-1 Research article Land use – land cover conversion, regeneration and degradation in the high elevation Bolivian Andes Jodi S. Brandt 1,2 and Philip A. Townsend 1,3, * 1 Appalachian Laboratory, Center for Environmental Science, University of Maryland, 301 Braddock Road Frostburg, MD 21532, USA; 2 Instituto Interuniversitario Boliviano de Recursos Hidricos, Universidad Autonomo Juan Misael Saracho, Tarija, Bolivia; 3 Current address: Department of Forest Ecology and Management, University of Wisconsin-Madison, 1630 Linden Drive, Russell Labs, Madison, WI 53706, USA; *Author for correspondence (email: [email protected]) Received 2 March 2005; accepted in revised form 14 October 2005 Key words: Classification, Desertification, Remote sensing, South America, Spectral mixture analysis (SMA) Abstract Regional land-cover change affects biodiversity, hydrology, and biogeochemical cycles at local, watershed, and landscape scales. Developing countries are experiencing rapid land cover change, but assessment is often restricted by limited financial resources, accessibility, and historical data. The assessment of regional land cover patterns is often the first step in developing conservation and management plans. This study used remotely sensed land cover and topographic data (Landsat and Shuttle Radar Topography Mission), supervised classification techniques, and spectral mixture analysis to characterize current landscape pat- terns and quantify land cover change from 1985 to 2003 in the Altiplano (2535–4671 m) and Intermediate Valley (Mountain) (1491–4623 m) physiographic zones in the Southeastern Bolivian Andes. Current land cover was mapped into six classes with an overall accuracy of 88% using traditional classification tech- niques and limited field data. The land cover change analysis showed that extensive deforestation, desertification, and agricultural expansion at a regional scale occurred in the last 20 years (17.3% of the Mountain Zone and 7.2% of the Altiplano). Spectral mixture analysis (SMA) indicated that communal rangeland degradation has also occurred, with increases in soil and non-photosynthetic vegetation fractions in most cover classes. SMA also identified local areas with intensive management activities that are changing differently from the overall region (e.g., localized areas of increased green vegetation). This indicates that actions of local communities, governments, and environmental managers can moderate the potentially severe future changes implied by the results of this study. Introduction South American Andean ecosystems are extremely vulnerable to climatic factors and anthropogenic activities (Brush 1982). Historically, global climate change cycles have been shown to profoundly influence shifting vegetation zones and hydrologic regimes (Barry and Seimon 2000). In contrast to temperate mountain regions, the South American highlands have a long history of human occupation and landscape transformation driven by anthro- pogenic activity (Ellenberg 1979; Messerli et al. 1997). The majority of its current inhabitants are subsistence farmers using traditional agricultural Landscape Ecology (2006) 21:607–623 Ó Springer 2006 DOI 10.1007/s10980-005-4120-z
Transcript
-1
Research article
Land use – land cover conversion, regeneration and degradation in the high
elevation Bolivian Andes
Jodi S. Brandt1,2 and Philip A. Townsend1,3,*1Appalachian Laboratory, Center for Environmental Science, University of Maryland, 301 Braddock RoadFrostburg, MD 21532, USA; 2Instituto Interuniversitario Boliviano de Recursos Hidricos, UniversidadAutonomo Juan Misael Saracho, Tarija, Bolivia; 3Current address: Department of Forest Ecology andManagement, University of Wisconsin-Madison, 1630 Linden Drive, Russell Labs, Madison, WI 53706, USA;*Author for correspondence (email: [email protected])
Received 2 March 2005; accepted in revised form 14 October 2005
Regional land-cover change affects biodiversity, hydrology, and biogeochemical cycles at local, watershed,and landscape scales. Developing countries are experiencing rapid land cover change, but assessment isoften restricted by limited financial resources, accessibility, and historical data. The assessment of regionalland cover patterns is often the first step in developing conservation and management plans. This studyused remotely sensed land cover and topographic data (Landsat and Shuttle Radar Topography Mission),supervised classification techniques, and spectral mixture analysis to characterize current landscape pat-terns and quantify land cover change from 1985 to 2003 in the Altiplano (2535–4671 m) and IntermediateValley (Mountain) (1491–4623 m) physiographic zones in the Southeastern Bolivian Andes. Current landcover was mapped into six classes with an overall accuracy of 88% using traditional classification tech-niques and limited field data. The land cover change analysis showed that extensive deforestation,desertification, and agricultural expansion at a regional scale occurred in the last 20 years (17.3% of theMountain Zone and 7.2% of the Altiplano). Spectral mixture analysis (SMA) indicated that communalrangeland degradation has also occurred, with increases in soil and non-photosynthetic vegetation fractionsin most cover classes. SMA also identified local areas with intensive management activities that arechanging differently from the overall region (e.g., localized areas of increased green vegetation). Thisindicates that actions of local communities, governments, and environmental managers can moderate thepotentially severe future changes implied by the results of this study.
