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Anthropological Center for Training and Research on Global Environmental Change

A Research Center of the Office of the Vice Provost for Research at Indiana University Bloomington
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2009– 2012: Advancing Land Use and Land Cover Analysis by Integrating Optical and Polarimetric Radar Platforms (Funded by NSF)


Description:  The Brazilian Amazon basin contains the largest continuous rain forest in the world. Since the 1970s, deforestation in this region has gradually increased from 152,000 km2 in 1978 to 707,000 km2 in 2006. Monitoring changes in forest cover, and subsequent land uses, has been challenging for researchers, who face extensive cloud cover that makes analysis of a given area very difficult. Building on 35 years of research experience in the Amazon, this study will advance our understanding of land use and land cover change, particularly in rain forest regions where cloud cover makes annual and seasonal monitoring of changes in land cover a challenge. We will integrate data from new  radar platforms (ALOS/PALSAR and RADARSAT2) with those from existing optical sensors (TM/ETM+, ASTER) to take land cover classification and assessment of land cover dynamics to a new frontier-- where cloud cover will no longer limit scientists’ efforts to provide timely and accurate assessment of land cover change. We believe that the multiple polarizations of ALOS/PALSAR and RADARSAT2, fused with analysis of optical data, will provide better and more timely vegetation classification accuracy, and will improve the temporal and spatial analysis of land use and land cover in areas characterized by persistent cloud cover.

Two study areas will be the focus of the study, Altamira and Santarem, both in the Brazilian Amazon state of Para. This will allow us to take advantage of data collected for several years by our team using a nested-georeferenced approach including soil analyses, vegetation stand structure and composition, land use histories, institutional analyses, demography and decision-making of hundreds of households, and land cover classification using multi-temporal remote sensing data since the 1970s (MSS, TM/ETM+, and IKONOS/QuickBird,) thereby reducing costs and to achieve results much more quickly. We have obtained recent images of RADARSAT2 and ALOS/PALSAR for both study areas, as well as IKONOS and Quickbird images, and these will be fused with archived data for the study areas from Landsat MSS, TM and ETM+ (and ASTER) to advance the state of knowledge and methods in land cover assessment applicable across any part of the world where cloud-cover constraints seasonal observations of changes in land cover. Separatibility analysis will be used to examine the capability of the data fused images, which are developed with different data fusion techniques, in distinguishing the land cover classes  of  interest based on the training sample plots from extensive field work and the intensive use of the IKONOS and Quickbird very high resolution data.