remote sensing Article
Detection of Cropland Change Using Multi-Harmonic Based Phenological Trajectory Similarity Jiage Chen 1, * 1 2
*
ID
, Jun Chen 2, *, Huiping Liu 1, * and Shu Peng 2
School of Geography, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China National Geomatics Center of China, Beijing 100830, China;
[email protected] Correspondence:
[email protected] (J.C.);
[email protected] (J.C.);
[email protected] (H.L.)
Received: 2 June 2018; Accepted: 22 June 2018; Published: 26 June 2018
Abstract: Accurate information on cropland changes is critical for food production and security, sustainable cropland management, and global change studies. The common change detection methods bi-temporal based, using remotely sensed imagery easily generate pseudo changes due to phenological or seasonal differences. Cropland exhibits a distinctive phenological trajectory that has strong periodic characteristics and seasonal paths. This paper proposes the use of phenological trajectory similarity to search for the overall changes between two time-series images instead of single change events between two dates of imagery. Due to the complex spectral–temporal characteristic of cropland, a phenological trajectory was constructed using a multi-harmonic model for capturing intra-annual variations. Then, phenological trajectory similarity was measured using coefficient vector difference (CVD), and used for detecting change/no-change areas when considering both the amplitude and phase difference. Finally, instead of the traditional classification method based on original images, we used the coefficient ratio vector (CRV) as the input for change type discrimination. The distance between the coefficient ratio vector (CRV) of the change pixel and of the reference change type was calculated to identify the exactly changed types. The performance of this proposed approach was tested using two sets of Landsat time-series images from 2010 and 2015. Moreover, the change area detection results of three other methods, namely, the continuous change detection and classification (CCDC), change vector analysis (CVA), and post-classification comparison (PCC), were also calculated for comparison and analysis. The results indicated that the proposed approach acquired the highest accuracy with an overall accuracy of 98.58% and a kappa coefficient of 0.82, which demonstrated that the method provides the capacity to detect real changes and estimate pseudo changes caused by season differences. Keywords: cropland; change detection; phenological trajectory similarity; multi-harmonic model; vegetation index (VI) time series
1. Introduction Cropland is the basic resource and condition of human existence, and its quantity and quality are an important basis for ensuring global food production security [1]. Cropland changes not only directly threaten the survival and development of mankind, food production, and security, but also affect the biogeochemical cycles, global warming, and lead to environmental problems [2–4]. However, large areas of cropland have been shrinking over the past decades due to rapid urbanization and forest plantations [5]. Therefore, timely, accurate, and cost-effective cropland change detection is critical for more effective policy making, yield estimation, and sustainable cropland management practices [6–9]. Remote sensing has proven to be useful for mapping and characterizing cropland information [10,11]. A variety of change detection methods utilizing remote sensing data have been developed for detecting changes [12–14]. The widely used methods including image differencing, change vector analysis (CVA), Remote Sens. 2018, 10, 1020; doi:10.3390/rs10071020
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and post-classification comparison (PCC), are based on a bi-temporal strategy, which is quite simple and straightforward [15–17]. However, since cropland changes dramatically with the season shifts, pseudo changes caused by phenological differences are more likely to be produced [18,19]. Recently, time series analysis has been proven to be superior to the bi-temporal methods for capturing land cover change, since it significantly reduces the impacts of seasonal differences and interference noises [20–22]. Accordingly, many algorithms based on time-series analysis have been proposed for assessing change. For example, the Landsat-based Detection of Trends in Disturbance and Recovery (Landtrendr) proposed by Kennedy et al., (2010) detected an abrupt forest disturbance using a temporal segmentation algorithm [23]. The Breaks for Additive Seasonal and Trend (BFAST) algorithm developed by Verbesselt et al., (2010) is capable of capturing multiple breakpoint changes by estimating time and magnitude of changes occurring within the seasonal or trend components [24–26]. The Continuous Change Detection and Classification (CCDC) estimated the trend of the time-series by using harmonic models and utilized this trend to characterize multiple changes, including abrupt [27]. Although these approaches are very effective in identifying abrupt or gradual changes, they only focused on the detection of forest disturbance and urban expansion, while neglecting agricultural or cropland variations [28,29]. This is mainly due to the fact that the cropland may possess not only more complicated spectral features but also more dynamic temporal characteristics due to the discrepancies between categories, phenology, and growth stage [30,31]. Therefore, the existing methods may be invalid for cropland change detection. To fully capture cropland changes, it is necessary to excavate more temporal information (e.g., phenological information). Fortunately, cropland phenology is a temporal feature that describes the seasonal growth and dynamic development of cropland, which is primarily featured by the obvious time phases of sowing, emergence, growing, ripening, and harvesting. A growing number of studies have focused on utilizing remote sensing data to extract phenology information [32–34]. However, most of them mainly focused on the extraction of key phenological parameters, such as the start of the growing season and the end of the season without considering the overall change of phenological characteristics, namely changes in phenology trajectory. Generally, phenological trajectory can be derived from the function model fitting time-series data [35,36]. However, most mathematical models (e.g., logistic model, Gaussian model, and the simple harmonic model) for fitting phenological trajectory have mainly focused on a single growth or senescence cycle that natural vegetation types possess [37,38]. In fact, cropland may present a more complex phenology cycle than natural vegetation, such as double- or triple-crop patterns within a year. Therefore, the existing models fail to fully describe and capture the phenological trajectory of cropland. In this paper, we propose a new method based on phenological trajectory similarity to solve the issue of pseudo changes caused by phenological differences. The basic idea of the proposed method is that we search for the overall changes between two phenological trajectories instead of exploiting single change events between two dates of imagery. Our method relies on a multi-harmonic model that remarkably matches VI time series due to its periodic changes and season paths. Although most studies mainly utilize the Normalized Difference Vegetation Index (NDVI) time series to capture phenological changes [39,40], the Enhanced Vegetation Index (EVI) still has a higher sensitivity than the NDVI in dense vegetation areas. For this study, we define ‘phenological trajectory’ as the fitted curve through the EVI time series of one year. The objectives of this study are thus (1) to establish a multi-harmonic model to fully capture phenological changes under different cropping patterns, and (2) to test the robustness and performance of the proposed method using Landsat time-series data of Shandong Province, China. 2. Methodology The core of our approach is that we designate finding comprehensive changes between two time-series trajectories rather than searching for the single change events between two dates of imagery. However, image preprocessing is prerequisite for change detection. In this paper, the Landsat
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2. Methodology Remote Sens. The 2018, core10, of1020 our
of 20 approach is that we designate finding comprehensive changes between 3two time-series trajectories rather than searching for the single change events between two dates of imagery. However, image preprocessing is prerequisite for change detection. In this paper, the Ecosystem Disturbance Adaptive Processing System (LEDAPS) algorithm was used for converting Landsat Ecosystem Disturbance Adaptive Processing System (LEDAPS) algorithm was used for DN to surface reflectance [41], and the Function of mask (Fmask) was use for detecting cloud and converting DN to surface reflectance [41], and the Function of mask (Fmask) was use for detecting cloud shadows. The detailed preprocessing procedure is described in Section 3 [42]. The main body cloud and cloud shadows. The detailed preprocessing procedure is described in Section 3 [42]. The ofmain the proposed method can method be split can intobethree (Figure 1). (Figure First, a1). multi-harmonic model is body of the proposed splitparts into three parts First, a multi-harmonic established to describe phenological changes for different cropping patterns. Second, based on the model is established to describe phenological changes for different cropping patterns. Second, based phenological trajectory of the of pixels, the model coefficient vector difference (CVD) is designed to on the phenological trajectory the pixels, the model coefficient vector difference (CVD) is designed measure similarity in order to detect change/no-change areas. Third, the coefficient ratio vector (CRV) to measure similarity in order to detect change/no-change areas. Third, the coefficient ratio vector of(CRV) reference changedchanged types and of changed pixels ispixels calculated, respectively. Then the change type is of reference types and of changed is calculated, respectively. Then the change discriminated by the minimum distance of the CRV. type is discriminated by the minimum distance of the CRV.
