Integration of multi-source remote sensing data for land cover change detection

The objective of this study is to develop a methodology to integrate multi- source remote sensing data into a homogeneous time series of land cover map s in order to carry out change detection. We developed a method to increase the comparability between land cover maps coming from panchromatic aerial photographs and SPOT XS (multi-spectral) data by equalizing their levels of thematic content and spatial details. The methodology was based on the hyp otheses that: (1) map generalization can improve the integration of data fo r change detection purpose, and (2) the spatial structure of a land cover m ap, as measured by a set of landscape metrics, is an indicator of the level of generalization of that map. Firstly, the methodology for data integrati on was developed by using land cover maps generated from near-synchronous d ata. Results revealed that, by controlling successively the parameters that influence the level of map generalization, the percentage of agreement bet ween the near-synchronous land cover maps can be increased from 42% to 93%. The computation of five landscape metrics for a set of generalized land co ver maps and for the target map allowed us to optimize the level of general ization by measuring the similarity in landscape pattern of the maps. The o ptimum level of generalization of the land cover map obtained from the aeri al photographs for comparison with a land cover map derived from SPOT XS da ta was found at a resolution of 41m for two generalization levels of the th ematic content. The spatial structure of a land cover map, as measured by a set of landscape metrics, is thus a good indicator of the level of general ization of this map. Secondly, the method was applied by integrating a land cover map obtained from aerial photographs of 1954 with a land cover map o btained from a SPOT XS image of 1992.