GLADAGLADAGlobal Land Degradation Global Land Degradation

AssessmentAssessmentWorking Session: GIS Techniques and Working Session: GIS Techniques and

Improved Global Land Degradation MappingImproved Global Land Degradation Mapping

Progress ReportProgress ReportChristopher M. Auricht and Christopher M. Auricht and

Freddy NachtergaeleFreddy Nachtergaele

22 January 200422 January 2004

AgendaAgenda Introduction BackgroundIntroduction Background Progress - Progress -

Updating GLASOD DatasetUpdating GLASOD Dataset• ASSOD & SOVEURASSOD & SOVEUR

Additional improvementsAdditional improvements• Protected Area, cold areas etcProtected Area, cold areas etc

Techniques for Identification of HotspotsTechniques for Identification of Hotspots• P/PETP/PET• LGPLGP

Other Issues – Clarification and Next StepsOther Issues – Clarification and Next Steps• Data & Info ProductsData & Info Products

Presentation/VisualizationPresentation/Visualization Data DisseminationData Dissemination

• Additional processing and analysis – options/requirementsAdditional processing and analysis – options/requirements

Introduction and Introduction and BackgroundBackground

Introduction / BackgroundIntroduction / Background

Need to appreciate that exercise will remain very Need to appreciate that exercise will remain very much a ‘product in progress’ I.e. On-going much a ‘product in progress’ I.e. On-going activityactivity

Requires a methodology that can accommodate Requires a methodology that can accommodate integration of new improved data layers as they integration of new improved data layers as they become availablebecome available

Work plan developed around a step-wised Work plan developed around a step-wised approach with outputs from each stage saved as approach with outputs from each stage saved as a discrete dataset -> possible to re-run whole a discrete dataset -> possible to re-run whole process. (Note: Could also develop a spatial process. (Note: Could also develop a spatial model that automates process)model that automates process)

ProgressProgressPart 1: ASSOD/SOVEURPart 1: ASSOD/SOVEUR

Basic GLASOD UpdateBasic GLASOD Update Input DatasetsInput Datasets

1:10 million 1990, GLASOD Dataset1:10 million 1990, GLASOD Dataset ASSOD, 1:5 Million 1995 (South East Asia)ASSOD, 1:5 Million 1995 (South East Asia) SOVEUR, 1:2.5 Million 2000 (Central & Eastern Europe)SOVEUR, 1:2.5 Million 2000 (Central & Eastern Europe)

Processing / MergingProcessing / Merging Reprojection, recoding and merging of new dataReprojection, recoding and merging of new data

OutputOutput Updated GLASOD datasetUpdated GLASOD dataset

GLASOD Update – Major CauseGLASOD Update – Major Cause

ASSODASSOD

Original GLASODOriginal GLASOD

GLASOD Update – DegreeGLASOD Update – Degree

ASSODASSOD

Original GLASODOriginal GLASOD

GLASOD Update – ExtentGLASOD Update – Extent

ASSODASSOD

Original GLASODOriginal GLASOD

GLASOD Update – Severity/ImpactGLASOD Update – Severity/Impact

ASSODASSOD

Original GLASODOriginal GLASOD

GLASOD Update – Major CauseGLASOD Update – Major Cause

SOVEURSOVEUR

Original GLASODOriginal GLASOD

GLASOD Update – DegreeGLASOD Update – Degree

SOVEURSOVEUR

Original GLASODOriginal GLASOD

GLASOD Update – ExtentGLASOD Update – Extent

SOVEURSOVEUR

Original GLASODOriginal GLASOD

GLASOD Update – DegreeGLASOD Update – Degree

SOVEURSOVEUR

Original GLASODOriginal GLASOD

GLASOD Update – Major TypeGLASOD Update – Major Type

SOVEURSOVEUR

Original GLASODOriginal GLASOD

Summary – ASSOD/SOVEURSummary – ASSOD/SOVEUR Issues during processing -Issues during processing -

Coding not standardised – took time to sort some Coding not standardised – took time to sort some things out! Would be a lot easier if some standards things out! Would be a lot easier if some standards were introduced and used!were introduced and used!

