Characterization of the alternative to slash-and-burn benchmark research area representing the Congolese rainforests of Africa using near-real-time SPOT HRV data

This study used four near-real-time multispectral Systeme pour l'Observatio n de la Terre (SPOT) high-resolution visible(HRV) images to establish land cover and forest classes of relevance to slash-and-burn agriculture. The st udy was conducted in 1.43 million ha of the Alternative to Slash-and-Burn ( ASB) global benchmark research area for Africa selected as representative o f the entire Congolese basin of Central Africa and located in Southern Came roon. The land cover and forest classes mapped have different impacts on gl obal phenomena as well as their own management challenges and include slash -and-burn dominant farmlands, Chromolenea orodata dominant short-fallows, I mperata cylindrica dominant weeds, long-fallows or regenerated forests, rap hia palm-dominant lowlands, permanently flooded swamp forests, and primary and secondary forests. In order to map these distinct and complex classes t he study proposes and implements a piecemeal approach to classification inv olving stratification of the image into several distinct discrete subsets o f forest corridors, lowlands, and uplands. The approach involves the use of image texture indicators in conjunction with ground-truth data to divide t he image into discrete subsets, performing unsupervised classification on t hese discrete subsets, masking problem classes and reclassifying them, edit ing certain spectrally inseparable areas, adopting post-classification stra tegies, and finally mosaicking the discrete subsets into one seamless image . This approach led to an overall mapping accuracy of 82.4% with individual classes mapped at accuracies above 72.9% (user's), and above 66.7% (produc er's).