Research project SR/00/133 (Research action SR)
Land abandonment and forest recovery is often taking place on marginal lands, such as mountain environments. Assessing the rate, spatial patterns and ecosystem impacts of forest cover change in these environments is challenging given the ruggedness and inaccessibility of mountains. Remote sensing methods are the privileged tool, and yet suffer from methodological challenges due to topographical and shadowing effects. Recent techniques have been developed to correct high and very high resolution imagery for radiometric and geometric distortions, and illumination and shade effects on accidented surfaces. These sophisticated correction methods are not only highly labour intensive, but also demand site-specific calibration which makes it particularly difficult to apply them in streamlined processing schemes. At present, it is not clear what the added value of complex preprocessing techniques is compared to relative simple empirical methods and to what extent more sophisticated processing enhances results of subsequent analyses.
Objective
This project will specifically address this methodological research question, and will develop an optimal preprocessing chain to be used for semi-automatic analyses of high resolution satellite data on mountainous terrain. The project aims at a better understanding of the impact of preprocessing techniques on the detection accuracy of forest transitions and the mapping accuracy of ecosystem services.
Method
The methods is divided in 5 steps:
- Remote sensing data acquisition, pre-processing and correction for topographic effects;
- Large area mapping with high resolution remote sensing data;
- Monitoring of forest-cover change and degradation;
- Mapping of ecosystem services with high resolution sensing data;
- Socio-economic responses to changes in environmental goods and services.
- We will conduct a sensitivity analysis of all these analyses to the level of topographic correction applied in the preprocessing of satellite data.
Result
The project will:
- evaluate the sensitivity of the parameterization of biophysical attributes from remote sensing to the level of correction for possible distortions due to topographic effects, illumination and shadowing, and preceding rainfall,
- test and apply an optimal preprocessing chain for monitoring forest cover change and ecosystem services,
- provide new insights in the impact and feedback mechanisms of forest transitions on ecosystem services.
The results of this study on forest transition are very relevant for climate change policies and possible future obligations of countries with respect to limitations of GHG emissions from land cover activities (reforestation and avoided deforestation) as part of the Reduced Emissions from Deforestation and forest Degradation (REDD) scheme.