Figure1. 1. Outline Outline of of the the overall overall methods methods adopted Figure adopted in in this this study. study.
2.1. Phenological Trajectory Description Using the Multi-Harmonic Model 2.1. Phenological Trajectory Description Using the Multi-Harmonic Model Numerous models have been applied to fit vegetation index (VI) time series for land cover Numerous models have been applied to fit vegetation index (VI) time series for land cover change change analysis, such as the local asymmetric Gaussian function (AG), the double logistic (DL), and analysis, such as the local asymmetric Gaussian function (AG), the double logistic (DL), and the the harmonic model [43]. Recently, de Beurs and Henebry (2010) presented 12 existing harmonic model [43]. Recently, de Beurs and Henebry (2010) presented 12 existing spatio-temporal spatio-temporal methods to estimate phenological parameters, and Atkinson et al., (2012) surveyed methods to estimate phenological parameters, and Atkinson et al., (2012) surveyed four time-series four time-series models to smooth time-series vegetation index data [44,45]. However, each model models to smooth time-series vegetation index data [44,45]. However, each model has its own has its own advantages and disadvantages, and the choice of model needs to be based on the land advantages and disadvantages, and the choice of model needs to be based on the land cover type. cover type. Figure 2 presents the fitting results from three models, the AG, DL, and harmonic model. Figure 2 presents the fitting results from three models, the AG, DL, and harmonic model. However, However, AG and DL are unable to accurately match different waveforms and capture growing AG and are unable to accurately match different waveforms and capture season variations season DL variations as they are always adapted to the upper envelope of thegrowing time series. Because the asannual they are always adapted to the upper envelope of the time series. Because the annual phenology phenology of cropland has periodic changes and season paths, the harmonic model ofremarkably cropland has periodic and VI season harmonic remarkably matches the changes intra-annual time paths, series. the Moreover, themodel harmonic model ismatches capable the of intra-annual VI time series. Moreover, the harmonic model is capable of decomposing a noise-affected decomposing a noise-affected time series into periodic signals in the frequency domain, each time seriesbyinto periodic signalsand in the frequency each defined by unique amplitude and phase defined unique amplitude phase values domain, [46]. values [46].
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Figure2.2.The Thethree threemodels modelsfitted fittedtototime-series time-seriesfrom fromhomogeneous homogeneouspixels pixelsofofcropland croplandderived derivedfrom from Figure Figure 2. The three models fitted to time-series from homogeneous pixels of cropland derived from theLandsat LandsatOperational OperationalLand LandImager Imager(OLI) (OLI)time-series time-seriesimage imageinin2015. 2015.The Thethree threemodels modelsare arethe thelocal local the the Landsat Operational Land Imager (OLI) time-series image in 2015. The three models are the local asymmetric asymmetricGaussian Gaussianfunction function(AG), (AG),the thedouble doublelogistic logistic(DL), (DL),and andthe theharmonic harmonicmodel modelrespectively. respectively. asymmetric Gaussian function (AG), the double logistic (DL), and the harmonic model respectively.
Recently,the thecontinuous continuous change detection and classification (CCDC) algorithm proposed by Recently, change detection and classification (CCDC) algorithm proposed by Zhu Recently, the continuous change detection and classification (CCDC) algorithm proposed by Zhu and Woodcock (2014) applied simple harmonic model (first order) to predict all land cover and (2014)(2014) applied simplesimple harmonic model (first predict land cover types by Zhu Woodcock and Woodcock applied harmonic modelorder) (firstto order) to all predict all land cover types by using all available Landsat images. The simple harmonic model is often not satisfactory for using The simpleThe harmonic is often not satisfactory the complex types all by available using all Landsat availableimages. Landsat images. simplemodel harmonic model is often not for satisfactory for the complex shape of phenological dynamics that has more intra-annual variation, and shape of phenological dynamics that hasdynamics more intra-annual and phenological trajectory the complex shape of phenological that hasvariation, more intra-annual variation, and phenological trajectory description therefore requires high frequency terms for full fitting. Figure description therefore requires high frequency terms for full fitting. Figure 3 shows the phenological phenological trajectory description therefore requires high frequency terms for full fitting. Figure 33 shows the theof phenological trajectory of cropland cropland collected from the the Landsat Landsat OLI time-series image in trajectory cropland collected from the Landsat OLI time-series image inOLI 2015, and illustrates the shows phenological trajectory of collected from time-series image in 2015, and illustrates the fitting results by including different components of the harmonic model. fitting results by including different components of the harmonic model. of the harmonic model. 2015, and illustrates the fitting results by including different components
Figure 3. The phenological trajectory of cropland collected from Landsat OLI time-series image in Figure 3. 3. The Thephenological phenologicaltrajectory trajectory cropland collected from Landsat time-series in Figure ofof cropland collected from Landsat OLI OLI time-series imageimage in 2015, 2015, and the fitting results by including different components of the harmonic model (1st harmonic 2015, and the fitting results by including different components of the harmonic model (1st harmonic and the fitting results by including different components of the harmonic model (1st harmonic model model and 1st + 2nd harmonic model). model 1stharmonic + 2nd harmonic and 1stand + 2nd model).model).
In this this paper, paper, the the multi-harmonic multi-harmonic model model including including the the firstfirst- and and second-order second-order harmonic, harmonic, was was In In this paper, the multi-harmonic model including the firstand second-order harmonic, was used used for for describing describing phenological phenological trajectory. trajectory. The The second second harmonic harmonic isis capable capable of of capturing capturing the the used for describing phenological trajectory. TheMoreover, second harmonic is capable ofseparates capturingcomponents the detailed detailed phenology of the biannual signal. the harmonic model of detailed phenology of the biannual signal. Moreover, the harmonic model separates components of phenology of the biannual signal. Moreover, the harmonic model separates components of the time the time time series series into into different different frequency frequency domains. domains. Therefore, Therefore, the the lower-order lower-order harmonics harmonics (mainly (mainly the series into different frequency domains. Therefore, the majority lower-order harmonics (mainly refer towhile first refer to first and second harmonics) accounts for the of the VI time series variance refer to first and second harmonics) accounts for the majority of the VI time series variance while the higher-order higher-order components components include include substantial substantial noise. noise. The The multi-harmonic multi-harmonic model model we we adopted adopted isis the describedas asfollows: follows: described
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and second harmonics) accounts for the majority of the VI time series variance while the higher-order Remote Sens. 2018, 10, x FOR PEER REVIEW 5 of 20 components include substantial noise. The multi-harmonic model we adopted is described as follows: 2π 2π 2π 2π 2π 2π 2π 2π ˆ VI(t) = a0 + a1 cos t + b sin t + a cos t + b sin t VI(t) = a + a cos T t + b1 sin T t + a 2 cos 0.5Tt + b 2sin 0.5Tt T T 0.5T 0.5T
(1) (1)
where where a is the coefficient of the overall value for the vegetation index; a0 is the coefficient of the overall value for the vegetation index; a and b capture intra-annual change for the vegetation index; aa1 and b1 capture forchange the vegetation index; and b indicateintra-annual intra-annualchange bimodal for the vegetation index; aVI2 (t) andisbthe intra-annual bimodal change for the vegetation index; 2 indicate index value for the vegetation index at the Julian dates. ˆ (t) is the index value for the vegetation index at the Julian dates. VI For each pixel, five coefficients of the multi-harmonic model can be estimated by using MPFIT, For each pixel, five coefficients the multi-harmonic model can be estimated by using MPFIT, an implementation of the iterativeofLevenberg–Marquardt technique to solve the least-squares an implementation of the iterative Levenberg–Marquardt technique to solveand the higher least-squares problem. problem. The MPFIT was selected because of its improved robustness computational The MPFITthan wasthe selected because its improved robustness and higher computational efficiency efficiency ordinary least of square (OLS) [47]. The initial estimates of the coefficient values than ordinary least square (OLS)between [47]. Theobservation initial estimates of thevalue coefficient values were input were the input into MPFIT. The fitness and fitted was estimated using the into MPFIT.ofThe fitness between and fitted was0.6, estimated using the coefficient of coefficient determination (R2).observation If the R2 value was value less than observations with maximum 2 ). If the R2 value was less than 0.6, observations with maximum residual error were determination (R residual error were removed, which returned the adjusted coefficients set that best fitted the removed, which returned the adjusted coefficients set that best fitted the phenological trajectory. phenological trajectory. 2.2. 2.2. Change Change Areas Areas Detection Detection Using Using Phenological Phenological Trajectory TrajectorySimilarity Similarity Generally, is characterized by baseline, amplitude, timingtiming and shape. Generally, the thephenological phenologicaltrajectory trajectory is characterized by baseline, amplitude, and Across different years, the changing of land cover (e.g., natural or anthropogenic disturbances) causes shape. Across different years, the changing of land cover (e.g., natural or anthropogenic the variation incauses amplitude, timing and of the timing phenological trajectories. For the pixel that changes disturbances) the variation in shape amplitude, and shape of the phenological trajectories. from cropland to urban built-up, the trajectory of the EVI shows biggestofdifference. Figurethe 4 For the pixel that changes from cropland to urban built-up, the the trajectory the EVI shows shows the phenological trajectory of cropland based on the EVI time series derived from Landsat biggest difference. Figure 4 shows the phenological trajectory of cropland based on the EVI time Enhanced Thematic Mapper Enhanced Plus (ETM+) imagesMapper in 2010 Plus and of urbanimages built-up on EVI time series derived from Landsat Thematic (ETM+) inbased 2010 and of urban series derived Landsat OLI images 2015. The examples illustrate that these changes could built-up based from on EVI time series derivedinfrom Landsat OLI images in 2015. The examples illustrate be detected by examining changes in amplitude, timing, and shape. Following this assumption, that these changes could be detected by examining changes in amplitude, timing, and shape. change detection is accomplished bydetection measuring similarity of phenological trajectory of of the Following this assumption, change is the accomplished bythe measuring the similarity the same pixel at different years. phenological trajectory of the same pixel at different years.
