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Mesoamerican Reef Alliance, ICRAN-MAR Project
Land use change modelling for
MAR
three scenarios for the region
Technical Report
Technical report on the collection of geographic data, the regression
analysis ofexplanatory factors ofland use patterns, the development ofa
set of three alternative scenarios, and the modelling ofland use changes
using the CLUES model. This work was carried out as part of the
ICRAN-MAR project's sub-result 1.2, "Trends in land use integrated with
spatial, hydrological andoceanographic models foruse in modelling".
Joep Luijten, Lera Miles and Emil Cherrington
UNEP World Conservation Monitoring Centre
5 October2006
***>
UNEP WCMC
SCRAN
This report was made possible through support provided by the office of Guatemala-
Central American Programs, Latin America and Caribbean Bureau, U.S. Agency for
International Development, under the terms of Grant No.596-G-00-03-00215-00. The
opinions expressed herein are those of the author(s) and not necessarily reflect the
views ofthe U.S. Agencyfor International Developmentorof UNEP.
dlooL
A In
Table of contents
List oftables v
List offigures vii
Summary
1
1 Data collection and preparation 2
1.1 Outline ofmethodology and preparation steps 2
1.1.1 Stage 1: Creation ofASCII gridswith identical numberofvalue cells 2
1.1.2 Stage 2: Conversion to a textfile for use by SPSS regression module 3
1.2 Grid extentand grid resolution 3
1.2.1 Creation ofwatershed boundaries shapefile 3
1.2.2 Conversion to raster masks 4
1.3 Land use/land coverclassification 5
1.3.1 Reduced number ofland coverclasses 5
1.3.2 Sources ofland coverdata 6
1.3.3 Output extent and cell size 7
1.3.4 Reclassification methodology using ArcGIS 7
1.3.5 Calculation ofarea by country and land covertype 9
1.3.6 Land coverfor use in N-SPECT 10
1.4 Explanatory factors ofland use patterns 10
1.4.1 Topographic factors - elevation and slope 10
1.4.2 Demographic factors- population density 13
1.4.3 Demographicfactors- location ofsettlements 13
1.4.4 Soil and geology factors 14
1.4.5 Climate factors - precipitation and length ofdry season 15
1.4.6 Contextual factors- protected areas 17
1.4.7 Contextual factors-access to roads and markets 18
1.4.8 Contextual factors-tourist hotspots and areas ofcoastal development 21
2 Analysis of drivers of land use change 22
2.1 Land Use ChangeAdjacenttothe Mesoamerican Reef 22
2.1.1 Mexico 22
2.1.2 Belize 23
2.1.3 Guatemala 25
2.1.4 Honduras 26
2.1.5 Regional Synthesis 26
2.2 Statistical analysis ofexplanatory factors for land use patterns 27
2.2.1 Methodology 27
2.2.2 Evaluating statistical significance and goodness offit 28
2.2.3 Regression results 29
3 GEO-4 scenarios and the ICRAN MAR project 34
3.1 Scenario summaries 34
3.1.1 Aboutthe GEO-4 scenarios 34
3.1.2 Markets First 35
3.1.3 Policy First 37
3.1.4 Sustainability First 38
3.2 Population and land cover change: comparisons between scenarios 39
3.3 Land cover quantification: the IFs and IMAGE models 44
3.3.1 Model background 44
3.3.2 Bringing the models together: methods 46
3.4 Future changes in protected areas 48
Modelling land use changes for scenarios using CLUE-S 52
4.1 Important aspects ofmodel development 52
4.1.1 Land use data 52
4.1.2 Probability Surfaces 52
4.1.3 Neighbourhood settings 53
4.2 Input data preparation 53
4.2.1 Files used by CLUE-S 53
4.2.2 Land use requirements fordifferent scenarios 54
4.2.3 Main model parameters 56
4.2.4 Regression parameters 57
4.2.5 Conversion elasticities 59
4.2.6 Conversion matrix 60
4.2.7 Dynamic location factorgrids: Protected Areas 62
4.2.8 Dealing with absence ofpine forest in Mexico 62
4.3 Simulation results 63
4.3.1 Simulated changes in land use and in forest cover 63
4.3.2 Average and maximum deviation ofsolution 63
Workshop, conclusions and recommendations 72
5.1 Technical Workshop 72
5.2 Conclusions and Recommendations 72
5.2.1 Application ofCLUE-S model to the MAR region 72
5.2.2 Workshop and training 73
References 74
Appendices 78
Appendix 1. Avenue scriptfor NoData filling and filtering 78
Appendix 2. Avenue scriptforcreating dynamic protected areas grids 82
