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Grant Agreement No.: 262775Doc. No.: REDDAF 310.3Issue/Rev.: 1.0Date: 15.März 2013

REDDAF D310.3: Data & Maps forPrototype Test Areas

REDDAF

Reducing Emissions from Deforestation andDegradation in Africa

Collaborative ProjectCo-funded by the European Commission

Seventh Framework ProgrammeFP7-SPACE-2009-1

Stimulating the Development of GMES Services in Specific Areas

REDDAF Doc. No.: REDDAF D310.3FP7 Grant Agreement No.: 262775 Issue/Rev-No.: 1.0

REDDAF D310.3: Data & Maps for Prototype Test Areas Page I

Partners:

Users of the REDD Services: National Focal Points for UNFCCC

Country Name of National Focal Point Organisation

Cameroon Dr. Joseph Armathé Amougou Ministry of Environment and NatureProtection (MINEP)

CentralAfricanRepublic

Mr. Igor Tola Kogadou Ministere de l' Environnement et de l'Ecologie(MEE)

Consortium Partners (Beneficiaries) for the REDD Service Developments

BeneficiaryNumber

Beneficiary name Beneficiaryshort name

Country Entrymonth

Exitmonth

1 GAF AG (Coordinator) GAF AG Germany 1 36

2 MESAconsult MESA Germany 1 36

3 Système d'Information àRéférence Spatiale SAS

SIRS France 1 36

4 Universite Paul SabatierToulouse III

CESBIO France 1 36

5 JOANNEUM ResearchForschungsgesellschaft mbH

JR Austria 1 36

6 Geospatial Technical GroupSARL

GTG Cameroon 1 36

7 Universite de Bangui,Laboratory of Climatology,Cartography and GeographyStudies

LaCCEG-UB Central AfricanRepublic

1 36

Disclaimer:The contents of this document are the copyright of GAF AG and Partners. It is released by GAF AG on thecondition that it will not be copied in whole, in section or otherwise reproduced (whether by photographic,reprographic or any other method) and that the contents thereof shall not be divulged to any other person otherthan of the addressed (save to the other authorised officers of their organisation having a need to know suchcontents, for the purpose of which disclosure is made by GAF AG) without prior consent of GAF AG.


REDDAF D310.3: Data & Maps for Prototype Test Areas Page II

Project Information

Project REDDAF Project ID 262775

Start of project 01.01.2011 Duration 36 months

FP7 Programme FP7 Cooperation Work Programme 2010: Theme 9 SpaceActivity: 9.1 Space-based applications at the service of European SocietyArea 9.1.1: Pre-operational validation of GMES services and productsSPA.2010.1.1.04 Stimulating the development of services in specific areas

Funding Scheme Collaborative Project

Document Preparation and Release

Affiliation Name(s) Date Signature

Author JR Janik Deutscher 13.03.2013

Contributions

Review JR Manuela Hirschmugl 15:03.2013

Endorsement GAF AG

Document Issue Record

Issue Date Author(s) Description of Change

1.0 15.03.2013 Janik Deutscher First Issue

Document Distribution

Affiliation Specification

REDDAF project all consortium partners

REA, EC Project Officer, Project Reviewers, Commission Services

Dissemination Level

Project co-funded by the European Commission within the Seventh Framework Programme (FP7)

PU Public

PP Restricted to other programme participants (including the Commission Services)

RE Restricted to a group specified by the consortium (including the Commission Services) xCO Confidential, only for members of the consortium (including the Commission Services)


REDDAF D310.3: Data & Maps for Prototype Test Areas Page III

Executive Summary

The main objective of WP310, Improving EO-based Forest Cover Change Mapping Services, is tooptimise the production of forest maps and IPCC compliant land cover classes (within forest changeareas). One of the main issues to resolve in this context is the problem induced by frequent cloudcover in tropical areas. This report presents the application of the methods and algorithms developedin REDDAf and described in REDDAf Deliverable D310.2. More specifically, the methods for SARand optical data integration by means of the classification-based trainer were applied on a prototypearea in the Centre Province in Cameroon. Data gaps from a RapidEye classification due to clouds werefilled with a classification result of ALOS PALSAR data. The necessary processing steps for the mapproduction are described and the result is presented. The work is completed by an accuracy assessmentof the final map.


