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Remote Sens. 2014, 6(1), 756-775; doi:10.3390/rs6010756
Article

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

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Received: 20 November 2013; in revised form: 23 December 2013 / Accepted: 30 December 2013 / Published: 9 January 2014
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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.
Keywords: forest degradation; time series analysis; REDD+ monitoring system; SMA; gap detection forest degradation; time series analysis; REDD+ monitoring system; SMA; gap detection
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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MDPI and ACS Style

Hirschmugl, M.; Steinegger, M.; Gallaun, H.; Schardt, M. Mapping Forest Degradation due to Selective Logging by Means of Time Series Analysis: Case Studies in Central Africa. Remote Sens. 2014, 6, 756-775.

AMA Style

Hirschmugl M, Steinegger M, Gallaun H, Schardt M. Mapping Forest Degradation due to Selective Logging by Means of Time Series Analysis: Case Studies in Central Africa. Remote Sensing. 2014; 6(1):756-775.

Chicago/Turabian Style

Hirschmugl, Manuela; Steinegger, Martin; Gallaun, Heinz; Schardt, Mathias. 2014. "Mapping Forest Degradation due to Selective Logging by Means of Time Series Analysis: Case Studies in Central Africa." Remote Sens. 6, no. 1: 756-775.


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