the 250 m global land cover change product from the ... 250m global land cover change product from...

int j remote sensing 2000 vol 21 no 6 amp 7 1433ndash1460

The 250 m global land cover change product from the ModerateResolution Imaging Spectroradiometer of NASArsquos Earth ObservingSystem

X ZHANdagger R DEFRIESdagger J R G TOWNSHENDdaggerDaggerC DIMICELIdagger M HANSENdagger C HUANGdagger and R SOHLBERGdagger

daggerDepartment of Geography University of Maryland College Park MD 20742USADaggerUniversity of Maryland Advanced Institute of Computing SciencesUniversity of Maryland College Park MD 20742 USA

Abstract One of the products to be derived from data acquired by the ModerateResolution Imaging Spectroradiometer (MODIS) onboard the NationalAeronautics and Space Administration (NASA) Earth Observing System (EOS)will identify locations where land cover changes attributable to human activitiesare occurring The product aims to serve as an alarm where rapid land coverconversion can subsequently be analysed with higher resolution sensors such asLandsat 7 This paper describes the production procedure and change detectionalgorithms for the 250m MODIS land cover change product Multiple changedetection algorithms including three spectral methods and two texture methodsare utilized to generate the product from the two 250m MODIS bands Thechange detection methods are implemented with look-up tables (LUTs) whichare initially generated using data from the Advanced Very High ResolutionRadiometer (AVHRR) and Landsat Thematic Mapper (TM) until MODIS databecome available The results from the ve methods are combined to improvecon dence in the product We test the performance of each method against severalsets of simulated MODIS data derived from Landsat TM image pairs The testdata represent tropical deforestation agricultural expansion and urbanizationThe commission errors of the ve methods and the combinationare approximately10 with reasonable omission errors less than 25

1 IntroductionThe Earthrsquos land cover exerts an important control on the planetrsquos environment

Land cover in uences surface roughness and albedo factors which consequentlya ect exchanges of sensible heat water vapour (latent heat) and carbon dioxide andother greenhouse gases between the land surface and the atmosphere In additionvegetation plays a physiological role in these exchange processes through stomatalresistance and photosynthetic capacity (Sellers et al 1996) These exchanges of energyand materials are major components of the hydrologic cycle the carbon cycle andthe global climate system Consequently many hydrological ecological and climato-logical models use geographically referenced land cover information as an essentialinput (Asrar and Dozier 1994 Denning et al 1996) Reliable land cover informationis thus increasingly recognized as having crucial relevance for understanding many

Also at Earth System Science Interdisciplinary Center University of Maryland CollegePark MD 20742 USA

International Journal of Remote SensingISSN 0143-1161 printISSN 1366-5901 online copy 2000 Taylor amp Francis Ltd

httpwwwtandfcoukjournalstf01431161html

X Zhan et al1434

aspects of Earth system science (Townshend et al 1991 1994) The signi cant e ectsof hypothetical land cover changes on the climate (Dickinson and Henderson-Sellers1988 Nobre et al 1991 Yukuan et al 1994 OrsquoBrien 1996 Xue 1996) indicate thatland cover change information is also important for global change studies As Earthsystem models become more sophisticated it will be necessary to incorporate landcover change as a variable In addition monitoring land cover change is increasinglyimportant for natural resource management biodiversity assessments and inventoriesfor implementing international agreements on greenhouse gas emissions

Several static global land cover products derived from remotely sensed data arenow available or are under production (Loveland et al 1991 DeFries and Townshend1994 DeFries et al 1995 Loveland and Belward 1997 DeFries et al 1998 Hansenet al 2000) These products were created primarily with remote sensing data fromthe National Oceanic and Atmospheric Administration (NOAA) AVHRR TheMODIS of NASArsquos EOS will provide an improved source of global information forthe study of land surfaces with spatial resolutions of 250 to 1000 m depending onthe bandwidth As one of the e orts of the MODIS Land Science Team a globalland cover change product at 1 km resolution will be created by Boston Universityto depict broad-scale land cover changes attributable for example to interannualvariability in climate (Lambin and Strahler 1994) The University of Maryland(UMd) will provide a product at 250m resolution to depict land cover changes dueto anthropogenic activities which generally occur at ner resolutions than 1 km(Townshend et al 1991) This paper describes the 250m resolution land cover changeproduct developed by the UMd Speci cally this paper presents the generationprocedure the change detection algorithms and the associated look up tables (LUTs)required for their implementation the data sets used for creating the LUTs andtesting the change detection algorithms and nally the results of the algorithm testing

2 Generation of the MODIS 250m land cover change productThe MODIS instrument onboard the EOS AM-1 platform is a scanning radio-

meter system with 36 spectral bands extending from the visible to the thermalinfrared wavelengths (Running et al 1994) The rst seven bands are designedprimarily for remote sensing of the land surface with spatial resolutions of 250m forband 1 (red 620ndash670nm) and band 2 (near-infrared (NIR) 841ndash876nm) and 500mfor bands 3ndash7 (459ndash479nm 545ndash565nm 1230ndash1250 nm 1628ndash1652nm 2105ndash2155 nm respectively) Its orbital con guration and its viewing geometry producefull global coverage every two days Because a very high proportion of land coverchanges due to human activities occur at spatial scales around or less than 250m(Townshend et al 1991) the land cover change product described in this paper isderived from the only two bands available at 250m resolution Although the numberof the available bands is limited the two bands are in the red and near-infraredwavelengthsmdashthe most important spectral regions for remote sensing of vegetation(Townshend and Justice 1988)

Considering that the information available in the two 250 m bands of MODISand the current scienti c knowledge in global scale land cover change detection areboth limited the MODIS 250 m global land cover change product is designed toserve as an alarm system rather than a comprehensive global scale land cover changemonitoring system This alarm system aims to provide users with indicators forwhere the major land cover changes might have occurred and then users can usehigher resolution remote sensing data of the indicated areas to examine the exact

Global and regional land cover characterization 1435

locations and types of the changes One of the direct applications of the alarmproduct is to help the data acquisition strategy of the Landsat 7 satellite With thisalarm system we aim to identify changes caused by human activities such asdeforestation urbanization agricultural expansion or contraction as well as byextreme natural events such as ooding and re (burn scars) Speci cally the typesof land cover change to be detected in this at-launch version of the product areconversions between the following land cover types forest (tall woody vegetationwith greater than 40 canopy cover) non-forest (short herbaceous vegetation orwoody vegetation with less than 40 canopy cover) bare ground water bodies andburn scars (table 1) These simpli cations are associated with the conservative goalof the current at-launch version of the product Once experience of detecting globalscale land cover change with real MODIS data is gained other detailed types ofland cover change such as conversions between non-forest land cover types will beincluded in the post-launch version of the product

Figure 1 shows the data processing scheme for the 250m global land coverchange product The processing procedure takes advantage of the gridded level 2(L2G) products of surface re ectance for the two 250m bands The L2G surfacere ectance product used as input in the processing chain has been atmosphericallycorrected for molecular scattering or absorption of atmospheric gases (such as watervapour carbon dioxide ozone and other trace gases) aerosols and thin cirrus clouds(Vermote et al 1995 1997) We chose to use a surface re ectance product that doesnot employ a bidirectional re ectance distribution function to correct for sensorgeometry in order to avoid assumptions about land cover type

The rst step in the processing chain involves compositing the surface re ectancefor each 32-day period to maximize the availability of cloud-free snow-free goodquality and close-to-nadir surface re ectance data The compositing procedure wehave chosen is shown in gure 2 After the 32-day surface re ectance data for thered and NIR bands have been generated for the two dates to be considered theyare used as input to ve change detection algorithms In the absence of MODISdata for developing and testing the algorithms we use multiple algorithms to buildcon dence in the result The change results from the ve algorithms are thenintegrated with an lsquoalgorithm integration rulersquo to determine whether to label thepixel as lsquochangersquo or lsquono changersquo The ve change detection algorithms and thealgorithm integration rule are described in sect3

The 250m global land cover change product detects changes between imagesacquired at both 3-month and annual intervals For the 3-month interval which isconfounded by phenological changes that do not represent a change in cover type