Appendix 3: Complete listofavailable spatial data 85
Appendix4: Ecosystem Map land coverclassification 93
Appendix 5. Land use requirements forfuture scenarios 94
Appendix 6. CLUE-S Training Package (Exercises) 110
IV
List of tables
Table 1-1: Spatialextentsforthe rasterdatasets, bycountry. Thecoordinatesarebasedon Universal
TransverseMercator(UTM)projection forzone 16 withtheNAD 1927CentralAmerican datum. 4
Table 1-2: Landuseclasses usedforthelanduse changemodelling. TheoriginalEcosystem Map
datasethada moredetailedclassificationthatwasreduced. 5
Table 1-3: Totalarea (km2) ofeachlandcovertype inthe reclassifiedandrasterizedEcosystemmap
data (finalversion 4createdon 6th February2006) 6
Table 1-4: Reclassification tableforthe2003EcosystemmapusingfieldDESCRIPTIO 8
Table 1-5: Reclassificationtableforthe 2004BelizeEcosystemmapusingfieldECOSYSTEM 8
Table 1-6: Potentialexplanatoryfactorsthatwillbeincludedin the regressionanalysisandsimulated
oflanduse changes. Thenumber(#) hasbeen usedfornumberingofthe CLUE-Sregression
resultsparameterfilesandthereforestartsat0. Costofaccesstoroads waseventuallyleftout
fromtheanalysisbecause itisstronglycorrelatedtocostofaccesstomarkets. There weareno
categoricalexplanatoryfactors 72
Table 1-7: Gridreclassification (resampling) scheme forthenumberofdrymonths 16
Table 1-8: PrevailingdesignationtypesofWDPA areasin Mexico, Guatemala, HondurasandBelize.
18
Table 1-9: Friction values forlandcoverwith 250mgridcells. Onlandcover, average walkingspeed
wasestimatedat4km/hr, butreducedto3km/hrinforestandincreaseto5km/hrin urbanareas
20
Table 1-10: Friction valuesfordifferentroadtype with250mgridcells 20
Table 1-11:Frictionmultipliers forslope. There isno accountingforslopedirection:itisassumedthat
travellingboth up-slope anddown-slopeincursareduction intravelspeed. 21
Table 2-1: SummaryofthelogicregressionanalysisforBelize. Foreachdynamiclanduse, the
regression coefficientsforallstatisticallysignificantexplanatorylocationfactorsarelisted, with
thefourmostsignificantonesinbold. Notethatthe absolute valueofa regressioncoefficientis
noindicatorofitslevelofsignificance, soevenrelativelysmallvaluesmaybeinthetopfour....30
Table 2-2: SummaryofthelogicregressionanalysisforMexico. Foreach dynamiclanduse, the
regression coefficientsforallstatisticallysignificantexplanatorylocationfactorsarelisted, with
thefourmostsignificantonesinbold. Notethattheabsolute valueofaregressioncoefficientis
noindicatorofitslevelofsignificance, soevenrelativelysmallvaluesmaybeinthetopfour....31
Table 2-3: Summaryofthelogicregression analysisforGuatemala Foreachdynamiclanduse, the
regressioncoefficientsforallstatisticallysignificantexplanatorylocationfactorsarelisted, with
thefourmostsignificantonesinbold. Notethattheabsolute valueofa regression coefficientis
noindicatorofitslevelofsignificance, soevenrelativelysmallvaluesmaybeinthetopfour....32
Table2-4: SummaryofthelogicregressionanalysisforHonduras Foreachdynamiclanduse, the
regression coefficientsforallstatisticallysignificantexplanatorylocationfactorsarelisted, with
thefourmostsignificantonesinbold. Notethatthe absolute valueofaregressioncoefficientis
noindicatorofitslevelofsignificance, soevenrelativelysmallvaluesmaybein thetopfour.... 33
Table 3-1:Forestcoverchangebyscenario 40
Table 3-2: Mappingoflandcovertypesbetween IFs, IMAGEandCLUE-S 46
Table4-1: Inputfiles usedby CLUE-S. The "created"columnindicates whichsoftwareis usedto
create thefilesandthe "mandatory"columnindicates whetherthefileisaminimuminputdata
requirement. Filescreatedusing CLUE-Sareplain textfilesandmayalso beeditedina text
editor. 53
Table4-2: Belize: Distributionofpresentlanduseandlanddemandforthescenarios. Blue coloured
landusetypes werekeptfixedatpresentvaluesandnotallowedtochangeovertime 54
Table 4-3:Mexico: Distributionofpresentlanduse andlanddemandforthescenarios. Bluecoloured
landusetypes werekeptfixedatpresentvaluesandnotallowedtochangeovertime. Notethat
thereisnopine forestin Mexico: thisrequiredsomeadjustmentstothemodel. 54
Table 4-4: Guatemala: Distributionofpresentlanduseandlanddemandforthescenarios. Blue
colouredlandusetypes were keptfixedatpresentvaluesandnotallowedtochange overtime.