Figure 4.4.The Thephenological phenological trajectory of cropland collected from Landsat Enhanced Figure trajectory of cropland collected from Landsat Enhanced ThematicThematic Mapper Mapper Plus (ETM+) time-series image in 2010 and the Enhanced Vegetation Index (EVI) of curve of Plus (ETM+) time-series image in 2010 and the Enhanced Vegetation Index (EVI) curve urban urban built-up from the Landsat OLI time-series image in 2015. built-up from the Landsat OLI time-series image in 2015.
2.2.1. Phenological Trajectory Similarity Indicator Once the phenological trajectory was generated, the coefficient vector 𝑪𝑽 = (a , a , b , a , b , RMSE) was extracted to fully excavate phenology information embedded in the temporal trajectory. The first five coefficients (a , a , b , a , and b ) in the multi-harmonic model
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2.2.1. Phenological Trajectory Similarity Indicator Once the phenological trajectory was generated, the coefficient vector CV = (a0 , a1 , b1 , a2 , b2 , T was extracted to fully excavate phenology information embedded in the temporal trajectory. RMSE) Remote Sens. 2018, 10, x FOR PEER REVIEW 6 of 20 The first five coefficients (a0 , a1 , b1 , a2 , and b2 ) in the multi-harmonic model have definite physical significance associated phenology. The zero-order coefficient The a0 iszero-order the arithmetic mean ofaVI is over have definite physicalwith significance associated with phenology. coefficient the the time series andofrepresents thetime overall productivity of cropland. Theproductivity first-term amplitude values arithmetic mean VI over the series and represents the overall of cropland. The afirst-term the variability of productivity over the year.ofThe first-term phase value b1The summarizes values a indicate the variability productivity over the year. first-term 1 indicate amplitude the timing of the growth stage. amplitude and phase b of second-order harmonic mainly phase value b summarizes theThe timing of the agrowth stage. The amplitude a and phase b of 2 2 describe the detailed phenological information of bimodal cropland activity (e.g., double or triple second-order harmonic mainly describe the detailed phenological information of bimodal cropland cropping a year) [48]. The root mean square error [48]. (RMSE) usedmean for assessing the fitness thatisindicates activity (e.g., double or triple cropping a year) Theisroot square error (RMSE) used for residual error. It is able to indicates reflect theresidual possibility of cropland to some extent. For assessing the fitness that error. It is ablechange to reflect the possibility of example, cropland when cropland changed to example, urban built-up, RMSE may biggertosince thebuilt-up, harmonic model is not change to someis extent. For when cropland is get changed urban RMSE may get the mostsince suitable for the urban areaiseven though can wellfor fit the trajectory ofitcropland. bigger the harmonic model not the mostit suitable the phenological urban area even though can well Figure illustrates thetrajectory model coefficients vector for four differentthe kinds of land cover classes. is clear fit the 5phenological of cropland. Figure 5 illustrates model coefficients vectorIt for four that cropland shows quite different shapes the that plotscropland of the sixshows variables, a different kinds of land cover classes. It is in clear quiteespecially different ashapes in the 2 showing lower plots value. of the six variables, especially a showing a lower value.
Figure5.5.The Themodel model coefficients coefficients vector vector of of different differentland landcover covertypes. types. The The model model coefficients coefficientswere were Figure derivedfrom fromthe theLandsat LandsatOLI OLItime-series time-seriesimage imageinin2015. 2015. derived
2.2.2.Change ChangeDetection DetectionUsing UsingCoefficient CoefficientVector VectorDifference Difference(CVD) (CVD) 2.2.2. Directional cropland cropland change, change, which which results results from from natural natural and and anthropogenic anthropogenic disturbances, disturbances, Directional could be detectable using harmonic analysis of the phenological trajectory by examining in could be detectable using harmonic analysis of the phenological trajectory by examiningchanges changes CV. represent the the amplitude amplitude of of in CV.InInthe thefrequency frequencydomain, domain,CV CV contains contains two two components: components: aa1,,aa2 represent trajectory and b , b represent the phase of trajectory. On the one hand, amplitude scaling can trajectory and b1 , b2 represent the phase of trajectory. On the one hand, amplitude scaling can causeintensity intensityvariations variationsand andoccurs occurswhen whenthe theseasonal seasonalpeak peakhas hasbeen beenstretched stretchedor orcompressed compressedin in cause they-axis. y-axis. These These intensity intensity variations variations could could be be related related to to changes changes in in vegetation vegetation vigor vigor or or coverage. coverage. the Amplitude translation (i.e., when the time series has been shifted in the y-axis) is related to shiftsin in Amplitude translation (i.e., when the time series has been shifted in the y-axis) is related to shifts the background reflectance. On the other hand, phase effects (i.e., changing the width of the seasonal the background reflectance. On the other hand, phase effects (i.e., changing the width of the seasonal trajectory)could couldbebe related changes in the length of growing the growing season due to an earlier later trajectory) related to to changes in the length of the season due to an earlier or lateroronset onset of the growing season. The shape or values change of the VI time series (referring to the of the growing season. The shape or values change of the VI time series (referring to the phenological phenological here) canofbeamplitude the resultand of amplitude and phase effects. The shapeisofimportant trajectory trajectory here)trajectory can be the result phase effects. The shape of trajectory is important for deriving cropland phenological features, and values are also indispensable to for deriving cropland phenological features, and values are also indispensable to describe the growth describe the growth and change of the cropland. Therefore, both shape and value (i.e., amplitude and change of the cropland. Therefore, both shape and value (i.e., amplitude and phase) differences and phase) differences are designed to quantitatively and qualitatively assessing changes of phenological trajectories of the same pixel at different years. The similarity measurement is based on exploiting the differences of coefficient vector by considering amplitude and phase. Accordingly, it is assumed that the coefficient vectors of the pixel at year t and year t are given by 𝑪𝑽 and 𝑪𝑽 , respectively, and the CVD is calculated as change magnitude as follows:
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are designed to quantitatively and qualitatively assessing changes of phenological trajectories of the same pixel at different years. The similarity measurement is based on exploiting the differences of coefficient vector by considering andPEER phase. of the Remoteamplitude Sens. 2018, 10, x FOR REVIEWAccordingly, it is assumed that the coefficient vectors 7 of 20 pixel at year t1 and year t2 are given by CVt1 and CVt2 , respectively, and the CVD is calculated as (𝑪𝑽 , 𝑪𝑽 ) CVDas = follows: change magnitude (2) =t1 , CVt2 (a − a ) + (b − b ) + |RMSE − RMSE | CV r r t1 t1 t2 2 t2 2 2 2 t2 + + formula RMSEt1is−the RMSE a − a b − b ∑ where k is the = order ∑ number of the harmonic, the first term of the amplitude k=0 k=1 k k k k
CVD =
(2)
difference and the second term is the phase difference while the third term indicates the residual error the harmonic model. where k is theoforder number of the harmonic, the first term of the formula is the amplitude difference Figure 6 shows the phenological trajectory of cropland collected from the Landsat ETM+ and the second term is the phase difference while the third term indicates the residual error of the time-series image in 2010 and Landsat OLI time-series image in 2015. Figure 6a shows amplitude harmoniceffects model. occur due to different cropland types, which lead to the changing height peak of the EVI Figure shows the phenological trajectory of cropland collected from6bthe Landsat curve6 (i.e., the seasonal peak has been stretched or compressed in the y-axis). Figure shows phase ETM+ effects occurinprimarily due to a change the rainy season, the time being time-series image 2010 and Landsat OLI of time-series imageresulting in 2015.inFigure 6aseries shows amplitude stretched or compressed in time, thus changing the width of the phenological trajectory. EVI curves effects occur due to different cropland types, which lead to the changing height peak of the EVI curve of cropland at 2010, urban built-up and water at 2015 are compared in Figure 6c, Figure 6d, (i.e., the seasonal peak has been stretched or compressed in the y-axis). Figure 6b shows phase effects respectively. Based on the calculation of CVD, change magnitudes of the four