Table 1 Types of land cover change to be detected by the MODIS 250m land cover changeproduct mdash indicates that the conversion is not of interest or is not likely to occur

Time 2 cover type

Time 1 cover type Forest Non-forest Bare Water Burn

Forest mdash Deforest Deforest Flooding BurnNon-forest Regrowth mdash Urban Flooding BurnBare Regrowth Agriculture mdash Flooding mdashWater Flood retreat Flood retreat Flood retreat mdash mdashBurn Regrowth Regrowth mdash mdash mdash

X Zhan et al1436

Figure 1 Data processing ow chart for the at-launch version of the MODIS 250m globalland cover change product

we apply the algorithms to three pairsmdasha 1-month interval 2-month interval and3-month intervalmdashin order to build con dence in the result (see gure 3) Thesethree results are then integrated into a nal result for the 3-month period using thetime integration rules that combine the various outcomes for the three time intervals

3 Change detection algorithmsDetection of land cover changes has been a major application of remote sensing

for more than a decade During this time period the role of remote sensing inmonitoring the Earthrsquos environment became emphasized (Townshend 1977) Severalchange detection techniques have been developed and applied These techniques fallinto two categories analysis of di erences in classi cation results between two datesand analysis of radiometric di erences between dates In the absence of very highaccuracies in classi cation results the classi cation approach must be used cautiouslyin order that misclassi ed pixels in either of the two dates are not erroneouslylabelled as change Radiometric methods include band di erencing band ratioingtransformed band di erencing principal component analysis and multispectral ormulti-temporal change vector analysis (see Singh (1989) for a review and Johnsonand Kasischke (1998) for examples) Although these methods have been successfulwith the local-scale samples to which they have been applied none of them havebeen tested at a global scale

The 250m MODIS land cover change product will be created as an alarm systemof land cover changes Because MODIS data will not be available until the EOS-


Figure 2 The processing procedure for compositing the 32 daily observations of the band 1and band 2 re ectance values for each pixel

Figure 3 Time intervals over which land cover changes will be detected in the at-launchversion of the MODIS 250m land cover change product

AM1 platform is launched it is only possible to develop and test the algorithmswith simulated data For this reason we employ multiple change detection methodsin the 250m MODIS land cover change product in order to have a reasonable levelof con dence in the result

X Zhan et al1438

Speci cally the change detection methods used in the 250m MODIS land coverchange product are the redndashNIR space partitioning method the redndashNIR spacechange vector method the modi ed delta space thresholding method changes in thecoe cient of variation and changes in linear features The rst three methods exploitthe spectral information from MODIS bands 1 and 2 for the two 32-day compositesbeing compared The other two methods exploit changes in spatial texture Wedescribe the theoretical basis for each of the methods in the following sections

31 T he redndashNIR space partitioning methodMost land cover changes caused by human activities ood or re are associated

with changes in the surface brightness and greenness Therefore the locations of apixel in the two-dimensional space of brightness and greenness at di erent timesshould indicate whether and what type of change has occurred Because the brightnesscan be represented by albedo (approximately the mean of MODIS bands 1 (red)and 2 (NIR) re ectances) and the greenness can be represented by the di erencebetween MODIS bands 2 and 1 the brightnessndashgreenness space is just a 45 szlig clockwiserotation of the red and NIR space ( gure 4) In addition the value of the NormalizedDi erence Vegetation Index (NDVI) of a point in the redndashNIR space is associatedwith the slope of the line connecting the origin and the point All points on one linegoing through the origin have the same value of NDVI For a given geographicregion and time of year the spectral signatures of various land cover types have

Figure 4 The relationship between the redndashNIR space and the brightnessndashgreenness spaceand the typical signatures of various land cover types


characteristic ranges in the redndashNIR space The redndashNIR space partitioning methodexploits these characteristic land cover signatures to detect change (Hansen et al1998)

The method partitions the redndashNIR space into ve classes forest (tall woodyvegetation with greater than 40 canopy cover) non-forest (short herbaceous andwoody vegetation with less than 40 canopy cover) bare ground water bodies andburn scars By comparing the locations of the pixel in the redndashNIR space at time 1(the composite derived from 32 daily observations for the rst month being consid-ered) and time 2 (the composite derived from 32 daily observations for the secondmonth being considered) we can determine whether conversions between the vecover types have occurred Though similar to the classic classi cation di erencemethod this method di ers in the following important ways (1) it distinguishes only ve classes which are spectrally distinct compared with more detailed land coverclassi cations (2) it labels pixels as changed only when it migrates in spectral spacefrom the core area for one cover type in time 1 (the spectral space with no confusionbetween classes see sect 51 for further explanation) to the core area for another covertype in time 2 Migration between a core area and an area in spectral space thatrepresents mixtures of cover types are not labelled as change This avoids the problemof overestimating pixels that have changed a problem with classic classi cation

di erencing methods To implement the redndashNIR space partitioning method for the250m land cover change product a set of LUTs to de ne the core areas is neededSection 5 describes the generation of these LUTs

32 T he redndashNIR space change vector methodThe redndashNIR space change vector method is based on the presumption that land

cover conversions can be characterized by a vector indicating a pixelrsquos change inlocation in the redndashNIR space from time 1 to time 2 The starting and endingpositions direction and magnitude of the change vector are used to determine ifand what type of change has occurred In other words the change vector methoduses both the state and dynamic information of location in the redndashNIR spacecompared with known spectral signatures of the ve cover types ( gure 5) Forexample if a forest does not undergo conversion between time 1 and time 2 and theseasonal change is not signi cant the spectral signature does not change substantiallyand the change vector has a magnitude of zero or close to zero If the forest iscleared for agriculture or urban development and becomes bare ground then the

change vector would generally move from low brightness and high greenness to highbrightness and low greenness in other words parallel with the red axis in the redndashNIR space If the forest is burned both the greenness and brightness decrease andthe corresponding change vector moves parallel but in the negative direction to the

NIR axis in the redndashNIR space These examples illustrate the utility of the dynamicsof the change vector the magnitude and direction for identifying land cover changeHowever the change vectors associated with di erent types of change may havesimilar values for magnitude and direction as shown in gure 5 for changes from

forest to bare ground and non-forest vegetation to bare ground In this case thestate information ie the starting and ending positions of the change vector canindicate the type of change (Huang et al 1998 Zhan et al 1998)

The redndashNIR change vector method di ers from the traditional multispectral ormulti-temporal change vector methods (eg Malila 1980 Colwell and Weber 1981

X Zhan et al1440

Figure 5 Typical change vectors associated with no forest change deforestation and forestburning processes in the redndashNIR space representing the brightnessndashgreenness spaceThe small circles show the typical signatures at Time 1 (T 1) and Time 2 (T 2)respectively

Lambin and Strahler 1994 Johnson and Kasischke 1998) in that the former (1) usesthe starting or ending position of the vector in addition to magnitude and direction(2) distinguishes the characteristic signatures for di erent types of change using adecision tree approach (see sect52) rather than simply setting a threshold for the changemagnitude andor direction

The magnitude A and direction h of the redndashNIR space change vector arecomputed from the re ectance values of band 1 and band 2 at time 1 and time 2with the following equations

A= Oacute (DrRed )2 +(DrNIR )2 (1)

h=

h0 if Dr

Red gt 0 and DrNIR

gt 0

90 szlig if DrRedgt 0 and DrNIR

=0

180szlig Otilde h0 if DrRedgt 0 and DrNIR

lt 0

180szlig +h0 if DrRed lt 0 and DrNIRlt 0

270szlig if DrRedlt 0 and DrNIR

=0

360szlig Otilde h0 if Dr

Redlt 0 and Dr

NIRgt 0

(2)


where

h0=arctan KDrRed

DrNIR K (3)

DrRed

=rT2RedOtilde rT1Red

(4)

DrNIR

=rT2NIROtilde rT1NIR

(5)

and rT1Red rT1NIR

rT1Redand rT1NIR

are the surface re ectance values of the MODIS band1 (red) and band 2 (NIR) at time 1 (T 1) and time 2 (T 2) respectively When thevalues of rT1Red rT1NIR rT1Red and rT1NIR are available and the values of A and h arecomputed the redndashNIR space change vector method is implemented with a set ofLUTs The LUTs provide the ranges for values of A h rT1Red or rT2Red andrT1NIR or rT2NIR associated with the various types of land cover change The generationof these LUTs is described in sect5

