ISPRS Journal of Photogrammetry and Remote Sensing 81 (2013) 31–43

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ISPRS Journal of Photogrammetry and Remote Sensing

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Independent two-step thresholding of binary images in inter-annual landcover change/no-change identification

Priyakant Sinha ⇑, Lalit KumarEcosystem Management, School of Environmental and Rural Science, University of New England, Armidale, NSW 2351, Australia

a r t i c l e i n f o a b s t r a c t

Article history:Received 5 March 2012Received in revised form 9 January 2013Accepted 31 March 2013Available online 9 May 2013

Keywords:Binary imagesNDVI differencingDistribution normalityThresholdingChange detection

0924-2716/$ - see front matter � 2013 Internationalhttp://dx.doi.org/10.1016/j.isprsjprs.2013.03.010

⇑ Corresponding author. Tel.: +61 2 6773 5217; faxE-mail address: psinha@une.edu.au (P. Sinha).

Binary images from one or more spectral bands have been used in many studies for land-cover change/no-change identification in diverse climatic conditions. Determination of appropriate threshold levels forchange/no-change identification is a critical factor that influences change detection result accuracy. Themost used method to determine the threshold values is based on the standard deviation (SD) from themean, assuming the amount of change (due to increase or decrease in brightness values) to be symmet-rically distributed on a standard normal curve, which is not always true. Considering the asymmetricalnature of distribution histogram for the two sides, this study proposes a relatively simple and easy ‘Inde-pendent Two-Step’ thresholding approach for optimal threshold value determination for spectrallyincreased and decreased part using Normalized Difference Vegetation Index (NDVI) difference image.Six NDVI differencing images from 2007 to 2009 of different seasons were tested for inter-annual or sea-sonal land cover change/no-change identification. The relative performances of the proposed and twoother methods towards the sensitivity of distributions were tested and an improvement of �3% in overallaccuracy and of �0.04 in Kappa was attained with the Proposed Method. This study demonstrated theimportance of consideration of normality of data distributions in land-cover change/no-change analysis.� 2013 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS) Published by Elsevier

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1. Introduction

Land-use change detection and reporting is critical for evaluat-ing and monitoring trends in natural resource conditions (Coppinet al., 2004; Tadesse et al., 2005). Change detection has become amajor application in the field of remote sensing as updated land-cover information can be extracted efficiently and cheaply fromremote sensing that can be used in various decision making pro-cesses (e.g. Arnold et al., 2000). However, investigation of theamount of change depends on the availability of consistent timeseries land-use data capable of highlighting the change in targetsof interest (Pax-Lenney and Woodcock, 1997). The activities car-ried out in different seasons provide a means to understand theland-use practice followed in any given year. This in turn providesan insight about the level of land operation carried out in an areafor crop or other vegetation production in different seasons (Reedet al., 1994; Potgieter et al., 2010; Lhermitte et al., 2011a,b) andalso in identifying the pattern and extent of land-cover classchanges from one season to another. Land-cover change is oftenconnected to land-use, while seasonal variability in land-cover isoften associated with vegetation phenological response to seasonal

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changes in temperature and rainfall (Bradley and Mustard, 2005;Lucas et al., 2007; Geerken, 2009). At latitude 30�S (in a temperateto subtropical subhumid climate), vegetation and managementrespond to seasonal changes in temperature as well as to varyingamounts of seasonal rainfall, therefore both seasonal land coverchange and seasonal land cover variability are prominent in thisstudy area and their changes need to be addressed for betterunderstanding of seasonal land-cover dynamism. The term land-cover change used in this study encompasses both land-coverchange and seasonal variability and can be understood in the con-text it is being used.

Multi-season remote sensing data are useful in understandingseasonal dynamism of land-cover and also in improving land-coverclassification accuracy (Schriever and Congalton, 1995; Wolteret al., 1995; Gilmore et al., 2008; Kleynhans et al., 2010; Chenet al., 2012). Numerous studies have shown the usefulness of NDVIor other vegetation indices in detecting and monitoring changes invegetation and subsequently in land-covers using different tech-niques (e.g., Lunetta et al., 2006; Morawitz et al., 2006; Wanget al., 2009; Lhermitte et al., 2011a,b; He et al., 2013). Land-coverchange from satellite images can be extracted through map-to-map comparison (detection of detailed ‘from–to’ changetrajectories) (e.g. Conchedda et al., 2008) or through image-to-image comparison (detection of binary change/non-change

emote Sensing, Inc. (ISPRS) Published by Elsevier B.V. All rights reserved.

32 P. Sinha, L. Kumar / ISPRS Journal of Photogrammetry and Remote Sensing 81 (2013) 31–43

features) (Green et al., 1994; Lu et al., 2004). Post classificationcomparison (PCC) technique is mostly used for the detection of de-tailed ‘from–to’ change trajectory; while techniques such as imagedifferencing, image ratioing and principal component analysis(PCA) often involve a transformation of the original spectral bandsso as to enhance the land-cover changes in the form of binarychange/no-change information (Lu et al., 2004). For example, inimage differencing, the same spectral bands or other transformeddata, such as vegetation indices (e.g., Hayes and Sader, 2001; Luet al., 2005) or principal components (Lu et al., 2005) of differentdates are subtracted from each other on a pixel by pixel basis. Con-sidering the land-cover changes are shown in the form of a signif-icant change in feature brightness values, pixels with values closeto 0 in the change image are noted as no-change while those great-er or less than 0 represent change (Jensen, 2005). Similarly, imageratioing uses division rather than subtraction to generate newimages, in which ratioed values deviating from 1 are noted aschange (Jensen, 2005; Lu et al., 2005). Principal components anal-ysis has also been applied in many applications when merged ori-ginal spectral bands or transformed images of two dates are usedfor transformation and minor components are interpreted forland-cover changes (Fung and LeDrew, 1987; Macleod andCongalton, 1998; Lu et al., 2005). In these transformed images, alight or dark tone usually represents pixels of change, clearly dis-tinguishable from the grayish background of the no-change pixels(Fung and LeDrew, 1988). The changes are located at both tails ofthe histograms of these transformed data with pixels of no-change,i.e. with minor brightness changes, distributed around the mean(Macleod and Congalton, 1998). Thresholding (or density slicing)is applied to transformed data to acquire quantitative informationabout the areas of land-cover changes by separating the pixels ofchange from those of no-change. It is obvious that the selectionof an optimal threshold should be based on the accuracy of classi-fying the pixels as change or no-change. Various accuracy indicessuch as overall accuracy, average accuracy, or combined accuracyhave been discussed in determining the threshold levels for changedetection along with Kappa coefficient of agreement as the stan-dard measure of accuracy (Fung and LeDrew, 1988).