Some polygons without any attributes!Some polygons without any attributes! Separating ‘compound’ attributes (eg. Cause/Second Separating ‘compound’ attributes (eg. Cause/Second

Cause Type, severity etc were all in one field in Cause Type, severity etc were all in one field in SOVEUR) took additional time -> much easier if each SOVEUR) took additional time -> much easier if each attribute has a separate fieldattribute has a separate field

New Dataset produced New Dataset produced Separate Grids (Datasets) for ASSOD & Separate Grids (Datasets) for ASSOD &

SOVEUR maintained SOVEUR maintained

Improving Rigour Improving Rigour based in Thematic based in Thematic

DatasetsDatasets

Part 2: Updating of New Master Part 2: Updating of New Master GLASOD Dataset -> Improving RigourGLASOD Dataset -> Improving Rigour

No of datasets available to improve the rigour – No of datasets available to improve the rigour – accuracy of GLASOD Datasetaccuracy of GLASOD Dataset

Run a series of iterations on dataset based on Run a series of iterations on dataset based on specific thematic material and assumptionsspecific thematic material and assumptions

Datasets available includeDatasets available include WCMC protected areasWCMC protected areas IFPRI’s Agriculture areas datasetIFPRI’s Agriculture areas dataset Kassel/FAO Irrigation DatasetKassel/FAO Irrigation Dataset Population Population SlopeSlope LivestockLivestock Urban areas etcUrban areas etc

Part 2: Updating of New Master GLASOD Part 2: Updating of New Master GLASOD Dataset -> Improving Rigour Cont.Dataset -> Improving Rigour Cont.

AssumptionsAssumptions Consider as non-degraded all areas identified as Protected in Consider as non-degraded all areas identified as Protected in

WCMS dataset regardless of what GLASOD infoWCMS dataset regardless of what GLASOD info Where agriculture is given as cause in GLASOD create new info Where agriculture is given as cause in GLASOD create new info

only retaining that part which is indicated as agriculture in only retaining that part which is indicated as agriculture in IFPRI’s map or if is irrigated according to KasselIFPRI’s map or if is irrigated according to Kassel

Where deforestation/loss of vegetation is given as cause only Where deforestation/loss of vegetation is given as cause only retain where forestry is significant (>25%)retain where forestry is significant (>25%)

Where overgrazing is problem only retain if livestock density Where overgrazing is problem only retain if livestock density exceeds a certain densityexceeds a certain density

Chemical Pollution – restrict where cause is indicated as Chemical Pollution – restrict where cause is indicated as ‘salinization’ to areas where there is irrigation‘salinization’ to areas where there is irrigation

Consider all areas <2% slope free of water erosionConsider all areas <2% slope free of water erosion Consider all areas of tropical rain forest as non-degraded except Consider all areas of tropical rain forest as non-degraded except

where deforestation is indicated in GLASODwhere deforestation is indicated in GLASOD Consider all areas of population density <2p/km2 as non Consider all areas of population density <2p/km2 as non

degradeddegraded Consider all areas with cold or polar climate as non-degradedConsider all areas with cold or polar climate as non-degraded

ProgressProgressPart 2: Results to datePart 2: Results to date

Change Area to NDA if WCMC Protected. Change Area to NDA if WCMC Protected.

Part 2: Results to date. Cont.Part 2: Results to date. Cont.

Change Area to NDA if Polar ClimateChange Area to NDA if Polar Climate

CommentsComments

Problems with aggregating 30 arc second Problems with aggregating 30 arc second datasets to 5 min for Population. Used GAEZ datasets to 5 min for Population. Used GAEZ Population Plate 62 – Class One person or less / Population Plate 62 – Class One person or less / kmkm2.2. Surprisingly large area! Surprisingly large area!

Similar problem for WCMC protected areas but Similar problem for WCMC protected areas but overcome by filtering so only cells > 50% overcome by filtering so only cells > 50% protected area retained.protected area retained.

Polar Climate – had minimal impact as low pop Polar Climate – had minimal impact as low pop areas anywayareas anyway

Comments. Cont.Comments. Cont.

Tropical Forest & No Forest. Problem Tropical Forest & No Forest. Problem summarizing 1 km dataset to 5 min. Used ‘Major summarizing 1 km dataset to 5 min. Used ‘Major Ecosystems’ Plate 55 from GAEZ. Combined Ecosystems’ Plate 55 from GAEZ. Combined Forest Class with Climatic Zones to get ‘Tropical Forest Class with Climatic Zones to get ‘Tropical Forest’. Combined Cause Gri, class Forest’. Combined Cause Gri, class ‘Deforestation’( Cause-f). -> problem in assiging ‘Deforestation’( Cause-f). -> problem in assiging non-degraded area status to all tropical forest non-degraded area status to all tropical forest areas no listed as having deforestation as a areas no listed as having deforestation as a primary cause of degradation.primary cause of degradation.