Introduction
South American Andean ecosystems are extremelyvulnerable to climatic factors and anthropogenicactivities (Brush 1982). Historically, global climatechange cycles have been shown to profoundlyinfluence shifting vegetation zones and hydrologic
regimes (Barry and Seimon 2000). In contrast totemperate mountain regions, the South Americanhighlands have a long history of human occupationand landscape transformation driven by anthro-pogenic activity (Ellenberg 1979; Messerli et al.1997). The majority of its current inhabitants aresubsistence farmers using traditional agricultural
and pastoral practices especially adapted to thesesensitive mountain ecosystems. With recent mod-ernization and increased population pressure, thebalance between humans and the land has beenthreatened (Ellenberg 1979; Seibert 1983; Baiedand Wheeler 1993; Hamilton and Bruijnzeel 1997;Roberts et al. 1998; Rundel and Palma 2000).Population growth stimulates an increased need forcrop and pasture area, resulting in deforestation,increased pressure on existing crop and grazinglands, and inappropriate use of marginal land.Modernization results in the abandonment of tra-ditional agricultural practices, such as the hus-bandry of endemic grazing animals (e.g. llama) andcommunal land management. Road construction,mining, and urbanization are additional causes ofserious, long-term degradation in the region.
Once degraded, vegetation and soil regenerationare restricted by climatic and topographic condi-tions. The result of landscape degradation in arid,mountainous environments is two-fold: (1) desert-ification, or a long-term reduction in the amount ordiversity of natural vegetation (UNEP 1992) and(2) disturbance of the hydrologic cycle. Vegetationcover and land use are important determinants ofinfiltration and erosive processes of precipitation,and therefore of biological integrity and streamwater quality (Roth et al. 1996; Allan et al. 1997;Johnson et al. 1997; Wang et al. 1997; Wang andYin 1997). Deforestation results in a decrease ininfiltration, evapotranspiration, and stream dryseason baseflows, and an increase in overland flow,erosion, stream peak flow, and stream sedimentloads (Hamilton and King 1983). Mountainstreams in regions with seasonal precipitationregimes are particularly vulnerable to influences ofcatchment vegetation cover and land use change asthey are smaller and relatively unable to bufferseasonal variability in precipitation, water flow andmaterial fluxes (Flecker and Feifarek 1994;Monaghan et al. 2000). In a region where peopledepend on untreated surface water for drinking,household use, and irrigation, the decrease in dryseason stream flows and the contamination ofdrinking water with harmful pathogens are devas-tating consequences of landscape degradation.
The assessment of regional patterns of currentland use and past land cover conversion is the firststep in developing sound land management plansthat could prevent broad scale, irreversiblewatershed and stream degradation. However, this
assessment is difficult in rapidly changing andremote regions of developing countries becauselimited field data are available and detailed fieldanalyses are not practical. Recently, the increasedavailability of remotely sensed (RS) data and thestandardization of satellite image analysis tech-niques has allowed the study of remote locations atscales not possible using traditional, field-intensivemethods (Roberts et al. 2003; Cingolani et al.2004; Townsend et al. 2004). A common applica-tion is classification of the landscape into landcover classes, producing a categorical and quanti-tative representation of a study area (Lillesand andKiefer 1994). A regional classification, linked withwater quality and stream data, can provide infor-mation on the impacts of observed landscapecondition on water resources (Ballester et al. 2003).Classification of past images allows a quantifica-tion of trends in land cover change over time,which can be used to estimate future conditions(Hall et al. 1995; Verburg et al. 2002b). A relativelynew image analysis technique, spectral mixtureanalysis (SMA), quantifies landscape componentsat a sub-pixel scale (Adams et al. 1995; Robertset al. 1998). Unlike image classification, which as-signs each pixel to a discrete land cover categorybased on its spectral reflectance, SMA identifiesmaterials of interest in the image (e.g. vegetation,soil, and water) and quantifies the proportion ofthese materials in each pixel, allowing an assess-ment of changing landscape condition. For exam-ple, it has been used detect changes in vegetationabundance in semi-arid environments (Elmoreet al. 2000; Okin et al. 2001), pasture condition inthe humid tropics (Numata et al. 2003), andrangeland degradation in semi-arid Mediterraneanregions (Hostert et al. 2003).
This study integrates a variety of geospatial dataand methods to assess patterns of land use andland cover conversion (LULCC) in a remote andextremely sensitive region of the southeasternBolivian Andes. The research was designed inconjunction with local environmental managersand The Nature Conservancy. The primaryobjectives of the study were to (1) accuratelycharacterize past and current land cover patternsand processes and (2) interpret the results to makethe research meaningful from a managementperspective. Topographic characterization of thestudy area was performed with Shuttle RadarTopography Mission (SRTM) elevation data.
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Satellite images from 1985 (Landsat TM) and 2003(Landsat ETM) were used to determine currentand past land cover patterns, to quantify types andrates of change through the past two decades, andto estimate future change based on observed ratesof past change (Verburg et al. 2002).
Methods
Study area
The study site is a 5367 km2 area of the UpperParana River basin in southeastern Bolivia sur-rounding the Sama Mountain Range BiologicalReserve (Sama) (Figure 1). Sama encompasses aunique area of transition between the EasternAndean Cordillera and the Altiplano (high plain).Elevations climb dramatically from 1400 m at theeastern edge of the study area to 4650 m at peaksof the Sama Cordillera in the center of the studyarea. The mountainous terrain drives considerableclimatic and topographic variability and isresponsible for the formation of the two physio-graphic zones. The Intermediate Mountain andValley (Mountain) zone, east of the peaks of theSama Cordillera, is at a lower elevation (2367 m),and has a temperate climate with higher averageannual temperature (18�C), compared to theAltiplano zone (3619 m elevation, 11 �C). Highelevations in the Altiplano are subject to extremetemperature fluctuations with intense solar radia-tion during the day and sub-freezing nighttimeconditions. The region has a seasonal precipitationregime with greater than 85% of the annual pre-cipitation falling between November and March(Carpio et al. 2002). Local variability is driven byorographic precipitation. Air systems moving fromeast to west generate relatively high annual pre-cipitation (1318 mm/year in Calderillas, Figure 1)in the Mountain zone (Carpio et al. 2002). As theair crosses the Sama mountain range, it is depletedof most of its moisture, forming a rain shadow inthe arid Altiplano, where average annual precipi-tation ranges from 350 to 500 mm.
