Hiroto Shimazaki

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.

Network analysis of potential migration routes for Oriental White Storks (Ciconia boyciana)

From 1998 through to 2000, we satellite-tracked the movements of 13 Oriental White Storks (Ciconia boyciana) on their autumnal migration in order to identify their important stopover sites for preserving links from the Russian Far East breeding sites to the wintering sites in south-eastern China. New analytical methods of satellite tracking data were employed to derive robust information on the locations of stay sites, the number of stopovers made during migration, and the distance traveled without making stopovers. Based on the derived information, we modeled a stay site network as an abstraction of the storks’ potential migration routes from their breeding sites to wintering sites. Using network analysis techniques, we explored how the loss of stopover sites could affect the connectivity of potential migration routes. The results suggested that if the seashore stopover sites facing Bohai Bay in eastern China were lost, the storks’ wintering sites along the Yangtze River in south-eastern China would be isolated. Among the seashore stopover sites, Jiantuozhi Gley Mire (39.185°N, 118.627°E), located on the northern seashore of Bohai Bay, was considered particularly important for migrating storks, because it was used every year by the storks we tracked. If conservation needs of this critically located site fail to be addressed, the stay site network of storks can create weak links in the chain of migration and, if ‘broken’, storks will have great difficulties in completing their autumnal migration.