33 T he modi ed delta space thresholding methodBecause the seasonal changes of vegetation are not among the types of land

cover change we are interested in we apply the modi ed delta space thresholdingmethod to compensate for seasonal di erences in order to avoid agging phenologicalvariations as real change (Zhan et al 1998) The change vector in the redndashNIR spacecan be converted to the delta-red (DrRed ) and delta-NIR (DrNIR) space (called thedelta space for short) where delta represents the di erence in re ectance betweentime 1 and time 2 With this conversion the change vectors for all pixels start at theorigin ( gure 6) The delta-brightness and delta-greenness space can be overlain withthe delta-red and delta-NIR space which aids the interpretation of the change vectorsin terms of the types of changes they represent The modi ed delta space is measuredin the coordinate system of drRed and drNIR which are DrRed and DrNIR modi ed toaccount for the expected seasonal variability in the re ectances Mathematically ifthe time 1 and time 2 averages of the red and NIR re ectance for the cover type ofthe pixel are MT1Red MT2Red MT1NIR and MT2NIR respectively then

drRed=DrRed

Otilde (MT2RedOtilde MT1Red )=rT2Red

Otilde rT1RedOtilde (MT2Red

Otilde MT1Red ) (6)

drNIR=DrNIR

Otilde (MT2NIROtilde MT1NIR)=rT2NIR

Otilde rT1NIROtilde (MT2NIR

Otilde MT1NIR ) (7)

With this seasonality compensation the e ects of uctuations in re ectance associ-ated with the seasonal changes are theoretically eliminated Values of drRed and drNIRsigni cantly larger than zero indicate real land cover changes

For the MODIS 250m land cover change product the modi ed delta spacethresholding method is employed to label whether and what type of change hasoccurred by the following steps

(1) Determine the cover type for each pixel using band 1 and 2 re ectances witha set of lsquocover typersquo LUTs which are the ranges of the band 1 and band 2re ectance values at time 1 for each of the ve cover types

(2) Compute the values of drRed and drNIR with equations (6) and (7)(3) Compute A and h by substituting DrRed and DrNIR in equations (1)ndash(3) with

drRed

and drNIR

X Zhan et al1442

Figure 6 Change vectors in the delta spaces The coordinates of change vector magnitudeand direction are overlaid with the delta spaces

(4) Determine whether change has occurred and type of change from the com-puted A and h in conjunction with the lsquochangersquo LUTs for the cover typedetermined in step (1)

The lsquocover typersquo LUTs and the lsquochangersquo LUTs are described in sect5

34 T he texture change detection methodTexture features describe the spatial distribution or heterogeneity of the

re ectances within one or in a combination of multiple bands Information aboutthe spatial distribution complements information available from spectral features(Strahler 1981) Most land cover changes that we aim to detect with this productcorrespond to a change in texture features For example deforestation caused bylogging or farming generally increases the heterogeneity of the deforested areas alongthe boundaries of such areas Agricultural expansion in the desert also increases theheterogeneity of the landscape

Many measures of texture features have been proposed (Haralick 1979 Lambin1996 Mayaux and Lambin 1996 Wulder et al 1998) After applying some of thesemeasures to the test data sets we nd that the coe cient of variation (CoV standarddeviation divided by the mean) using NDVI values of neighbouring pixels in a 3


pixel by 3 pixel kernel to be an e ective texture measure for change detection Forthe at-launch version of the 250m MODIS land cover change product we use thefollowing criteria to label changed pixels if |CoV

T2Otilde CoV

T1| 4 then label the pixel

as changed where CoVT1

and CoVT2

are the values of the CoV for the NDVI in a3 Ouml 3 pixel kernel surrounding the pixel at T 1 and T 2 respectively

35 T he linear feature change detection methodThe linear feature change detection method is based on the observation that

many land cover changes caused by human activities are associated with explicitboundaries such as roads power line rights-of-way or the edges of elds If a linearfeature is observed at time 2 but was not present at time 1 it can be inferred thatchange has occurred

The linear feature change detection method has three steps (1) highlight linearfeatures by manipulating the grey levels of neighbouring pixels with an edge enhance-ment method (2) identify linear features in both the time 1 and time 2 edge-enhancedimages (3) label the pixel as changed if a linear feature is identi ed in time 2 butnot time 1

Exploratory analysis identi ed band 1 (red) re ectance as better at discriminatinglinear features than either band 2 or metrics based on combinations of bands 1 and2 The step (1) edge enhancement of band 1 re ectance computes the mean of theabsolute di erence between the grey level value at each pixel and at each neighbourin a 3 Ouml 3 image kernel surrounding the pixel In step (2) a continuous indicator ofthe presence of a linear feature is derived using the following rule in each of thefour directions through the centre pixel in a 3 Ouml 3 image kernel nd the minimumedge enhanced value then save the maximum of these four minimum values Step(3) identi es the pixel as containing an edge if the result of step (2) is greater thanor equal to 37 and then labels the pixel as changed if an edge exists in time 2 butnot time 1

4 Data sets for implementing and testing change detection algorithmsThe 250m MODIS land cover change product will be generated from data

acquired by MODIS bands 1 and 2 using the change detection algorithms describedabove However to derive the LUTs required by the algorithms to develop this at-launch product a global data set of the red and NIR surface re ectances are neededbefore real MODIS data are available Also to test the performance of the changedetection methods simulated MODIS data for test sites are needed For thesepurposes we use two types of existing remotely sensed data First the LUTs for thethree spectral methods (the redndashNIR space partitioning method the change vectormethod and the modi ed delta space thresholding method) were generated mainlyusing data from the AVHRR together with spectral data from Landsat TM Secondtesting of the ve change detection methods was conducted with data sets simulatedfrom Landsat TM image pairs Sections 41 and 42 describe these data sets

41 AVHRR dataThe AVHRR sensor is a relatively simple scanning radiometer with ve bands

in the red NIR and thermal spectral regions (Kidwell 1988) It is a lsquoheritageinstrumentrsquo of MODIS with an orbital con guration and viewing geometry produ-cing daily full Earth coverage The AVHRR red (channel 1 580ndash680nm) and NIR(channel 2 725ndash1100 nm) bands overlap the MODIS red (band 1 620ndash670nm) and

X Zhan et al1444

NIR (band 2 841ndash876) bands but have larger bandwidths than the MODIS bandsSimilar AVHRR sensors onboard NOAArsquos satellite series have been continuouslyobserving the Earthrsquos surface since 1981 The historical record has been preservedin an archive to produce continuity needed for land surface studies Many e ortshave been made to calibrate and atmospherically correct the data such as NASArsquosAVHRR Land Path nder project (Agbu and James 1994 James and Kalluri 1994)AVHRR Path nder data are calibrated and corrected for the atmospheric e ects ofmolecules aerosols trace gas absorption and scattering However they are notcorrected for water vapour absorption Because no other remotely sensed and atmo-spherically corrected red and NIR surface re ectance data are available globally weused the AVHRR Land Path nder data for generating the LUTs of the changedetection algorithms

Two sets of AVHRR data were used for generating the LUTs the 8 km data for12 years from 1982 to 1993 (Agbu and James 1994) and the 1 km AVHRR data for1992ndash1993 (Eidenshink and Faudeen 1994) The 1 km data set has a closer spatialresolution than MODIS and a 1 km pixel should have better purity than an 8 kmpixel However the 1 km data set has more cloud contamination than the 8 km datasets Since each of the two data sets has advantages and disadvantages and becauseno better data are available we used both for the prototypes of the LUTs neededfor the change detection method We composited the decadal 8 km and 1 km datato monthly values based on maximum NDVI values (Holben 1986) In order toderive representative red and NIR re ectances from the 12 years of the 8 km datafor each month we ranked the values from low to high NIR re ectances Then thevalue least likely a ected by both atmospheric and bidirectional e ects (the oneyielding the composite image with the least speckle) was chosen for each month Thecorresponding red and NIR re ectance values for that year were then extracted toconstruct a representative monthly data set of red and NIR re ectances

