Climate Land Interaction Project (CLIP) - East Africa
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Land Use Change Modeling

We are using different approaches and quantitative tools to generate a set of plausible land use/cover change scenarios for East Africa. Results of our analysis and synthesis will be an assessment, through the historical interpretation of land use change, of a range of plausible future scenarios which can be linked to land surface properties required by the regional climate model.

Expert Consensus of Future Land Cover Scenarios for East Africa. In order to allow the climate modelers to begin work early in the project, the land use team will use a current land cover map, population change data and maps of the biophysical attributes (e.g., elevation, agroecological zones) to develop two future (year 2020) land cover digital maps using the GIS and expert judgment. We will attempt to determine where agriculture will expand, urban areas expand, where forests will be lost and where wildlife preserves threatened.

Analysis of Drivers of Land Use Change from Case Studies. The land use/ cover change analysis of driving forces will be informed by past research on the historical context, including political, economic, social and biophysical factors, within which land use change has occurred. This context has been derived from long-term socioeconomic and biophysical field research in our case study sites across the East Africa region. The research methods include household, agricultural, soil and vegetative surveys in transects crossing altitudinal gradients, interpretation of aerial photographs and satellite imagery, and consulting secondary sources such as population censuses, agricultural statistics and other literature. The research has focused on social/ environmental interaction issues such as the impact of land use change on ecosystems and soil degradation, and the impact of drought on pastoral communities.

Model Land Use Scenarios for the East Africa Region using Spatial Databases. MSU’s Land Transformation Model (LTM) is a spatial allocation tool that can be used to assess variables associated with of historical land use change (Pijanowski et al., 2001a; 2001b; 2002). The models uses neural nets which train on data processed by a GIS to numerically solve spatial interactions between surrogates (e.g., distance from the nearest road, size of a parcel) of land use change drivers. The model has several strengths. First, very few spatial drivers (e.g., 3-7) can be used to build an accurate model. Second, neural nets are able to generalize across datasets and across spatial regions (Pijanowski et al., 2001) and thus can be a useful tool to scaling up from small training sets. Third, it has been already shown (Pijanowski, Olson, and Campbell, in prep.) to perform very well on predicting locations of historical agricultural extensification in two key East African case study areas: Embu/Mbeere and Loitokitok.

We will use the LTM to explore how the information from the case study areas can be used to create forecasts of land use across East Africa. Information on time series land use change from case study sites will be used to conduct “learning exercises” following Pijanowski et al. (1991a) that will establish a set of initial parameters (e.g., distance from nearest town, elevation) for the model. Once the initial LTM is established, the team will introduce information from time series climate data to simulate the impact of climate change on land use. The introduction of climatic data represents an innovation in such modeling.




1973 Land Use, Loitokitok Area, Kajiado District, Kenya


LTM Predicted 2005 Rainfed Agriculture


LTM Predicted 2010 Rainfed Agriculture