Integration of Landsat TM and SPOT HRG Images for Vegetation Change Detection in the Brazilian Amazon.
Traditional change detection approaches have been proven to be difficult in detecting vegetation changes in the moist tropical regions with multitemporal images. This paper explores the integration of Landsat Thematic Mapper (TM) and SPOT High Resolution Geometric (HRG) instrument data for vegetation change detection in the Brazilian Amazon. A principal component analysis was used to integrate TM and HRG panchromatic data. Vegetation change/non-change was detected with the image differencing approach based on the TM and HRG fused image and the corresponding TM image. A rule-based approach was used to classify the TM and HRG multispectral images into thematic maps with three coarse land-cover classes: forest, non-forest vegetation, and non-vegetation lands. A hybrid approach combining image differencing and post-classification comparison was used to detect vegetation change trajectories. This research indicates promising vegetation change techniques, especially for vegetation gain and loss, even if very limited reference data are available.
Dengsheng Lu and Emilio Moran are with the Center for the Study of Institutions, Population, and Environmental Change (CIPEC) and the Anthropological Center for Training and Research on Global Environmental Change (ACT), Indiana University, Bloomington, Indiana ( dlu@indiana.edu ).
Dengsheng
D
Lu
Mateus
M
Batistella
Emilio
E
Moran