Degradation of land resources in the drainage basin of Lake Malawi and other lakes of the African Rift Valley has been widespread over the past several decades. Causes for this degradation include inappropriate agricultural practices, indiscriminate disposal of wastes, and increasing population pressure. These factors pose serious threats to water quality in these important lakes. It is difficult and costly to monitor relevant water quality conditions, such as concentrations of chlorophyll-α in these large lakes using ground-based methods, and consequently, information on temporal and spatial variations in water quality conditions of these water bodies is quite limited.

Methods that use satellite-based remote sensing data to infer lake clarity and chlorophyll levels have been developed in recent years and have potential for alleviating the problem of inadequate water monitoring data in large lakes in poor and developing countries. In addition to providing data on water quality and trophic state conditions of lakes, satellite imagery has potential for determining water circulation patterns in lakes through maps of lake surface temperature (LST). In turn, LST can be used to locate upwelling zones that may enhance commercial fishing in large deep lakes. Satellite imagery also may be used to characterize and map land use and land cover (LULC). Such information can serve as a basis for studies of the relationships between changes in LULC and lake conditions, such as lake level and water quality.

This paper describes results of research to develop remote sensing tools to improve the understanding and management of Lake Malawi. It includes three major components, namely: estimating chlorophyll-α concentrations in Lake Malawi from MODIS/Terra satellite imagery; assessing the accuracy of existing split-window algorithms to estimate lake surface temperature from AVHRR and MODIS/Terra satellite data; and determining trends in forest cover and other categories of land cover in the Lake Malawi drainage basin based on a normalized vegetation index (NDVI) using a series of AVHRR and MODIS/Terra images acquired over the period 1982-2005, and exploring possible relationships between lake level rise and the degree to which forestland is being converted to cropland in the Lake Malawi Basin.