Different methods have been proposed for the determination ofoptimal thresholds values. For example, one method is by applyinga trial and error procedure to select appropriate thresholds in thelower and upper tails of the histogram of the resultant image toseparate change/no-change areas. Another method is to determinethe threshold values based on the standard deviation (SD) from themean and test the results empirically (Macleod and Congalton,1998; Lu et al., 2005). However, a major constraint of this methodis that it assumes the amount of change (due to increase or de-crease in brightness values) to be symmetrically distributed onstandard normal curve and at a given SD, the locations of changesat either side of the histogram are equidistant from the mean,which is not always true. In many cases, the histogram of the resul-tant image is not normally distributed due to the nature of changein the study area. Many studies have been conducted without con-sidering the asymmetry of distribution histograms for change/no-change identification (e.g. Fung and LeDrew, 1988; Hayes andSader, 2001) and did not raise the issue of unequal amounts ofchange in the two opposite directions, which is often the case.However, there are studies that have attempted to deal with the is-sue of asymmetrical distribution of change histogram duringchange/no-change thresholding (e.g. Mas, 1999; Pu et al., 2008).For example (Mas, 1999) applied independent two-step threshold-ing method for change/no-change detection by considering onlyone part of the histogram at a time for thresholding by maskingthe other half. In the process, he obtained a limited accuracyimprovement over the traditional mean ± SD thresholding. How-ever, he pointed out two major shortcomings of the two-step inde-

pendent thresholding: (a) reduced subset of sample points used forthe determination of each threshold level, and (b) optimal thresh-old level selected independently are not always most accuratebased on optimal Kappa, which takes into account entire changeagreement. Pu et al. (2008) applied a different thresholding tech-nique by considering the two sides of the histogram to be indepen-dent and computed the means and SD for each part. The authorsused the assumption that values on each side are normally distrib-uted, which leads to similar conclusion regarding the unreliabilityof the traditional assumption about the amount of change on bothsides.

The current study addresses the above issues through the devel-opment of improved independent two-step thresholding byconsidering decrease and increase parts separately for change/no-change identification. The purpose of this study include (a)demonstrating the use of NDVI differencing image for land-coverchange detection, (b) determining the optimal threshold levelsusing error matrices generated based on thresholding of NDVI dif-ference images with different threshold values using improvedtwo-step thresholding techniques, without masking one part ofthe histogram and without considering the two parts of the histo-gram to be normally distributed, (c) using the entire reference dataset for optimal threshold level determination, which was the majorissue in Mas (1999) study obtaining more reliable accuracy indices(i.e. Kappa), (d) comparing the relative performances of the Pro-posed Method in this study with the existing methods, and (e)identifying and quantifying changes that occurred because of spec-tral increases and decreases in pixel brightness values on differentdates and relating the changes to varying seasonal land-coverconditions.

2. Study region

The study region is in northern NSW, Australia, bounded be-tween 29�300S and 31�00S latitude and 150�150E and 152�150E lon-gitude, covering an area of nearly 34,200 km2 (Fig. 1). The areaincludes major parts of the New England Tablelands and Nandewarbioregions and a small portion of the Brigalow Belt South biore-gion, mostly of homogenous characteristics of country landscape.The climate of the region is mainly temperate to cool temperate,characterized by mild summers with slight summer dominancein rainfall (BoM, 2011). Monthly average temperature ranges froma minimum of �3.6 to 6.0 �C in winter to 21.0–31.0 �C in summerand average annual rainfall is around 1000 mm. The area is a highplateau of hills and plains with an elevation between 600 and1500 m sloping to the west (DECC, 2008). The hilly landscapesare warmer and drier than the tablelands, with vegetation commu-nities typical of the western slopes of the Great Dividing Range andsome tableland species. Rainfall decreases from east to west acrossthe region. The dual summer–winter rainfall and temperature vari-ations from warm summer to frosty winter support herbaceousvegetation dominated by both summer-active (C4) and winter-ac-tive (C3) plants (native and introduced). C3 plants are adapted tocool-season climates while C4 plants are adapted to warm to hotseasonal conditions (Ode et al., 1980). Improved pastures on thetablelands and slopes are generally dominated by sown, winter-ac-tive and yearlong green (C3) grasses and clovers or lucerne. Pas-tures on the plains, on the other hand, are increasingly sown totropical summer-active (C4) grasses.

Eight broad LULC classes viz. Bare Soil (BS), Cropland (CL), Na-tive Pasture (NP), Improved Pasture-(IP), Evergreen Forest (EF)and Evergreen Woodland (WL), Waterbody (WB) and Marshland(ML), were visually interpreted on satellite imagery with the helpof digital LULC map of the year 2000. Intra-annual changes inland-cover classes at latitude 30�S (in a temperate to subtropical

Fig. 1. Location of the study region.

P. Sinha, L. Kumar / ISPRS Journal of Photogrammetry and Remote Sensing 81 (2013) 31–43 33

climate) are mostly due to different vegetation and managementresponse in all four seasons (summer, autumn, winter and spring)to seasonal changes in temperature as well as to varying amountsof seasonal rainfall. The region is characterized by summer andwinter rainfall, with only a slight dominance of summer rain. Inspring, winter-active and yearlong growing crop and pastureplants and winter weeds are all in full growth mode until latespring, and become dormant during the hot dry months of sum-mer. Further, the summer crops (mainly cotton) and pastures (na-tive and introduced) have a growing period from mid-spring tomid-summer and being harvested in autumn. The preparation ofsowing of winter crops (mainly wheat) starts in autumn whichcontinues to grow till mid-spring before being harvested in latespring. Therefore, there are two major transition periods when ma-jor changes in vegetation cover are observed between the seasons:first transition from winter through spring up to early summer andthe second transition is observed between early spring throughsummer and then up to autumn. In mid-summer the reliabilityof the summer irrigation of crops and pastures, and the reliabilityof at least some summer rainfall stimulates growth in the range ofsown and native pasture types present, as well as access to deepsoil moisture by evergreen forests and woodlands.

3. Materials and methods

3.1. Image acquisition and pre-processing

Fig. 2 shows flow diagram of different steps followed in thisstudy. Landsat-TM data for six dates (January 2007, March 2008,May 2007, August 2007, September 2007 and November 2009);path 90, row 81, were acquired for the study (note that the imagesused were from three different years as no cloud-free data for theselected months within a single year were available). The vegeta-tion identified in the region had typical phenological response tothe four distinct seasonal changes in temperature as well as tovarying amounts of seasonal rainfall. Under normal conditions,the sensitivities of vegetation types towards seasonal rainfall andtemperature conditions do not change much in two consecutiveyears and as not much difference was observed in average rainfall

and temperature for the March of 2007 and 2008, and November of2007 and 2009, the vegetation response for these months was as-sumed similar in the different years, and did not have significantimpact on the change results. Each Landsat scene comprisedapproximately 80% of the New England Tablelands and NandewarBioregions of NSW (Fig. 1). Images were selected covering all fourseasons, summer (January), autumn (March and May), winter (Au-gust) and spring (September and November) from 2007 to 2009 tocompare land-cover classes and phenological differences betweenscenes. One of the major land-cover categories identified in the re-gion was grazing pasture dominated by both summer-active (C4)and winter-active (C3) plants or a mix of both; therefore, carewas taken in the selection of dates of satellite images. November(late spring), January (midsummer) and March (early Autumn)images were selected to identify summer-active pastures as theyhave rapid growth in late spring to early autumn. September (earlyspring) and November (late spring) images were used to identifywinter-active plants as they reach peak productivity in spring,becoming less active or dormant in the hottest months of summer.An additional late-autumn (May) image was used to identify win-ter-active pastures, which have commenced growth by this time,depending on rainfall.

