Improving change vector analysis in multitemporal space to detect land cover changes by using cross- correlogram spectral matching algorithm Yuanyuan Zhao, Chunyang He, Yang yang Beijing Normal University, Beijing, China, IEEE International Geoscience and Remote Sensing Symposium

Outline  Introduction  Methods  Case study  Effectiveness analysis of the new method  Accuracy assessment  Conclusions and discussion

Land cover change detection is of great significance  Land cover plays an important role in energy balance as well as biogeochemical and hydrological cycles in the Earth system (Avissar and Pielke, 1989; Lunetta et al., 2006).  Timely and accurate detection of land cover changes can a) provide essential information to enhance our understanding of the mechanisms that drive the spatial- temporal processes of land cover change. b) support the simulation and evaluation of the associated environmental impacts.

Traditional change vector analysis (TCVA)  TCVA has then been widely adopted in land cover change detection using VI data (Lambin and Strahler, 1994).  TCVA is sensitive to temporal fluctuations in VI values, which greatly limits the method’s accuracy. It may overestimate the actual changes. It is not able to determine whether the results represent land cover conversion or simply VI variation of the same type of land cover. Land cover conversion growth vigor changes comparable change magnitude ?

The CCSM algorithm has demonstrated its merits in estimating the similarity of two VI profile curves  A cross-correlogram can be constructed for each pair of VI profiles and a goodness-of-match value can be calculated accordingly.  Advantage ： it is able to capture the shape similarity of two VI profiles even if there is a time lag between the two.

Objectives  Proposing a new approach that improves TCVA with an adapted CCSM analysis.  The proposed approach was applied and validated through a case study of land cover conversion detection in the Beijing–Tianjin–Tangshan urban agglomeration district (BTT-UAD), China, using the MODIS Enhanced VI data (EVI) for 2000–2008.

Outline  Introduction  Methods  Case study  Effectiveness analysis of the new method  Accuracy assessment  Conclusions and discussion

Traditional change vector analysis The VI time series data in the period R ： A greater M indicates a higher possibility of land cover change for pixel i. A specific threshold is used to distinguish change pixels from no-change pixels （ Lambin and Strahler, 1994a ）。 The VI time series data in the period S ：

 As comparable magnitude values of change vectors may also result from phenological variation of a vegetative type of land cover or change in its growth vigor, a threshold is not always effective in extracting land cover conversions. （ e ） No-change M =0.13 （ d ） Vegetation growth status change M =0.77 （ c ） Phenology change M =0.78 （ b ） From ‘winter wheat-summer maize’ to ‘spring maize’ （ a ） From cropland to built-up M=0.78 M=0.82

Improved change vector analysis(ICVA) Three step: TCVA is employed to preliminarily detect land cover changes. The CCSM approach is used to identify and eliminate areas in which the land cover type did not really change but only experienced some degree of VI variation. The type of land cover conversion (e.g., from cropland to built-up area) is determined by further analyzing the change vectors for the remaining pixels of interest. Preliminary detection of land cover change using traditional change vector analysis Determination of land cover change types Identification and elimination of land cover modifications using cross-correlogram spectral matching analysis Time series data in time r Time series data in time s Flow chart

Preliminary land cover change detection using TCVA  The change magnitude of VI time series was calculated using the TCVA.  An optimal threshold was determined to extract the preliminary change information. A semi-automatic method called Double-windows Flexible Pace Search method (DFPS) (Chen et al., 2003). VI time series in time r VI time series in time s Change magnitude Change information TCVA DFPS

Identifying and eliminating pseudo-conversion by CCSM analysis  The correlation coefficients (R m ) of the two VI profile curves between time r and s at different match positions (m) are calculated. where λ s and λ r are VI profile curve values for period r and s, respectively. m is the match position. n is the number of overlapping positions. λsλs λrλr RmRm time λrλr λsλs

Eliminating land cover modification using the CCSM algorithm  The maximum correlation coefficient (R max ) is chosen as the shape similarity index of the two curves (Wang et al., 2009). where R max ranges from 0 to 1. The R max is equal to 1 when the shape of the VI profile curves between period r and s are completely the same. A larger R max indicates a smaller difference between the two shapes of the VI profile curves. Time r Time s | △ V|= 0.78 Time R max =0.996 Match position (m)

 The land cover modification is eliminated by an optimal threshold for R max using a manual trial-and-error procedure. Selecting sample areas with the help of the ancillary data. Assessing the effectiveness of eliminating land cover modification for different thresholds. Assigning the optimal threshold for R max to the value at which the eliminating effect is best. Threshold t1t1 … tntn change 1 …… change n reference compare Kappa 1 …… Kappa n Kappa k max tktk The optimal threshold Change information Eliminating land cover modification using the CCSM algorithm