REDDAF D310.3: Data & Maps for Prototype Test Areas Page IV

Table of Contents

1 INTRODUCTION......................................................................................................................... 1

2 SERVICE OPERATION .............................................................................................................. 1

2.1 DATA ORDER HANDLING AND ACQUISITION .......................................................................... 12.2 SOURCE DATA ......................................................................................................................... 1

2.2.1 Optical Satellite Data ........................................................................................................................ 12.2.2 SAR Satellite Data ............................................................................................................................ 12.2.3 Field Data .......................................................................................................................................... 1

2.3 PROCESSING METHODS ........................................................................................................... 22.3.1 Data Input.......................................................................................................................................... 22.3.2 Pre-Processing................................................................................................................................... 22.3.3 Thematic Processing ......................................................................................................................... 22.3.4 Accuracy Assessment of the Forest / Non-Forest Maps.................................................................... 72.3.5 Accuracy Assessment of the IPCC Land Cover Maps ...................................................................... 7

3 RESULTS....................................................................................................................................... 8

3.1 MAP PRODUCTS ....................................................................................................................... 8

REFERENCES .................................................................................................................................... 11

ANNEXE 1 – SOURCE DATA LIST .................................................................................................. 1

ANNEXE 2 – FIELD WORK DATA................................................................................................... 2

List of FiguresFigure 1: Workflow for thematic processing using the classification-based trainer ............................... 3Figure 2: Cropland and grassland from field work as visible in the GeoEye image ............................... 4Figure 3: left: deforested area in GeoEye (cyan); right: deforested area after 10 months RapidEye(cyan)....................................................................................................................................................... 5Figure 4: left: deforested area in GeoEye (cyan); right: deforested area after 10 months RapidEye(cyan)....................................................................................................................................................... 5Figure 5: left: Vegetated area in GeoEye image (04/2010); right: bare (burnt) area in RapidEye image(01/2011); in field work campaign assigned as grassland (yellow point). .............................................. 6Figure 6: PALSAR data overlaid with forest (transparent) /nonforest (yellow) classification ............... 9Figure 7: Final classification result with merged data sets of forest / nonforest ................................... 10

List of Tables

Table 1: Accuracy assessment of the RapidEye forest / nonforest map.................................................. 7Table 2: Accuracy assessment of the multi-temporal dualpol PALSAR forest / nonforest map ............ 7Table 3: Accuracy assessment of the RapidEye IPCC land cover map .................................................. 7Table 4: Accuracy assessment of the multi-temporal dualpol PALSAR IPCC land cover map ............. 8Table 5: Source Data List ........................................................................................................................ 1Table 6: Field work data obtained in the area of GeoEye image ............................................................ 2


REDDAF D310.3: Data & Maps for Prototype Test Areas Page V

List of Abbreviations

ALOS Advanced Land Observing Satellite

CESBIO Centre des Etudes Spatiales de la Biosphere

CoV Coefficient of Variation

DEM Digital Elevation Model

EO Earth Observation

FBD Fine Beam Dual

FP7 Framework Programme 7

GAF GAF AG, Consultant for Geo-Spatial Services

GMES Global Monitoring for Environment and Security

GTG Geospatial Technology Group

HH Transmitted and Received Polarisation Horizontal

HV Transmitted Polarisation Vertical and Received Polarisation Horizontal

IPCC Intergovernmental Panel on Climate Change

ISODATA Iterative Self-Organizing Data Analysis Technique

JAXA Japanese Aerospace Exploration Agency

JR Joanneum Research

K&C Kyoto & Carbon (Initiative)

LaCCEG Laboratory of Climatology, Cartography, and Geography Studies

LaCCEG-UB Laboratory of Climatology, Cartography, and Geography Studies, University ofBangui