Ecosystems in the study area are unique andextremely sensitive. The reserve contains fourdistinct eco-regions and is home to severalplant and animal species endemic to the uniquecombination of climate, altitude and geomorphol-ogy. In addition to biodiversity, the park region
contains the headwaters for downstream rivers thatsupplywater for local communities and for Tarija, acity of 110,000 inhabitants. The arid climate andmountainous terrain control and limit human landuse. Soils suitable for agriculture are restricted tothe flat, moist, fertile soils of the valleys. Forestedareas are scarce, and limited to very steep slopeswhere cultivation and grazing are not possible. Lowseasonal rainfall and cold nighttime temperatureslimit agricultural production and forest regenera-tion.
Historically, the region was densely populatedby subsistence farmers using traditional agricul-tural and pastoral practices. Recent populationgrowth and development driven by the exploita-tion of natural gas reserves in Tarija is causingrapid environmental and social change throughoutsoutheastern Bolivia, resulting in an increase inimmigration, infrastructure, and urban develop-ment. Due to the environmental constraints andthe present dense population, land available andappropriate for increased use and production areextremely limited, and potential for land coverconversion is very low. Yet, population and pres-sure on the land continues to grow, resulting inintensification of current land use in addition toland use conversion. Throughout the study area,substantial areas are devoid of vegetation andtopsoil. Information from local inhabitants, inaddition to historical Landsat images and topo-graphic maps developed in the 1970s, indicate thatthese areas were, in the recent past, productivegrasslands and forest, suggesting ongoing forestloss, rangeland desertification, and agriculturalexpansion. The main threats to land and waterresources in the region as identified by localenvironmental managers are: (1) the advance ofthe agricultural and pastoral frontier caused bypopulation growth, (2) deterioration of the land-scape due to unsustainable agricultural practices,overgrazing, and the presence of non-nativegrazing species; and (3) deforestation due tologging for firewood (Ayala Bluske 1998).
Data collection and image processing
Field datasets for geocorrection, image classifica-tion, and accuracy assessment were collectedthroughout the study area in January – March2004 using a GPS. Easting, northing, altitude, and
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descriptive information were recorded for 35ground control points (GCPs). To characterizevegetation cover and to test classification accuracies,85 training points (TPs) and 64 field verificationpoints (FVPs) were collected. TPs were selected in
the field as homogeneous areas representative ofthe various land covers present in the study area.FVPs were randomly chosen prior to fieldwork,and subset to include only points within 2 km of aroad. When possible, each point in this subset was
Figure 1. The Sama Reserve and elevation contours in the study area.
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visited in the field. However, due to private prop-erty boundaries, rugged terrain, and time con-straints, many points could not visited. In thesecases, the point was collected as close to thecoordinates as possible. In addition to FVPs andTPs, incidental points (IPs) were collected astraining data for image analysis and classification.IPs included a description of the land cover and itsdistance and direction from a GPS or referencepoint while driving or walking.
We recorded land use and land cover (LULC)observations and took photographs at each FVPand TP to aid in image processing, classification,and accuracy assessment. LULC observationswere recorded for an approximate 100 · 100 msampling area surrounding each GPS point(Justice and Townshend 1981).
Two rainy season satellite images, April 1, 1985(Landsat-5 TM sensor) and April 29, 2003,(Landsat-7 ETM+) were used for the land coveranalyses. The 2003 image was geocorrected usingthe GCP’s collected during fieldwork. Root meansquare (RMS) error for the 2003 image was 5.7 m(x=4.0 m and y=4.0 m) using 29 field GCPs. The1985 image was co-registered to the corrected 2003image. RMS error was less than 1 pixel (21.4 m)(x=19.8 m and y=8.0 m) using 18 GCPs.
DNs were used for all image analyses. Spectralnormalization of the two images was necessary toaccount for differences in atmospheric conditions,sensor variation, or other factors. Spectralnormalization was performed according to theRelative Normalization method (Collins andWoodcock 1996) by extracting cell values fromboth images of temporally invariant areas ofextreme brightness (sand dunes) and darkness(deep water) to encompass the entire reflectancerange. A linear regression model was generatedfrom the extracted pixel values and applied to the1985 image to calibrate it to the 2003 image. Cloudand shadow masks for the 1985 and 2003 imageswere developed using the software eCognition(Baatz et al. 2003), and were applied prior toimage analysis.
Topographic characterization of the study areawas performed with a 90 m-resolution digital ele-vation model (DEM) derived by the US GeologicalSurvey (USGS) from data collected in February2000 on the Shuttle Radar Topography Mission(SRTM). Gaps in the satellite data caused byincomplete SRTM sensor coverage were filled with
a DEM developed from 1:50,000 topographicmaps of the study area.
Maximum likelihood classification
Supervised classification of the 2003 and 1985images was performed using the maximum likeli-hood classification (MLC) parametric rule (Jensen1996). Input layers for classification included theLandsat bands 1, 2, 3, 4, 5, 7 and slope from theSRTM DEM. The final classification included (1)forest, (2) agriculture (crop and lowland pastureplots), (3) pasture (upland grassland used ascommunal rangelands), (4) bare (land withextremely sparse or no vegetation), and (5) water.Land use and land cover conversion (LULCC)analysis was performed by comparing land coverclassification in 1985 and 2003 for each pixel, andwas summarized for the entire study area, and theMountain and Altiplano zones separately.