Theareasavannais verysmallbutnotexactlyzero (thedistinction issignificant) 55
Table 4-5: Honduras: Distribution ofpresentlanduseandlanddemandforthescenarios.
Blue=forcedfixedatinitialarea, notallowedtochangedovertime 55
Table4-6: Presentlanduse distribution with redcolouredvaluesforthosetypesforwhich thedemand
waskeptconstantovertimebecause thedemandchanges weresmallerthantheiteration
tolerance ofCLUES, orsosmallthatthemodelwaspreventedfromreachingasolution. The
requiredchangeinlandwasaddedtoanotherlandusetype, asindicatedwithinparenthesis... 56
Table 4-7a: Mainmodelparametersasusedforthe simulations 56
Table4-8: Defaultconversionelasticitiesforthelanduse types 60
Table 4-9: defaultconversionmatrix. Note thatsomeadjustmentshadtobemadeforallcountriesto
allowforsufficientchange options, asindicatedin blue in thenextfourtables 60
Table 4-10:ModifiedconversionmatrixforBelizeconversion. Themediumgreycolouredrows and/or
columnsareassociatedwithlandusetypesthatwerekeptconstantanddidnotchange 61
Table 4-11:modifiedconversion matrixforMexico. Themediumgreycolouredrowsand/orcolumns
areassociatedwithlandusetypesthatwere keptconstantanddidnotchange 61
Table 4-12: modifiedconversionmatrix forHonduras. Themediumgreycolouredrowsand/or
columnsareassociatedwithlandusetypesthatwerekeptconstantanddidnotchange 61
Table 4-13: modifiedconversionmatrixforGuatemala. Themediumgreycolouredrowsand/or
columnsareassociatedwithlandusetypesthatwerekeptconstantanddidnotchange 62
Table 4-15: meanandmaximumdeviation between demandandallocatedlanduse, inpercentageof
aebvseorlyustiemaurleaat,edfoyrelaarndbuutsperiensethnetefdinhalerseimounllaytefodrytehaer,fi2na0l2y5ea).r)T.hTehseemstaaxtiismtiucsmsar(e2cndalacnudla3treddfor
columns) arespecifiedin themainparameterfileandareslightadjustments fromthedefault
settingsin CLUES, respectively, 0.35%and3.0%. Inalmostallcasesthehighestdeviation
appliestolandusethatoccupiestheleastarea andisnotkeptconstant, whichalmostalwaysis
Urban 63
VI
List of figures
Figure 1-1: Spatialextentsanddata areaforrasterdatasetsforthecounties withintheMARregion. 4
..