MESA MESAconsult

MINEP Ministry of Environment and Nature Protection

MODIS Moderate Resolution Imaging Spectroradiometer

EO Earth Observation

PA Producer’s Accuracy

PALSAR Phased Array type L-band Synthetic Aperture Radar

RADAR Radio Detection and Ranging

REDD Reducing Emissions from Deforestation and Degradation

REDDAf Reducing Emissions from Deforestation and Degradation in Africa

SAR Synthetic Aperture Radar

SIRS System d’Information à Reference Spatiale

UA User’s Accuracy

UNFCCC United Nations Framework Convention on Climate Change

WP Work Package


REDDAF D310.3: Data & Maps for Prototype Test Areas Page 1

1 Introduction

One of the aims of WP 310 aside from the development of new methods is also the application of themethod to a prototype area. For this purpose, the classification-based trainer is applied on a prototypearea in the centre province in Cameroon. This report gives an overview of the used data sets, thenecessary pre-processing and thematic processing steps and the results.

2 Service Operation

2.1 Data Order Handling and Acquisition

RapidEye data was acquired by GAF AG through the data warehouse. ALOS PALSAR dualpolarization data and the GeoEye scene were acquired through the data warehouse by JR. K&C datawas made available by JAXA through CESBIO.

2.2 Source Data

A complete list of source data as well as associated access and licensing conditions is given in Annex2. A summary of the main data used is provided in the following sections.

2.2.1 Optical Satellite Data

RapidEyeA RapidEye scene from 28.01.2011 was used to provide an input classification for the classification-based trainer. The scene was classified based on GeoEye reference areas. Spatial resolution is 5m.

GeoEyeA GeoEye scene from 04.04.2010 was ordered to be used as reference for the analysis and to enablequality assurance of the results. Spatial resolution is 0.5m.

2.2.2 SAR Satellite Data

Three PALSAR scenes were used as raster input for the gap classification. They were ordered asproduct H1.1, so Single Look Complex images with ‘Precision’ orbit data. Three scenes were neededto perform a temporal averaging over different vegetation cycles, which increases the accuracycompared to mono-temporal data (see comparison in REDDAf Deliverable D310.2).

The PALSAR scenes were acquired on 23.06.2010, 08.08.2010 and 23.09.2010, thus several monthsafter the GeoEye scene and several months before the RapidEye Scene was acquired. Image resolutionafter multilooking is 30m.

2.2.3 Field Data

A field campaign done within REDDAf by GTG in the area was used for the interpretation of theGeoEye image for both training and validation data. Altogether 42 points in the area of the GeoEyeimage were found, described and located with GPS measurements by GTG (see Annexe 2 – FieldWork data).



2.3 Processing Methods

In the following sections, the main techniques and standards used for service operation aresummarised.

2.3.1 Data Input

Data processing starts with selection of appropriate EO satellite scenes. For all input data, qualitychecks are performed according to defined procedures in order to detect anomalies, artefacts andinconsistencies.

2.3.2 Pre-Processing

Geometric Processing of GeoEye image:The GeoEye scene was orthorectified and pansharpened using JR’s RSG software. As geometricalreference, the RapidEye scene was used. The adjustment based on 18 tie points resulted in an RMSEof 6.59 m in X and 2.98 m in Y direction. Considering the spatial resolution of RapidEye with a pixelspacing of 5 m, this is an acceptable accuracy.

Pre-processing of the PALSAR data:Pre-processing of the PALSAR FBD data was carried out with JR’s RSG software. The three dual-polarization ALOS PALSAR images are ingested, then multilooked to a defined output resolution of30m. Digital number values are used. A multiresolution filter is applied to further despeckle theimages. Difference images are then calculated from the different polarization images. The followingband combinations are produced and stacked as individual layers in the output file: HH, HV, HH-HV,HH+HV. DEM based geocoding is then performed based on the SRTM4 90m model. A temporalaverage of the three ALOS PALSAR layer stacks is calculated by averaging the amplitude images foreach band combination. This reduces seasonal effects and radiometric artefacts (i.e. fromprecipitation) inherent in the images and thereby further reduces speckle noise. More details on all pre-processing steps can be found in REDDAf Deliverables D310.1 and D310.2.

