHZLWKWKHH[WHQVLRQRI³

07/W[W´

Next, we move onto processing with ATCOR.
① 7XUQ RQ $7&25 $7&25 LV IRU ÀDW DUHDV DQG $7&25 LV IRU PRXQWDLQRXV DUHDV &UHDWHDQHZSURMHFW¿OH
②&KRRVHLQSXWDQGRXWSXW¿OHV )LJXUH )LJXUH,QSXWDQGRXWSXW¿OHPHQX

9

Vol.5

Satellite Image Analysis for Area Estimation Using Remote Sensing③6HOHFWWKHVHQVRUDQGFKRRVHWKHFDOLEUDWLRQ¿OHWKDWZDVSUHSDUHGHDUOLHU )LJXUH )LJXUH&DOLEUDWLRQ¿OHPHQX

10

REDD-plus COOKBOOK ANNEX④ Use the solar position calculator (Figure 8) to calculate the solar zenith and apply. Here,input the date and time of the data acquisition as well as the latitude and longitude at the cenWHURIWKHLPDJHLQWKHKHDGHU¿OH7KHFDOFXODWLRQUHVXOWLVDSSOLHGDFFRUGLQJWRWKHJHRPHWU\¿HOGIURPWKHPDLQPHQX )LJXUH 

Figure 8. Solar position calculator.

Figure 9. Results of applying the geometry.

11Vol.5

Satellite Image Analysis for Area Estimation Using Remote Sensing⑤ Select the type of aerosol in the atmospheric selections menu (Figure 10) and estimate thevisibility. Click on “Run Correction.”

Figure 10. Atmospheric selections menu.
⑥ Click on “Atmospheric Correction” on the atmospheric correction screen (Figure 11) toexecute atmospheric correction.

Figure 11. Atmospheric correction screen.

12

REDD-plus COOKBOOK ANNEX+HUHRQHFDQREWDLQWKHLPDJHRIUHÀHFWDQFHZLWKWKHDWPRVSKHULFFRUUHFWLRQSHUIRUPHGThe present example is ATCOR2 without a digital terrain model (DTM). In this case, radiancedue to topography is not corrected. ATCOR corrects radiance due to topography, but as an input
value, DTM and topographic feature values calculated from a DTM are necessary. In developing countries SRTM, ASTER GDEM, and ALOS DSM can be used as a DTM. Correction ofradiance due to topography could be overcorrected, thus the corrected results must be carefully
examined.
ATCOR
ATCOR for IMAGINE is an add-on module for ERDAS IMAGINE.
$VDSUHSURFHVVRIGLIIHUHQFHRIH[WUDFWLRQRUFODVVL¿FDWLRQ$7&25IRU,0$*,1(FDQSHUIRUP
atmospheric correction of satellite data. This product is exclusively supplied by GEOSYSTEMS
Germany.
For more details, please see https://www.geosystems.de/en/products/atcor/.
Contact: info@geosystems.de■ 4.2 Cloud masking [Recipe-T06]
Satellite images contain clouds, haze, and their shadows. Satellite image pixels coveredby these cannot be used to estimate areas and must be removed. This is called masking. For
Landsat image data, a mask to remove clouds, haze, and their shadows can be prepared withfree software called Fmask, which can be obtained from the following site.
https://github.com/prs021/fmask
Fmask is executed by copying Fmask.exe (execution) to the folder containing the downloaded Landsat data and then decompressed, via command prompt. Results are outputted to the
executed folder with a format that can be opened with the image analysis software ENVI (Figure12). Pixel values of the resulting image show the following classes.
0 => Clear land area
1 => Transparent water area
2 => Shadow of clouds
3 => Snow
4 => Cloud
255 => Pixel without observed value

13Vol.5

Satellite Image Analysis for Area Estimation Using Remote Sensing

Figure 12. Example of extracted water area, clouds, and shadows of clouds.
Since the value of “1” in mask image indicates cloud free areas, masking is performed with
the image analysis or GIS softwares.

TIPS

There was a period during which TIRS (thermal band) of Landsat 8 was broken.
Thus, Fmask has versions with and without TIRS.

■ 4.3 Seasonality [Recipe-T06]
Since satellite images are captured routinely as the satellite passes, these images are obtained throughout the year. However, since there are evergreen and deciduous forests, theappearance of the image differs depending on the season. As the solar elevation changes, thepositional relationships between the sun, observation target, and the satellite also change, andthe radiance input into the sensor also changes. Though atmospheric correction can reducesome of these effects, generally these effects are taken into consideration, the acquisition time
LVVSHFL¿HGDQGWKHVDWHOOLWHGDWDDUHVHOHFWHGLQWLPHVHULHVDQDO\VLV :KLWH ,IDQLPDJH
from a period of defoliation is selected, it is easy to differentiate evergreen forests and deciduous
forests. But deciduous forests can be easily mistaken with other land uses, such as agriculturallands. On the other hand, if an image from other seasons is selected, it is easy to differentiate
IRUHVWIURPQRQIRUHVWEXWLWLVGLI¿FXOWWRGLIIHUHQWLDWHHYHUJUHHQIRUHVWVIURPGHFLGXRXVIRUHVWVTherefore, the season used for analysis must be selected based on the objective of the study,
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14

