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Land degradation highly affects the sustainability of agricultural production, food security and ecosystem services in the natural environment. Various attempts have been made to assess land degradation at multiple spatial scales from local to global. Prior methodologies, in general, show a strong bias towards assessing only biological and physical factors of land degradation, ignoring the equally significant social and economic factors. In this book, a new probabilistic approach to assess land degradation and its causes in dry lands is introduced. The suitability of Bayesian Networks for modelling the causality of land degradation intensity and extent through the integration of driving forces, pressures, states impacts and responses (DPSIR) is evaluated. In an attempt to describe the relationships between bio-physical states of degradation to their social, economic and demographic causes, the proposed framework offers a new probabilistic approach to the establishment of the major root causes of the states of degradation, resulting in a practical GIS and Bayesian network modeling application and implementation to land degradation data.