The idea behind image pre-processing is to make all the imagessimilar or nearly similar so that they can be considered to be takenat the same time, in the same environmental conditions and by thesame sensors (Hall et al., 1991). The importance of accurate imageregistration of the multi-date imageries is important to avoid anyspurious results of change detection due to misregistration(Townshend et al., 1992; Dai and Khorram, 1999; Stow, 1999;Verbyla and Boles, 2000). In the current study, the images weregeometrically referenced to UTM projection system keeping RMSerrors below half a pixel between the images. Previous studieshave indicated that a registration accuracy of less than one-fifthof a pixel (0.2) is required to achieve a change detection error ofless than 10% (e.g. Townshend et al., 1992; Dai and Khorram,1998). However, Dai and Khorram (1998) indicated that the impactof misregistration varied between image pairs and also was morepronounced in heterogeneous areas located around the edges. Fur-ther, Townshend et al. (1992) suggested that at landscape scale the

Fig. 2. Flow chart showing different methodology steps followed in the study.

34 P. Sinha, L. Kumar / ISPRS Journal of Photogrammetry and Remote Sensing 81 (2013) 31–43

gross estimation of change may not be substantially affected bymisregistration and was less of the problem for more homoge-neous classifications. Considering the homogenous characteristicsof country landscape of the study region, where land cover classesstretch over large areas, typical of rural country landscape, andlarge sized agricultural fields, and the objective of the study to esti-mate gross seasonal land cover variability, the RMS error of 0.5 wasdeemed acceptable and assumed not to have impacted much onland cover change results. This within-pixel shift was reportedinherent to any digital change detection technique (Coppin andBauer, 1994) and was used successfully in numerous studies usingTM data (e.g. Chavez and MacKinnon, 1998; Lu et al., 2005;Kennedy et al., 2007).

Conversion of digital numbers into radiance or surface reflec-tance is a requirement for quantitative analysis of multiple imagesacquired on different dates (Song et al., 2001; Lu et al., 2004;Bodart et al., 2011) and numerous methods have been developedand reported for image normalization (Markham and Barker,1987; Chavez, 1996; Song et al., 2001; Lu et al., 2002; Bodartet al., 2011). In the current study, all six TM image Digital Numbers(DNs) were converted to top-of-atmosphere (TOA) reflectance val-ues using the equation suggested by Chander and Markham(2003). Images of the same area acquired on two different datesshow differences in brightness values due to LULC change. How-ever different atmospheric and land surface conditions, sun angleand phenological stages also contribute to different brightness val-ues, thus shows areas under change which have not actually been

changed. Therefore attempts are made to identify actual changes inLULC areas, while normalizing all other changes caused by otherexternal factors mentioned above. Many researchers have demon-strated the importance and process of normalizing radiometric val-ues between two-date images (e.g. Collins and Woodcock, 1996;Prakash and Gupta, 1998; Pu et al., 2008). In this study, a linearregression method was adopted for normalizing images on two dif-ferent dates assuming the image pixel values on date1 as a linearfunction of the values of the same area on date2. Normalizationtargets were selected from a wide range of pixel brightness valuesas suggested by Eckhardt et al. (1990) to develop more accurateregression models. Finally a linear regression equation was com-puted by matching a given pixel’s DN of selected targets on date1images with the corresponding pixel’s DN on date2 (e.g. Jan–Mar,May–Mar, etc.) for TM bands 3 and 4 and for NDVI images.

3.2. NDVI differencing

NDVI was computed for assessing the type, extent, and condi-tion of vegetation in the study region as part of land-use changeinvestigations and to separate vegetated and non-vegetated areasand its usefulness in normalizing the variable vegetation condi-tions and the background effect (Veraverbeke et al., 2012). TheNDVI was calculated as the ratio between measured reflectivityin the red and near infra-red (NIR) portions of the electromagneticspectrum as NDVI = (NIR � Red)/(NIR + Red). These two spectralbands were chosen because chlorophyll in leafy green vegetation

P. Sinha, L. Kumar / ISPRS Journal of Photogrammetry and Remote Sensing 81 (2013) 31–43 35

absorbs red light for photosynthesis and reflects the NIR wave-lengths due to scattering caused by internal leaf structure (Tucker,1978). The contrast between vegetation and soil is also at a maxi-mum in these two bands. NDVI has values ranging from �1 to +1.NDVI differencing image is often considered and used in many re-search as an effective method for land-cover change identification(Lu et al., 2005; Kleynhans et al., 2011). NDVI, measure of green-ness, image has capability in enhancing the difference among spec-tral features and also in minimizing topographical and shadoweffects. Therefore, difference of NDVI images between two dateshas the potential to detect land-cover change. NDVI difference(NDVI_Diff) image was generated for each of the six change periodsin this study as, NDVI_Diff = NDVI (t1) � NDVI (t2).

Before the selection of NDVI and image differencing techniquesfor binary change/no-change detection for this study, a series ofchange detection techniques ranging from image differencing(simple and improved), image rationing (IR) (simple and im-proved) (Lu et al., 2005), principal component analysis (PCA), andchange vector analysis (CVA) were tested on a number of changeimages. The selected change images ranged from individual 3, 4,5 TM bands, different vegetation index images such as NDVI, SoilAdjusted Vegetation Index (SAVI) and Tasseled Cap greenness in-dex (TC2). The results indicated that NDVI images outperformedall other images in change detection in the study region. For imagedifferencing method, the change/no-change accuracies from Janu-ary to March images were: NDVI (OA = 97.3%, Kappa = 0.95); SAVI(OA = 87.1%, Kappa = 0.84); TC2 (OA = 93%, Kappa = 0.87); TM B3(OA = 86.2%, Kappa = 0.75); TM B4 (OA = 79.1%, Kappa = 0.62); TMB5 (OA = 68.3%, Kappa = 0.48). Similar trends were observed forother change periods. Based on these results, NDVI was selectedas the index for further work. Veraverbeke et al. (2012) also foundNDVI to be more useful index than SAVI in the region of varyingvegetation conditions and for the normalization of background ef-fect. SAVI was found more suitable for single vegetation type. Im-age differencing technique was found marginally better than PCAand IR and any of these techniques could be used for change detec-tion. However, because of the simplistic nature and relative ease inidentifying both negative and positive changes from differenceimages, NDVI differencing technique was selected for seasonalland-cover change identification in the study region. The choiceof NDVI for land-cover change detection was supported by manyresearchers (e.g., Lunetta et al., 2006; Morawitz et al., 2006; Wanget al., 2009; Lhermitte et al., 2011a,b; He et al., 2013).

