Predicting consumption of resources along the gradient of nutrients and moisture

Biomass and quality of plant resources influence the distribution of wildlife, livestock, and humans in savanna rangelands. Multi-temporal imagery can map intra-seasonal biomass development over time, and airborne hyperspectral imagery helps map vegetation quality. We hypothesize that the spatial variation in resource biomass and quality thus mapped, enables us to predict both herbivore distribution and human use of vegetation and animal resources. We furthermore hypothesize that, in areas with higher human densities or external support, the predictive power of resource quantity and quality on its consumption will be lower. These predictions will be tested across Competing Claims research sites.

PhD researcher: Abel Ramoelo (MSc). Co-funded project in collaboration with ITC.

Soil carbon distribution and change as indicator of land quality, sustainability and viability of land use

This project, which uses the Limpopo National Park in Mozambique as a pilot study site, is based on the hypothesis that soil carbon is a good proxy indicator of both environmental goods and services (such as crops, grass for range, but also water storage, soil biology and nutrient cycling) and therefore of the sustainability of land use in southern Africa.

Spatial distribution of soil organic carbon and dynamics will be studied. Traditional soil survey and analysis are expensive and time-consuming. The project’s approach aims at covering larger areas at low cost and time investment, though perhaps a little less accurate. The approach will combine the estimation of soil organic carbon from diffuse reflectance spectroscopy and digital soil mapping using secondary information, especially terrain parameters derived from digital elevation data, along with spectral information captured by remote sensors, supported by limited field observations, to map the distribution of soil organic carbon. The project will also make use of geo-statistical techniques have been developed to study the spatial structure of regionalized variables and to use this structure to map.

PhD researcher: Armindo Cambule (MSc). Co-funded project in collaboration with ITC.

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