Pixels sampled from the 8 km and 1 km AVHRR data were subsequently used astraining data to construct the LUTs This will be described in sect5

42 L andsat T hematic Mapper (T M) dataLandsat TM data were used for two purposes (1) to extract sample re ectance

values in the red and NIR bands for water bodies and burned areas and (2) to testthe change detection methods Landsat TM has seven spectral bands ranging fromthe visible bluendashgreen to the thermal infrared spectral regions Band 3 (red 630ndash690nm) and band 4 (NIR 750ndash900nm) of Landsat TM are the lsquoheritage bandsrsquo ofthe MODIS bands 1 and 2 and can be used to simulate MODIS data The pixelsize of the TM images for the red and NIR bands is 285m Landsat 4 and 5 havea 705km near-circular Sun-synchronous orbit with a repeat period of 16 daysGlobal coverage of Landsat data is not available However we selected cloud-freeTM images or image subsets in several locations for use in the development of thechange detection algorithms

A TM image of an area in north-eastern China where a boreal forest had beenburned massively was used to extract the sample values of red and NIR re ectancesfor burn scars The sample re ectance values for water bodies and ooding wereobtained from TM images of the Washington DC area Manaus Brazil and theSan Francisco Bay area We delineated polygons to identify water bodies oods andburn scars The TM images were then degraded to MODIS 250m resolution usinga specially designed lter that approximates the point spread function of MODIS


(the computer code for this transformation was obtained from Dr Kai Yang and isreferred to as the TM-MODIS code hereafter) The re ectance values of the delin-eated areas on the simulated MODIS images were extracted to represent red andNIR values of water and burn scars The seasonal variations of these re ectancevalues were considered to be negligible

To validate the change detection methods presented previously more than adozen pairs of cloud-free or nearly cloud-free Landsat TM images were obtained fordi erent locations around the world where various types of land cover conversionsare occurring The characteristics of some of these locations are listed in table 2 Toreduce the size of this paper only three pairs of these data sets representing agricul-tural encroachment into desert agricultural expansion into tropical rainforest andtemperate urban development are going to be presented

To utilize these TM image pairs for testing the performance of the changedetection methods preliminary data processing is required The most importantprocessing requirements for change detection are the accurate registration and theradiometric normalization of the images As the rst step the original TM imagepairs were coregistered using ground control points A root mean square (rms) errorof less than 067 corresponding to approximately 20 m was achieved using a linear

Table 2 Characteristics of the Landsat TM images used to create the test data sets (indicatesthat the data set is presented in this paper)

Location Dates of TM images Main types of land cover change

Alexandria Egypt 7 June 1984 Agricultural expansion in desert13 June 1992

Beijing China 26 September 1987 Urban growth into agricultural area25 October 1992

Charlotte NC 8 July 1984 Temperate mixed woodland to urban28 July 1997 development

Manaus Brazil 2 August 1989 Conversions between forest and20 September 1995 non-forest and between water and

landOrlando FL 9 January 1985 Forest to agricultural elds or

26 January 1997 residential areasOntario site 1 Canada 17 August 1985 Boreal forest deforestation and

20 October 1985 regrowth20 August 1992

Ontario site 2 Canada 17 August 1985 Boreal forest deforestation and20 August 1992 regrowth

Ontario site 3 Canada 27 July 1987 Boreal forest deforestation and3 July 1996 regrowth

Rondonia Brazil 10 August 1986 Tropical rain forest to agricultural20 July 1996 elds

Santa Cruz Bolivia 2 July 1986 Rain forest to agricultural use10 July 1992

Parana River Basin Brazil 22 November 1986 Patches of forest converted into20 November 1991 agricultural elds

Washington DC 26 May 1985 Temperate mixed woodland to urban8 May 1990 development

Yellowstone WY 22 September 1987 Forest to burn scars10 October 1988

Yucatan Mexico 14 April 1986 Urban growth in arid or semi-arid4 April 1994 area

X Zhan et al1446

or second-order polynomial equation The TM data were then degraded to theMODIS spatial resolution of 250m using the TM-MODIS code Based on the rmserror of 20m for the TM data the rms errorof the coregistration of the simulatedMODIS data is less than 01 pixel

Radiometric normalization of the test data is of equal importance to geometricregistration We applied a radiometric normalization procedure based on thatreported by Hall et al (1991) to most of the pairs of coregistered simulated MODISdata For each pair of images black target pixels such as water bodies and brighttarget pixels such as bare soil were selected If their re ectance values at time 1 andtime 2 were signi cantly di erent (more than 5 of the mean value) we determinedthat the pair of data required radiometric normalization and the procedure wasapplied For the three pairs of data listed in table 2 the Washington DC and Egyptpair were normalized The Bolivia pair was not because the dark and bright targetre ectance values were comparable

For each test data pair we delineated where land cover change had occurred byvisual inspection of the original TM images The change bitmaps at TM resolutionwere then converted to 250m using the TM-MODIS code If the 250m simulatedMODIS pixel included at least 25 change based on the TM resolution changebitmap the 250m pixel was labelled as change These change bitmaps at MODISresolution were used to test the performance of the change detection methods (sect6)

5 Implementation of the change detection methodsFor the generation of the 250m MODIS land cover change product the ve

algorithms are implemented with look up tables (LUTs) To account for the season-ality di erences between the latitudes the Earth is split into four regions the northregion (gt 235szlig N) northern tropical region (0ndash235szlig N) southern tropical region(0ndash235szlig S) and south region (gt 235szlig S) The LUTs associated with each of the vemethods were created for each of these four regions and for each of the 12 monthsThus for each of the ve methods there are 12 Ouml 4=48 LUTs These LUTs weredesigned and generated as follows

51 L UT s for the redndashNIR space partitioning methodThe LUTs for implementing the redndashNIR space partitioning method are a set of

tables which determine the land cover class membership from the red and NIRre ectance values They are used according to the following mathematical function

Cover type (rRed rNIR) (8)

where rRed

and rNIR

are the surface re ectance values of MODIS bands 1 and2 respectively and the value of the function is one of the following cover typesforest non-forest bare water burn scar and mixtures of each These functions aredetermined for each of the four latitude regions for each of the 12 months

To determine the values for the LUTs we used decision trees a techniquedescribed by Breiman et al (1984) and implemented for land cover classi cationusing remotely sensed data (Hansen et al 1996 2000 Friedl and Brodley 1997DeFries et al 1998) Decision trees predict class membership by recursively parti-tioning training data into more homogeneous subgroups A deviance measure iscalculated for all possible splits in the training data and the split that yields thegreatest overall reduction in deviance is chosen to partition the data The procedureis repeated with the subgroups until a decision tree is created with terminal nodes


with no misclassi cation errors or until preset conditions are met for terminatingthe treersquos growth By selecting those terminal nodes with no misclassi cation errors(pure nodes) we identi ed the spectral signature in the redndashNIR space for the lsquocorearearsquo for each cover type Terminal nodes with high classi cation errors representspectral signatures of pixels with mixtures of cover types The pixel is labelled aschange only when the location of a pixel in the redndashNIR space migrated from alsquocore arearsquo at time 1 to a di erent lsquocore arearsquo in time 2 Otherwise it is labelled asno change

The 8 km monthly composited AVHRR Path nder data were used to train thedecision tree to determine values associated with lsquocore areasrsquo and lsquomixed areasrsquo usingthe following steps (1) aggregate the UMd 1 km land cover product (Hansen et al2000) into the ve cover types (2) randomly select 1000 pixels (after bu ering thepolygons of contiguous land cover types by 1 pixel to avoid mixed pixels) for theforest non-forest vegetation and bare cover types (3) extract the red and NIRre ectance values for the selected pixels of the three cover types and the re ectancevalues of water bodies and burn scars from the Landsat TM data (4) for each regionand each month generate a decision tree from the 1000 pixels for each of the vecover types to nd the boundaries of the lsquocore areasrsquo and lsquomixed areasrsquo (see Hansenet al 1998)