The visual interpretation method reported by Cohen et al.(1998) was used for developing reference data for LULC changeevaluation. Initially, change pixels were identified on each of theJanuary, March and May TM color composites (RGB 453) throughvisual inspections. Then, a three-date (e.g. Jan–Mar–May)change-detection classification scheme (Table 1) was developed

Table 1Example of three-date (January, March and May) land-cover change detection scheme for th(Jan–Mar and Mar–May) change/no-change reference data (e.g. code YYN was merged as YYto no-vegetation cover as ‘negative change’ between date2 and date3). Similarly, code YNY wand date2) and NY (no-vegetation to vegetation as ‘positive change’ between date2 and d

Jan–Mar–May change

Description Code

No change in high biomass NCHBMNo change in low-medium biomass NCLMBMNo change in no biomass NCNBMCleared before Jan, regrow Jan–May NYYCleared Jan–Mar, regrow Mar–May YNYCleared Mar–May YYNCleared before Jan, regrow Mar–May NNYCleared before Jan, regrow Jan–Mar, cleared Mar–May NYNCleared Jan–Mar, no regrow YNN

to identify changes between LULC classes during that period. Forexample, areas with existing vegetation cover are coded as ‘Y’and those with no-vegetation as ‘N’ on each date, thus on three-date change scheme, YNY code implied vegetation cover to bepresent before or in January, harvested between January andMarch and again re-grow between March and May. The referencepixels were further modified with the help of each date NDVI im-age displayed as RGB (Sader and Winne, 1992). The advantage ofthis method is its ease in change identification as any combinationof primary colors of similar brightness produces a complementarycolor as a change (e.g. if NDVI images of three different dates aredisplayed in sequence as red, green and blue, then if a crop is pres-ent in date1 and date2 and is harvested before date3 can be iden-tified by yellow color and so on). Finally, Global Positioning System(GPS) based field surveys were conducted during Jan–May, 2010 toconfirm the LULC classes and their change/no-change status. Nec-essary modifications and corrections were made to improve thereference data. Finally, the entire LULC change and no-changeinformation were grouped into seasonal ‘change’ and ‘no-change’categories. 759 sample points (BS, 107; CL, 95; NP; 121; IP; 110;EF, 112; EWL, 104; WB, 54 and ML, 56) were finalized as referencedata for change/no-change categories for Jan–Mar–May period.Further, these three-date reference points were grouped into twoparts by merging similar classes on two successive dates intoone. For example, code YYN was merged as YY (i.e. no change be-tween date1 and date2 as ‘no change’) and YN (i.e. vegetation coverto no-vegetation cover as ‘negative change’ between date2 anddate3). Similarly, code YNY was merged as YN (i.e. vegetation tono-vegetation as ‘negative change’ between date1 and date2) andNY (no-vegetation to vegetation as ‘positive change’ between date2and date3) and so on. The details of this are explained in Table 1.Merged reference pixels were used for the evaluation of changesbetween January to March and also between March to May sepa-rately. Similarly, based on field survey conducted during Aug/Septand Nov, 2010, 694 sample points (BS, 91; CL, 83; NP; 104; IP; 96;EF, 108; EWL, 101; WB, 59 and ML, 52) and 761 sample points (BS,98; CL, 88; NP; 134; IP; 118; EF, 101; EWL, 98; WB, 63 and ML, 61)were used for two remaining three-date change periods (May–Aug–Sept and Sept–Nov–Jan), respectively, and identical classeson two consecutive dates were merged as above to get two-datereference data for change evaluation. Table 2 explains the numberof sample points used for two-date change periods.

3.3. Determination of thresholds

Ideally, the differences between feature brightness are theplaces for change identification, but in reality a threshold value isrequired to separate these changed areas from those of no-changeones. The task of determining suitable thresholds is one of the most

e study region. Similar classes on two consecutive dates were merged to get two-date(i.e. no change between date1 and date2 as ‘no change’) and YN (i.e. vegetation coveras merged as YN (i.e. vegetation to no-vegetation as ‘negative change’ between date1

ate3) and so on.

Jan–Mar change Mar–May change

Class Class

No change No changeNo change No changeNo change No changePositive change No changeNegative change Positive changeNo change Negative changeNo change Positive changePositive change Negative changeNegative change No change

Table 2Sample points used for change/no-change accuracy assessment for different change periods.

Jan–Mar–May May–Aug–Sept Sept–Nov–Jan

Jan–Mar Mar–May Total May–Aug Aug–Sept Total Sept–Nov Nov–Jan Total

C NC C NC C NC C NC C NC C NC

346 413 218 541 759 279 415 318 376 694 344 417 389 372 761

C, change; NC, no change.

36 P. Sinha, L. Kumar / ISPRS Journal of Photogrammetry and Remote Sensing 81 (2013) 31–43

critical steps in identifying and separating change and no-changeareas from a resultant image. A commonly used method involvesselection of appropriate threshold values using standard deviation(SD) from the mean and testing the results empirically. However, amajor constraint of this method is that it assumes the amount ofchange (due to increase or decrease in brightness values) to besymmetrically distributed on standard normal curve and at a givenSD, the locations of changes at either side of the histogram areequidistant from the mean, which is not always true. Anotherproblem associated with this method is that the mean and theSD of the entire study area are greatly affected by the extreme val-ues (outliers) and also in many cases the histogram of the resultantimage is not normally distributed due to the nature of change inthe study area. However, the histogram of unchanged pixels in astudy area is normally distributed. Fig. 3 explains the distributionof histogram of NDVI difference image for Mar–May, Aug–Septand Sept–Nov change periods. From the histograms of all the sixchange periods, the above three NDVI difference histograms wereselected as each of them represented one of the three differenttypes of distributions, negatively skewed, positively skewed andsymmetrical. The rest of the histograms were assumed falling intoone of these categories with similar characteristics. The three se-lected histogram distributions were tested using the two existingmethods and the Proposed Method.