Accuracy assessment for the 2003 classificationused 112 field reference points, including therandomly selected FVPs and TPs not used astraining data during image classification. The GPScoordinates for each reference point were locatedon the classification to determine the land coverassignment for an individual pixel. Mapped landcover and the actual (field reference) land cover atthat point were compared and quantitativelysummarized into a confusion matrix. Detailed fielddata to test the accuracy of the 1985 classificationwere not available.
Spectral mixture analysis
Spectral mixture analysis (SMA), an image anal-ysis technique that quantifies relative abundanceof specific landscape components (called end-members), was conducted to assess landscapechange and degradation within land cover classesby determining changes in endmember abundance.Endmembers were selected from the 2003 imageusing a combination of the pixel purity index (PPI)(RSI 2000), field data, and analysis of spectralsignatures (a pixel’s spectral reflectance in eachimage band). Endmember selection is an iterativeprocess. Upon selection of an endmember set,SMA is performed to determine the proportion ofeach endmember in each pixel. SMA produces a
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fraction image for each endmember and an imagedepicting root mean square (RMS) error by pixel.RMS values indicate the ability of the SMA modelto explain the composition of each pixel. To beused, endmember data must satisfy the followingconditions: (1) patterns of fraction images coincidewith actual field conditions; (2) endmember frac-tions for the landscape components of interest arebetween 0 and 1, indicating that the purest pixelswere selected as endmembers; (3) endmemberfractions for a pixel sum to one, indicating that theendmember set adequately characterizes thematerials in the field; and (4) the error band showslow RMS error for the landscape components ofinterest.
Target endmembers were: (1) green vegetation(GV), (2) non-photosynthetic vegetation (NPV),(3) bare soil, and (4) shade. Due to the aridityand geomorphologic variability within the studyarea, there were three distinct bare soil materialsin the images. Due to limits in the dimensionalityof the data (six bands), one of these soil end-members, sand, was excluded from the endmem-ber set since it was less of interest than the otherendmembers. The final set, extracted from the2003 image, included five endmembers (Figure 2).The light soil endmember, taken from the exten-sive formations of severely eroded sedimentary‘badlands’ north of Tarija, represents the lightand erodible lacustrine soils of the Tarija valley.The dark soil endmember, extracted from a bare
ridge top, represents the igneous and metamor-phic formations of the ridges. The GV endmem-ber was taken from an agricultural field in theTarija Valley. The NPV endmember was takenfrom a flat area of senesced grass near the run-way of the Tarija airport. The shade endmember,although not a physical material, was necessaryto account for illumination effects, and was takenfrom Laguna Tajzara, the deepest and clearestwater body in the image.
A principal components (PC) transformationwas performed on the Landsat images to demon-strate dimensionality of the spectral mixing spaceas represented by orthogonal scatterplots of thefirst three PC bands (Figure 3). Plotting the loca-tion of endmembers in the spectral mixing spaceindicates the endmembers’ relationship to theimage pixels and their ability to effectively modelthe image (Small 2004). Ideally, the mixing space isbounded by the endmembers, indicating that theendmembers are pure representations of thedifferent materials present on the landscape. In thisanalysis, four of the five endmembers (GV, darksoil, light soil, and shade) plot near the edge ofspectral mixing space in at least one of the threeprojections of PC bands 1, 2, and 3 (Figure 3).However, NPV consistently plots within themixing space, possibly introducing error into themixing model. Thus, the model has the potential tomistakenly represent NPV-dominated pixels as amixture of the other four endmembers. The final
Figure 2. Endmember spectra used in the spectral mixture analysis.
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endmember sets in the spectral mixing spaces ofboth the 1985 image and the 2003 image exhibitedsimilar patterns.
The central location of NPV in the mixingspace, as well as the spectral similarity betweensome soils and NPV (Adams et al. 1995; Numataet al. 2003; Souza et al. 2003), made the selectionof appropriate NPV and soil endmembers themost challenging aspect of the endmember selec-tion process. The final NPV and dark soil end-members are almost identical in Landsat bands1–4, but diverge in bands 5 and 7 (Figure 2). Inaddition to inspection of spectral signatures ofpotential endmembers throughout the endmemberselection process, fraction images were closelyexamined to determine separability between thepotential NPV and soil endmembers. For example,large variability exists within communal range-lands in the study area. The region surroundingthe city of Tarija is characterized by sparse vege-tation and severe erosion, whereas the sparselypopulated, mountainous region of the SamaCordillera is composed of abundant grasslands.Fraction images produced from the finalendmember set closely corresponded to actual fieldconditions, showing high NPV and low soilfractions in the abundant mountain grasslands,and low NPV and high soil fractions in the erodedand sparsely vegetated Tarija valley.
Average RMS for the 1985 image (6.1) wasrelatively high compared to the 2003 image (0.81).Lower RMS values for the 1985 image were ob-tained with mixing models that used endmembersextracted from the 1985 image. However, using adifferent endmember set for each image resulted ininconsistent fraction results between the 2 yearsfor temporally invariant areas, such as undis-turbed forest, sand dunes, and urban features.Similar to using reference endmembers from aspectral library, using identical image endmembersfor different images allows a direct comparison ofthe resulting endmember proportions. Using thesingle endmember set extracted from the 2003image produced consistent endmember propor-tions for temporally invariant areas for both years,which was an important criterion of endmemberselection. Despite its high average RMS relative tothat of the 2003 image, the 1985 fraction imagesmet all other criteria of an acceptable mixingmodel. Scatter plots showed a slightly negativerelationship between GV proportion and RMS in1985, and no significant relationship in 2003.
