Mapping Forest Degradation due to Selective Logging by Means of Time Series Analysis: Case Studies in Central Africa

Remote Sensing

Manuela Hirschmugl¹  Martin Steinegger²  Heinz Gallaun² 

[1] Remote Sensing and Geoinformation, Institute for Information and Communication Technologies, Joanneum Research, Steyrergasse 17, A-8010 Graz, Austria;

关键词: forest degradation;    time series analysis;    REDD+ monitoring system;    SMA;    gap detection;   

DOI  :  10.3390/rs6010756

来源: mdpi

【 摘 要 】

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.

【 授权许可】

CC BY   
© 2014 by the authors; licensee MDPI, Basel, Switzerland