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ICESat vegetation product evaluation with airborne Lidar Data in China  ( CPCI-S收录 EI收录)  

文献类型:会议论文

英文题名:ICESat vegetation product evaluation with airborne Lidar Data in China

作者:Pang, Yong[1,2] Li, Zengyuan[1] Lefsky, Michael[2] Sun, Guoqing[3] Liu, Qingwang[1] Xu, Guangcai[1]

第一作者:Pang, Yong;庞勇

通信作者:Pang, Y[1]

机构:[1]Chinese Acad Forestry, Inst Forest Resource Informat Tech, Beijing 100091, Peoples R China;[2]Colorado State Univ, Coll Nat Resources, Ctr Ecol Applicat Lidar, Ft Collins, CO 80523 USA;[3]Univ Maryland, Dept Geol, College Pk, MD 20742 USA

会议论文集:6th International Symposium on Digital Earth - Data Processing and Applications

会议日期:SEP 09-12, 2009

会议地点:Chinese Nat Comm Int Soc Digital Earth/Ctr Earth Observ Digital Earth, Chi, Beijing, PEOPLES R CHINA

主办单位:Chinese Nat Comm Int Soc Digital Earth/Ctr Earth Observ Digital Earth, Chi

语种:英文

外文关键词:ICESat Vegetation Product; forestry; airborne Lidar

年份:2010

摘要:Spaceborne large footprint lidar (ICESat GLAS) has acquired over 250 million lidar observations over forest regions globally, an unprecedented dataset of vegetation heights. The ICESat Vegetation Product (IVP) was developed aimed at a global forest height dataset. Because of its high vertical resolution, large spatial extent and 70 m footprint characteristics, it is difficult to validate this product with other available remote sensing product or field measurements. To evaluation the IVP product in China, airborne waveform data was collected along several ICESat GLAS obits in the Southwest and West of China. The preliminary results show that the R2 is 0.41 and RMSE is 2.69 m between the vegetation height from airborne data and ICESat Vegetation Product at forest stand level in most cases. For those cases where the GLAS shots and airborne datasets gave very difference results, the airborne lidar data were synthesis to GLAS waveform and compared with the GLAS waveform. Those waveforms from fluctuated terrain also have several peaks and large waveform extent, which cased error in forest height estimation.

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