52 L UT s for the redndashNIR space change vector methodThe redndashNIR space change vector method uses the red and NIR re ectances in

time 1 and time 2 and the change direction and magnitude to distinguish the di erenttypes of change The LUTs have the following form

Change type (rT1Red rT2Red rT1NIR rT2NIR A h) (9)

where A and h were computed using equations (1)ndash(5) and rT1Red rT2Red

rT1NIR rT2NIR

arethe red and NIR re ectance values of the beginning and ending positions of thevector To generate this set of LUTs the 1 km AVHRR data were used to determinethe re ectance values for forest non-forest and bare ground The values for waterbodies and burn scars were obtained from Landsat TM images as described previ-ously because these types are not identi able in the AVHRR data For each of the ve cover types listed in table 1 500 randomly selected pixels in the agreed areas ofthe UMdrsquos (Hansen et al 2000) and Eros Data Centerrsquos (Loveland and Belward1997) 1 km land cover classi cation product were used to determine values forrT1Red rT2Red rT1NIR rT2NIR for each month For each possible type of change in table 1 thecorresponding values for change direction h and magnitude A were computed usingequations (1)ndash(5) for the 500 pixels Applying the decision tree approach to the dataof rT1Red

rT2Red rT1NIR

rT2NIRand A and h for each possible land cover change the charac-

teristic ranges of these variables were determined and entered into the LUTs (Huanget al 1998) Examples of these LUTs are listed in table 3 for the regions and monthscorresponding to the test data listed in table 2

53 L UT s for the modi ed delta space thresholding methodTo implement the modi ed delta space thresholding method two sets of LUTs

are needed One set lsquocover type LUTsrsquo is used to determine the cover type for time1 from the red and NIR re ectances Mean values of red and NIR re ectances foreach month and region are used to modify the delta values in equations (6) and (7)to compensate for seasonal changes in re ectances These LUTs are similar to the

X Zhan et al1448

Table 3 The LUT used to detect the speci c land cover changes in the test data sets listedin table 2 with the redndashNIR space change vector method

Bolivia(South Tropical

Region July) Egypt Washington DCData set and forest to (North Region June) (North Region May)change type non-forest vegetation bare to vegetation Forest to urban

(1) Agt 30 and (1) Agt 233 (1) hgt 172szlig andhgt 318szlig and rT1Red

lt 69 andrT1Red

lt 36 and rT1NIRgt 276

rT1NIRgt 126

Criteria forChange Type or or orLUT

(2) Agt 30 and (2) (2) Agt 123 and210szlig lt h lt 318szlig and 108lt Alt 233 127szlig lt h lt 178szlig andrT1Red

lt 36 and and hgt 106szlig and rT1Redlt 46 and

rT1NIRgt 126 rT1Red

lt 246 rT1NIRgt 276

LUTs represented by function (8) for the redndashNIR space partitioning method butthe latter include only the lsquocore areasrsquo without the lsquomixed areasrsquo The other set ofLUTs the lsquochange type LUTsrsquo are used to determine the type of land cover changefrom the values of direction h and magnitude A These LUTs are computed fromthe modi ed delta values (drRed and drNIR) substituting for the DrRed and DrNIR inequations (1) (2) and (3)

The randomly selected pixels for forest non-forest and bare and the LandsatTM data for water bodies and burn scars as described in sect52 for deriving LUTsfor the redndashNIR space change vector method are also used to derive the LUTs forthe modi ed delta space thresholding method The lsquocover typersquo LUTs were deter-mined with a simple classi er from the randomly selected pixels The lsquochange typersquoLUTs were determined with the same simple classi er using equations (6) and (7)Examples of the two types of LUTs are shown in table 4 for the speci c types ofland cover changes in the regions and months corresponding to the test data setsin table 2

Table 4 The Look-Up Tables used to detect the speci c land cover changes in the test datalisted in table 2 with the modi ed delta space thresholding method



Criteria for the 0 lt rT1Redlt 6 and 0 lt rT1Red

lt 60 0 lt rT1Redlt 10 and

cover type 10 lt rT1NIRlt 100 for bare 10 lt rT1NIR

lt 100LUT for forest for forest

Criteria for the 2 lt Alt 100 and 2 lt Alt 100 and 7 lt Alt 100 andchange type 0 szlig lt hlt 150szlig 0 szlig lt hlt 150szlig 0 szlig lt hlt 175szligLUT


54 L UT s of the coe cient of variation and linear feature methodsThe LUTs for the texture and linear feature methods simply identify a threshold

for the di erence in texture or linear feature between time 1 and time 2 Above thisthreshold a pixel would be labelled as change Because the spatial resolution of theAVHRR data precludes a possibility of determining texture and linear features at250m resolution the only available data for deriving the threshold are simulatedMODIS data derived from Landsat TM images Landsat TM data for all monthsin all regions are not available Consequently the threshold values were based onempirical examination of the available Landsat TM data

55 L UT s for integrating various change detection resultsThe ve change detection methods produce ve results of detected changes These

results may not agree on whether or not change has occurred To integrate theresults and create a nal result for the 250m MODIS land cover change product aLUT for integrating the results from the di erent algorithms is needed Based onexperiments with di erent combinations of the results from the ve methods on thethree test data sets in table 2 the following rule was found to give an acceptableresult if at least any three of the ve methods label a pixel as lsquochangersquo then the pixelis labelled as lsquochangersquo This lsquoalgorithm integration rulersquo is implemented with a LUTThis LUT can be updated for any other lsquoalgorithm integration rulesrsquo

A simple lsquotime integration rulersquo for the 3-month result states that if at least anyone of the time intervals (1-month 2-months or 3-months) labelled the pixel aslsquochangersquo then the pixel is labelled lsquochangersquo in the 3-month change detection productThese integration rules require further testing with additional test sites

6 Performance of the change detection methodsWe tested the ve change detection methods implemented with the LUTs on

three pairs of MODIS data simulated from Landsat TM images The test datarepresent tropical deforestation near Santa Cruz Bolivia ( gure 7) agriculturalexpansion into desert areas around Alexandria Egypt ( gure 8) and conversion oftemperate mixed woodland to residential and commercial uses ( gure 9) The vealgorithms were run on each of these test data sets The result from the lsquoalgorithmintegration rulersquo applied to the ve change detection results and an integration resultwas obtained for each of these data sets The results from each of the ve methodsand the integration were then compared with the validation data generated frombitmaps of known changed pixels as described in sect43 to test the performance ofthese methods It should be noted that because the change bitmaps were generatedby visual inspection false errors can result due to errors in the change bitmapsthemselves

Errors in the test results can be of two types commission errors where pixelsthat are not labelled as change by the change bitmap are falsely labelled as changeor omission errors where pixels labelled as change in change bitmaps are not labelledas change by the algorithm These measures used to evaluate the performance of thealgorithms are

Commission error ()=NcommitNpredict

(10)

Omission error ()=Nomit

Nbitmap(11)

X Zhan et al1450

Figure 7 The MODIS 250m resolution images simulated from the Landsat TM band 3 and4 re ectance data of the Santa Cruz Bolivia area on 2 July 1986 (the left panel) and10 July 1992 (the right panel) The colour images were composited with the MODISband 1 (red) and band 2 (NIR) re ectance data Band 1 is displayed in both greenand blue colours and band 2 in red The black bar represents 10 km in length

Figure 8 The MODIS 250m resolution images simulated from the Landsat TM band 3 and4 re ectance data of the Alexandria Egypt area on 7 June 1984 (the left panel) and14 June 1990 (the right panel) The colour images were composited with the MODISband 1 (red) and band 2 (NIR) re ectance data Band 1 is displayed in both greenand blue colours and band 2 in red The black bar represents 10 km in length

where Ncommit is the number of pixels where the method labelled change but theactual change bitmap did not Npredict is the total number of pixels that the methodlabelled as change N

omitis the number of pixels where the change bitmap labelled


Figure 9 The MODIS 250m resolution images simulated from the Landsat TM band 3 and4 re ectance data of the Washington DC area on 2 July 1986 (the left panel) and10 July 1992 (the right panel) The colour images were composited with the MODISband 1 (red) and band 2 (NIR) re ectance data Band 1 is displayed in both greenand blue colours and band 2 in red The black bar represents 10 km in length

change but the method did not and Nbitmap is the total number of the change pixelsin the change bitmap The rate of correctly detected change pixles is the compensationof the omission error rate that is (100 Otilde omission error)

