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SVM-based forest mapping of Wolong Giant Panda Habitat using SAR data |
| ZHOU Xiaoyu1, 2, CHEN Fulong1, 3, JIANG Aihui4 |
1. Key Laboratory of Digital Earth Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100094, China;
2. University of Chinese Academy of Sciences, Beijing 100049, China;
3. International Centre on Space Technologies for Natural and Cultural Heritage Under the Auspices of UNESCO, Beijing 100094, China;
4. College of Geomatics, Shandong University of Science and Technology, Qingdao 266590, China |
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Abstract Generally, the conservation of Wolong Giant Panda Habitat (Natural World Heritage site) is significant for the sustainability of the rare species of Giant Panda. As we know, forest coverage can be an essential impact on the suitability of the habitat. Owing to the all-weather, all-day operation capability of radar systems, in this study, the authors investigated the performance of Synthetic Aperture Radar (SAR) images in fine mapping of forests using multi-temporal/polarization PALSAR data. The authors firstly corrected the radiometric distortion of SAR data induced by the cliffy topography; then the authors selected 5 different temporal acquisitions for the forest mapping using the Support Vector Machine (SVM) approach. 5 multi-temporal/dual-polarization indexes, i.e., HHm,HVm,TSD,HHm-HVm and HHm/HVm, were applied for the training and classification. Experimental results demonstrated that the combination of HHm, HVm, TSD and HHm-HVm derived an optimal classification (e.g., total accuracy and user accuracy of forest/non-forest were 86.90%,82.34% and 92.83%, respectively), better than the single-temporal/polarization mode (total classification accuracy of 55.47%). This study shows the effectiveness of multi-temporal/polarization SAR data in forest fine mapping, particularly in the monitoring and evaluation of natural heritage sites located in cloudy and rainy environments.
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| Keywords
remote sensing geological map
standard legend
design
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Issue Date: 15 August 2017
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2017,
Vol. 29