Not all image components can be effectivelymodeled using a simple endmember model (Adams
Figure 3. Scatterplots of the first three principal components of
the 2003 image showing the dimensionality of the mixing space.
Endmembers are included to show their relationships within the
mixing space: S = Shade, GV = Green Vegetation, NPV
= Non-photosynthetic Vegetation, DS = Dark Soil, LS
= Light Soil.
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et al. 1995; Elmore et al. 2000; Souza et al. 2003).The endmember set was selected to maximizemodel performance in the forest, rangelands andagricultural regions of the study. Greater than90% of each image was modeled within thephysically meaningful range from 0 to 1 for allendmembers. Fraction values that were greaterthan 1.0 or less than zero occurred predominantlyin areas of high RMS that were not of interest inthe study, such as clouds, sand dunes, the heavilyurbanized city of Tarija, and turbid surface waters.The 1985 image also showed high RMS error inthe ‘badlands’, an extensive area that had experi-enced severe degradation (conversion from pastureto bare land) between 1985 and 2003. Inclusion ofan incorrect number of endmembers can increaseRMS error (Roberts et al. 1998), and possibly theinclusion of the light soil endmember (extractedfrom the degraded sedimentary formations of the2003 image) to model the 1985 image (beforeserious vegetation loss and erosion had occurred)caused the relatively high average RMS error ofthe 1985 image. As for the 2003 image, RMSerrors in 1985 were relatively low for the regions ofinterest in the SMA, including unconvertedpasture, forest, and agricultural areas.
All bands were truncated so that any negativevalues were assigned to be zero and values greaterthan 1.0 were assigned to be 1, and then rescaledso that all endmembers for a pixel summed to 1.For the change analysis, the light soil and dark soilfractions were added together to form a single baresoil band. The shade fractions from both imagedates were compared. An overall increase in shadefraction from 1985 to 2003 was evident, possiblydue to differences in the sensor or atmosphericconditions on specific image dates. Therefore,shade was removed from the images and the otherendmembers, GV, NPV, and soil, were rescaled tosum to 1 by apportioning the shade fraction to theremaining endmembers. Fraction bands from 1985and 2003 were stacked to perform change detec-tion in endmember fractions on a pixel-by-pixelbasis.
Average endmember fraction for each landcover class was calculated to determine thecoincidence of the land cover mapping with thespectral mixture analysis. A non-parametricequivalent of a 1-way ANOVA (Kruskal–Wallistest) and pairwise comparisons of the ranked data(Tukey’s studentized range test) were performed to
determine if endmember proportions variedsignificantly according to land cover class. Aver-age endmember fraction change for each conver-sion class was calculated to determine endmemberchanges over time and with land cover conversion.Areas of extreme change were identified by calcu-lating the mean and standard deviation for eachfraction change image. Pixels with values between+1 and –1 standard deviation from the mean wereconsidered to be areas of relatively little or nochange to account for potential error due toco-registration (Washington-Allen et al. 1998;Elmore et al. 2000).
Results
Land use and land cover classification and changeanalysis
Overall classification accuracy of the 2003 classi-fication was 88%, with a Kappa statistic (KHAT)of 0.82 (Table 1). Although overall accuracy wasabove the 85% target accuracy (Foody 2002),forest cover was poorly predicted (40% accuracy)because small forest plots and narrow treeboundaries in the intensively used floodplainvalleys were often misclassified as agriculture orpasture. Historical information collected duringfieldwork was considered along with close analysisof the image and showed that unchanged features,such as sand dunes, lakes, major roads, the airportrunway, and the city of Tarija, classified accord-ingly in 1985. In addition, land cover changescharacterized in the classified image matchedpatterns of known desertification, agriculturaldevelopment and forest regeneration (Table 2).
Comparison of the classifications indicatessubstantial land cover conversion between 1985and 2003 (Table 2, Figure 4). Net land use con-version as a proportion of each zone was higher inthe Mountain zone (17.3% of Mountain zoneexperienced conversion) than the Altiplano (7.2%converted). The Mountain zone experienced a4.7% decrease in forest, a 4.5% increase in bareland, and a 4.0% increase in agricultural land.Compared to the land cover in 1985, forestdecreased by more than half, and bare groundalmost doubled. The increase in surface water inthe Mountain zone (99.2%) is a result of theconstruction of the San Jacinto reservoir. Net
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changes in land cover for the Altiplano included a2% loss in agricultural land, a 2% gain in bareland, and a 2% gain in pasture. Although small asa percentage of the entire Altiplano zone, the rateof change as a proportion of area in 1985 was veryhigh. For example, the reduction in forest wasalmost 100%. The shrinkage of the Altiplano lakesaccounts for the 80% reduction in water.