The three NDVI differencing image histograms displayed inFig. 3 shows that Aug–Sept NDVI image (Fig. 3b) has an approxi-mately symmetrical distribution, while the distribution of Mar–May NDVI (Fig. 3a) and Sept–Nov (Fig. 3c) look asymmetric. Thesymmetric distribution of a histogram means the number of pixelsfrom NDVI decrease and increase parts are similar. Otherwise thedistribution of the histogram is asymmetric. Such distributionproperty can be described by skewness statistics of a histogram dis-tribution, which is a measure of the asymmetry of the data aroundthe sample mean. If a distribution is asymmetric, it is either posi-tively skewed or negatively skewed. A distribution is said to bepositively skewed if the data tend to cluster toward the lowerend of the histogram and with a negatively skewed distribution,most of the data tend to occur toward the upper end of the histo-gram. The skewness of the normal distribution (or any perfectlysymmetrical distribution) is zero. Skewness statistics were

Fig. 3. Histogram of NDVI difference image (a) Mar NDVI– May NDVI (left skewed), (b) Au

computed for distribution of histograms of all change images alongwith standard errors of skewness (SES). These statistics were usedto determine if the degree of skewness was significant. The compar-ison was made by taking the numerical value for skewness withtwice the SES and included the range from minus twice the SES toplus twice the SES (i.e. test of normality = skewness ± 2�SES, at 95%level of significance). If the value for skewness falls within thisrange, the skewness of distribution of the histogram is consideredto be nearly normal and the concept of normality is considerednot to be seriously violated. The skewness statistics and results ofthe test of normality of distribution of histograms of NDVI differ-ence images are given in Table 3.

Since most of the distributions were found to be significantlyskewed, a new approach was applied to determine differentthreshold values for changes due to spectrally increase and de-crease parts and to separate them from no-change pixels. Themethod was based on the fact that, in most cases, due to dissimilaramounts of change occurring due to spectrally increase and de-crease in brightness values of pixels between the two dates, thetwo portions of the histogram should be treated separately andthe use of the same mean and SD to determine the threshold inboth sides would not give appropriate results. Therefore, there ex-ist separate means and standard deviations of spectrally decreas-ing and increasing parts, and the two can easily be computedand used for change/no-change threshold determination in bothdirections. These issues are addressed in this study by determiningthreshold values from mean and SD for the spectrally decreasedand spectrally increased parts separately. The process includescomputing the minimum, maximum, mean and SD of NDVI differ-ence image. By taking the values between minimum and mean,and values between mean and maximum, image masks were cre-ated for spectrally decreased and spectrally increased parts,respectively. Mean and SD for each part were computed separatelyusing a mask on the opposite part. Further, the histogram of eachimage was checked for the concentration of distributions and iso-lated pixels mainly at the two tails were discarded by rescalingtheir values to match the higher concentrations range.

Fig. 4 illustrates an example of the probability density functionof a difference image for the entire study area with a mean valueat the middle. Two separate means and SDs were calculated for

g NDVI–Sept NDVI (nearly symmetrical) and (c) Sept NDVI – Nov NDVI (right skewed).

Table 3The skewness statistics and results of the test of normality of distribution ofhistograms of NDVI difference images.

Change Period S SES Significance (95%)

Jan NDVI–Mar NDVI �3.14 0.152 YesMar NDVI–May NDVI +1.48 0.163 YesMay NDVI–Aug-NDVI +2.03 0.171 YesAug NDVI–Sept NDVI �0.42 0.290 NoSept NDVI–Nov NDVI �1.38 0.153 YesNov NDVI–Jan NDVI �4.94 0.287 Yes

S, skewness; SES, standard error of skewness.

P. Sinha, L. Kumar / ISPRS Journal of Photogrammetry and Remote Sensing 81 (2013) 31–43 37

spectrally decreased, i.e. left part as (MeanL, SDL) and spectrally in-creased, i.e. right part as (MeanR, SDR). Thus the two sides of thehistogram would be treated separately for change detection andchange thresholds can be determined based on respective meanand SD on either side of the histogram. Thus, for the left part (neg-ative change), the threshold value can be determined as pixelvalue < MeanL + C�SDL and for right part (positive change) as pixelvalue > MeanR � C�SDR (where, C is critical value (C-value)) andno-change pixels would lie in-between these two thresholds(Fig. 4). For effective comparison of the performances of threshold-ing techniques such as conventional Mean ± C�SD (Method 1) andthe two step independent Mean ± C�SD by masking opposite part(Mas, 1999) (Method 2) were compared with the proposed two-step independent thresholding technique (Proposed Method). Bin-ary classifications were done using C-value thresholds set at 0.2–3,with an interval of 0.2 for Methods 1 and 2 and 1.2 to �3.0, with aninterval of 0.2, for the Proposed Method.

3.4. Accuracy assessment

Accuracy assessment is an important step in remote sensing todetermine the fitness of classification and change detection pro-cesses (Janssen and van der Wel, 1994). The threshold images, pro-duced at each C-value, were verified with reference sample datacollected for positive, negative and no-change areas for each meth-od implemented. Change/no-change classification accuracies wereexpressed as an error matrix in terms of producer’s error, user’s er-ror and overall accuracy (Congalton et al., 1983; Congalton, 1991;Smits et al., 1999). Kappa coefficient (K^) was also used as a mea-sure of overall statistical agreement of error matrix (Congaltonet al., 1983). It takes non-diagonal elements into account and isrecognized as a powerful technique for analyzing a single error ma-trix and comparing the difference between different error matrices(Congalton, 1991; Smits et al., 1999). The Kappa coefficient and

Fig. 4. Probability density functions of NDVI differencing image. Data present in thetwo sides from mean are assumed to be normally distributed to compute means (ML

and MR) and standard deviations (SDL and SDR) for spectrally decrease andspectrally increase parts respectively. C-value can be determined by an optimalKappa or other accuracy indices (Fung and LeDrew, 1988).

variances of the error matrices of each method were used to com-pare the performance among different change detection methods(Gong and Howarth, 1990; Congalton, 1991). The Kappa coefficient(K^) was computed as follows:

K^ ¼ NPr

i¼1xii �Pr

i¼1xiþxþi

N2 �Pr

i¼1xiþxþi

where r is the number of rows in the matrix, xii is the number ofobservations in row i and column i (the ith diagonal elements), xi+

and x+i are the marginal totals of row r and column i respectively,and N is the number of observations. Different critical values (C-val-ues) were tested, ranging from 1.4 to �3 (with an interval of 0.2) forthe Proposed Method and 0.2 to 3.0 (with an interval of 0.2) for theother methods, using reference data for change and no-change cat-egories and an optimal C and threshold value was determined basedon highest Kappa or optimal Kappa (e.g. Fung and LeDrew, 1988;Hayes and Sader, 2001; Pu et al., 2008). The performances ofchange/no change accuracy of different methods were comparedby means of a pairwise test of significance. The Z statistics com-puted in this test compares a pair of Kappa statistics obtained fromthe error matrices of two techniques, to determine if they are signif-icantly different. The test statistic Z is obtained by:

Z ¼ K1 � K2

ðV1 þ V2Þ1=2

where K1 and K2 are the estimated Kappa statistics for the two tech-niques and V1 and V2 are the large sample variances of the respec-tive Kappa statistics. The Z statistics follows a normal distribution.For instance, if the Z value obtained was greater than 1.96, the clas-sification results (error matrices) were significantly different at a95% confidence level.