pair EVI curves were occur primarily a change of 6e, thethe rainy in the time stretched or computed.due As to shown in Figure CVDseason, betweenresulting different cropland typesseries shows being a relatively smallinvariance. Similarly, the CVD cropland withphenological different time location of theEVI seasonal peak compressed time, thus changing theofwidth of the trajectory. curves ofalso cropland at has abuilt-up small variance. In contrast, the CVD of change type cropland to urban built-up and waterBased on 2010, urban and water at 2015 are compared in from Figure 6c, Figure 6d, respectively. is much larger. It means that the pseudo changes caused by differences of cropland type and the calculation of CVD, change magnitudes of the four pair EVI curves were computed. As shown influence of weather events (change of rainy season resulting in an earlier or later onset of the in Figuregrowing 6e, theseason) CVD between different cropland types shows a relatively small variance. Similarly, can be eliminated effectively based on CVD. the CVD of As cropland with different time location of the seasonal peak also has small variance. for the magnitude of the change, a larger CVD indicates a greater possibility for achanging. Giventhethe magnitude image change, a self-adaptive threshold In contrast, CVD of change type of from cropland to urban built-up andalgorithm water is called much larger. (EM) caused can be used detect the change/no-change areas. Theinfluence EM algorithm It meansExpectation-Maximization that the pseudo changes bytodifferences of cropland type and of weather applies an unsupervised assessment of prior probability density functions to automatically select the events (change of rainy season resulting in an earlier or later onset of the growing season) can be threshold to minimize the change-detection error probability. The pixels with greater magnitude eliminated effectively based CVD. than the threshold were on labelled as changed pixels.
(a)
(b)
(c)
(d)
Figure 6. Cont.
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(e) 6. Change magnitude of phenological trajectory (or EVI curve) between 2010 and 2015. (a) FigureFigure 6. Change magnitude of phenological trajectory (or EVI curve) between 2010 and 2015. phenological trajectory difference caused by different cropland type; (b) phenological trajectory (a) phenological trajectory difference caused by different cropland type; (b) phenological trajectory difference caused by time scaling due to change of the rainy season; (c) trajectory difference caused difference caused by time scaling due to change of the rainy season; (c) trajectory difference caused by by change from cropland to urban built-up; (d) trajectory difference caused by change from cropland change from cropland to urban built-up; (d) trajectory difference caused by change from cropland to to water; (e) change magnitude of four pair curves based on CVD. water; (e) change magnitude of four pair curves based on CVD.
2.3. Change Type Discrimination Using Coefficient Ratio Vector (CRV)
As for magnitude the change, a larger CVD change indicates a greater possibility It isthe more important of to acquire the detailed “from-to” information after detecting for changing. theHowever, magnitude of has change, a self-adaptive algorithm called change Given areas [49]. for aimage pixel that undergone a change fromthreshold one class to another, the CV will definitely show(EM) a bigger land cover change typeareas. has different Expectation-Maximization can difference. be used to Different detect the change/no-change The EMchange algorithm patterns of CV. Therefore, the coefficient ratio vector (CRV) was proposed to determine change applies an unsupervised assessment of prior probability density functionshere to automatically select the type both qualitatively and quantitatively. Firstly, the reference CRV of typical change types could threshold to minimize the change-detection error probability. The pixels with greater magnitude than be constructed based on the reference image. Then, the change type of the changed pixel could be the threshold were labelled as changed pixels. determined based on the minimum distance between reference CRV and target CRV.
2.3. Change Type Discrimination Using Coefficient Ratio Vector (CRV) 2.3.1. Reference CRV Construction
It is more important to acquire the detailed “from-to” change information after detecting change To determine the change types of changed pixels, the reference CRV is required for determining areas the [49]. However, pixel that hasInundergone a change fromthe one class to another, the any CV will change type offor theachanged pixel. this study, we assume that CRV differences between definitely show a bigger Different land cover change has different change patterns of two land cover typesdifference. on t1 are approximately equal to their CRVtype differences from t1 to t2. Therefore, CV. Therefore, theofcoefficient ratiowas vector (CRV)onwas here at tot1determine change typeofboth the reference a changed CRV calculated the proposed reference image . First, a certain number samplesand for each class was selected on a known map ontypes t , and the CV of each qualitatively quantitatively. Firstly,based the reference CRVclassification of typical change could be constructed was derived image. from the harmonic model.type Then, value 𝑪𝑽𝒊could of each class could be basedclass on the reference Then, the change of the the mean changed pixel be determined based calculated. Finally, the reference 𝑪𝑹𝑽 between any two kinds of land cover types i and j were on the minimum distance between reference CRV and target CRV. calculated based on 𝑪𝑽 and 𝑪𝑽 . The reference 𝑪𝑹𝑽
can be used for distance measuring, in
to discriminate change types from t to t . 2.3.1. order Reference CRV Construction b , reference a , b , RMSE , a , the 𝑪𝑽changed apixels, To determine the change types=of 𝑪𝑹𝑽 =( , , , , , CRV) is required for determining (3) 𝑪𝑽 a a b , a , bthat , , assume , RMSE the change type of the changed pixel. In this study, we the CRV differences between any two land cover t1 are approximately equalcoefficient to their CRV differences from t51 was to t2used . Therefore, Based types on theon calculation of CRV, the model of examples in Figure to the reference of acoefficient changed CRV was calculated on the reference image atBecause t1 . First,different a certaintypes number acquire the ratio vector between different land cover types. of of land coefficient vectors, certain change types between cover typesclass samples forcover eachhave classdistinctive was selected based on a known classification map on t1 , two and land the CV of each also have distinctive change patterns. Figure the 7b shows CRV between different cropland types, was derived from the harmonic model. Then, mean the value CV i of each class could be calculated. ref and Figure 7c,d shows the coefficient ratio vector of two change types fromj were cropland to urban Finally, the reference CRVij between any two kinds of land cover types i and calculated based built-up and from cropland to water bodies respectively. As shown in Figure 7b, the CRV between
on CVi and CVj . The reference CRVref can be used for distance measuring, in order to discriminate different cropland types have theijsame signs (positive). When the changes occur from cropland to change types from t to t . 2 (i.e., urban or water), the CRV signs are inconsistent and differ in different other land cover1 types coefficients. This finding implies that different land cover change types ! have different CRV. T a b a a b Moreover, the value difference of CRV between the two land cover types shows much larger RMSE i,0 i,1 i,1 i,2 i,2 i ref
CRVij = CVi CVj =
, , , , , aj,0 aj,1 bj,1 aj,2 bj,2 RMSEj
(3)
Based on the calculation of CRV, the model coefficient of examples in Figure 5 was used to acquire the coefficient ratio vector between different land cover types. Because different types of
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land cover have distinctive coefficient vectors, certain change types between two land cover types also have distinctive change patterns. Figure 7b shows the CRV between different cropland types, and Figure 7c,d shows the coefficient ratio vector of two change types from cropland to urban built-up and from cropland to water bodies respectively. As shown in Figure 7b, the CRV between different cropland types have the same signs (positive). When the changes occur from cropland to other land cover types (i.e., urban or water), the CRV signs are inconsistent and differ in different coefficients. This finding implies that different land cover change types have different CRV. Moreover, the value Remote Sens. 2018, 10, x FOR PEER REVIEW 9 of 20 difference of CRV between the two land cover types shows much larger variance than between different cropland types. Therefore, the reference CRV should be constructed according to different variance than between different cropland types. Therefore, the reference CRV should be constructed signs (qualitatively) andsigns values (quantitatively). according to different (qualitatively) and values (quantitatively).