Spectral mixture analysis
Spectral mixture analysis (SMA) was performed todetermine relative proportions of green vegetation(GV), bare soil, and non-photosynthetic vegetation(NPV) throughout the study area. In both 1985 and2003, average endmember fractions were signifi-cantly different (p<0.05) in each land cover class
in the Mountain zone (Table 3). 1985 data showthat soil fraction was lowest in the forest class(0.04) and reached a maximum in the bare landclass (0.52). GV fraction complimented the soilfraction well, with the highest value in the forestclass (0.51), and the lowest value in the bare class(0.13). NPV trends are variable because NPV rep-resents many different components on the land-scape, and therefore many different processes canaffect its pattern (Adams et al. 1995; Roberts et al.1998; Okin et al. 2001). In this study area, NPV onthe landscape includes senesced pasture, matureannual crops, perennial crops (grape trees), woodymaterial from living vegetation (tree branches,trunks, etc), and plant material from dead vegeta-tion (debris from deforestation or from crop har-vest). Its variability is highly dependent onvegetation stage (Numata et al. 2003) as well as
Table 2. Net changes in land cover as a proportion of each physiographic zone and as a proportion of land cover in 1985 for each
zone.
Land use class Percent of physiographic zone Change in percent
Table 1. Error matrix from comparison of the land cover classification and reference data.
Classification data Actual LULC – reference data User’s accuracy
F A P B Total
F 6 6 1.00
A 7 37 1 1 46 0.80
P 2 2 41 45 0.91
B 1 14 15 0.93
Total 15 39 43 15 112 –
Producer’s accuracy 0.40 0.95 0.95 0.93 – –
Overall 0.88
KHAT 0.82
F = Forest, A = Agriculture, P = Pasture, B = Bare.
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land use practices. For example, a deforestationevent will result in an immediate decrease in GV.However, the trend in NPV fraction will depend onwhether the branches and dead leaves are left be-hind (increased NPV), or scavenged for firewood(decrease in NPV and increase in soil fraction).
In the Altiplano zone, average endmemberfractions were significantly different (p<0.05) ineach land cover class except for the forest class,which covered an extremely small proportion ofthe Altiplano zone in both 1985 and 2003.
The change in endmember fraction for each landcover conversion class was quantified to (1) verifythe ability of the mixture model to detect land useconversion on the landscape, and (2) determinehow endmember proportions change with landcover conversion (Table 4). Changes in endmem-ber proportions also occurred in areas that havenot been converted, possibly due to vegetationmaturity, climatic conditions, or degradationwithin a land cover class. The differences in theimage dates of the two different years (April 1,1985 vs. April 29, 2003) must be considered when
comparing the fraction images. The rainy seasonin this region is both pronounced and short, withgreater than 85% of the precipitation fallingbetween November and March (Carpio et al.2002). At the beginning of April, rainfall has beenplentiful and moisture available for vegetationgrowth is still abundant. Conversely, at the end ofApril available moisture is more limited. Envi-ronmental conditions change rapidly during thisperiod, and considerable changes in vegetationdevelopment that alter relative proportions of GVand NPV (e.g. plant maturity and senescence) inresponse to these dynamic environmental condi-tions almost certainly occur. For example, in theMountain region, there was a decrease of 0.14 inGV fraction complemented by an increase of 0.16in NPV for areas that were forest in both 1985 and2003 (Table 4). In order to isolate endmemberfraction changes due to land cover conversion,fraction changes were calculated relative to areasof no conversion, and rounded to one significantdigit for simple comparison. For example, areasconverted from forest to agriculture experienced a
Figure 4. Areas converted (a) from forest and pasture and (b) to agriculture and bare ground in the Mountain zone from 1985 to 2003.
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0.27 gross loss in GV, but only a 0.13 loss relativeto forest that was not converted (rounded to 0.1).
Clear and expected trends were obvious in thesoil and GV fractions. Soil fractions increased andGV fractions decreased with forest conversion(Table 4). Conversion of forest to bare showed thelargest increase in soil fraction (0.4) and decreasein GV (�0.4). In the agriculture class, soil fractiondecreased and GV fraction increased with con-version to forest. Soil fraction increased with
conversion of agriculture to both pasture and bare.A decrease in soil fraction occurred with the con-version of bare ground to agriculture (�0.3) andforest (�0.4), accompanied by increases in GV.Patterns in the Altiplano were similar to those inthe Mountain region.
In order to detect changes in landscape condi-tion leading to conversion, net average changes inGV, NPV and Soil fractions in unchanged pixelsfor each cover class were calculated (Table 5).
Table 3. Mean endmember fractions for each land cover class in the Mountain and Altiplano regions in 1985 and 2003.
NC = No Change, For = Forest, Ag = Agriculture, Past = Pasture.
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Changes in endmember proportions in areas thathave not been converted can occur for variousreasons, such as vegetation maturity, climaticconditions, or degradation within a land coverclass. In the Mountain zone, both the forest andagriculture classes experienced GV loss compli-mented by NPV gain and accompanied by a slightdecrease in soil fraction. This may be attributed tovegetation maturity and senescence rather thandegradation, because loss of GV due to degrada-tion in agricultural and forested land would intu-itively be accompanied by higher soil fractions(e.g. forest degradation due to tree extraction)(Adams et al. 1995; Lu et al. 2003). The pastureand bare classes showed a different pattern. Theloss of GV cannot be entirely attributed to senes-cence, as increases in NPV do not fully account forGV loss. Instead, a gain in soil fraction accom-panied the loss in GV, suggesting actual vegetationloss and replacement by bare soil in unconvertedMountain rangelands.
The Altiplano zone exhibited a similar, but morepronounced, pattern for areas that did not expe-rience conversion (Table 5). As in the Mountainzone, the loss in GV in Altiplano forest wasaccompanied by a gain in NPV and a loss in soil,typical of vegetation senescence. The agricultureclass experienced a larger gain in soil fraction thanin NPV, which was most likely due to a post-harvest image date in 2003 (April 29) compared to1985 (April 1). However, the pasture and bareclasses experienced a loss in GV, but no increasesin NPV. Instead, loss of GV was accounted forcompletely by increases in soil fraction, indicatingthe replacement of vegetation by bare soil.
