Reliable data on deforestation patterns and their spatial and temporal variability are of highest importance as basis for the development of efficient and precise counter-measures for affected regions and countries. In this context, revealing direct and underlying causes of deforestation are a fundamental prerequisite for the implementation of REDD+.
While the development of reference scenarios for deforestation is currently pushed by several countries, there is still enormous need for revealing causes of deforestation on subnational and local level in relation to current and historical land use and related socio-economic and institutional stakeholders (LAMBIN et al. 2001).
Further on, many countries with net-deforestation on national level frequently exhibit strata with increasing forest cover on subnational levels, which have to be considered in integrated and effective policy-approaches. The project contributes improving efficiency of global and national forest related policies and helps identifying potential leakage-effects.
For this purpose, we aim at combining historical time series of satellite images of lower spatial resolution with recent high resolution images, in order to distinguish the most important forestry, agroforestry and agricultural land-use types within their landscape context.
Landsat-images (and NDVI or others) will be analyzed on national level in high temporal resolution, in order to determine spatially delimited strata of de- and reforestation for the subsequent work steps (WP 2-6), and to verify if global drivers of de- forestation patterns can be detected also on national and subnational levels.
The strata will serve as analytical units for cross-checking relationships with governance structures (WP 4) and regional price variability of agricultural products (WP 1a).
In a next step (WP 1b) several landscape sections will be selected randomly within defined strata of typical de- and reforestation patterns and will be analyzed with images of high spatial resolution (e.g. Quickbird, Rapid Eye).
Calibration and ground truthing in the field will be carried out in combination with inventory teams of WP 2.
Derived land use maps will be combined with information on drivers of de- and reforestation as basis for land- use models and the implementation of policy scenarios of WP 6.