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Abstract
Detecting and monitoring forest degradation in the tropics has implications for various fields of interest (biodiversity, emission calculations, self-sustenance of indigenous communities, timber exploitation). However, remote-sensing-based detection of forest degradation is difficult, as these subtle degradation signals are not easy to detect in the first place and quickly lost over time due to fast re-vegetation. To overcome these shortcomings, a time series analysis has been developed to map and monitor forest degradation over a longer period of time, with frequent updates based on Landsat data. This time series approach helps to reduce both the commission and the omission errors compared to, e.g., bi- or tri-temporal assessments. The approach involves a series of pre-processing steps, such as geometric and radiometric adjustments, followed by spectral mixture analysis and classification of spectral curves. The resulting pixel-based classification is then aggregated to degradation areas. The method was developed on a study site in Cameroon and applied to a second site in Central African Republic. For both areas, the results were finally evaluated against visual interpretation of very high-resolution optical imagery. Results show overall accuracies in both study sites above 85% for mapping degradation areas with the presented methods
Original language | English |
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Pages (from-to) | 756-775 |
Journal | Remote Sensing |
Volume | 6 |
Issue number | 1 |
DOIs | |
Publication status | Published - 2014 |
Externally published | Yes |
Fields of Expertise
- Sustainable Systems
Treatment code (Nähere Zuordnung)
- Basic - Fundamental (Grundlagenforschung)
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Dive into the research topics of 'Mapping Forest Degradation due to Selective Logging by Means of Time Series Analysis: Case Studies in Central Africa'. Together they form a unique fingerprint.Projects
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Publications of the Institute of Remote Sensing and Photogrammetry
Hollinger, G., Avian, M. & Prüller, R.
1/01/01 → …
Project: Research area