(a)
(b)
(c)
(d)
(e)
(f)
Figure 7. The signs and value differences of the coefficient ratio vector (CRV) between different land Figure 7. The signs and value differences of the coefficient ratio vector (CRV) between different land cover types. (a) model coefficient difference caused by different cropland type; (b) CRV between cover types. (a) model coefficient difference caused by different cropland type; (b) CRV between different cropland type; (c) model coefficient difference caused by change from cropland to urban different cropland type; (c) model coefficient difference caused by change from cropland to urban built-up; (d) CRV between cropland and urban built-up; (e) model coefficient difference caused by built-up; (d) CRV between cropland and urban built-up; (e) model coefficient difference caused by change from cropland to water bodies; (f) CRV between cropland and water bodies. change from cropland to water bodies; (f) CRV between cropland and water bodies.
2.3.2. Change Type Discrimination by CRV Distance After the change areas were identified, the changed pixel’s 𝑪𝑹𝑽 computed: 𝑪𝑹𝑽
=(
a a
, ,
,
a a
, ,
,
b b
, ,
,
a a
, ,
,
b b
, ,
,
RMSE ) RMSE
from t to t could also be
(4)
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2.3.2. Change Type Discrimination by CRV Distance After the change areas were identified, the changed pixel’s CRVt01 t2 from t1 to t2 could also be computed: !T at01 ,0 at01 ,1 bt01 ,1 at01 ,2 bt01 ,2 RMSEt01 0 (4) CRVt1 t2 = , , , , , at02 ,0 at02 ,1 bt02 ,1 at02 ,2 bt02 ,2 RMSEt02 Suppose the pixel belongs to cropland at t1 , the corresponding reference CRV from cropland to others can be selected. Then, the change type is be determined based on the minimum distance Remote Sens. 2018, 10, x FOR PEERref REVIEW 10 of 20 between the reference CRVij and the CRVt01 t2 of the changed pixel. o n 𝑪𝑽𝑹 𝑪𝑽𝑹 0 − 𝑪𝑽𝑹 ref ref ) = min 0 ref , 0 𝑪𝑽𝑹 ref D t0 𝑪𝑽𝑹 , 𝑪𝑽𝑹 = min − 𝑪𝑽𝑹 , ⋯ 𝑪𝑽𝑹 − D(CVR , CVR − CVR − CVR · · · − CVR (5)(5) CVR , CVR , CVR ∑ ∑ ∑ t1 t2 t1 t2 t1 t2 12 13 ij ij 1 t2
A and Asmaller smallerD Dvalue valueindicates indicatesaahigher highersimilarity, similarity, and the thechange change type type of of the the changed changed pixel pixel is is then then 0 ref assigned D 𝑪𝑽𝑹 assigned to to the the class class with with the the minimum minimum D CVR ,,𝑪𝑽𝑹 CVR . . t1 t2
ij
3. 3. Study StudyArea Areaand andData Data Our Our study study area area belongs belongs to to the the North North China China Plain Plain located locatedin in central central Shandong ShandongProvince, Province,China, China, ◦ 0 ◦ 0 ◦ 0 ◦ 0 ◦C bound bound by by 35°39′ 35 39 to to 36°12′N 36 12 Nand and115°55′ 115 55 to to116°23′E 116 23 E(Figure (Figure8). 8).Mean Meanannual annualtemperature temperatureisis 12.9 12.9 °C and and mean mean annual annualprecipitation precipitationisis687 687mm mm32% 32%falling fallingin inJuly. July. There There are are some some zones zones of of mountainous mountainous areas, but the region is dominated by agriculture. The main land cover types of the area areas, but the region is dominated by agriculture. The main land cover types of the area are are cropland, cropland, water, urban built-up, forest, and barren. There are two types of cropping systems in our study water, urban built-up, forest, and barren. There are two types of cropping systems in our studyarea. area. The The dual dual cropping cropping system system is is composed composed of of winter winter wheat wheat and and summer summer corn, corn, and and the the single single cropping cropping system systemis is mostly mostly peanuts. peanuts. Over Overthe thepast pastfew few decades, decades, rapid rapid industrialization industrializationand and urbanization urbanizationhave have greatly changed the agricultural land pattern in this study area. greatly changed the agricultural land pattern in this study area.
Figure Figure8.8.Location Locationof ofthe thestudy studyarea, area,showing showingthe theland landcover coverclassification classification from from Globeland30 Globeland30 2010. 2010.