2.3.3 Thematic Processing

All steps of the processing chain are briefly outlined below. The overall workflow is illustrated inFigure 1. The core of the workflow is the classification-based trainer, which is based on the principleof labeling the result of an ISOData clustering process (in the prototype area based on the PALSARdata) with the class labels of a known classification (obtained from the RapidEye image in theprototype area). To avoid mislabeling the workflow applies a number of filtering methods to removeproblematic areas or pixels which have a high probability to be miss-classifications. The workflow isexplained in detail in REDDAf Deliverable D310.2.



Figure 1: Workflow for thematic processing using the classification-based trainer

The classification-based trainer requires and initial classification as input. The RapidEye image wasclassified using reference areas for the required classes selected visually from the GeoEye scene and adata set from a field work campaign performed within REDDAf by GTG. The GeoEye scene wasdivided in two halves: areas identified in the northern part were used as training areas for theclassification, while areas identified in the southern part were used as validation areas. A supervisedmaximum likelihood classification was carried out based on the RapidEye image and the training areasfrom the northern half of the GeoEye image.

The aim was to classify all IPCC land cover classes that occur in the area:

1. Settlement (including roads)2. Forest (including swamp forest)3. Cropland4. Grassland5. Water

The methodological procedure for the ascertainment of verification areas (both for training andvalidation) has followed a strict procedure. The areas have been identified and digitized in the GeoEyeimage under inclusion of field information. The transfer of the verification areas to the RapidEyescene has been performed under following considerations: first checking the location accuracy, andsecondly omit areas with strong deviations (e.g. due to changes between acquisition dates).

A separation of the classes ‘grassland’ and ‘cropland’ was attempted for the area, but even visualinterpretation of the GeoEye data could not give accurate interpretation for these two classes. Themain reason is that most areas are under shifting cultivation, which means that it is cropland for some

Classification-basedtrainer



time, then left to fallow and turning into grassland before being burnt to serve as cropland again. Thiswas also noted by GTG in the field work report (see REDDAf Deliverable D430.1): These differentcroplands were at times noted to be replaced by dense grassland especially where the cropland hasbeen abandoned to fallow. Mosaic land use classes were rampant with cropland mixed with shrubs,grassland mixed with scattered trees and settlements with cropland. Croplands as identified in thefield work have a wide range: from bare soil to banana farm (plants 3m-5m mixed with grass 1.5mheight); Palms (plants 5m - 15m mixed with grass 1.5 - 2m height) even to a class cropland, where thedescription says Fallow land; Thick cover of grass; Dominated by fern plants. This fact is proven bythe comparison of the GeoEye image with the field work data, where grassland and cropland wereattributed, but these two classes often could not be distinguished in the GeoEye image (see examplesin Figure 2).

Figure 2: Cropland and grassland from field work as visible in the GeoEye image

In addition, it has to be emphasized that in the Tropics the development in the vegetation has a strongdynamic component. That means that areas visited during a particular period or obtained from imagesof a specific date can only be used reliably for a very short time. Some examples are given in thefollowing section. One example is shown in Figure 3: an area deforested shortly before imageacquisition of GeoEye (4.4.2010) and the same area after 10 months in the RapidEye image(28.1.2011). It can be seen that part of the area is covered with vegetation (either crops or grass), whilethe other part is still relatively bare soil with only scattered vegetation. Another example of the hightemporal dynamics is shown in Figure 4, where not only the vegetation regrowth, but even moredistinctly the cultivation practices of burning or harvesting are visible. Burning would change thespectral properties of the area completely and not only croplands, but also grasslands are burnt (seeFigure 5). Therefore no distinction between the two classes could be made. Therefore the RapidEyeimage is classified into the following target classes:

• Settlement (including roads)• Forest (including swamp forest)• Cropland & Grassland• Water



Figure 3: left: deforested area in GeoEye (cyan); right: deforested area after 10 months RapidEye (cyan).

Figure 4: left: deforested area in GeoEye (cyan); right: deforested area after 10 months RapidEye (cyan).



Figure 5: left: Vegetated area in GeoEye image (04/2010); right: bare (burnt) area in RapidEye image(01/2011); in field work campaign assigned as grassland (yellow point).

The ALOS PALSAR temporal average layer stack is produced as described in the pre-processing ofthe PALSAR data and is used as raster input file for the classification using the classification-basedtrainer.In detail the following steps are performed by the classification-based trainer:

• Resample the classification input to the resolution of the alternate raster input file. An outputresolution of 30m was therefore used.

