Synergistically monitoring the snowstorm damaged forest with polarimetric SAR and optical remote sensing data

doi:10.3969/j.issn.1000-2006.2014.04.001

JOURNAL OF NANJING FORESTRY UNIVERSITY ›› 2014, Vol. 38 ›› Issue (04): 1-6.doi: 10.3969/j.issn.1000-2006.2014.04.001

Synergistically monitoring the snowstorm damaged forest with polarimetric SAR and optical remote sensing data

XU Maosong¹, LI Kun^2*, XIE Chou², ZHU Song³, LUO Hongzhang³, ZHANG Fengli², WANG Xuejun¹, XIA Zhongsheng³, DANG Yongfeng¹

1. Academy of Forestry Inventory, Planning and Designing, State Forestry Administration, Beijing 100714, China;
2. Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China;
3. Forest Resource Management and Conservation Station, Department of Forestry of Guizhou Province, Guiyang 550001, China

Online:2014-07-31 Published:2014-07-31

Abstract

Abstract: In this study, polarimetric SAR and optical remote sensing data were synergized to monitor snowstorm damaged forests in Zhazuo Forest Farm, Guizhou province, southwest of China. The backscatter coefficients of the snowstorm damaged forests, healthy forests and their difference were analyzed. In order to fully explore the potentiality of polarimetric SAR data, the polarimetric signature and scattering mechanism were investigated for both snowstorm damaged forests and healthy forests. Then, Freeman-Durden polarimetric decomposition was applied for the identification of snowstorm damaged forest based on the change of their scattering behaviors caused by snowstorm destruction. In order to further improve the results of snowstorm damaged forests identification, high-resolution optical data was introduced to synergize with polarimetric SAR data. The results showed that it was feasible using polarimetric SAR and optical remote sensing data to synergistically monitor the snowstorm damaged forests. Most notably, fully polarimetric SAR data had remarkable advantages in forest type identification and deforestation monitoring and was very promising for the survey of forest resources and disaster monitoring in southwestern of China.

CLC Number:

S757
TP79

XU Maosong, LI Kun, XIE Chou, ZHU Song, LUO Hongzhang, ZHANG Fengli, WANG Xuejun, XIA Zhongsheng, DANG Yongfeng. Synergistically monitoring the snowstorm damaged forest with polarimetric SAR and optical remote sensing data[J]. JOURNAL OF NANJING FORESTRY UNIVERSITY, 2014, 38(04): 1-6.

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Synergistically monitoring the snowstorm damaged forest with polarimetric SAR and optical remote sensing data

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