Pan-tropical Monitoring of Deforestation

Abstract

This paper reviews the technical capabilities for monitoring deforestation from a pan-tropical perspective in response to the United Nations Framework Convention on Climate Change (UNFCCC) process, which is studying the technical issues surrounding the ability to reduce greenhouse gas emissions from deforestation in developing countries. The successful implementation of such policies requires effective forest monitoring systems that are reproducible, provide consistent results, meet standards for mapping accuracy, and can be implemented from national to pan-tropical levels. Remotely sensed data, supported by ground observations, are crucial to such efforts. Recent developments in global to regional monitoring of forests can contribute to reducing the uncertainties in estimates of emissions from deforestation. Monitoring systems at national levels in developing countries can also benefit from pan-tropical and regional observations, mainly by identifying hot spots of change and prioritizing areas for monitoring at finer spatial scales. A pan-tropical perspective is also required to ensure consistency between different national monitoring systems. Data sources already exist to determine baseline periods in the 1990s as historical reference points. Key requirements for implementing such monitoring programs, both at pan-tropical and at national scales, are international commitment of resources to increase capacity, coordination of observations to ensure pan-tropical coverage, access to free or low-cost data, and standardized, consensus protocols for data interpretation and analysis. Data sources exist to determine base periods in the 1990s as historical reference points. Key constraints for implementing programs to monitor deforestation at pan-tropical to national scales are international commitment of resources to increase capacity, coordination of observations to ensure pan-tropical coverage, access to free or low-cost data, and standard and consensual protocols for data interpretation and analysis.