Automated object-based change detection for forest monitoring by satellite remote sensing : applications in temperate and tropical regions

by Desclée, Baudouin

Forest ecosystems have recently received worldwide attention due to their biological diversity and their major role in the global carbon balance. Detecting forest cover change is crucial for reporting forest status and assessing the evolution of forested areas. However, existing change detection approaches based on satellite remote sensing are not quite appropriate to rapidly process the large volume of earth observation data. Recent advances in image segmentation have led to new opportunities for a new object-based monitoring system.

This thesis aims at developing and evaluating an automated object-based change detection method dedicated to high spatial resolution satellite images for identifying and mapping forest cover changes in different ecosystems. This research characterized the spectral reflectance dynamics of temperate forest stand cycle and found the use of several spectral bands better for the detection of forest cover changes than with any single band or vegetation index over different time periods. Combining multi-date image segmentation, image differencing and a dedicated statistical procedure of multivariate iterative trimming, an automated change detection algorithm was developed. This process has been further generalized in order to automatically derive an up-to-date forest mask and detect various deforestation patterns in tropical environment.

Forest cover changes were detected with very high performances (>90 %) using 3 SPOT-HRVIR images over temperate forests. Furthermore, the overall results were better than for a pixel-based method. Overall accuracies ranging from 79 to 87% were achieved using SPOT-HRVIR and Landsat ETM imagery for identifying deforestation for two different case studies in the Virunga National Park (DRCongo). Last but not least, a new multi-scale mapping solution has been designed to represent change processes using spatially-explicit maps, i.e. deforestation rate maps. By successfully applying these complementary conceptual developments, a significant step has been done toward an operational system for monitoring forest in various ecosystems.