Study on land cover change detection method based on NDVI time series batasets: change detection indexes design

Abstract

The normalized difference vegetation index (NDVI) time-series database, derived from NOAA/AVHRR, SPOT/VEGETATION, TERRA or AQUA/MODIS, is increasingly being recognized as a valuable data source for extracting land cover and its change information at global, continental and large regional scale. However, existing approaches, such as principal component analysis (PCA) and change vector analysis (CVA) present considerable difficulties in taking full advantage the NDVI dataset for land cover change detection. Based on the assumptions that different land cover types have different NDVI temporal profiles and that the NDVI profile curve can be regarded as a spectrum in which an NDVI value for a certain date corresponds to on band value of this spectrum, we analyzed the existing change detection indexes and develop a new land cover change detection method based on Lance distance and a cross correlogram spectral matching (CCSM) technique. The new method was validated in the simulation experiments and a case study area of Beijing. From the results, we have demonstrated that the new method takes the shape and value features of NDVI profile curve into consideration. The relatively better performance of the new method can be attributed to two advantages: (1) the new method can discriminate long-term land cover changes form other changes by excluding false changes caused by vegetation phenology changes, climate events, atmospheric variability and sensor noise; (2) it is similarly sensitive to all kinds of land cover changes no matter where the changes have occurred. The better results compared with the CVA method suggest that the new method is effective and has potential for land cover change detection using an NDVI time-series dataset. Furthermore, it is worth noting that the method can not only be applied to NDVI datasets but also to other index datasets reflection surface conditions sampled at different time interval. It can also be applied to datasets for different satellites without the need to normalized sensor differences.