REDD-plus COOKBOOK ANNEX

'H¿QLWLRQRIWKHFODVV>Recipe-T07]
Forest carbon stock in REDD+ is obtained with the product of activity data and emission
factors. Forest area estimated via remote sensing, is equivalent to the activity data. Thus, to
FODVVLI\IRUHVWVWKHHPLVVLRQIDFWRUVPXVWEHGHWHUPLQHGIRUHDFKFODVVL¿HGFODVV,IWKHUHLV
an existing map, class could be set accordingly, but class without an emission factors would bemeaningless. Considering the performance of a satellite sensor, it must be examined if there are
FODVVHVWKDWFDQQRWEHFODVVL¿HGE\WKHDQDO\VLV

6 Ground truth [Recipe-T08]
Ground truth refers to data that are considered to be true values used to understand spectral
characteristics of each class and to analyze accuracy. Therefore, a representative location for
HDFK FODVV LV VHOHFWHG IRU ZKLFK JURXQG FRYHU LV FRQ¿UPHG RQ WKH JURXQG DQG IRUHVW W\SHbiomass, etc., are surveyed. In a case where a landcover map is being prepared from medium
resolution satellite data, if the ground truth is only land cover, it may be substituted by aerial
photographs or the interpretation of high-resolution satellite images.
7KH VXUYH\ SRLQWV DUH UDQGRPO\ RU V\VWHPDWLFDOO\ VDPSOHG WR SUHYHQW DUELWUDU\ LQÀXHQFHVDIIHFWLQJWKHGDWDDQDO\VLV)RUPHWKRGVRIVDPSOLQJWKHUHLVFRPSOHWHO\UDQGRPRUVWUDWL¿HG
sampling. As a systematic and completely random sampling, the most common method is tochoose all grid points at a certain interval to be the survey points (Figure 13). If funds and labor
DUHOLPLWHGVWUDWL¿HGVDPSOLQJVXFKDVVHOHFWLQJDSRLQWSHUODQGFRYHULVHIIHFWLYH)RUIRUHVWV
limited access often prevents the ability to reach the survey points, causing a bias of a small
number of samples from areas with poor access which is a sampling issue.

If image capture date and ground truth capture date (day of the ground survey

TIPS

or the day aerial photographs were taken) are different, land cover might havechanged during that time, which should be noted.

TIPS

15

For more details on ground truth survey methods, please see [Cookbook Annex
Vol. 4].

Vol.5

Satellite Image Analysis for Area Estimation Using Remote Sensing

Figure 13. Ground truth points arranged systematically.

&ODVVL¿FDWLRQ>Recipe-T09]
Presently, in tropical countries, color composite images are prepared from satellite data, such
as Landsat. By visually interpreting these images, a land cover map is prepared. However,this method has problems, such as the need for an experienced interpreter, results dependent
XSRQWKHLQWHUSUHWHUDQGWKHUHTXLUHPHQWRIVXI¿FLHQWWLPH7KRXJK/DQGVDWKDVPXOWLSOHEDQG
data, color composite images only have three bands; thus, the information in the other bands
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clustering, maximum likelihood, and minimum distance methods which are installed as major
IXQFWLRQVRIUHPRWHVHQVLQJVRIWZDUH)RUFOXVWHULQJZKLFKLVDQXQVXSHUYLVHGFODVVL¿FDWLRQparameters such as the number of initial clusters and repeats must be set. These default values

16REDD-plus COOKBOOK ANNEX

should be tried, then parameters should be adjusted until appropriate results are obtained.
Classes are assigned to the obtained clusters while referring to the original images, aerial phoWRJUDSKV¿HOGVXUYH\UHVXOWVRU*RRJOH(DUWKLPDJHV:LWKVXSHUYLVHGFODVVL¿FDWLRQWUDLQLQJDUHDV PXVW EH VHOHFWHG IRU WKH WDUJHW FODVVHV 7UDLQLQJ DUHDV UHTXLUH D VXI¿FLHQW QXPEHU RI
pixels and must be selected from pure areas. Size and region of the training areas must be seOHFWHGDSSURSULDWHO\ZKLOHUHIHUULQJWRFODVVL¿FDWLRQUHVXOWVRULJLQDOLPDJHVDHULDOSKRWRJUDSKV¿HOG VXUYH\ UHVXOWV DQG *RRJOH (DUWK LPDJHV )XUWKHUPRUH LQ UHFHQW \HDUV PHWKRGV KDYH
developed that incorporate neural networks and machine learning, but these methods must be
executed with designated statistical software and programming.