4. Results

Table 4 shows the linear regression equations simulated byusing a least-squares method with unchanged pixels in the studyarea for NDVI images for each change period. These regressionequations basically reflect the effect of phenology on image spectrabetween the two different dates. For Jan–Mar pair, NDVI for un-changed pixels decreases, reflected in 0.88 slope coefficients, thusmore changes were observed during this period. The histogramfor Jan–Mar NDVI image pair showed more concentration on theright side, indicating more positive change as compared to nega-tive change. The reason for such a pattern of change during Jan–Mar period can be attributed to continuous growth of summeractive plants till February and then plants starting to die after Feb-ruary with falling temperatures. NDVI slope coefficient of 0.95 forMar–May pair indicates more areas under no change category. Thesmall changes that occurred were mostly due to decline in vegeta-tion cover (decrease in brightness value) mainly due to completeharvesting or dormancy of summer plants and some positivechange (i.e. increase in vegetation cover) due to winter activepastures which often have a growth period in early autumn. ForMay–Aug pair, slope coefficient of 0.91 showed a larger decline

Table 4Regression equation of NDVI image pairs.

Change period Regression equation R2

Jan–Mar 0.88x + 0.001 0.96Mar–May 0.95x � 0.005 0.94May–Aug 0.91x + 0.01 0.86Aug–Sept 1.08x�0.005 0.92Sept–Nov 0.97x + 0.002 0.90Nov–Jan 1.04x � 0.001 0.89

Fig. 5a. Change/no-change thresholds based on Mean ± C�SD (Method 1) at different C-values for Mar–May, Aug–Sept and Sept–Nov change periods. The optimal thresholdvalues were determined by highest Kappa value or overall accuracy (OA). The figure shows that an optimal C-value for Mar–May, Aug–Sept and Sept–Nov can be at 2.0, 1.6and 1.6, respectively.

Fig. 5b. Change/no-change thresholds based on two step Mean ± C�SD (Method 2) at different C-values by masking opposite part (Mas, 1999). Data present at the two sidesfrom mean are treated independently. Mask applied on right part while change/no-change thresholding of left part is carried out and vice versa. The optimal threshold value(C-value) was determined by highest Kappa or overall accuracy (OA) of left and right part of histogram separately. Based on optimal Kappa identified at a given C-value for leftand right part separately, change/no-change areas are identified. For example, for Mar–May period C-value can be 2.0 and 2.2 for left and right parts, respectively, and ifMean = 0.18 and SD = 0.15, pixel values less than�0.12 and pixel values greater than 0.51 are identified as change and pixel values falling in between these two values are no-change areas. Similarly, the range for change/no-change classification for Aug–Sept and Sept–Nov periods can be determined at 1.8 (left part) and 2.0 (right part) and change/no-change classification can be done based on these two values as described earlier.

Fig. 5c. Change/no-change thresholds using proposed independent two-step thresholding technique (Proposed Method) at different C-values. The optimal threshold value(C-value) was determined by highest Kappa or overall accuracy (OA). Data present at the two sides from mean are treated independently to compute means (ML and MR) andstandard deviations (SDL and SDR) for spectrally decrease and spectrally increase parts, respectively. For example, for Mar–May period C-value can be �1.4, and if ML = 0.04,SDL = 0.06 and MR = 0.3, SDR = 0.08, at C = �1.4, pixel values < �0.044 = negative change, values between �0.044 and 0.412 = no change and values > 0.412 = positive change,see Fig. 3. Similarly, the range for change/no-change classification for Aug–Sept and Sept–Nov periods can be determined at �2.0 and �1.2 C-values, respectively.

38 P. Sinha, L. Kumar / ISPRS Journal of Photogrammetry and Remote Sensing 81 (2013) 31–43

in unchanged areas and changes were mainly from active wintergrowing crops. Aug–Sept period showed increase in unchangedareas (NDVI slope coefficient of 1.08) and changes were mostlydue to harvesting of winter crops and also growing of summer ac-tive plants. Slope coefficients of Sept-Nov and Nov–Jan pairs (0.97and 1.04 respectively), reflects not much effect of phenology onNDVI and whatever changes observed between these periods weredecrease in vegetation cover due to senescence of winter activeplants with increasing temperature and growing of summer activeplants.

Figs. 5a, 5b and 5c show the process of optimal threshold iden-tification taking NDVI_Diff image for Mar–May, Aug–Sept and forSept–Nov using conventional Mean ± C�SD (Method 1); two-stepindependent thresholding based Mean ± C�SD by masking oppositepart of distribution (Method 2) and the method developed in thisstudy (Proposed Method), respectively. The optimal threshold va-lue was determined by highest Kappa value or overall accuracy(OA). Fig. 5a shows that an optimal C-value for Mar–May, Aug–Septand Sept–Nov, from Method 1, can be at 2.0, 1.6 and 1.8, respec-tively, as Kappa or OA were the highest in the series at these

values. For Method 2, the optimal threshold value (C-value) wasdetermined by highest Kappa or overall accuracy (OA) of left andright part of histogram separately. Based on optimal Kappa identi-fied at a given C-value for left and right part separately, seasonalchange/no-change areas are identified. For example, for Mar–Mayperiod, C-value can be 2.0 and 2.2 for left and right parts, respec-tively, and if Mean = 0.18 and SD = 0.15, pixel values less than�0.12 and pixel values greater than 0.51 are identified as changeand pixel values falling in between these two values are no-changeareas. Similarly, the range for change/no-change classification forAug–Sept and Sept–Nov periods can be determined at 1.8 (leftpart) and 2.0 (right part) and seasonal change/no-change classifica-tion can be done based on these two values as described earlier. Forthe Proposed Method, a range of C-values from 1.4 to �3 weretested in terms of highest Kappa value and OA achieved afteraccuracy evaluation. Fig. 5c shows that an optimal Kappa valuefor Mar–May period can be at C-value of �1.4, and if ML = 0.04,SDL = 0.06 and MR = 0.3, SDR = 0.08, then pixel values<�0.044 = negative change, values between �0.044 and0.412 = no change and value >0.412 = positive change, see Fig. 4.

Table 5Change/no-change accuracy comparison for different thresholding techniques for selected change periods. It can be seen that accuracy varies significantly for asymmetricaldistributions between existing and the Proposed Method.

Change period Normality of distribution Method 1 Mean ± C�SD(conventional method)(a)

Method 2 Mean ± C�SD(masking oppositepart) (b)

Proposed Method(independent two-stepthresholding) (c)

Pair-wise Test of significance

Kappa OA (%) Kappa OA (%) Kappa OA (%) Pair Z-stats

Mar–May Left skewed 0.892 92.14 0.906 92.71 0.934 95.63 (a):(b); (a):(c); (b):(c) 0.68; 2.65*; 2.31*

Aug–Sept Nearly symmetrical 0.893 93.64 0.904 93.84 0.908 94.18 (a):(b); (a):(c); (b):(c) 0.75; 1.36; 1.64Sept–Nov Right skewed 0.891 92.57 0.897 92.66 0.936 96.60 (a):(b); (a):(c); (b):(c) 0.33; 3.15*; 3.68*

C, critical value; SD, standard deviation; OA, overall accuracy.* Significant at 95% level.