We We used used aa series series of of Landsat Landsat sensors sensors Thematic Thematic Mapper Mapper (TM), (TM), Enhanced Enhanced Thematic Thematic Mapper Mapper Plus Plus (ETM+), and Operational Land Imager (OLI) image for Path 122, Row 35 for the years 2009, 2010, (ETM+), and Operational Land Imager (OLI) image for Path 122, Row 35 for the years 2009, 2010, 2015, 2015, 2016 (Table 1) assuming no cropland change had occurred within neighboring two years. Two 2016 (Table 1) assuming no cropland change had occurred within neighboring two years. Two years years (2009 and 2010) of Landsat TM and ETM+ images (11 images) were selected as time-series data (2009 and 2010) of Landsat TM and ETM+ images (11 images) were selected as time-series data at at t1 year, and another two years (2015 and 2016) of Landsat OLI images (12 images) were used as time-series data at t2 year. Image preprocessing is critical to the change detection, facilitating comparison of time series images. Image preprocessing mainly includes geo-referencing, atmospheric correction, and cloud and cloud shadows detection. First, to convert all raw imagery from the DN to surface reflectance values, we used the Landsat Ecosystem Disturbance Adaptive Processing System (LEDAPS)
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t1 year, and another two years (2015 and 2016) of Landsat OLI images (12 images) were used as time-series data at t2 year. Table 1. Description of Landsat images (including Thematic Mapper (TM), Enhanced Thematic Mapper Plus (ETM+), and Operational Land Imager (OLI) image) used in the analysis. Number
Date
Sensor
Number
Date
Type
1 2 3 4 5 6 7 8 9 10 11
10 January 2009 10 March 2010 3 April 2010 16 April 2009 27 April 2010 3 June 2009 22 June 2010 17 August 2010 30 August 2009 10 September 2010 17 October 2009
ETM TM ETM ETM TM ETM ETM TM TM ETM TM
1 2 3 4 5 6 7 8 9 10 11 12
19 January 2015 4 February 2015 24 March 2015 25 April 2015 13 May 2016 12 June 2015 14 July 2015 1 August 2016 2 September 2016 2 October 2015 3 November 2015 21 December 2015
OLI OLI OLI OLI OLI OLI OLI OLI OLI OLI OLI OLI
Image preprocessing is critical to the change detection, facilitating comparison of time series images. Image preprocessing mainly includes geo-referencing, atmospheric correction, and cloud and cloud shadows detection. First, to convert all raw imagery from the DN to surface reflectance values, we used the Landsat Ecosystem Disturbance Adaptive Processing System (LEDAPS) developed by the United States Geological Survey (USGS) to perform automatic atmospheric correction. The LEDAPS algorithm is a highly standardized preprocessing tool that ensures the compressibility of reflectance values from different sensors. The algorithm also requires some ancillary data such as water vapor and ozone concentration. LEDAPS source code and ancillary data can be downloaded from USGS. Second, cloud, cloud shadows and snow were detected using the object-based Fmask algorithm. We used Bands 1, 2, 3, 4, 5, 7 and Band 6 Brightness Temperature (BT) as input data. Fmask ulitizes rules based on cloud physical properties and an object matching approach to extract cloud, cloud shadows, and snow. Finally, relative radiometric normalization was performed using multivariate alteration detection and calibration (MADCAL) algorithms [48]. After the cloud and cloud shadow masking steps were completed for all images, pixels labelled as cloud or cloud shadows were not used in the proposed method. One strategy to complete the integrity of the time-series was the linear-interpolation technology: the masked pixels were given a value according to the trajectory of the clear observations. Specifically, for each masked pixel in a particular day i, the temporally nearest clear observations acquired before (b) and after (a) day i were used to drive its value as follows: Ri = R b + (i − b ) ×
R a − Rb a−b
(6)
where a and b are before and after day i, respectively; and R a , Rb , andRi are the reflectance values on a, b, and i, respectively. There are several non-cropland types in our study area, such as water bodies, barren land, and artificial surfaces. To avoid confusion between cropland and other land cover types, we extracted a cropland mask and only assessed these pixels for change. We detected cropland change from t1 year to t2 year, and cropland mask was made only for images from the t1 year. That is to say, the changes we detected are from cropland to other types. In our experiment, the Landsat images from 2010 were selected as time-series data at t1 year. Therefore, the cropland mask of images at the t1 year was produced using Globeland30 2010. Meanwhile high spatial resolution images from Google Earth were used to help manual interpretation. If there was confusion in comparing the two Landsat images, high spatial resolution images before and after 2010 (can be a few years apart) from Google Earth were
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Remote Sens. 2018, 1020 12 of of 20 northwest and 10, southwest parts. These changes in the landscape of Tai’an are mainly the outcome the continuous urban sprawl of the urban district region over the past five years (2010–2015). Change detection results only including change and no-change classes showed high performance for used to help determine what was happening at the specific locations. The land cover of each reference detection accuracy, with an overall accuracy (OA) value of 98.58% and kappa coefficient of 0.82 point was checked against the Google Earth image with sub-meters spatial resolution. (Table 2).
4. Results
Table 2. Confusion matrix of change/no-change detection results.
4.1. Change Areas Detection Reference Changed (Pixels) Classified Changed (Pixels) The proposed change detection method was used to estimate change magnitude No-Change Change Sum theCommission Error of the phenological trajectory between the two years (t and t ). Generally, pixels having relatively large 2 Nochange 48,130 1 715 48,845 1.46 magnitudes were labelled smaller magnitudes Changeas changed areas, and 2 those pixels 1672 with1674 0.12 were classified as unchanged areas.Sum The EM algorithm was used to determine the threshold for change detection. 48,132 2387 50,519 These images ofOmission change area were formatted 0.00 as binary images error 29.95 and used as mask files for further change type discrimination (Figure 9).OA(%) = 98.58%, Kappa coefficient = 0.82
Figure 9. 9. Detected Detected change change areas areas based based on on phenological phenological trajectory trajectory similarity similarity (in Figure (in black). black).
To the improvement andchange effectiveness of indicated our method, thereduction change in areas detection Theevaluate results show signs of cropland pattern, as by the cropland area results of the three other methods, CCDC, CVA, and PCC, were calculated for comparison and (Figure 9). However, most of the reduction in cropland can be found geographically in the northwest analysis. In the CCDC method, two years (2009 and 2010) of time-series images (11 images) were and southwest parts. These changes in the landscape of Tai’an are mainly the outcome of the continuous used inputof data describe theregion trend,over andthe predicted value at 2015. Then,detection the differences urbanas sprawl the to urban district past fiveimages years (2010–2015). Change results between model predicted and observed value were used to detect the change areas. CVA and PCC only including change and no-change classes showed high performance for detection accuracy, with an were separately conducted based on several Landsat images acquired on 3 April 2010 and 2 October overall accuracy (OA) value of 98.58% and kappa coefficient of 0.82 (Table 2). 2015. To visually inspect areas of change or no-change in detail, four subset areas of the change detection results were selected (Figures 10 and 11). Table 2. Confusion matrix of change/no-change detection results. Figure 10 shows cropland related changes from cropland to urban built-up are the primary Reference changes that occurred between the two time periods for theChanged subset-1(Pixels) and subset-2 area. The area Classified Changed (Pixels) inside the yellow polygon changed from 2010 to 2015 according to Google earth imageError with higher No-Change Change Sum Commission resolution. In subset-1 area, the results48,130 show that changes development are Nochange 715 associated 48,845 with urban1.46 correctly identified using the proposed method. Although the 1674 obvious changes were very well Change 2 1672 0.12 48,132not well 2387 detected in CCDC,Sum the subtle changes were detected in50,519 this image. By contrast, CVA and error changes instead 0.00 of true29.95 PCC detectedOmission many pseudo changes. This might be because the change = 98.58%, Kappa coefficient 0.82change from cropland to urban magnitude of cropland pseudo OA(%) changes is greater than that of =real built-up. Similarly, for subset-2 area, although the CCDC also correctly identified real changes, it also To produced lotimprovement of pseudo changes or pepperofand noises. achieved results indicate evaluateathe and effectiveness oursalt method, theThe change areas detection results superiority of the proposed method to other methods in detecting real changes. of the three other methods, CCDC, CVA, and PCC, were calculated for comparison and analysis. In the Asmethod, shown two in Figure 11 (the subset-4 area,(11 respectively), two areas CCDC years (2009 andsubset-3 2010) of and time-series images images) werethe used as subset input data to remained unchanged from 2010 to 2015 according to the Google earth image. The result shows that describe the trend, and predicted images value at 2015. Then, the differences between model predicted pseudo changes are were not produced in thethe proposed method. It isand clear that ourseparately method outperforms and observed value used to detect change areas. CVA PCC were conducted the other three methods in the acquired capacity on of 3elimination change by seasonal based on several Landsat images April 2010 of andpseudo 2 October 2015.caused To visually inspect
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difference. difference. For For cropland cropland that that likely likely has has aa large large spectral spectral change change but but not not aa real real cover cover change, change, our our method significantly eliminated these changes by combining the difference of the phenological method significantly eliminated these changes by combining the difference of the phenological trajectories. However, CCDC, captured spurious because of areas of change or no-change in CVA, detail,and fourPCC subset areas these of theapparent change detection results were selected trajectories. However, CCDC, CVA, and PCC captured these apparent spurious changes changes because of aa larger change magnitude resulting from crop rotation, irrigation, and seasonality. (Figures 10 and 11). larger change magnitude resulting from crop rotation, irrigation, and seasonality. Subset-1 Subset-1 area area
(a) (a)
(b) (b)
(c) (c)
(d) (d)
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(h) (h)
Subset-2 Subset-2 area area
Figure 10. Change detection results for two subset (a) Landsat images on 2010; (b) Figure 10.Change Changedetection detectionresults results for for two two subset subset areas: areas: (a) Landsat images onon33 3April April 2010; (b)(b) Figure 10. areas: (a) Landsat images April 2010; Landsat Landsat images images on on 22 October October 2015; 2015; (c) (c) Google Google earth earth image image at at 2010; 2010; (d) (d) Google Google earth earth image image at at 2015; 2015; Landsat images on 2 October 2015; (c) Google earthmethod image at 2010; (d) Google earth image at 2015; and and and change change detection detection result result of of the the proposed proposed method (e), (e), Continuous Continuous Change Change Detection Detection and and change detection result of the proposed method (e), Continuous Change Detection and Classification Classification Classification (CCDC) (CCDC) (f), (f), change change vector vector analysis analysis (CVA) (CVA) (g) (g) and and post-classification post-classification comparison comparison (PCC) (PCC) (CCDC) (f), change vector analysis (CVA) (g) and post-classification comparison (PCC) (h). (h). (h).
