NDLI: Polarimetric SAR Data for Forest and Deforestation Mapping in Guizhou Province, Southwest of China

Content Provider	IEEE Xplore Digital Library
Author	Maosong Xu Fengli Zhang Zhongsheng Xia Huaze Gong
Copyright Year	2008
Description	Author affiliation: Forest Resource Manage. & Conservation Station (Zhongsheng Xia) \|\| Inst. of Remote Sensing Applic., Chinese Acad. of Sci., Beijing (Fengli Zhang; Huaze Gong) \|\| Planning & Designing, Acad. of Forestry Inventory (Maosong Xu)
Abstract	Forestry inventory in southwest of China often suffers from the cloudy cover, and Synthetic Aperture Radar (SAR) can play an important role because of its all-weather and all-day capabilities. In this paper, we studied the potential of polarimetric SAR data for forest mapping in heavy cloud prone and rainy areas with polarimetric TerraSAR-X, Radarsat 2 data and how to improve the forestry classification accuracy through integration of SPOT 5 image and polarimetric SAR data. Field works were carried out in late August of 2007, and parameters were collected using a ground based Lidar and field measurements. For the high relief condition at the test site, a geometric correction strategy using two side looking direction SAR images and high resolution digital elevation model was proposed to overcome the geometric distortion of SAR image such as foreshortening, layover and shadow in hilly areas. Neural net method was suggested for classification of the SAR and SPOT images.
File Size	525328
File Format	PDF
ISBN	9781424428076
DOI	10.1109/IGARSS.2008.4779483
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-07-07
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Forestry Synthetic aperture radar Testing Image analysis Remote sensing Clouds Digital elevation models Radar imaging Chaos Resource management MIMICS Forest polarimetric SAR two side looking geometric correction classification
Content Type	Text
Resource Type	Article

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