Remote sensing and GIS modeling for selection of a benchmark research areain the inland valley agroecosystems of West and Central Africa

This paper presents and illustrates a methodology for rational selection of benchmark research areas (or benchmark watersheds) for technology developm ent research activities in the inland valley (IV) agroecosystems of West an d Central Africa. This was done through a two-tier characterization approac h. The Level I characterization involved macro-scale sub-continental-level secondary agroclimatic and soil datasets to produce 18 agroecological and s oil zones (AESZ), each of over 10 million hectares, spread across West and Central Africa. The Level II characterization involved the use of Landsat T M or SPOT high-resolution visible (HRV) "windows" within each Level I AESZ, as well as other spatial datasets to determine locations of the representa tive benchmark research areas. The focus here is a methodology for Level II characterization for benchmark research-area selection using SPOT HRV data, secondary Gls datasets, and d etailed ground-truth data with GPS locations. The spatial datalayers were a nalyzed in a GIS modeling framework. The study wets conducted in an area of 0.39 million hectares around Gagnoa, southwestern Cote d'Ivoire which is l ocated in AESZ number 16 (humid forests with acrisols). A toposequence orie nted land-use-land-cover mapping was suggested and implemented. The spatial distribution of the 16 land-use classes was mapped across toposequence: up lands (40.1 percent of total geographic area), valley fringes (40.3 percent ), valley bottoms (18 percent), and others (1.6 percent). The broad land-us e/land-cover classes as a percentage of total geographic area (393112 hecta res) comprised (1) 58.2 percent of areas in pristine humid forests, (2) 23 percent of areas in humid forest-cropland mosaic, and (3) 15.4 percent of a reas in significant farmlands in humid forests. Expert knowledge was incorp orated through an appropriate weighting criterion for classes in various la nd-use/land-cover datalayers and other spatial datalayers. GIS modeling was then performed on various spatial datalayers leading to the selection of r epresentative benchmark research areas. It is expected that the research co nducted or technologies developed in these benchmark research areas can the n be extrapolated or transferred to other areas within the same agroecologi cal and soil zones like AESZ number 16.