Subset-3 Subset-3 area area
(a) (a)
(b) (b)
(c) (c) Figure 11. Cont.
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(f)
(g)
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Subset-4 area
(a)
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(c)
(d)
(e)
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(g)
(h)
Figure 11. Change detection results for two subset areas: (a) Landsat images on 3 April 2010; (b) Figure 11. Change detection results for two subset areas: (a) Landsat images on 3 April 2010; (b) Landsat images on 2 October 2015; (c) Google earth image at 2010; (d) Google earth image at 2015; Landsat images on 2 October 2015; (c) Google earth image at 2010; (d) Google earth image at 2015; and and change detection result of the proposed method (e), CCDC (f), CVA (g) and PCC (h). change detection result of the proposed method (e), CCDC (f), CVA (g) and PCC (h).
The accuracy assessment with the same test samples is shown in Table 3. It is clear that the Figure 10 shows cropland urban built-up the primary proposed method acquired therelated highestchanges accuracyfrom with cropland an overalltoaccuracy of 98.58%are and a kappa changes thatofoccurred the two time periods forthe the subset-1 andmethod subset-2 area. The area coefficient 0.82. Thebetween CCDC has a higher accuracy than CVA and PCC with the overall accuracy of 95.23% and a kappa coefficient of 0.79. By contrast, thetoCVA andearth PCC image methodwith withhigher the inside the yellow polygon changed from 2010 to 2015 according Google lower accuracy provided relatively less show reliablethat results for cropland change detection. resolution. In subset-1 area, the results changes associated with urban development are
correctly identified using the proposed method. Although the obvious changes were very well detected Table 3. Accuracies of the fourin change detection in CCDC, the subtle changes were not well detected this image. Bymethods. contrast, CVA and PCC detected many pseudo changes instead of true changes. This might Change be because the change magnitude of The Continuous Change cropland pseudo changes is greater than that of real change fromVector cropland to urban built-up. Similarly, Post-Classification Proposed Detection and for subset-2 area, although the CCDC also correctly identifiedAnalysis real changes, it also produced a lot of Comparison (PCC) Method Classification (CCDC) (CVA) pseudo changes or pepper and salt noises. The achieved results indicate superiority of the proposed Thresholds 1.5 in detecting real changes. 1.0 82.57 78.14 method to other methods (%) in Figure 98.58 95.23 93.96 the two subset 93.42 AsOA shown 11 (the subset-3 and subset-4 area, respectively), areas remained Kappa unchanged from 2010 to 2015 according to the Google earth image. The result shows that pseudo 0.82 0.79 0.66 0.78 coefficient changes are not produced in the proposed method. It is clear that our method outperforms the other three methods in the capacity of elimination of pseudo change caused by seasonal difference. Change that Typelikely Discrimination For4.2. cropland has a large spectral change but not a real cover change, our method significantly eliminated changesareas by combining the difference of the phenological trajectories. However, CCDC, Oncethese the change were identified, the change type could be discriminated based on CRV CVA, and PCC captured2010 these because of adatasets larger change magnitude distance. Globeland30 wasapparent used as aspurious baseline changes for acquiring training from unchanged pixels. from The classification the change was performed by first calculating the difference resulting crop rotation,ofirrigation, andtype seasonality. between the CRV assessment of the changewith pixelthe andsame each test reference pixelisusing Equation (5).3.The The accuracy samples shown in Table It change is cleartype thatofthe pixel was then assigned to the withaccuracy the minimum proposed method acquired theclass highest withD. an overall accuracy of 98.58% and a kappa coefficient of 0.82. The CCDC has a higher accuracy than the CVA and PCC method with the overall
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accuracy of 95.23% and a kappa coefficient of 0.79. By contrast, the CVA and PCC method with the lower accuracy provided relatively less reliable results for cropland change detection. Table 3. Accuracies of the four change detection methods. The Proposed Method
Continuous Change Detection and Classification (CCDC)
Change Vector Analysis (CVA)
Post-Classification Comparison (PCC)
Thresholds
1.5
1.0
82.57
78.14
OA (%)
98.58
95.23
93.96
93.42
0.79
0.66
0.78
Kappa coefficient 0.82 Remote Sens. 2018, 10, x FOR PEER REVIEW
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4.2. Change Type Discrimination The change type discrimination results by using the CRV distance are presented on Figure 12. For comparison, the CCDC classification method was used for thebesame training data foronimage Once the change areas were identified, the change type could discriminated based CRV classification. The CCDC uses theused model as the inputs training for land cover classification. As an distance. Globeland30 2010 was as coefficients a baseline for acquiring datasets from unchanged example, results of change type discrimination by using the CRV distance and CCDC are pixels. Thethe classification of the change type was performed by both first calculating the difference between presented Asand shown in the subset-1 area,Equation the apparent are from urban the CRV ofon the Figure change 13. pixel each reference pixel using (5). Thechanges change type of pixel was developments that typically consume surrounding agricultural land. The region experiences then assigned to the class with the minimum D. apparent cropland changes caused byresults both urban developments in suburban areas. Most The change type discrimination by using the CRV distance are presented on landscape Figure 12. change was related urbanclassification developmentmethod to meetwas theused growing population in thedata region. Other For comparison, theto CCDC for the same training for image obvious changes are from cropland to water bodies (subset-2 and subset-3 areas). The changed areas classification. The CCDC uses the model coefficients as the inputs for land cover classification. mayanbeexample, seasonalthe rivers. Cropland areas arediscrimination actually the lake surface areathe that was dried before 2010. As results of change type by using both CRV distance and CCDC The expanded on water surface may beindue an increase rainfall. are presented Figure 13. area As shown thetosubset-1 area,inthe apparent changes are from urban To evaluate abilityconsume to discriminate change type and land. assessThe theregion accuracy of the proposed developments thatthe typically surrounding agricultural experiences apparent method, computed a confusion matrix and derivedin the overall accuracy andlandscape overall kappa index. croplandwe changes caused by both urban developments suburban areas. Most change was Tables 4 and 5 show the confusion matrixes for change areas classifications by CRV difference and related to urban development to meet the growing population in the region. Other obvious changes CCDC, The classification accuracy and kappa coefficient are 90.13%, and be 0.71 by the are fromrespectively. cropland to water bodies (subset-2 and subset-3 areas). The changed areas may seasonal CRV and 88.55% and 0.82the bylake the surface CCDC area method. This dried indicates that the The CRVexpanded distance rivers.distance, Cropland areas are actually that was before 2010. exceeds the CCDC method in overall accuracy. water surface area may be due to an increase in rainfall.
Figure Figure 12. 12. Classification Classification results results for for changed changed types. types.