Figure 1-2: RasterizationoftheEcosystemmap vectordata onlinkedfieldNUMforthe 2003
EcosystemMapdata (left) andthe 2004BelizeEcosystemMap (right) 9
Figure 1-3: SRTMtilenumbersthatweredownloaded. Tile 20_10wasincludedas the earlierversions
ofthe watershedsboundariesindicatedthatitextendedmoreto theeastandsoutheast 11
Figure 3-1: Roleofthree modelsusedtosimulatelandcoverchange 40
Figure 3-2: Nationalhumanpopulationat 2005and2025byscenarios(IFs) 41
Figure 3-3: Percentagechange innationalpopulations, 2000to2025, byscenario (IFs) 42
Figure 3-4: Changeinlandcover, 2005to2025, allcountriescombined 42
Figure 3-5: Percentagechangeinlandcover, 2005to2025, allcountriescombined 43
Figure 3-6: Landcoverforwatershedarea at2005: allcountriescombined 43
Figure 3-7: Landcoverforwatershedarea at2025byscenarios: allcountriescombined 44
Figure 3-8: Landcoveratbaselineyear(2004forBelize, 2000forGuatemala, HondurasandMexico)
45
Figure3-9: ChangeinlandcoverforBelize, 2005to2025, scenarios 49
Figure3-10: Change in landcoverforGuatemala, 2005to2025, scenarios 49
Figure 3-11: Change in landcoverforHonduras, 2005to2025, scenarios 50
Figure3-12: Changein landcoverforMexico, 2005to2025, scenarios 50
Figure4-1: Presentlandcoverandsimulatedlandcoverforthethreescenariosin 2025 64
Figure4-2: Baseline (2000/2004) landuse 65
Figure4-3: Simulatedlandcoverforscenario 1, MarketsFirst, in 2025 66
Figure4-4: Simulatedlandcoverforscenario2, PolicyFirst, in 2025 67
Figure 4-5: Simulatedlandcoverforscenario4, SustainabilityFirst, in2025 68
Figure 4-6: Simulatedareasofchange with 2025landcoverforscenario 1, Markets First 69
Figure 4-7: Simulatedareaofchange with 2025landcoverforscenario2, PolicyFirst 70
Figure 4-8: Simulatedareasofchange with 2025landcoverforscenario4, SustainabilityFirst 71
Figure 1: SpatialextentsandmaskforrasterdatasetsforthefourMARcounties. Coordinatesarein
UTMzone 16withNAD 1927CentralAmericandatum 134
VII
Summary
Mesoamerica - the region in which the Mesoamerican Barrier Reef Systems fall - is
recognized internationally for its biodiversity. For example, Conservation International has
identified the area as a biodiversity hotspot, with a high proportion of endemic species
(Myers ef al. 2000). The area's natural ecosystems are also recognized to be threatened.
The World Bank-funded Central America Ecosystems Mapping Project, which concluded in
2002, estimated that 49% of Central American land had been converted to agriculture
(Vreugdenhil efal. 2002).
With a focus on the Mesoamerican Reef, the International Coral Reef Action Network's
Mesoamerican ReefAlliance (ICRAN-MAR) project is focusing its attention on how changing
land use affects the health of the region's reef ecosystems. The project region includes
southern Mexico, and all ofBelize, Guatemala, and Honduras.
This report details the steps undertaken to map current and potential future land cover for
this ICRAN MAR region. Geographic data was collated, three alternative land cover
scenarios for 2005 to 2025 were developed, a regression analysis was undertaken to identify
the strength ofdifferentfactors affecting land use patterns and land use changes underthese
scenarioswere modelled.
The land cover maps for the present day and for 2025 were used as a key input to a
hydrological model ofwatersheds discharging adjacent to the Mesoamerican Reef, prepared
by the World Resources Institute (WRI). A hydrologic modelling report is also available on
this CD.
A workshop was held in August 2006 to disseminate project results and to provide training in
the use of the models. A preliminary version of this report was distributed to workshop
participants.
1 Data collection and preparation
1.1 Outline of methodology and preparation steps
To identify drivers of deforestation, a regression analysis was undertaken in SPSS. The
method involves a comparison of land use with the explanatory factors on a cell-by-cell basis
within a raster map. Consequently, it is important that all raster data associated with the
explanatory factors are prepared consistently: all raster maps must have exactly the same
extent, same cell size, and the same numbers of grid cells that are not Null (NoData). A
difference ofjust one cell will cause an offset in the order in which the statistical analysis are
carried out and resultswill be meaningless.
To assure consistency across the raster inputs, the same preparation and conversion
procedurewas applied to every dataset. The data preparation involves two stages as follows.
1.1.1 Stage 1: Creation ofASCII grids with identical numberofvalue cells
Stage 1 involves the creation ofthe raster datasets in Arc/Info ASCII format so that they can
be used (i) by the CLUE-S model and (ii) forthe subsequent Stage 2 processing steps.
1. Identify and acquire the best available and most suitable data, in vector or raster
formats. Different data from different sourceswill be used.
2. Review the quality of the dataset, and edit the dataset to resolve any data errors or
other problems (areas with missing data; non-adjacent polygons; misclassification of
data). Ifnecessary reclassify the data into a more appropriate system.
3. Create any derived datasets, if applicable. An example of this is the creation of a
datasetforthe number ofdry months from monthly precipitation data.
4. Convert vector data or resample raster data to the same raster grid resolution and
spatial extent (see Section 1.2).