• Remove small regions (i.e., regions below a user defined number of pixels) form the resampledclassification layer based on minimum mapping units or to remove potential classification errors.

• Calculate a mask of homogenous regions in the alternate raster input file by calculating theCoefficient of Variation (CoV) within a user defined window, and removing areas where the CoVis above a user-defined threshold ( normally 0.1)

• Perform and ISOData clustering process with user-defined parameters controlling the number ofiterations, cluster variance, maximum intercluster distance, minimum number of pixels in a clusterand the limit of pixels used for the clustering process. The clustering process randomly picks anumber of pixels within the mask calculated in the previous step to perform the clustering. Oncethe clustering is complete the entire alternate raster input frame is classified according to thestatistic parameters found in by this clustering process. Along with this classification for eachpixel the probability to belong to each cluster is calculated.

• Based on the above, calculation of cluster probability pixels where the best probability is below auser defined threshold, are removed.

• For high-probabilty clustering from the previous step and the resampled and masked classificationinput a co-occurence matrix is calculated which is then used to decide to which class label eachcluster belongs.

• The cluster output is then recoded to the class labels with the information from the previous step.This layer is the classification of the raster input file and was used for evaluating the classificationresult.

• The recoded result is resampled to the resolution of the original classification input• The resampled recoded result is stitched below the original input into the desired output frame to

fill the gaps (e.g., from clouds)



2.3.4 Accuracy Assessment of the Forest / Non-Forest Maps

An accuracy assessment is then carried out to evaluate the classification performance. An evaluationof the RapidEye classification is also carried out, as this classification is used as input for theclassification-based trainer and therefore its accuracy is directly related to the accuracy of the radardata classification. The assessment is carried out for both the forest/nonforest classification and theclassification of 4 IPCC classes. Reference areas were visually derived from the southern half of theGeoEye scene and a minimum mapping unit of 0.5ha was used to define the minimum size of thereference areas.

Table 1: Accuracy assessment of the RapidEye forest / nonforest map

classificationreference Nonforest forest % correct (PA)Nonforest 37329 4709 88,80forest 5676 107505 95,80% correct (UA) 86,80 95,80 93,31

The input RapidEye classification map for forest/nonforest has an overall accuracy of 93.3%.Producer’s Accuracies are higher for forest areas than for nonforest areas, there is a slightoverestimation of nonforest areas.

Table 2: Accuracy assessment of the multi-temporal dualpol PALSAR forest / nonforest map

classificationreference Nonforest forest % correct (PA)

Nonforest 14087 27951 33,51forest 3895 109339 96,56

% correct (UA) 78,34 79,64 79,49

Accuracy assessment results for the forest/nonforest map from the PALSAR data have an overallaccuracy of 79.5%. There is a strong overestimation of forest areas which leads to low producer’saccuracy for the nonforest class.

2.3.5 Accuracy Assessment of the IPCC Land Cover Maps

In a second accuracy assessment the IPCC classification is compared to reference areas.

Table 3: Accuracy assessment of the RapidEye IPCC land cover map

classificationreference

settlement forest

grassland &cropland

water

% correct (PA)settlement 569 1 413 0 57,88

forest 1 107505 5674 0 94,99grassland &

cropland 24 4808 34518 0 87,72water 0 0 0 1805 100

% correct (UA) 95,79 95,72 85,00 100 92,97

There is some confusion between the grassland & cropland class and the settlement class, leading to alow user’s accuracy for the settlement class. This is due to the small scale of the settlements and theirinterlinkages with croplands (see field work report, REDDAf deliverable D430.1).



