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The need to increase water productivity is a growing global concern as the World Commission on Water has estimated that demand for water will increase by c. 50% over the next 30 years and approximately half of the world's population will experience conditions of severe water stress by 2025.
Land markets, including land sales and short-term land rentals, have an important role to play for efficient and sustainable land management and agricultural development, especially where markets for other factors of production are imperfect or missing.
Geographic information systems and remote sensing technologies have become an important tool for visualizing conservation management and developing solutions to problems associated with conservation.
The three East African (EA) countries Tanzania, Kenya and Uganda have a population of about 95 million people and Gross Domestic Product (GDP) of USD 34.2 billion.
As the losses of tropical forests have accumulated over the past three decades, largely deforested areas have increased in extent in the tropics, and observers have begun to ask questions about the circumstances under which tropical forests reemerge in deforested areas.
The FAO land-cover classification system (LCCS) represents an innovative approach to standardizing and harmonizing land-cover classifications based on remote sensing data.
RothC and Century are two of the most widely used soil organic matter (SOM) models. However there are few examples of specific parameterisation of these models for environmental conditions in East Africa.
Processes of deforestation, known to threaten tropical forest biodiversity, have not yet been studied sufficiently in East Africa.
This work presents a new four-tier hierarchical change-point algorithm designed to detect land-cover change from satellite data. We tested the algorithm using Global Inventory Modelling and Mapping Studies (GIMMS) data for eastern Africa.
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