The performance statistics of each of the ve change detection methods and theirintegration are listed in tables 5 6 and 7 for the three respective test data sets Thespatial distributions of the commission and omission errors of these change detectionresults for the three data sets are demonstrated in gures 10 11 and 12 Commissionerrors were smaller than omission errors There were trade-o s between the commis-sion and omission errors during the selection of the criteria for growing the decisiontrees of the change detection methods The criteria were chosen in favour of smaller

Table 5 Commission and omission errors of the ve change detection methods and theintegration of them tested against the Bolivia data set (the size of the data set is308 Ouml 342 pixels and the change bitmap has 14 822 actual change pixels)

Commission Omission Commission Omissionerror error error error

(per pixel) (per pixel) (3 Ouml 3 window) (3 Ouml 3 window)Method () () () ()

RedndashNIR space 11 678 01 213partitioning

RedndashNIR space change 77 426 07 42vector

Modi ed delta space 84 171 08 07thresholding

CoV texture change 347 298 17 44detection

Linear feature change 196 740 03 241detection

The integration of the 30 490 02 104 ve methods

X Zhan et al1452

Table 6 Commission and omission errors of the ve change detection methods and theintegration of them tested against the Egypt data set (the size of the data set is137 Ouml 160 pixels and the change bitmap has 5977 actual change pixels)









Table 7 Commission and omission errors of the ve change detection methods and theintegration of them tested against the Washington DC data set (the size of the dataset is 342 Ouml 297 pixels and change bitmap has 4331 actual change pixels)









commission errors which would make the change detection product moreconservative

Figures 10 11 and 12 show that the pixels incorrectly identi ed as no changeare in close proximity to pixels correctly identi ed as change Because the intent ofthe product is to ag areas undergoing change for further analysis with high-resolution data rather than to identify change at each 250m pixel we calculate theomission error for a 3 Ouml 3 pixel moving window in addition to the error on a per-pixel basis For this calcualtion if a pixel is labelled as change in the bitmap andany pixel in the 3 Ouml 3 window around the bitmap pixel is identi ed as change fromthe algorithm we consider the result to be correct By this criteria omission errors


Figure 10 Comparison of the change detection results from each of the ve methods andtheir integration with the actual change bitmap for the Bolivia data set The greenpixels are detected and actual change the red pixels are detected but not actual changeand the blue pixels are actual change but not detected The black bar represents 10kmin length

for the integrated results were 66 239 and 104 for the Bolivia Egypt andDC scenes respectively The omission errors computed on the 3 Ouml 3 window basisare listed in column 5 of tables 5 6 and 7

The commission errors can also be computed on the 3 Ouml 3 window basis Forthis calculation if a pixel is labelled as change by an algorithm and any pixel in the3 Ouml 3 window around the pixel was not labelled as change by the change bitmapthen we consider the labelling is a commission error If there is one or more pixelslabelled as change by the change bitmap around the pixel labelled as change by analgorithm then we consider the labelling by the algorithm to be correct Based onthis 3 Ouml 3 window calculation the commission errors ( listed in column 4 of tables 56 and 7) are all less than 10

For the Bolivia and Egypt data sets the three spectral methods gave commissionerrors around or less than 10 while omission errors could be as low as 20 (eg

X Zhan et al1454

Figure 11 Comparison of the change detection results from each of the ve methods andtheir combination with the actual change bitmap for the Egypt data set The greenpixels are detected and actual change the red pixels are detected but not actual changeand the blue pixels are actual change but not detected The black bar represents 10kmin length

the modi ed delta space thresholding method) on a per-pixel basis and 1 whenconsidering the error in a 3 Ouml 3 window The redndashNIR space partitioning methodgave the smallest commission errors (around or less than 10) for all the three datasets However the omission errors for the redndashNIR space method were relativelyhigh (from 30 to 90 on a per-pixel basis and from 6 to 63 on the 3 Ouml 3window basis) compared to the other two spectral methods

The CoV change detection method labels a pixel as changed when its CoV attime 1 is signi cantly larger or smaller than its CoV at time 2 This occurredprevailingly in the Bolivia data set where the land cover changed mainly fromdeciduous forest with higher level heterogeneity to more homogeneous crop landsand in the Egypt data set where the homogeneous desert were converted to irrigatedsmall agricultural patches which have higher heterogeneity Consequently the


Figure 12 Comparison of the change detection results from each of the ve methods andtheir combination with the actual change bitmap for the Washington DC data setThe green pixels are detected and actual change the red pixels are detected but notactual change and the blue pixels are actual change but not detected The black barrepresents 10 km in length

method worked relatively well for the Bolivia data set (35 commission error and30 omission error) and the Egypt data set (40 commission error and 30omission error) If the change bitmaps are bu ered by one pixel around the edge toconsider the e ect of the 3 Ouml 3 kernel used by the CoV method the commission errorof the method can be reduced to 6 for the Bolivia data set and 25 for the Egyptdata set On the 3 Ouml 3 window basis the omission errors of the CoV method are lessthan 5 for all three data sets

The linear feature method identi es boundaries present at time 2 but not at time1 In the Bolivia data set the vegetation covers are relatively homogeneous Thusthe linear feature method had a relatively small commission error (20) for theBolivia data set compared with other data sets The method misses the central pixelsof changed areas thus its omission errors are high for all the three data sets on per-pixel basis (45ndash75) However if we compute the omission errors on the 3 Ouml 3window basis the 7ndash24 omission rates are acceptable

The Washington DC data set is di erent from the other two in terms of heterogen-eity of the land cover (see the time 1 and time 2 images in gure 9) In this temperateurban area the land cover is characterized by many small patches small agricultural elds with di erent crop types various kinds of roads and highways commercial

X Zhan et al1456

build-ups residential areas power line rights-of-way etc The changed areas markedby the change bitmap are also small patches Because many pixels may consist ofdi erent cover types the spectral di erence between its time 1 and time 2 valuesmay not be signi cant compared with the signal noises retained in the data Thereforethe commission and omission error rates of all the ve methods are large on per-pixel basis (30ndash90) except the redndashNIR space partitioning method had a 11commission error The small patches especially damage the performance of thetexture change detection methods (the CoV and linear feature methods) which detectchanges based on the emerging of border lines The commission errors of thesetexture methods are larger than 80 for this data set However all of the vemethods correctly identi ed the major changed areas (see the green areas in gure 12)The spatial patterns of the detected change areas by the three spectral methods andof the actual change bitmap matched reasonably well Thus on the 3 Ouml 3 windowbasis the omission errors for all the methods are reasonable (see the last columnof table 7 )

The commission and omission errors of the integrated change detection resultsfrom the ve methods are reasonable for the Bolivia and Egypt data sets (commissionerrors of 3 and 5 and omission errors of 49 and 28 respectively) For theWashington DC data set the omission error of the integrated result is 69 whichis higher than the omission errors of three of the ve change detection methodsThis indicates that the di erent methods missed di erent change pixels in otherwords di erent methods identi ed di erent pixels as change This is the rationalefor using multiple methods to gain con dence in the change detection results

7 DiscussionThe at-launch version of the 250m MODIS land cover change product is based

on an approach using multiple change detection methods to build con dence in theresult The methods are implemented with LUTs the data of which are currentlydetermined with re ectance values from the AVHRR Path nder data sets andLandsat TM images The results of the test for a limited number of sites show theability of the methods to identify change We expect the methods to become morereliable when data for training the LUTs can be derived from more advanced sensorssuch as the Sea-viewing Wide Field-of-view Sensor (SeaWiFS) and the futureNational Polar-orbiting Operational Environmental Satellite System (NPOESS)and especially real MODIS data available after launch

There are several possibilities for improving the change detection algorithmsdescribed in this paper as well as for improving the evaluation of results First thealgorithms are based on comparisons between only two dates Incorporation ofmulti-temporal information would likely improve performance because there aretimes of year when di erent cover types display similar re ectances For exampledense crops in the growing season can appear as green as forest or deciduousvegetation in the winter can appear as bright as bare ground Because of theselimitations the at-launch version of the product will serve as only an alarm systemTo create a comprehensive global land cover change monitoring system based onMODIS more experience and knowledge of global scale change detection and realMODIS data are needed