Table 4: Accuracy assessment of the multi-temporal dualpol PALSAR IPCC land cover map

classificationreference

settlement forest

grassland &cropland

water

% correct (PA)settlement 534 145 304 0 54,32

forest 828 108969 3437 0 96,23grassland &

cropland 3852 26687 8711 0 22,19water 188 167 1450 0 0

% correct (UA) 9,89 80,14 62,66 100 76,13

The overall accuracy for the radar data IPCC classification is only slightly less accurate than theforest/nonforest classification (76.1 %). However, it shows a low producer’s accuracy for settlementsand grassland & cropland. The class water was not classified due to the small extent of occurrence.The achieved results are in accordance with recent publications on PALSAR classifications. [Donget al., 2012] achieved overall accuracies of 89% for four classes with highest values for water andforest and lowest values for cropland using 50m pixel size data. [Rakwatin et al., 2012] also use 50mdata, where best results show an overall accuracy of 73% for five classes not considering settlement.Compared to targeted maximum likelihood classification with special signature analysis, the resultsare clearly inferior. Therefore the use of the classification-based trainer for generation of IPCC landcover classes in this region is not recommended.

3 Results

3.1 Map Products

The output map product for forest / nonforest based on PALSAR multitemporal data is shown inFigure 6.



Figure 6: PALSAR data overlaid with forest (transparent) /nonforest (yellow) classification

The final product of the classification process is to combine the classification from the optical datawith the RADAR data based classification in order to fill the gaps. The final map result is given inFigure 7.



Figure 7: Final classification result with merged data sets of forest / nonforest



References

[Dong et al., 2012] Dong, J., Xiao, X., Sheldon, S., Biradar, C., and Xie, G. (2012). Mappingtropical forests and rubber plantations in complex landscapes by integrating PALSAR andMODIS imagery. ISPRS Journal of Photogrammetry and Remote Sensing, 74:20–33.

[Rakwatin et al., 2012] Rakwatin, P., Longepe, N., Isoguchi, O., Shimada, M., Uryu, Y., andTakeuchi, W. (2012). Using multiscale texture information from ALOS PALSAR to maptropical forest. International Journal of Remote Sensing, 33(24):7727–7746.


REDDAF D310.3: Data & Maps for Prototype Test Areas Page 1 Page 1

Annexe 1 – Source Data List

Table 5: Source Data List

JRSource Data Service

CategoryIPR & Ownership/Data Distribution

Specifications License Conditions Access and DistributionMechanism and Priorities, Period ofValidity

Status DataOrdering

EO DataGeoEye scene orthorectified Data warehouse (by

JR)0.5m pan, 2m ms,Cameroon

REDDAf 04.04.2010 In houseavailable

RapidEye scene orthorectified Data warehouse (byGAF)

5m, Cameroon REDDAf 28.01.2011 In houseavailable

3 ALOSPALSAR scenes

H1.1. data Data warehouse (byJR)

H1.1 data REDDAf 23.06.2010, 08.08.2010,23.09.2010

In houseavailable

Other dataGround truth dataset

Processeddata

GAF 2011 REDDAf In houseavailable



Annexe 2 – Field Work data

Table 6: Field work data obtained in the area of GeoEye image

No x y Landcover Leaf_type Tree_Grass V_strata Dominant_u Secondary_ Short_desc Historic_i Additional

1 803554 427572 Non-forest

N/A Grass 2m-3m

N/A Cropland onfallow

Fallow land withdense grass cover

Old fallow &existed as such atthe time of imageacquisition

Vast area leftto fallow,Few housesat the NorthEast

2 803175 426423 Non-forest

N/A Grass1.5m-3m

N/A Cropland Corn farm partiallyunder fallow, Baresurfaces, Farmhouses withaluminum roof