Table 6Accuracy of NDVI differencing images for different change periods.

Change period NDVI_Diff

OA K^

Jan–Mar 97.3 0.95Mar–May 95.2 0.92May–Aug 98.3 0.97Aug–Sept 94.1 0.88Sep–Nov 98.0 0.96Nov–Jan 94.3 0.90

OA, overall accuracy; K^, Kappa.

P. Sinha, L. Kumar / ISPRS Journal of Photogrammetry and Remote Sensing 81 (2013) 31–43 39

Similarly, the range for change/no-change classification for Aug–Sept and Sept–Nov periods can be determined at �2.0 and �1.2C-values, respectively. The method of determining optimal thresh-old values by this means was found very effective as it simulta-neously computes negative change, no-change and positive

Fig. 6. Change/no-change in Site 1 in the study area where results from the two existinperiods.

change through an easy computing process and also the resultantKappa at each C-level can be considered to be reasonably valid asit accounts for the entire reference data.

To demonstrate the effectiveness of the Proposed Method tothreshold change/no-change areas, its performance was comparedwith those of Methods 1 and 2. Table 5 shows the accuracies interms of overall accuracy and Kappa for selected change periods.The change period selected here was to demonstrate the relativeperformances and consistencies of these methods with differenttypes of distribution histograms (Fig. 3). Overall accuracy of >90%and Kappa of 0.89 were achieved from all the methods used inchange/no-change determination. However, as for relative perfor-mances, the results clearly indicate not much significant differencein accuracies in case of nearly symmetric distribution for Aug–Septperiod between the three methods. Though the performances ofthe two earlier methods were also not found significantly differentin the other two asymmetric distributions, i.e. for Mar–May (leftskewed) and Sept–Nov (right skewed) periods, the performance

g methods (Methods 1 and 2) differ from the Proposed Method in different change

Fig. 7. Change/no-change in Site 2 in the study area where results from the two existing methods (Methods 1 and 2) differ from the Proposed Method in different changeperiods.

40 P. Sinha, L. Kumar / ISPRS Journal of Photogrammetry and Remote Sensing 81 (2013) 31–43

of the Proposed Method differs significantly for these two asym-metrical distributions. An improvement of �3% in overall accuracyand of �0.04 in Kappa was attained with the Proposed Methodover the existing ones in these conditions. The NDVI_Diff imageaccuracy showed very good to excellent agreement with referencedata across all the change periods and was greater than 94% (OA)with Kappa being greater than 0.88 in each case (Table 6).

Figs. 6 and 7 show two different sites in the study area as illus-trative examples where the results from the two existing methods(Methods 1 and 2) differ mostly from the Proposed Method in dif-ferent change periods. For Site 1, the visual comparisons of resultsshowed that the two existing methods underestimated positivechange during Jan–Mar, while Method 2 has overestimated posi-tive change for May–Aug change period. During Mar–May, Aug–Sept and Sept–Nov periods, the two methods have estimated morepositive and negative changes as compared to the Proposed Meth-od. Similarly, for Site 2, the two existing methods either over esti-mated or underestimated the change areas, however, the observeddifference was more for Method 1. For both sites, the amount andpattern of the two types of changes vary between the seasonsdepending upon the land-use practice and other vegetation sea-sonal phenology.

5. Discussion and conclusion

There are situations where information on land-cover change/no-change in short intervals is required for better understandingof seasonal land-use practices followed in a given area. The mainrequirements here are to highlight the areas where land-cover fea-tures’ brightness values have increased or decreased (mostly dueto changes in vegetation density), or remained unchanged. Such

information is valuable in regions where land-use features’ bright-ness changes rapidly due to the presence of both summer and win-ter active plants, susceptible to varying amounts of temperatureand rainfall. However, identifying a suitable approach for seasonalland-cover binary change detection through determination of opti-mal thresholding for change/no-change in both directions is founddifficult. The aim of this paper was to provide a new approach fordirectional binary change detection thresholding based on a simpleconcept and easy computation process.

Since the advent of digital remote sensing data processing, bin-ary images from one or more spectral bands have been used forchange/no-change identification in diverse climatic conditions.For example Jensen and Toll (1982) found the usefulness of visiblered band data in change detection analysis in both vegetated andurban environments. Chavez and MacKinnon (1994) also indicatedthat red band image differencing provided better vegetationchange detection results than using NDVI in arid and semi-aridenvironments of the south-western United States. Pilon et al.(1988) concluded that visible red band data provided the mostaccurate identification of spectral change for their semi-arid studyarea of north-western Nigeria in sub-Sahelian Africa. Ridd and Liu(1998) compared image differencing, regression method, tasselledcap transformation (TC), and Chi-square transformation for urbanland-use change detection in the Salt Lake Valley area using Land-sat TM. Prakash and Gupta (1998) used image differencing, imageratioing and NDVI differencing to detect land-use changes in a cor-al mining area of India. Hayes and Sader (2001) used NDVI-differ-encing and PCA analysis along with RGB-NDVI method formonitoring tropical forest clearing and vegetation regrowth inGuatemala’s Maya Biosphere Reserve (MBR). In a recent study, Luet al. (2005) compared several binary change detection methodsfor use in the moist tropical region of the Amazon.

Table 7Percentage change due to spectral decrease and spectral increase in pixel brightnessvalues and also percentage area that remained unchanged between two date changeperiods based on NDVI_Diff images.

Change/no-change

Jan–Mar

Mar–May

May–Aug

Aug–Sept

Sept–Nov

Nov–Jan

Negativechange

0.9 3.6 4.3 2.0 0.8 1.3

No change 90.6 94.6 91.7 95.5 97.8 97.7Positive

change8.5 1.8 4.0 2.5 1.4 1.0

P. Sinha, L. Kumar / ISPRS Journal of Photogrammetry and Remote Sensing 81 (2013) 31–43 41

This study built on the above existing knowledge regarding theuse of binary images in diverse climatic conditions in land-coverchange/no-change identifications, and developed a new methodof bi-directional change/no-change thresholding in a simplisticand effective manner. NDVI differencing method was found veryeffective in change/no-change identification as accuracies obtainedfrom NDVI images were very high. Since the study region is char-acterized by vegetation with different seasonal phenology, anychange related to vegetation can be detected easily by theseimages.

