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This study presents a predictive modelling technique to map population distribution and abundance for rural areas in Africa. Prediction models were created using a generalized regression analysis and spatial prediction (GRASP) method that uses the generalized additive model (GAM) regression technique. Dwelling unit presence–absence was mapped from airborne images covering 98 km2 (30% of the study area) and used as a response variable. Remote-sensing-based (reflectance, texture and land cover) and geospatial (topography, climate and distance) data were used as predictors. For the rest of the study area (228 km2; 70%), GAM models were extrapolated, and prediction maps constructed. Model performance was measured as explanatory power (adj.D 2, adjusted deviance change), predictive power (area under the receiver operator curve, AUC) and kappa value (κ). GAM models explained 19–31% of the variation in dwelling-unit occurrence and 28–47% of the variation in human population abundance. The predictive power for population distribution GAM models was good (AUC of 0.80–0.86). This study shows that for the prediction of dwelling-unit distribution and for human population abundance, the best modelling performance was achieved using combined geospatial- and remote-sensing-based predictor variables. The best predictors for modelling the variability in human population distribution using combined predictors were angular second moment image-texture measurement, precipitation, mean elevation, surface reflectance for Satellite Pour l'Observation de la Terre (SPOT) red and near-infrared (NIR) bands, correlation image-texture measurement and distance to roads, respectively. The population-abundance modelling result was compared with two existing global population datasets: Gridded Population of the World version 3 (GPWv3) and LandScan 2005. The result showed that for regional and local-scale population-estimation probability, models created using remotely sensed and geospatial data were superior compared to GPWv3 or LandScan 2005 data products. Population models had high correlation with Kenyan population census data for 1999 in mountainous sub-locations and low correlation for sub-locations that also extended into the lowlands. [ABSTRACT FROM AUTHOR]

Fire suppression associated with decades of cattle grazing can result in bush encroachment in savannas. Textural analyses of historical, high resolution images was used to characterize bush densities across a South African study landscape. A control site, where vegetation was assumed to have changed minimally for the duration of the image record (1955-1996), was used to standardize textural values between multidate images. Standardized textural values were then converted to estimates of percent woody canopy cover using a simple linear regression model. Results indicate a 30% relative increase in percent woody cover between 1955 and 1996. [ABSTRACT FROM AUTHOR]

This study used four near-real-time multispectral Systeme pour l'Observation de la Terre (SPOT) high-resolution visible (HRV ) images to establish land cover and forest classes of relevance to slash-and-burn agriculture. The study was conducted in 1.43 million ha of the Alternative to Slash-and-Burn (ASB) global benchmark research area for Africa selected as representative of the entire Congolese basin of Central Africa and located in Southern Cameroon. The land cover and forest classes mapped have different impacts on global phenomena as well as their own management challenges and include slash-and-burn dominant farmlands, C hromolenea orodata dominant short-fallows, Imperata cylindrica dominant weeds, long-fallows or regenerated forests, raphia palm-dominant lowlands, permanently flooded swamp forests, and primary and secondary forests. In order to map these distinct and complex classes the study proposes and implements a piecemeal approach to classification involving stratification of the image into several distinct discrete subsets of forest corridors, lowlands, and uplands. The approach involves the use of image texture indicators in conjunction with groundtruth data to divide the image into discrete subsets, performing unsupervised classification on these discrete subsets, masking problem classes and reclassifying them, editing certain spectrally inseparable areas, adopting post-classification strategies, 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% (producer's). [ABSTRACT FROM AUTHOR]