The interpretation of changes derived from SMAmust take into consideration the possibility that anentire region may undergo landscape changeaccording to regional climatic or demographicconditions. Therefore, the SMA results were fur-ther examined to identify areas of change differentfrom the rest of the study area by distinguishingpixels of moderate (1–2 standard deviations fromthe mean change) and extreme change (greater than2 standard deviations from the mean change) ingreen vegetation (GV) (Figure 5).
Development of irrigation has clearly stimulatedchange, as indicated by the extreme increases inGV fraction in the recently developed agriculturalareas in stream valleys of the Mountain zone(Figure 5). Moderate and severe GV loss is evidentin the steep mountain headwater stream valleys,where high rates of deforestation were observedfrom the land cover conversion analysis (Fig-ure 2). In contrast, a large area in the center of thestudy area, the upper Victoria River watershed,has experienced substantial increases in greenvegetation. In the late 1980s, protection of theVictoria River was undertaken because it is theprimary potable water source for the city of Tarija.Restricted access and daily patrol of the entireupper watershed curtailed livestock grazing andhuman activity. Therefore, the Upper Victoria hasbeen undisturbed (relative to its surroundings) forthe past several years, providing a valuable refer-ence area for the rest of the study region. Theregeneration of the protected area, in contrast withthe wide-scale vegetation degradation of adjacent,unmanaged watersheds, was not detected from theanalysis of LULC conversion. The SMA resultswere able to identify an area that has experiencedregeneration without actual conversion.
Discussion
Land cover conversion analysis from maximumlikelihood classification (MLC)
The Mountain zone of Southern Bolivia experi-enced increases in agricultural and bare land at theexpense of forest and pasture (Table 2). Patterns ofland cover change in the Mountain zone (Figure 4)indicate that agricultural expansion has been madepossible by forest clearing in the fertile valleys.Development of irrigation canals near perennial
Table 5. Results of spectral mixture analysis showing changes
in endmember fractions for areas that were in the same land
cover in both 1985 and 2003 (NC = No conversion).
Land cover type Change in
soil fraction
Change in
GV fraction
Change in
NPV fraction
Mountain zone
NC Forest �0.01 �0.14 0.16
NC Agriculture �0.01 �0.18 0.19
NC Pasture 0.04 �0.16 0.13
NC Bare 0.05 �0.08 0.04
Altiplano zone
NC Forest �0.04 �0.23 0.28
NC Agriculture 0.10 �0.16 0.08
NC Pasture 0.14 �0.13 0.00
NC Bare 0.09 �0.08 0.00
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streams and downstream of newly constructed res-ervoirs stimulated agricultural expansion through-out the flat areas of the Mountain zone. Moreover,the results show that at elevations above 2000 mand slopes greater than 5%, the amount of culti-vated land is extremely low, indicating topographicthresholds for agricultural production. Continuedagricultural expansion is limited by topographicand climatic conditions to discrete zones whereagricultural production is possible.
Forest cover in the Mountain zone decreased byhalf from 1985 to 2003. At current rates of change
(849 ha/year), forest will disappear by 2020.Clearing occurred throughout the Mountain zone,and the most accessible areas suffered alarmingforest losses. Deforestation rates were high in theflat, low elevation areas due to agricultural con-version and at intermediate slopes and elevationsdue to conversion to pasture. Only the steepest,most inaccessible terrain maintained forest cover.This is a classic pattern of forest loss and retentionin mountainous regions.
The extent of bare land doubled in the Moun-tain zone between 1985 and 2003. At current rates
Figure 5. Change in Green Vegetation endmember fraction from 1985 to 2003.
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of change (697 ha/year), the replacement of pas-ture by bare land will take less than 300 years. Thelarge increase in bare land is of grave concernbecause vegetation regeneration on steep, aridslopes can take hundreds to thousands of years(Janzen 1973; Ellenberg 1979; Seibert 1983; Baiedand Wheeler 1993; Kok et al. 1995). Much of thisdesertification has occurred in communal pasturesof the most densely populated areas. Althoughsteep, dry slopes are generally considered morevulnerable to erosion and desertification, this workshows that bare ground is more prominent on flatslopes and on land with high moisture levels. Infact, bare ground closely corresponds to theprevalence of agriculture near water sources andhighly populated areas, indicating that intenseagricultural use and high population densities aredriving desertification.
The Altiplano zone has experienced minimalconversion relative to the Mountain zone, proba-bly due to environmental conditions (e.g. soils,topography, climate, and water availability) thatseverely restrict land use activities. Forest hasdecreased to near-zero proportions (0.01%), andcommunal pasture and bare land compose 97% ofthe entire Altiplano zone (Table 2).
Deterioration of communal rangeland from spectralmixture analysis
The evaluation of LULC conversion clearlydemonstrated that bare ground is replacing com-munal rangeland at a rapid rate. However, thisanalysis was unable to detect changes in landscapecondition prior to conversion. From a manage-ment perspective, the identification of areas in theprocess of degradation is essential to preventirreversible damage.
