全球智库信息集成服务系统(G2TT): The BIOMASAR algorithm: An approach for retrieval of forest growing stock volume using stacks of multi-temporal SAR data.

G2TT

来源类型	Conference or Workshop Item (UNSPECIFIED)
规范类型	其他
	The BIOMASAR algorithm: An approach for retrieval of forest growing stock volume using stacks of multi-temporal SAR data.
	Santoro M; Beer C; Cartus O; Schmullius C; Shvidenko A; McCallum I; Wegmueller U; Wiesmann A
发表日期	2010
出处	Proceedings of ESA Living Planet Symposium, 28 June-2 July 2010
出版年	2010
语种	英语
摘要	Retrieval of forest growing stock volume (GSV) is a major topic of investigation in the remote sensing community due to the necessity of accurate and updated information on forest resources, which is not achievable using traditional survey methods at the regional and global level. Radar remote sensing has the advantage of being able to acquire images over any part of the Earth with a high repetition frequency. While an image can be formed regardless of the cloud cover and the solar illumination, the environmental conditions can play a significant role on the measurements collected by the sensor. This aspect is of particular relevance in case of forest-related studies and for high frequencies. C-band SAR backscatter is generally deemed as useless when aiming at forest resources assessment due to the weak sensitivity with respect to biophysical properties. Furthermore, the strong sensitivity to the dielectric properties of the scattering objects make C-band SAR backscatter an unreliable tool for monitoring forests. With increasing number of studies on extraction of forest biophysical properties from SAR data, the research community tends toward a clear focus on the use of low frequency SAR.
主题	Forestry (FOR)
URL	http://pure.iiasa.ac.at/id/eprint/9430/
来源智库	International Institute for Applied Systems Analysis (Austria)
资源类型	智库出版物
条目标识符	http://119.78.100.153/handle/2XGU8XDN/132345
推荐引用方式 GB/T 7714	Santoro M,Beer C,Cartus O,et al. The BIOMASAR algorithm: An approach for retrieval of forest growing stock volume using stacks of multi-temporal SAR data.. 2010.