Land cover change detection with a cross-correlogram spectral matching algorithm

Timely extraction of reliable land cover change information is increasingly needed at a wide continuum of scales. Few methods developed from previous studies have proved to be robust when noise, changes in atmospheric and illumination conditions, and other scene- and sensor-dependent variables are present in the multitemporal images. In this study, we developed a new method based on cross-correlogram spectral matching (CCSM) with the aim of identifying interannual land cover changes from time-series Normalized Difference Vegetation Index (NDVI) data. In addition, a new change index is proposed with integration of two parameters that are measured from the cross-correlogram: the root mean square (RMS) and (1-R max), where R max is the maximum correlation coefficient in a correlogram. Subsequently, a method was proposed to derive the optimal threshold for judging 'change' or 'non-change' with the acquired change index. A pilot study was carried out using SPOT VGT-S images acquired in 1998 and 2000 at Xianghai Park in Jilin Province. The results indicate that CCSM is superior to a traditional Change Vector Analysis (CVA) when noise is present with the data. Because of an error associated with the ground truthing data, a more comprehensive assessment of the developed method is still in process using better ground truthing data and images at a larger time interval. It is worth noting that this method can be applied not only to NDVI datasets but also to other index datasets reflecting surface conditions sampled at different time intervals. In addition, it can be applied to datasets from different satellites without the need to normalize sensor differences.