Comments. Cont.Comments. Cont.

Sealing – Used ‘Major Ecosystems’ Plate 55 Sealing – Used ‘Major Ecosystems’ Plate 55 from GAEZ: Class 11: Urban Agglomerates – from GAEZ: Class 11: Urban Agglomerates – seemed to work well for Europe and North seemed to work well for Europe and North America, but didn’t pick up some areas in AsiaAmerica, but didn’t pick up some areas in Asia

Water Erosion in Flat Area – Combined Water Water Erosion in Flat Area – Combined Water Erosion (Major_Type, Grid class ‘w’) with slope Erosion (Major_Type, Grid class ‘w’) with slope grid. Worked well.grid. Worked well.

Part 2: Results to date. Cont.Part 2: Results to date. Cont. Collective results to dateCollective results to date

IssuesIssues

Datasets kept discrete process can be Datasets kept discrete process can be repeated if/when better info available e.g. repeated if/when better info available e.g. population – urban / rural at 5 minpopulation – urban / rural at 5 min

Livestock model not yet available / Livestock model not yet available / implementedimplemented

Techniques for Techniques for Identification of Identification of

HotspotsHotspots

Part 3: Techniques for Identification Part 3: Techniques for Identification of Hotspotsof Hotspots

30 Year Datasets for LGP and P/PET 30 Year Datasets for LGP and P/PET obtained from IIASAobtained from IIASA

Possible to determine hotspot areasPossible to determine hotspot areas Recently commenced this workRecently commenced this work

ProgressProgressPart 3: Techniques for Identification of HotspotsPart 3: Techniques for Identification of Hotspots

Converted all datasets to ESRI Grid Converted all datasets to ESRI Grid formatformat

Explored methods for analysing and Explored methods for analysing and visualizing time series datavisualizing time series data *.avi movie*.avi movie Flash movie Flash movie

Click Here to demo *.avi

movie

Enter Alt+F4 to close the link windowClick Here to demo *.flash

movie

Enter Alt+F4 to close the link window

Summary of Outputs to Summary of Outputs to DateDate

Summary of Outputs to dateSummary of Outputs to date

Updated GLASOD with ASSOD & Updated GLASOD with ASSOD & SOVEURSOVEUR

Individual GRID datasets for various Individual GRID datasets for various thematic layersthematic layers

30 Year LGP and P/PET Grids30 Year LGP and P/PET Grids Demo of movie visualization optionsDemo of movie visualization options

Next Steps and Issues Next Steps and Issues for Clarificationfor Clarification

Attention AreasAttention Areas

Other Issues – Clarification and Next Other Issues – Clarification and Next StepsSteps

• Data & Info ProductsData & Info ProductsPresentation/Visualization –> Presentation/Visualization –> CD with CD with

dynamic viewing tool dynamic viewing tool (Terrastat,Dynamic Atlas, Arc Reader) (Terrastat,Dynamic Atlas, Arc Reader) or pdf etc?or pdf etc?

Movies –> Global, regional ? Movies –> Global, regional ? Screen resolution?Screen resolution?

Data Dissemination –> Data Dissemination –> Terrastat Terrastat Integration and/or separate CD?Integration and/or separate CD?

Attention Areas. ContAttention Areas. ContAdditional processing and analysis Additional processing and analysis

for 30 year datasets: for 30 year datasets: options/requirements options/requirements

-> -> combine grids combine grids Identify hotspots by determining Identify hotspots by determining

max, min, mean for each cell.max, min, mean for each cell. Analyse based on mean class for Analyse based on mean class for

P/PET and LGP. P/PET and LGP. Compare results or summarize by Compare results or summarize by

FS, AEZ etc?FS, AEZ etc?

Attention Areas. ContAttention Areas. Cont

Other Datasets –Other Datasets – Use of Remote Sensing products – Use of Remote Sensing products –

MODIS, SPOT NDVI, Global MODIS, SPOT NDVI, Global Landsat (GeoCover), Africover etc?Landsat (GeoCover), Africover etc?

Others ?Others ?