Various characteristics of the study area sug-gested the use of SMA to investigate landscapedynamics, including the prevalence of the ‘mixedpixel’, changing land cover condition (e.g. pas-ture degradation), and the absence of field datafor 1985. Instead of a subjective assignment ofland cover class to the pixels, the proportion ofeach endmember is quantified on a pixel-by-pixelbasis in both images, allowing assessment ofchanges in endmember proportions. The utilityof SMA to accurately quantify vegetation coverhas been explored in other semi-arid environ-
ments. Elmore et al. (2000) determined thatestimates of percentage of live cover in a Land-sat pixel is accurate to within ±4% (one stan-dard deviation from the mean), and thatestimates of change in live cover are accurate towithin ±3.8% (one standard deviation from themean). SMA correctly determined the directionof change (i.e. positive or negative) in 87% ofthe samples. Okin et al. (2001) found that SMAaccurately models vegetation cover at propor-tions greater than 10%.
Although the primary interest of the SMA wasto determine changing proportions of GV andsoil, the inclusion of an NPV endmember wasessential due to the seasonal difference betweenthe two images (collected April 1, 1985 andApril 29, 2003). The spectral similarity betweenNPV and some soils when using broadbandimagery such as Landsat can cause confusionbetween NPV and soil in SMA results (Adamset al. 1995; Numata et al. 2003; Souza et al.2003). A rigorously quantified measure of thiserror is impossible without extensive fieldwork.During endmember selection, soil and NPVseparability was verified using their spectralcharacteristics, as well as by confirming thatfraction images corresponded to actual fieldconditions. In addition, the results of the landcover classification and change analysis wereconsidered to determine the validity of the NPVand soil results. GV, Soil, and NPV proportionsfrom the SMA follow expected patterns whencompared to the land cover classifications foreach region and year (Table 3), and to observedland cover conversion (Table 4).
Changes in endmember proportions in areasthat have not been converted may result fromvegetation maturity/senescence, climatic stress, ordegradation within a land cover class. In both theMountain and Altiplano zones, forest and agri-culture classes show losses in GV accompanied bygains in NPV, indicating that changing endmem-ber proportions are a result of vegetation matu-ration. However, the pasture and bare land classes,especially in the Altiplano zone, show vegetationloss and bare soil gain, suggesting actual vegeta-tion loss in the communal rangelands. The landcover conversion analysis detected little change inthe Altiplano zone. However, the SMA resultsindicate a large region in the process ofdegradation.
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Identifying areas of extreme change
SMA facilitated the identification of areas thathave changed disproportionately from the rest ofthe study area. Irrigation development in flat,floodplain stream valleys, deforestation in thesteep headwater stream valleys, and vegetationregeneration in the upper Victoria watershed werethe most obvious zones of extreme change. Al-though areas of agricultural expansion anddeforestation were apparent from the conversionanalysis, regeneration of the upper Victoria wasnot detected. SMA was therefore used to identifyan area that experienced regeneration withoutactual conversion, in clear contrast to the wide-scale degradation of vegetation in adjacentunmanaged watersheds. The influence of irrigationdevelopment and the protection of the Victoriawatershed clearly indicate that management candrive local-scale land cover processes that differfrom those occurring at the regional scale.
Summary
This study was designed to characterize past andcurrent land cover patterns to identify processes oflandscape change that are important to land man-agers. The LULCC analysis showed that extensivedeforestation and desertification at a regional scalehas occurred in the last 20 years. Deforestation hasbeen caused primarily by agricultural expansion,stimulated in part by the development of irrigationsystems. Continued agricultural expansion is lim-ited by topographic and climatic conditions. Highrates of conversion of communal rangeland to bareland were observed. If current trends persist, forestsin the Mountain zone will disappear by 2020 anddesertification of the communal rangelands willoccur in less than 300 years. The land cover con-version analysis detected little change in the Alti-plano zone, but the spectral mixture analysis(SMA) indicated that large areas of Altiplanorangeland are in the process of degradation. Ifeventual conversion of these communal rangelandsto bare land occurs, regeneration will be extremelylimited by the extreme climatic conditions andmarginal soils in the Altiplano. Measures to man-age grazing practices in this area and to protectremaining forests will reduce the risks of continueddeforestation and desertification in the immediate
future. SMA was also able to identify areas thatdeviate from the overall direction of landscapechange in the region. This research demonstratedthat local management activities drive much of themoderate and extreme changes observed from theSMA analysis, including both degradation andimprovements in land condition. Therefore, theactions of the local government, communities, andenvironmental managers may moderate the poten-tially extensive future changes implied by this study.
An important product of this research was thedevelopment of a land cover and land use change(LULCC) analytical methodology that uses lim-ited resources and integrates different remotelysensed data sources and analysis techniques togenerate expedient, accurate, regional assessments.The dramatic landscape transformations reportedin this study have consequences for both landscapeproductivity and the hydrologic cycle, decreasingthe quantity of base flows, the quality of surfacewater, and stream biological integrity. Apart fromthe impacts of land use change at the local scale,broad-scale degradation of landscapes and waterresource degradation may also affect regional,continental and global climate cycles.
Acknowledgements
Funding for this research was generously providedby the Bolivia Country Program of The NatureConservancy, an Appalachian Laboratory (AL)Graduate Fellowship, an AL graduate researchaward, and NSF. We are grateful to Ricardo VitoAguilar and PROMETA for helping to coordinatethe logistical aspects of the fieldwork. The authorsespecially wish to thank Clayton Kingdon for hisexpert and patient assistance with the fieldworkand remote sensing/GIS analyses, and Jane Fosterfor her help in the final stages of manuscriptrevision and submission. Thanks also to CarolGarner and Claude de Patoul for help throughoutthe study, and to Chris Small and an anonymousreviewer for helpful comments on this paper.
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