A Look at Amazon Basin Seasonal Dynamics with the Biophysical ...

Agricultural land use in 2000-2001 Amazonia using new methods formerging agricultural census data with satellite reflectances: obtainingland use data from satellite informationJeffrey A. Cardille, Center for Sustainability and the Global Environment andEnvironmental Monitoring Program, University of Wisconsin, Madison, WI 53706 USA,Tel: +1-608-262-4775, Fax: +1-608-265-4113, E-mail: cardille@students.wisc.eduJonathan A. Foley, Center for Sustainability and the Global Environment, University ofWisconsin, Madison, WI 53706 USA, Tel: +1-608-265-5144, Fax: +1-608-262-5964, E-mail: jfoley@facstaff.wisc.eduMarcos Heil Costa, Department of Agricultural Engineering, Universidade Federal deViçosa. Viçosa, MG, 36571-000. Brazil. Tel: +55-31-3899-1899. Fax: +55-31-3899-2735.E-mail: mhcosta@ufv.brAbstractAs part of our research within the Large-scale Biosphere-Atmosphere Experiment inAmazonia (LBA), we are developing a time series of the spatial distribution andabundance of major agricultural activities within the large (6 million square km) Amazonand Tocantins basins. In earlier work, we described a new method for integratingremotely sensed land cover classifications with land use information from agriculturalcensuses. Here we present the preliminary results of merging unclassified satelliteimagery and ancillary data with agricultural census data for Rondonia. In particular, weexplore the ability of 16-day band information and NDVI composites from the 2000-2001to identify areas of active agricultural land use. By investigating the statisticalrelationship between density of agricultural area, composite reflectance-based values,and ancillary data, we train a classifier algorithm to identify likely agricultural land useareas within Rondonia. The adopted technique differs from typical classificationalgorithms that identify “pure” pixels of desired classes and seek similar characteristicsin the image. Instead, this method considers the similarity between sensor-based valuesand agricultural census totals across administrative units, and optimizes the relationbetween them to produce the classification. This new method of fusing data sources willbe of likely use in areas too inaccessible for adequate ground truthing, but whereoccasional comprehensive information (like that in agricultural censuses in developingnations) is collected.