Land-Change Driver Analysis

A proximate cause is a direct human activity that physically changes land cover at the site, such as clearing forest for crops, extracting timber, or building infrastructure. An underlying driving force is the broader and often distant factor that motivates or enables that activity, such as demographic change, commodity prices, technology, policy, or culture. Geist and Lambin's key point is that explanation needs both layers: the proximate cause tells you what happened on the ground, the underlying force tells you why, and skipping the second layer leaves the change unexplained.

Because the evidence demands it. The 2002 meta-analysis of tropical deforestation found that single-factor causation is the exception; most deforestation results from several underlying forces acting together and reinforcing one another, such as roads plus markets plus migration. Mono-causal explanations, like blaming population growth alone, were not supported across cases. Analytically this means modeling interactions among drivers or classifying the recurrent causal configurations, so the explanation reflects synergistic bundles rather than one dominant variable.

Change detection produces the maps that show where and how cover changed, but it stops at the pattern. LUCC analysis treats that detected change as the thing to be explained and adds a structured causal attribution: linking each major transition to its proximate human activity and then tracing that activity to its underlying social, economic, and policy drivers. Remote sensing supplies the dependent variable; LUCC analysis supplies the social-science explanation of it, which is why it sits at the intersection of geography, ecology, and the social sciences.