To build more con dence in each of the ve methods and their di erent integra-tion approaches more test data sets are required Although the three test data setsused in this paper represent di erent landscapes in di erent geographic areas we


are processing test data sets for boreal forest changes urbanization in rapidlydeveloping countries ooding and ood retreat forest burning irrigation agriculturaldevelopment and deforestation in di erent areas The methods were designed tolabel di erent types of changes simutaneously with the LUTs Test data sets in whichdi erent types of land cover changes occurred within the same scene will be used toevaluate the capabilities of the methods in detecting these di erent types of changeIn addition more test data are needed to test the redundancy of the methods andto study the feasibility of combining the three spectral methods into one integratedmore robust spectral change detection method

The compensation for seasonal changes in vegetation were included in the LUTsfor the ve change detection methods Time 1 and time 2 for the three test data setsused in this paper were the same month in di erent years and thus do not depictseasonal changes Data sets with both real land cover changes and seasonal changesare needed to test the capabilities of these methods in distiguishing them

The performances of the texture change detection methods are not satisfactoryas shown in the above section One inherent reason may be that we were usingglobally uniform thresholds for labelling changes with the texture measures (CoV oredges) The thresholds identi ed real change from texture in the Bolivia data setbut they identi ed false change in the Washington DC area (see gure 12) and themountain areas in the north of Egypt data (see gure 11) If larger thresholds wereused the commission errors for the Washington DC and Egypt data sets could bereduced but the real borders in the Bolivia data set would be missed This indicatesthat more area-speci c thresholds rather than the single one are needed for thetexture change detection methods A full set of LUTs of the thresholds for the globaltexture change detection should be obtained from more remote sensing data availablein the near future

Another test for the performance of the change detection methods is the e ectsof misregistration of the time 1 and time 2 images The geolocation accuracies forthe MODIS 250m bands are designed to 20 of a pixel ie 50 m If this level ofgeolocation accuracy can actually be achieved then the maximum georegistrationerror between the time 1 and time 2 images will be 100m A preliminary simulationstudy on misregistration e ects shows that error due to misregistration is within thenoise due to a combination of atmospheric and bidirectional e ects Further testingfor the e ects of misregistration needs to be conducted using the test data withsimulated misregistration errors

For the post-launch version of the MODIS land cover change product in additionto the outputs of the metric layers for users to judge changes we will also directlyoutput a lsquoChange Probabilityrsquo measure considering the uncertainties associated withthe intensities of various types of land cover change Once real MODIS data areavailable and more knowledge of global scale land cover change detection is obtainedthe post-launch version of the product is expected to be more comprehensive andreliable

AcknowledgmentsThe Landsat Thematic Mapper images from Dr Mary Pax Lenney and Jordan

Borak at Boston University Ms Tang Lingli at the China Remote Sensing SatelliteGround Station of the Chinese Academy of Science the University of MarylandLandsat 7 Science O ce and the University of Maryland Landsat Path nder Labare acknowledged and highly appreciated Dr Kai Yang is thanked for providing

X Zhan et al1458

the TM-MODIS code We also thank Jenny Hewson-Scardelletti and April Tompkinsfor assistance in data processing This research was supported by the NationalAeronautics and Space Administration Grants under contract numbers NAS596060and NAGW4206 The authors are also grateful to the anonymous reviewers for theircomments

References

Agbu P A and James M E 1994 T he NOAANASA Path nder AVHRR L and Data SetUserrsquos Manual (Greenbelt MD NASA Goddard Distributed Active Archive CenterGoddard Space Flight Center)

Asrar G and Dozier J 1994 EOS Science Strategy for the Earth Observing System(Woodbury NY American Institute of Physics)

Breiman L Freidman J Olshend R and Stone C 1984 Classi cation and RegressionT rees (Monterey CA Wadsworth)

Colwell J and Weber F 1981 Forest change detection Fifteenth International Symposiumon Remote Sensing of Environment 11ndash15 May 1981 (Ann Arbor ERIM) pp 839ndash852

DeFries R and Townshend J R G 1994 NDVI-derived land cover classi cation at aglobal scale International Journal of Remote Sensing 15 3567ndash3586

DeFries R Hansen M and Townshend J R G 1995 Global discrimination of land covertypes from metrics derived from AVHRR Path nder data Remote Sensing ofEnvironment 54 209ndash222

DeFries R Townshend J R G and Hansen M 1998 Continuous elds of vegetationcharacteristics at the global scale Journal of Geophysical Research 104 16911ndash16923

Dennings A S Collatz G J Zhang C Randall D A Berry J A Sellers P JColelloG D and Dazlich D A 1996Simulation of terrestrial carbon metabolismand atmospheric CO2 in a general circulation model Part 1 surface carbon uxesT ellus B 48 521ndash542

Dickinson R E and Henderson-Sellers A 1988 Modelling tropical deforestation a studyof GCM land-surface parameterizationsQuarterly Journal of the Royal MeteorologicalSociety 114 439ndash462

Eidenshink J C and Faudeen J L 1994 The 1 km AVHRR global land data set rststages in implementation International Journal of Remote Sensing 15 3443ndash3462

Friedl M A and Brodley C E 1997 Decision tree classi cation of land cover fromremotely sensed data Remote Sensing of Environment 61 399

Hall F Strebel D Nickeson J and Goetz S 1991 Radiometric recti cation towarda common radiometric reponse among multidate multisensor images Remote Sensingof Environment 35 11ndash27

Hansen M DeFries R DiMiceli C Huang C Solhberg R Zhan X and TownshendJ R G 1998 Red and infrared space partitioning for detecting land cover changeProceedings of the 1998 IEEE International Geoscience and Remote Sensing SymposiumIGARSSrsquo98 Seattle Washington 6ndash10th July 1998 Vol V (IEEE) pp 2512ndash2514

Hansen M C DeFries R S Townshend J R G and Sohlberg R 2000 Global landcover classi cation at 1 km spatial resolution using a classi cation tree approachInternational Journal of Remote Sensing 21 1331ndash1364

Hansen M Dubayah R and DeFries R 1996 Classi cation trees an alternative totraditional land cover classi ers International Journal of Remote Sensing 17 1075ndash1081

Haralick R M 1979 Statistical and structural approaches to texture Proceedings of theIEEE 67 786ndash804

Holben B N 1986 Characteristics of maximum-value composite images from temporalAVHRR data International Journal of Remote Sensing 7 1417ndash1434

Huang C Townshend J R G Zhan X Hansen M DeFries R and Sohlberg R1998 Developing the spectral trajectories of major land cover change processesHyperspectral Remote Sensing and Applications R Green and Q Tong (eds)Proceedings of SPIE vol 3502 pp 155ndash162

Huete A Justice C and van Leeuwen W 1996 MODIS Vegetation Index (MOD13)Algorithm Theoretical Basis Document version 20 NASA EOS Document p105


James M E and Kalluri S N V 1994 The path nder AVHRR data set an improvedcoarse resolution data set for terrestrial monitoring International Journal of RemoteSensing 15 3347ndash3364

Johnson R D and Kasischke E S 1998 Change vector analysis a technique for themultispectral monitoring of land cover and condition International Journal of RemoteSensing 19 411ndash426

Kidwell K B 1988 NOAA Polar Orbiter Data (TIROS-N NOAA-6 NOAA-7 NOAA-8 NOAA-9 NOAA-10 and NOAA-11) Users Guide National Oceanic andAtmospheric Administration Washington DC

Lambin E F 1996 Change detection at multiple temporal scales seasonal and annualvariations in landscape variables Photogrammetric Engineering and Remote Sensing62 931ndash938

Lambin E and Strahler A 1994 Change-vector analysis in multitemporal space a toolto detect and categorize land-cover change processes using high temporal-resolutionsatellite data Remote Sensing of Environment 48 231ndash244

Loveland T R J and Belward A S 1997 The IGBP-DIS global 1 km land cover dataset DISCover rst results International Journal of Remote Sensing 18 3289ndash3295