The initial number of clusters is generally 30 to 50, though the cluster number
depends on the number of target classes. If cluster images include agriculturallands and urban areas, a larger number of clusters will focus on these areas, andTIPS

there will not be many clusters in forested areas. Therefore, in the initial classi¿FDWLRQ YHJHWDWLRQV DQG QRQYHJHWDWLRQV LQFOXGLQJ FORXG VKDGRZ DQG ZDWHU 
DUHFODVVL¿HGWKHQWKHDUHDRIYHJHWDWLRQVVKRXOGEHFODVVL¿HGDIWHUFUHDWLQJD
vegetation masking image.

■  2EMHFWEDVHGFODVVL¿FDWLRQ
7KHREMHFWEDVHGFODVVL¿FDWLRQPHWKRGVWDWLVWLFDOO\SURFHVVHVVSHFWUDOLQIRUPDWLRQIRUHDFKEDQGSHUREMHFWDQGFODVVL¿HVWKHLQIRUPDWLRQLQWRVLPLODUJURXSV2EMHFWVUHIHUWRDVHWRISL[HOV
obtained by dividing regions. Objects have similar characteristics and are generated with spatial
grouping. Thus, the results are similar to maps created through visual interpretation. Whencreating objects, the parameters must be set, e.g., colors and scales. Attention should be paid
when setting parameters, especially since the patch size of objects are related to minimum
mapping unit (MMU).
,QSL[HOEDVHGFODVVL¿FDWLRQSL[HOYDOXHVDUHXVHG+RZHYHULQREMHFWEDVHGFODVVL¿FDWLRQVSHFWUDODWWULEXWHVDUHXVHGIRUFODVVL¿FDWLRQVXFKDVWKHPHDQDQGVWDQGDUGGHYLDWLRQRISL[HO
values included in objects, spatial attributes, circumference, extensibility (aspect ratio), andtexture attributes (e.g., data range, dispersion, and entropy). These attributes are automatically
FDOFXODWHGE\WKHVRIWZDUHDQGIRUFODVVL¿FDWLRQWKHVHOHFWHGDWWULEXWHVDUHXVHG,IDWWULEXWHVXVHIXOIRUFODVVL¿FDWLRQDUHQRWFOHDUVRPHVRIWZDUHKDVIXQFWLRQVWRSURYLGHVXSHUYLVLRQVRWKDWHIIHFWLYHDWWULEXWHVIRUFODVVL¿FDWLRQFDQEHDXWRPDWLFDOO\VHOHFWHG
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provided as an extension or as dedicated software.

17Vol.5

Satellite Image Analysis for Area Estimation Using Remote Sensing

Color and scale parameters in eCognition are affected by the resolution of satellite

TIPS

images but also by the bit number of the data. Thus, the same parameters cannot
be applied to Landsat 7 ETM + data (8 bits) and Landsat 8 OLI data (16 bits).

8 Accuracy assessment [Recipe-T09]$ ODQG FRYHU PDS EDVHG RQ WKH FODVVL¿FDWLRQ RI VDWHOOLWH LPDJHV LV QRW FRQVLVWHQW ZLWK WKHactual land cover, as it contains a certain amount of error. To use such a land cover map, its
FODVVL¿FDWLRQDFFXUDF\DQGELDVPXVWEHNQRZQ7KXVWKHSHUVRQSUHSDULQJWKHPDSPXVWDVVHVVWKHDFFXUDF\RIWKHFODVVL¿FDWLRQUHVXOWVDQGFODULI\WKHFODVVL¿FDWLRQDFFXUDF\DQGELDVHV
7RHYDOXDWHWKHFODVVL¿FDWLRQDFFXUDF\DWDEOHRIGLVFULPLQDWLRQHI¿FLHQF\LVXVHGIURPZKLFKindices of accuracy are derived, such as the overall accuracy, user accuracy, producer accuraF\DQG.DSSDFRHI¿FLHQW0RVWVRIWZDUHLVHTXLSSHGZLWKDQDFFXUDF\DVVHVVPHQWIXQFWLRQDV
a standard or as a plugin.
Ground truth data used for accuracy assessment needs data that is systematically or randomly acquired, as discussed in Chapter 6.

References
White, J.C., Wulder, M.A., Hermosilla, T., Coops, N.C., Hobart, G.W. (2017). A nationwide annual characterization of 25 years of forest disturbance and recovery for Canada using Landsat
time series. Remote Sensing of Environment,194: 303-321

18

Authors
Editors
VersionCitation

Hideki Saito (Forestry and Forest Products Research Institute)
Tamotsu Sato & Masayoshi Takahashi
1.01enThis document may be cited as:
Saito H (2019) REDD-plus Cookbook Annex.
Vol. 5 Satellite Image Analysis for Area Estimation Using Remote Sensing.
REDD Research and Development Center,
Forestry and Forest Products Research Institute Japan, 18pp.