Farming atindustrial scale,Shiftingcultivationpracticed

Surroundedby forest

3 804294 426539 Non-forest

N/A Palms 5m-6m

N/A PerennialCropland

Oil palm plantationwith surface cover ofgrass

About 5 years oldoil palmplantation

Presence offew tree,Poorlymaintained

4 804397 426221 Non-forest

N/A Palms 6m,Grass1.5m-2m


Oil palm plantationwith few spottedplantain trees

Young palmplantation atimage acquisition

Surroundedby forest

5 804041 424741 Non-forest

N/A Cassava1.5m-2m

N/A AnnualCropland

Cassava farm withfew palms andplantains

Cropland at timeof imageacquisition

Sitesurroundedby forest,National roadpassing by

6 804903 427372 Forest Mix ofmediumand broadleaves

Tree 40m-45m

Threelayers

Forest Tree diameters rangebetween 40cm-45cm,40% canopy cover

Young secondaryforest at time ofimage acquisition

N/A

7 805903 428314 Non-forest

N/A Grass1.5m,

N/A Grassland Savannah grassland,Dominated by fern

Savannahgrassland,at the

Sitesurrounded



Trees 5m-7m

plants time of imageacquisition, Partsof the area oncecultivated

by forest,Isolate palms& bananas

8 806235 427770 Non-forest

N/A Cassava2m

N/A AnnualCropland

Cassava farm , Partlyburnt grassland

Old cropland withperiodicallygrassland by theside

N/A

9 806608 427617 Non-forest

N/A Grass 1.5m N/A Grassland Savannah grassland,Dominated by fernplants, Dense grasscover

Savannahgrassland, at thetime of imageacquisition

surroundedby forest inall directions

10 806475 427340 Non-forest

N/A Cassava1m, Shrubs3m-4m

N/A Cropland Mixed cropland withplantain and cassava

Young farmpossibly clearedand or burnt timeof imageacquisition

Aspect ofmass burningand cuttingof wood,Sitesurrounded

11 806848 427334 Non-forest

N/A Cassava3m, Grass2m

N/A AnnualCropland

Cassava farm withfew palms andplantains

Cassava farm attime of imageacquisition

Bordered bytrees in theSouth East &Swamp in theSouth

12 806997 427326 Forest Medium& Broadleaves

Tree 35m-40m,Palms 8m

Threelayers

Forest Tree diameters rangebetween 40cm-50cm,45% canopy cover

Young Secondaryforest at timeimage acquisition

N/A

13 807252 428090 Non-forest

N/A Grass1.5m-3m

N/A AnnualCropland

Currently burntcropland withcassava farm in theNorth

Cropland wasfallowed at thetime of imageacquisition


14 804688 42447 Non-forest

N/A N/A N/A Settlement Buildings of localmaterials andaluminum & ironroof

Old villagesettlement &existed as such atthe time of imageacquisition

N/A

15 805585 424353 Non- N/A Palms 8m- N/A Perennial Oil palm plantation Old plantation houses of



forest 10m, Grass1m

Cropland with surface cover ofgrass

that existed assuch at the timeof imageacquisition

localmaterials andaluminumroof in theEast

16 806790 423645 Non-forest

N/A Grass 1m N/A Grassland Savannah grassland,Dominated by fernplants

Grassland at thetime of imageacquisition

surroundedby forest

17 807050 423849 Non-forest

N/A Grass2.5m-3m

N/A AnnualCropland

Pineapple farm withthick cover of grass

Mature farmlandat time imageacquisition

Sitesurroundedby forest,Farm lacksmaintenance

18 807636 42392 Non-forest

N/A Plantain2m-5m,Palms 5m-8m; Grass2.5m


Plantain farm withthick cover of grass

Site was undercultivation at timeof imageacquisition

Farmlandsurroundedby forest

19 808202 423922 Non-forest

N/A N/A N/A Baresurface

Bare surfacesurrounded byvegetation &building

Excavated atimage acquisition

Close to aneducationalestablishment

20 809294 422527 Non-forest

N/A Palms 7m,Grass 2.5m

N/A Wetland Temporal swampwith raffia palms,Thick surface coverof grass

Periodically burnt, Temporarilyswampy area

surroundedby forest

21 809244 425435 Non-forest

N/A Palms 8m-10m, Grass1.5m-3m

N/A Wetland Temporal swampwith raffia palms,Thick surface coverof grass

Periodicallyburnt;Temporarilyswampy area

Site has oncebeencultivated,surroundedby forest

22 809325 425269 Non-forest

N/A Palms10m-15m,Grass 1m

N/A Wetland Swamp with raffiapalms; Thick surfacecover of grass

Periodicallyburnt, swampyarea

Large swamp

23 805569 426377 Non- N/A Grass 3m N/A Annual Fallow land ; Thick Fallow land at the surrounded



forest Cropland cover of grass time of imageacquisition

by forest

24 813045 416001 Non-forest

N/A Grass 1.5m N/A Grassland Savannah grassland,Very dense cover ofgrass; Dominated byfern plants