5. Apply a focal mean filter (continuous data) or a focal majority filter (categorical data)
to fill any occasional Null cells1 and "add a few grid cells width" of data on the edges
of the maps. This critical step ensures that when data are clipped in the next step,
there are absolutely no Null cells within the watershed boundaries. An Avenue script
was developed for use in ArcView 3.3 (Appendix 1).
6. Clip all rasters to the MAR extent, and then clip them further to the individual extents
ofthe countries (Table 1-1). This step can be carried out using the Raster Calculator
in ArcMap.
7. Export all data from GRID to ASCII text format. This can be carried out using the
conversion tools in ArcToolBox (Conversion Tools > From Raster > RastertoASCII2)
' ITheconversion ofvectordatatorasterdatasometimes resultsin Nullcellswheretheywould notbeexpected.
This reasonforthisappearstobenon-adjacencyofpolygonsinthevectordata. GridcellsareassignedasNull
whentheircentrepointsfall intheemptyarea betweenthetwo polygons.
2 Step7-10requiredseveral GigabytesofdiskspacebecausetheASCII fileswerequitelargeandtherewere
manyofthem.
1.1.2 Stage 2: Conversion to a textfile for use by SPSS regression module
Stage 2 involves the further processing ofthe output datasets from stage 2 into a number of
different formats to obtain plain text files that can be imported by SPSS. The CLUE-S user
manual and exercises (Verburg 2004, Verburg efal. 2004), offer a more detailed explanation.
8. Separate grids must be created for every land cover type because binary logistic
regression analysis is used. This can be carried out using the Raster Calculator. For
the ICRAN MAR region, there were 4 countries * 10 land use types = 40 different
grids. Each grid is then converted to ASCII format, as in step 7.
9. Using the File Converter program that is supplied with CLUE-S, convert the ASCII
grids to text files in which all raster values are listed in a single column, with no
header. This must be undertaken for all land use types and all explanatory factors,
creating a large number of files. For example, for 10 land use types and 15
explanatory factors, there are 4*(10+15) = 100 single-column files. A consistent file
naming convention should be used to avoid confusion and mistakes.
10. Copy the contents of the single-column files into an overall file that can be loaded in
SPSS (this file is called stats.txt by the CLUE-S File Converter). The total number of
columns in this file must equal the sum ofthe number of land use types and the total
numberofexplanatory factors. This file was created using the TextPad text editor (the
option to create this file using the CLUE-S File Converterresulted in a runtime error,
possibly as a result ofthe large grid size. Record the orderofthe data columns.
1.2 Grid extent and grid resolution
1.2.1 Creation ofwatershed boundaries shapefile
WRI provided a base watershed boundaries shapefile. This illustrates that not all the
watersheds in the four MAR countries drain to and have a direct impact on the
Mesoamerican Reef1 and is a vital component in analysing the impacts ofland coverchange
on the reef system. A, version delineated from the 90 m DEM was completed on 4 August
2005 and a version based on the 250 m DEM on 24 January 2006. Neither shapefile was
readily usable in this exercise because WRI had removed watersheds less then 80 ha in
size. This had resulted in an erratic boundary that did not correctly represent the water/land
boundary. Furthermore, to retain flexibility in the final resolution used for modelling, it was
considered undesirable to restrictthe boundaries to a particular DEM extent.
Several edits were carried out to create an improved and more flexible boundaries shapefile
for preparation of data for the regression analysis and the land use modelling. The overall
area of WRI's 90 m and 250 m shapefiles (for inland boundaries) was combined it with the
best land/water/country boundary shapefile (Iand_country_20july05.shp, used for the mask's
coastline). Next, the combined shapefile was improved in January 2006 by extensively
editing the coastline of Mexico and Honduras so that it better matched the coastline from the
Ecosystem map and the Landsat TM colour composites. The final MAR watershed shapefile
MAR_BASIN_3B_RECLASSMASK_5FEB06 SHP was Created.
.
The shapefile was converted to a raster at 250 m resolution as basin250. This raster has
NoData values outside the catchment area and has fourdifferent grid values: 1 for Mexico, 2
1 GISanalysis(usingtheWRIwatersheddelineationsandtheadministrativeboundaries provided byCCAD)
revealsthatallofBelize'ssixdistricts, fourteenofGuatemala'stwenty-twodepartments, sixteen ofHonduras'
eighteendepartmentsandthreeofMexico'sthirty-twostatespossess landsinthe hundredorsowatersheds
drainingtothe reef