Determination of appropriate threshold levels for change/no-change identification is a critical factor that influences changedetection result accuracy. Since a majority of the total area belongsto no-change category (assuming only 5–10% of changes occurringin a given period) and also the near normal distribution of histo-grams of no-change features, it is much easier and accurate toidentify threshold for no-change areas than those for changed ones(Lu et al., 2005). Various studies have used statistical methods todetermine appropriate threshold values for change images usingmean and standard deviation (SD) of distribution histogram.Threshold levels for change/no-change areas were then deter-mined by multiplying a constant value (C) to SD and computinga range from the mean at both sides of the histogram (i.e.Mean ± C�SD) (e.g. Fung and LeDrew, 1988; Ridd and Liu, 1998).However, the process was found highly subjective and scenedependent and also assumed that the amount of change on bothsides of the histogram to be the same. This assumption is not al-ways applicable, particularly in environments where vegetationhas different seasonal phenology. Thus the amounts of changecaused by increase or decrease in feature’s brightness values be-tween two dates are not always the same, making the histogramdistribution asymmetrical. Therefore, the traditional approach ofdetermining threshold was found not suitable in this case. Thereare a few studies that have addressed the issue of asymmetricaldistribution, but they had issues of reliability of the resultant Kap-pa from the reduced sample size (Mas, 1999) or considering left orright parts of the distributions to be normal (Pu et al., 2008), whichis not true. To overcome this, a relatively new approach based onPu et al. (2008) was developed in this study with some modifica-tions. The method treated both left and right parts of the histogramseparately to compute the mean and SD for each side. The meanand SD computed for the two sides were then used to determinethreshold values for spectrally decreased (negative change) andspectrally increased (positive change) parts separately, with no-change areas lying in-between these two at different C-values asexplained in Fig. 5c. An optimal C and threshold value was deter-mined based on highest Kappa or optimal Kappa (e.g. Fung and Le-Drew, 1988; Hayes and Sader, 2001; Pu et al., 2008).

To demonstrate the effectiveness of the Proposed Method tothreshold change/no-change areas, the performances of conven-tional one step thresholding based on Mean ± C�SD (Method 1);the two step thresholding based on Mean ± C�SD by masking oppo-site part (Mas, 1999) (Method 2); and the new approach proposedin this study (Propose Method) based on separate means and SD forspectrally decrease and increase parts, were compared for selectedchange periods with different types of distribution histogram(Fig. 3). For symmetrical distribution, the results clearly indicatednot much difference in accuracies for Aug–Sept period from allthe three methods. Though the performances of Methods 1 and 2were also not found significantly different in the other two asym-metric distributions of Mar–May (left skewed) and Sept–Nov (rightskewed) periods, the performance of the Proposed Method variessignificantly for these two asymmetrical distributions. Animprovement of �3% in overall accuracy and of �0.04 in Kappawas attained with the Proposed Methods over the existing onesin these conditions. Such pattern of accuracy variations for differ-

ent types of distribution histograms demonstrated the importanceof consideration of normality of data distributions. In case of asym-metrical distributions, which are often the case, thresholding canbe carried out by treating spectral decrease and spectral increaseparts independently, as demonstrated in the Proposed Method.The advantages of such a concept not only resulted in improve-ment in accuracy of change/no-change thresholding but also pro-vided a means of understanding the directional change throughquantification of spectrally decreased and increased areas, alongwith no change areas, between two change periods. The accuraciesfrom other two methods in case of asymmetrical distributionswere also high; however, the policy makers and resource managerswill most likely prefer the binary change product with highestaccuracy and also with directional information about the changes.For this community, the remote sensing analyst can consider theirneeds with the threshold selection based on the Proposed Methodin this study.

Further, the method was found effective in addressing theshortcomings of independent two-step thresholding method ofMas (1999); where a reduced subset of sample points were usedto determine each threshold level and thus the reliability of Kappa.These two issues were efficiently dealt with in this study using im-proved dependent two-step thresholding without masking onepart of the histogram. Thus the entire reference data set can beused for optimal threshold level determination.

This modified approach was found useful in understanding sea-sonal land-cover change patterns in terms of amount and directionof changes (negative or positive) between two dates compared totraditional change/no-change reporting, which combines thesetwo types of changes (positive and negative) into one as ‘Change’and provides the results for change and no-change areas onlyand not the directions of change. For example, histogram of NDVIdifference image for Mar–May period showed more concentrationon the left side (Fig. 3a), indicating that changes occurred more dueto spectral decrease in pixel values as compared to spectrally in-creased part. Table 7 gives the percentage change in spectral in-creased and decreased parts in different change periods fromNDVI_Diff images.

The reason for such a pattern of change during Jan–Mar periodcan be attributed to continuous growth of summer active plants tillFebruary and then plants starting to die after February with fallingtemperatures. Therefore, with a total of 10% change observed dur-ing this period in the region, 8.5% changes were from growing veg-etation cover, thus increasing pixel brightness values. The patternof change during Mar–May period indicated�4% decline in vegeta-tion cover (decrease in brightness value) mainly due to completeharvesting or dormancy of summer plants. However, the periodalso showed�2% of positive change (i.e. increase in vegetation cov-er) due to winter active pastures which often have a growth periodin early autumn. The period between May and Aug showed equalamounts of change in both directions (�4% each). The spectral in-crease in brightness values here are mainly from active wintergrowing crops, e.g. wheat, which is sown in May and continues

42 P. Sinha, L. Kumar / ISPRS Journal of Photogrammetry and Remote Sensing 81 (2013) 31–43

growing till August. However, winter active plants, which have agrowth period in early autumn, starts dying during this period,leading to a decrease in brightness values. This pattern was foundto continue during August–September period, when changes weremostly due to harvesting of winter crops and also growing of sum-mer active plants. September–November period experienced a de-crease in vegetation cover due to dying of winter active plants withincreasing temperature, while growing of summer active plantscontinues till end of summer, thus contributing more positivechanges in vegetation cover. 90% of the region experienced no-change in land-cover during the entire period.

This study introduces a new approach of independent two-stepthresholding of binary images in inter-annual or seasonal land cov-er change/no-change identification. The method was found veryefficient through the use of NDVI difference images. The relativelysimple concept and ease in computation means that this methodcan be applied to other types of binary images generated fromPCA, band differencing, image ratioing, CVA, etc. for land-coverchange/no-change identification. The method not only provides ahigher accuracy over other methods for all types of distributions,it also has an added advantage of identification of directionalchanges in terms of both negative and positive changes. The rela-tive performances of the Proposed Method and the two existingtechniques demonstrated that the assumption that the amount ofchange to be equal at the two sides of the histogram is not alwaystrue and there may be cases when the changes at the two sides aredissimilar. The study recommends the use of separate thresholdvalues for the two sides and showed how accuracy results can beimproved by treating the two sides of the histogram separately.The classification approach used in this study is based on densityslicing of change image and hence is different from other super-vised classifications (both parametric (e.g. MLC) and non paramet-ric (e.g. ANN)) as no training of sample data is performed. TheProposed Method did not differentiate between data being nor-mally distributed or not but applies the same procedure for bothtypes of distributions.

However, the method has not been tested for bimodal distribu-tions of change histograms and this could be assumed as a limita-tion of this method at present. For this, split up bimodalhistograms, as proposed by Loyarte (2002), could be used; how-ever, no such attempt was made in this study to discuss this specialcase. Nevertheless, the confidence in binary products based on thefull use of reference data in accuracy evaluation process and thepattern of seasonal land-cover change identification make thisstudy unique from others.

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