Loveland T R J Merchant J W Ohlen D O and Brown J F 1991 Developmentof a land-cover data characteristics database for the conterminous USPhotogrammetric Engineering and Remote Sensing 57 1453ndash1463

Malila W A 1980 Change vector analysis an approach for detecting forest changes withLandsat Proceedings of Machine Processing of Remotely Sensed Data SymposiumPurdue University West L afayette Indiana (Ann Arbor ERIM) pp 326ndash335

Mayaux P and Lambin E F 1996 Tropical forest area measured from global land-coverclassi cations inverse calibration models based on spatial textures Remote Sensing ofEnvironment 59 29ndash43

Nobre C A Sellers P J and Shukla J 1991 Amazonian deforestation and regionalclimate change Journal of Climate 4 957ndash988

OrsquoBrien K L 1996 Tropical deforestation and climate change Progress in PhysicalGeography 20 311ndash332

Running S W Justice C O Salomonson V V Hall D Barker J Kaufman Y JStrahler A H Huete A R Muller J-P VanderbiltV Wan Z M TeilletP and Carneggie D 1994 Terrestrial remote sensing science and algorithms plannedfor EOSMODIS International Journal of Remote Sensing 15 3587ndash3620

Sellers P J Bounoua L Collatz G J Randall D A Dazlich D A Los S OBerry J A Fung I Tucker C J Field C B and Jensen T G 1996Comparison of radiative and physiological e ects of doubled atmospheric CO2 onclimate Science 271 1402ndash1406

Singh A 1989 Digital change detection techniques using remotely sensed data InternationalJournal of Remote Sensing 10 989ndash1003

Strahler A H 1981 Strati cation of natural vegetation for forest and rangeland invertoryusing Landsat digital imagery and collateral data International Journal of RemoteSensing 2 15ndash41

Townshend J R G 1977 A framework for examining the role of remote sensing inmonitoring the Earthrsquos environment In Monitoring Environmental Change by RemoteSensing edited by W G Collins and J L van Genderen (Reading UK RemoteSensing Society) pp 3ndash5

Townshend J R G and Justice C O 1988 Selecting the spatial resolution of satellitesensors required for global monitoring of land transformations International Journalof Remote Sensing 9 187ndash236

Townshend J R G Justice C O Li W Gurney C and McManus J 1991 Globalland cover classi cation by remote sensing present capacities and future possibilitiesRemote Sensing of Environment 35 243ndash256

Townshend J R G Justice C O Skole D Malingreau J-P Cihlar J TeilletP Sadowski F and Ruttenberg S 1994 The 1 km AVHRR global data set needsof the InternationalGeospherendashBiosphere Programme InternationalJournal of RemoteSensing 15 3417ndash3441

Global and regional land cover characterization1460

Vermote E F El Saleous N Justice C O Kaufman Y J Privette J L RemerL Roger J C and Tanre D 1997 Atmospheric correction of visible to middle-infrared EOS-MODIS data over land surfaces background operational algorithmand validation Journal of Geophysical Research 102 17131ndash17141

Vermote E F Remer L A Justice C O Kaufman Y J and Tanre D 1995 Atmosphericcorrection algorithm spectral re ectances (MOD09) version 20 AlgorithmTheoretical Basis Document NASA EOS-ID 2015 Greenbelt MD USA

Wulder M A LeDrew E F Franklin S E and Lavigne M B 1998 Aerial imagetexture information in the estimation of northern deciduous and mixed wood forestleaf area index Remote Sensing Environment 64 64ndash76

Xue Y 1996 The impact of deserti cation in the Mongolian and the Inner MongolianGrassland on the regional climate Journal of Climate 9 2173ndash2189

Yukuan S Longxun C and Min D 1994 Numerical simulation for the impact ofdeforestation on climate in China and its neighboring regions Advances in AtmosphericSciences 11 212ndash234

Zhan X Huang C Townshend J R G DeFries R Hansen M DiMiceli C SohlbergR Hewson-Scardelletti J and Tompkins A 1998 Land cover change detectionwith change vector in the red and near-infrared re ectance space Proceedings of the1998 IEEE International Geoscience and Remote Sensing Symposium IGARSSrsquo98Seattle Washington 6ndash10 July 1998 Vol II (IEEE) pp 859ndash861

X Zhan et al1434

aspects of Earth system science (Townshend et al 1991 1994) The signi cant e ectsof hypothetical land cover changes on the climate (Dickinson and Henderson-Sellers1988 Nobre et al 1991 Yukuan et al 1994 OrsquoBrien 1996 Xue 1996) indicate thatland cover change information is also important for global change studies As Earthsystem models become more sophisticated it will be necessary to incorporate landcover change as a variable In addition monitoring land cover change is increasinglyimportant for natural resource management biodiversity assessments and inventoriesfor implementing international agreements on greenhouse gas emissions

Several static global land cover products derived from remotely sensed data arenow available or are under production (Loveland et al 1991 DeFries and Townshend1994 DeFries et al 1995 Loveland and Belward 1997 DeFries et al 1998 Hansenet al 2000) These products were created primarily with remote sensing data fromthe National Oceanic and Atmospheric Administration (NOAA) AVHRR TheMODIS of NASArsquos EOS will provide an improved source of global information forthe study of land surfaces with spatial resolutions of 250 to 1000 m depending onthe bandwidth As one of the e orts of the MODIS Land Science Team a globalland cover change product at 1 km resolution will be created by Boston Universityto depict broad-scale land cover changes attributable for example to interannualvariability in climate (Lambin and Strahler 1994) The University of Maryland(UMd) will provide a product at 250m resolution to depict land cover changes dueto anthropogenic activities which generally occur at ner resolutions than 1 km(Townshend et al 1991) This paper describes the 250m resolution land cover changeproduct developed by the UMd Speci cally this paper presents the generationprocedure the change detection algorithms and the associated look up tables (LUTs)required for their implementation the data sets used for creating the LUTs andtesting the change detection algorithms and nally the results of the algorithm testing

2 Generation of the MODIS 250m land cover change productThe MODIS instrument onboard the EOS AM-1 platform is a scanning radio-

meter system with 36 spectral bands extending from the visible to the thermalinfrared wavelengths (Running et al 1994) The rst seven bands are designedprimarily for remote sensing of the land surface with spatial resolutions of 250m forband 1 (red 620ndash670nm) and band 2 (near-infrared (NIR) 841ndash876nm) and 500mfor bands 3ndash7 (459ndash479nm 545ndash565nm 1230ndash1250 nm 1628ndash1652nm 2105ndash2155 nm respectively) Its orbital con guration and its viewing geometry producefull global coverage every two days Because a very high proportion of land coverchanges due to human activities occur at spatial scales around or less than 250m(Townshend et al 1991) the land cover change product described in this paper isderived from the only two bands available at 250m resolution Although the numberof the available bands is limited the two bands are in the red and near-infraredwavelengthsmdashthe most important spectral regions for remote sensing of vegetation(Townshend and Justice 1988)

Considering that the information available in the two 250 m bands of MODISand the current scienti c knowledge in global scale land cover change detection areboth limited the MODIS 250 m global land cover change product is designed toserve as an alarm system rather than a comprehensive global scale land cover changemonitoring system This alarm system aims to provide users with indicators forwhere the major land cover changes might have occurred and then users can usehigher resolution remote sensing data of the indicated areas to examine the exact







Time 2 cover type



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Red gt 0 and DrNIR

gt 0


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lt 0



=0


Redlt 0 and Dr

NIRgt 0

(2)


where

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DrNIR K (3)

DrRed


(4)

DrNIR


(5)

and rT1Red rT1NIR

rT1Redand rT1NIR




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Otilde MT1NIR ) (7)





drRed

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Time 2 cover type



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h=

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(4)

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(5)

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rT1Redand rT1NIR




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(10)


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(10)


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(10)


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(10)


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(10)


Nbitmap(11)

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(10)


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(10)


Nbitmap(11)

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(10)


Nbitmap(11)

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Nbitmap(11)

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(10)


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(10)


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the 250 m global land cover change product from the ... 250m global land cover change product from...

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