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20110723IGARSS_ZHAO-yang.ppt
1. 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, 100875 Email : [email_address] 2011 IEEE International Geoscience and Remote Sensing Symposium
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8. 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 ：
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17. Study area Latitude: 38°28′ N - 41°05′ N Longitude: 115°25′ E -119°53′ E Total area: 55774.5 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
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20. Extracting preliminary pixels of land cover change Change magnitude image of the study area, 2000-2008 Preliminary extraction of land cover change (2000-2008) in the study area EVI time series in 2000 EVI time series in 2008 TCVA Calculating Change magnitude Preliminary change information DFPS
21. 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 Land cover conversion in the study area, 2000-2008 R max calculated by CCSM using the EVI profile curves in 2000 and 2008
the land cover for an image pixel in a period is represented by a multi-dimensional vector of NDVI, while the number of dimensions is dependent on the number of NDVI observations in the period. The Euclidean distance between the NDVI vectors for two different periods (e.g., different years) is used to measure the change in magnitude. A threshold is then applied to the change magnitudes to separate the significant land cover changes from the rest (Chen et al . , 2003). TCVA has then been widely adopted in land cover change detection using VI data.
, because both the values and the profile shape of a yearly series of VI tend to change significantly in such case although the land cover type remains to be the same. Future efforts should be directed to further increase the accuracy of detecting land cover conversion.
![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, 100875 Email : [email_address] 2011 IEEE International Geoscience and Remote Sensing Symposium](https://image.slidesharecdn.com/20110723igarss-zhao-yang3-110726013119-phpapp02/85/20110723igarsszhaoyangppt-1-320.jpg)
![Outline ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]](data:image/gif;base64,R0lGODlhAQABAIAAAAAAAP///yH5BAEAAAAALAAAAAABAAEAAAIBRAA7)