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(a)
(b)
(c)
(d)
(a)
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(c)
(d)
(a)
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(d)
Figure 13. Change types discrimination results for three subset areas, respectively (from top to
Figure 13. Change types discrimination results for three subset areas, respectively (from top to bottom): (a) Landsat images on 3 April 2010; (b) Landsat images on 15 October 2015; (c) change type bottom):discrimination (a) Landsatresult images onproposed 3 April 2010; (b)(d)Landsat images on 15 October 2015; (c) change type of the method; change type discrimination result of the CCDC. discrimination result of the proposed method; (d) change type discrimination result of the CCDC. Table 4. Confusion matrix of classification results from the proposed method.
To evaluate the ability to discriminateReference change Data type(%) and assess the accuracy of the proposed Classified Data (%) User’s Accuracy Cropland Forest Urban Wateraccuracy and overall kappa index. method, we computed a confusion matrix and derived the overall Cropland 96.93 for3.57 0.00 35.95 Tables 4 and 5 show the confusion matrixes change10.22 areas classifications by CRV difference and Forest 0.54 90.19 1.66 0.08 99.70 CCDC, respectively. The classification accuracy and kappa coefficient are 90.13%, and 0.71 by the CRV Urban 2.54 5.23 87.95 1.27 71.96 distance, and 88.55% and 0.82 by the CCDC method. This indicates that the CRV distance exceeds the Water 0 1.01 0.17 98.65 47.14 CCDC method inProducer’s overall accuracy. accuracy 96.93 90.19 87.95 98.65 OA(%) = 90.13%, Kappa coefficient = 0.71
Table 4. Confusion matrix of classification results from the proposed method. Table 5. Confusion matrix of classification results from the CCDC method.
Reference Data (%) Reference Data (%) User’s Accuracy Classified Data (%) Classified Data (%) Cropland Forest Urban WaterUser’s Accuracy Cropland Forest Urban Water Cropland 96.93 74.82 3.57 1.83 10.22 Cropland 2.68 0.300.00 97.21 35.95 Forest 0.54 90.19 1.66 0.08 99.70 Forest 17.74 96.85 5.49 0.21 40.02 Urban 2.54 5.23 87.95 1.27 71.96 Urban 7.44 1.33 91.65 4.80 74.02 Water 0 1.01 0.17 98.65 47.14 Water 0.18 94.70 99.95 Producer’s accuracy 96.93 0.00 90.190.00 87.95 98.65 Producer’s accuracy 74.82 96.85 91.65 74.82 OA(%) = 90.13%, Kappa coefficient = 0.71 OA(%) = 88.55%, Kappa coefficient = 0.82
Table 5. Confusion matrix of classification results from the CCDC method. 5. Discussion The experimental results (Table 3, Figures 10 and 11) indicate that the proposed method Reference Data (%) provides the capacity caused by season User’s Accuracy Classified Data (%)to detect real changes and estimate pseudo changes Cropland Forest Urban Water Cropland Forest Urban Water Producer’s accuracy
74.82 17.74 7.44 0.00 74.82
1.83 96.85 1.33 0.00 96.85
2.68 5.49 91.65 0.18 91.65
OA(%) = 88.55%, Kappa coefficient = 0.82
0.30 0.21 4.80 94.70 74.82
97.21 40.02 74.02 99.95
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5. Discussion The experimental results (Table 3, Figures 10 and 11) indicate that the proposed method provides the capacity to detect real changes and estimate pseudo changes caused by season differences. The possible reasons are as follows. First, the proposed method effectively weakens the influence of seasonal differences and noise because data are collected throughout the growing season. Although the CCDC also makes full use of time-series images, the simple sinusoidal model used for CCDC fails to fully describe the complex features of cropland. Second the multi-harmonic model was used for describing the phenological trajectory, which widens the gap between cropland and other land cover types to some extent. Finally, the proposed method is mainly based on the similarity between temporal trajectories instead of direct comparison between the two points. Thus, the final change detection result is equivalent to the comprehensive results from different season differences. In addition, the proposed method discriminated change type by CRV distance instead of only model coefficients as with the CCDC. The classification accuracy of the proposed method is better than that of the CCDC (Figure 13, Tables 4 and 5). One possible explanation is that the large spectral variance within the cropland class makes accurate classification difficult. In the CCDC, several variables from the simple harmonic model are used as inputs for classification. However, cropland possesses complicated spectral features and unique phenological characteristics. The simple sinusoidal model including several variables is fully unable to discriminate cropland from other land cover type. The same land cover type is still more likely to have different model variables. The CRV including six coefficients is capable of maximizing the difference between cropland and other land cover types. Therefore, the result indicates that the CRV distance is a more effective method for change type discrimination compared with the CCDC method. However, we should note that since the result of the proposed method depends on the study area and data, it is necessary that further analysis is conducted to validate the extent to which the method holds true in other regions or with different resolutions. This work indeed provided a novel method to detect changes but could be further improved and enhanced. However, further research needs to be considered as follows. (1)
(2)
(3)
Our method focused on the intra-annual variations within the EVI time series. Further research is necessary to study inter-annual variations related to plant phenology. However, the multi-harmonic model including first and second order harmonics may not be sufficient to fully capture its inter-annual trend. An advanced time-series model should be constructed to capture both intra-annual and inter-annual trend. Future algorithm improvements may include the capacity to eliminate phenological changes caused by change of cropland types or transformation of the farming system. In this study we focused on the shape similarity of the phenological trajectory while neglecting the phenological value difference. The main phenological parameters changes should be also considered as part of the change magnitude. This illustrates that further work is needed to extract the key phenological parameters. Although our method acquired higher accuracy on change type discrimination, reference CRV only includes three change types (“from cropland to” changes). To identify the decrease and increase of cropland, a knowledge base of reference CRV including all change types is necessary.
6. Conclusions One of the major challenges in cropland change detection is how to detect true changes while reducing false changes caused by seasonal differences and other interference factors. The change detection method based on time series images effectively weakens the influence of seasonal differences and noise because data are collected throughout the growing seasons. In this research, a new change detection method based on phenological trajectory similarity was designed and implemented to capture cropland changes. The core concept of the proposed method is detecting true cropland changes based on the similarity between temporal trajectories instead of the direct comparison between the two
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images. Due to the complex spectral-temporal characteristic of cropland, the phenological trajectory was constructed using a multi-harmonic model for capturing intra-annual variations. Then, to make full use of the spectral–temporal–phenological information, the CVD was calculated to measure similarity and identify change/no-changes areas. Moreover, it will be more important if we know the detail “from cropland to” change information. Instead of the traditional classification method based on original images, we used the coefficient ratio vectors (CRV) as the inputs for change type discrimination. The proposed method integrates spectral information, phenological information, and temporal characteristics to acquire a maximum possible change map with the goal of minimizing pseudo changes caused by seasonal differences. Our method was applied to the EVI time series derived from Landsat images for a cropland study area in eastern China. Results showed that the proposed method has the highest accuracy of change detection (98.58%) and a lower commission error than the other methods. The proposed method can avoid the strict requirement of CVA or PCC for image acquisition in that two images acquired in different years should be from the same seasonal period. In addition, this study presents a change type discrimination method based on the distance between the CRV of the changed pixel and of the reference change type. The result of the change type classification (OA of 90.13% and kappa index of 0.71) confirmed the efficiency of the method in discriminating the main change types. Author Contributions: J.C. (Jiage Chen) and J.C. (Jun Chen) designed the methodology. J.C. (Jiage Chen) performed the experiments and wrote the manuscript. H.L. and S.P. reviewed and edited the manuscript in detail. Funding: This study was funded by the National Key Research and Development Program (during the 13th Five-year Plan) under Grant (NO. 2016YFA0601500 and NO. 2016YFA0601503), Ministry of Science and Technology, PRC. Acknowledgments: The authors wish to thank all the anonymous reviewers for their constructive comments. Conflicts of Interest: The authors declare no conflict of interest. The founding sponsors had no role in the design of the study; in the collection, analyses or interpretation of data; in the writing of the manusctipt; nor in the decision to publish the results.
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