Discriminating the land cover conversion type  Unsupervised clustering approach (Bruzzone and Prieto, 2000) 。 Having no requirement for training data Partitioning remotely sensed data with multi-spectral or multi- temporal information Transforming the partitioned classes into a thematic map of interest by a posteriori The unsupervised clustering method is adopted in this study to the actual land cover conversion types with the support of some ancillary data. Change vector image Class map Unsupervised clustering Change Type map Ancillary data

Outline  Introduction  Methods  Case study  Effectiveness analysis of the new method  Accuracy assessment  Conclusions and discussion

Study area Latitude: 38°28′ N - 41°05′ N Longitude: 115°25′ E -119°53′ E Total area: km 2 Climate: Sub-humid and temperate monsoon climate Main land cover type: cropland, built-up, forest Over the past several decades, significant land cover changes have taken place in the BTT-UAD, mainly driven by rapid economic development and unprecedented urbanization (Tan et al., 2005). Beijing–Tianjin–Tangshan urban agglomeration district (BTT-UAD), China

Data  MODIS_EVI data (specifically MOD 13Q data version 004) The spatial resolution is 250m The time spans from 2000 to 2008 They were downloaded from the Earth Resources Observation Science Center of United States Geological Survey (USGS EROS)  Landsat ETM+ data ： 123/32 20 August 2000 and 11 September /33 10 June 2000 and 3 August 2008 They were downloaded from EROS data center  Other data ： The land use/cover data in 2000 Field survey data Images obtained from Google Earth MODIS_EVI

MODIS_EVI data preprocessing  Image mosaicing The four tiles (h26v04 、 h26v05 、 h27v04 、 h27v05) covering the study area were mosaiced to a complete EVI image covering the study area.  Projection converting The mosaiced images were converted to the map projection format commonly used in China, the Albers Conical Equal Area format.  Noise removing The Harmonic Analysis of Time Series (HANTS) was performed on the image time series.  Image clipping Image mosaicking Projection converting Noise removing Image clipping 1234 MODIS_EVI of the study area

Extracting preliminary pixels of land cover change Change magnitude image of the study area, Preliminary extraction of land cover change ( ) in the study area EVI time series in 2000 EVI time series in 2008 TCVA Calculating Change magnitude Preliminary change information DFPS

Land cover conversion in the study area, R max calculated by CCSM using the EVI profile curves in 2000 and 2008 Eliminating land cover modification in the study area using the CCSM algorithm The preliminary change information EVI time series in 2000 EVI time series in 2008 Calculating the shape similarity index R max Manual trial- and-error procedure land cover conversion

Obtaining the land cover conversion map  The land cover conversion map was obtained by classifying the change vector image of land cover using an unsupervised classification technique. (a) from water to cropland ; (b) from cropland to built-up ; (c) from water to built-up 2000 ETM ETM+

Outline  Introduction  Methods  Case study  Effectiveness analysis of the new method  Accuracy assessment  Conclusions and discussion

Effectiveness analysis of the ICVA  The TCVA could not distinguish the land cover modification from land cover conversion accurately only by selecting the threshold for the change magnitude.  The ICVA can effectively eliminate partial land cover modification information by thoroughly use of the shape variation of the EVI profile curves and determining an optimal threshold for R max.

Outline  Introduction  Methods  Case study  Effectiveness analysis of the new method  Accuracy assessment  Conclusions and discussion

Visually comparing  (a) Differences in the case of vegetation vigor change.  (b) Differences in the case of phenological change.  The TCVA misinterpret the vegetation vigor change and phenological change as land cover conversion, while the ICVA eliminate the two types of changes.

The ICVA performed better than the TCVA in detecting the land cover conversion in the study area  The TCVA achieved a kappa coefficient of 0.29 and an overall accuracy of 60.40%, whereas the ICVA achieved a kappa coefficient of 0.42 and an overall accuracy of 71.20%.

Outline  Introduction  Methods  Case study  Effectiveness analysis of the new method  Accuracy assessment  Conclusions and discussion

Conclusions  We have proposed a new approach, named ICVA, that improves TCVA with an adapted use of cross-correlogram spectral matching (CCSM) analysis.  ICVA was applied to detect land cover conversion in BTT- UAD, China from two time series of MODIS EVI data for 2000 and The results showed that ICVA is able to map land cover conversion with a significantly higher accuracy (71.20%, kappa = 0.42) than TCVA (60.40%, kappa = 0.29).  The higher accuracy has been achieved by analyzing the multi- temporal VI information with the consideration of not only change magnitude but also profile similarity.

Discussion  The application of ICVA has some limitations: The approach is best used in distinguishing land cover modifications resulting from phenological and/or growth vigor changes. More complex types of land cover changes, such as the cultivation pattern change of double cropping land to single cropping land, pose challenge to the proposed approach.

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