Natural grassland;Periodically burnt

surroundedby forest;Has stands ofburnt trees

25 813344 414482 Non-forest

N/A Palms 7m;Grass 1m-1.5M

N/A Wetland Permanent swampwith raffia palms;Thick surface coverof grass

Permanent swamp surroundedby forest

26 812891 417007 Non-forest

N/A Grass1.5m-2.5m

N/A AnnualCropland

Fallow land; Thickcover of grass;Dominated by fernplants

Old fallow land atthe time of image


27 808933 423966 Non-forest

N/A Palms 4m-5m


Oil palm plantationwith surface cover ofgrass

Palm plantationabout 5 years old

surroundedby forest

28 813183 42106 Non-forest

N/A Grass 2m N/A AnnualCropland

Fallow land ,Partially undercultivation

Cropland possiblyunder fallow attime imageacquisition

Partiallysurroundedby forest

29 805187 425550 Non-forest

N/A Banana4m-5.5m


Banana plantationwith few fruit trees& oil palms

Farm existed atthe time imageacquisition

Extensivefarm withforest in theNorth

30 813551 420874 Forest Small,medium& broadleaves

Trees 30m Threelayers

Forest Canopy cover 45%,Tree diameter 45cm-50cm, Denseundergrowth

Young Secondaryforest at timeimage acquisition

Presence offew palms

31 812418 49335 Non-forest

N/A N/A N/A Settlement EducationalestablishmentBuildings of mixmaterials andaluminum roof,Surface cover of

Educationalestablishment,Existed as such attime of imageacquisition

surroundedby forest



grass

32 810183 423925 Non-forest

N/A N/A N/A Settlement Buildings of localmaterials andaluminum & ironroof, Presence of fewtrees

Old villagesettlement &existed as such atthe time of imageacquisition

Villagesurroundedby forest

33 81065 423301 Non-forest

N/A Cassava4m

N/A AnnualCropland

Cassava farm withcurrently clearedarea by the side.Spotted stands oftrees

Cassava farmexisted as such atthe time of imageacquisition

N/A

34 811447 418892 Non-forest

N/A Cassava2m

N/A AnnualCropland

Cassava farm withspotted trees, palmsand plantains

Young cassavafarm at the timeof imageacquisition

surroundedby forest

35 812847 420617 Non-forest

N/A Palms 5m-7m

N/A Cropland Fallow landpresently burnt withstands of plantainsand palms

Fallow land attime of imageacquisition


36 818388 415912 Forest Small-Mediumleaves

Tree 35m-40m

N/A Forest Primary forest withcanopy of 50%/ treesdiameters of 30cm-70cm/ less denseundergrowth/ lianas

Natural forestsince period ofimage acquisition

N/A

37 818463 415994 Non-Forest

N/A Grass1.5m-2m

N/A Grassland Savannahgrassland

Unique grass type(fern plant)

Natural savannahsince period ofimage acquisition

Surroundedby forest

38 816136 417800 Non-Forest

N/A Palms 7m,Grass 2m

N/A Cropland Perennialcropland

Palm plantation/grass cover/ fewplantains stands

Same at imageacquisition

Few standsof trees in theplantation

39 817885 426309 Non-Forest

N/A Plants 3m-5m, Grass1.5m

N/A Cropland Perennialcropland

A banana farm/ grassgrowing / pineapplefarm in the East

Same at imageacquisition

Surroundedby forest

40 803821 416490 Non-Forest

N/A Grass 2m N/A Grassland Savannahgrassland

Area dominated byfern plant/few palms

Same at imageacquisition/ has

N/A



once beencultivated

41 803852 417225 Non-Forest

N/A Grass 3m,Plants 3m-7m, Trees30m-40m

N/A Cropland Old Fallow Grass cover partiallyburnt/few cassavastems/trees andpalms

Was under fallowat the time ofimage acquisition

Surroundedby forest

42 804805 416644 Non-Forest

N/A Grass 2m N/A Cropland Old Fallow Old cassavastems/thick grasscover partially burnt/isolated palms

Was under fallowat the time ofimage acquisition

Surroundedby forest

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