Humid tropical forest monitoring with multi-temporal L-, C- and X-band SAR data

Humid tropical forest monitoring with EO is limited by frequent cloud cover and rapid forest regrowth. Both can be overcome by using temporally dense SAR image stacks. We present a method that uses the coefficient of variation of multi-temporal SAR data stacks to map tropical forest disturbances. Th...

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Bibliographic Details
Published in2017 9th International Workshop on the Analysis of Multitemporal Remote Sensing Images (MultiTemp) pp. 1 - 4
Main Authors Deutscher, Janik, Gutjahr, Karlheinz, Perko, Roland, Raggam, Hannes, Hirschmugl, Manuela, Schardt, Mathias
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.06.2017
Subjects
ALOS PALSAR
Backscatter
Earth
forest degradation
Monitoring
Optical sensors
Remote sensing
Satellites
Sentinel-1
Synthetic aperture radar
TerraSAR-X
time series analysis
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Summary:Humid tropical forest monitoring with EO is limited by frequent cloud cover and rapid forest regrowth. Both can be overcome by using temporally dense SAR image stacks. We present a method that uses the coefficient of variation of multi-temporal SAR data stacks to map tropical forest disturbances. The SAR data pre-processing and the forest change detection workflows are described and illustrated. The method is tested at a humid tropical forest site in the Republic of Congo. At this test site we use data from three different SAR sensors: ALOS PALSAR, Sentinel-1 and TerraSAR-X. The forest disturbance maps are validated by visual interpretation and compared to the Landsat based Humid Tropical Forest Disturbance Alerts available from Global Forest Watch. Change mapping accuracies for plots larger than 0.5 ha are very high: 76% for ALOS PALSAR, 96% for TerraSAR-X and 98% for Sentinel-1. For Sentinel-1, producer accuracies were derived for different forest disturbance types. The overall accuracy is 81.8%, with highest values for deforestation in oil palm plantations and burnt areas. The results are similar to the accuracy of the Humid Tropical Forest Disturbance Alert layer, which detects 85.6% of all reference areas. We also show that fusion of the disturbance maps on a result level is possible. The presented method could be adapted to near real-time processing and to a combined processing with optical EO data.
DOI:10.1109/Multi-Temp.2017.8035264