Estimation of Overstory and Understory Leaf Area Index by Combining Hyperion and Panchromatic QuickBird Data Using Neural Network Method
This paper presented a neural network method for combining Hyperion and panchromatic QuickBird data to retrieve the Overstory and Understory Leaf Area Index (OU-LAI) of forest stands in the Long-menhe forest nature reserve in China. A field survey was firstly carried out to collect thirty sampling sites located in typical forest stands in the study area. Then a Multi-Layer Perception artificial neural network model was used to integrate hyperspectral domain fusion and high spatial domain fusion techniques so as to deal with the non-linear canopy scattering between overstory and understory vegetation. Various combination of selected twenty-one optimal Hyperion bands and panchromatic QuickBird data were tested to evaluate the OU-LAI retrieval accuracy. The results show that nine Hyperion bands (3 VIS, 3 NIR and 3 SWIR bands) and standard deviation (SD) within each 50 by 50 mobile window from panchromatic QuickBird data have the best retrieval results for OU-LAI. This study also indicates that the non-linear artificial neural network can be utilized to retrieve OU-LAI parameters in the forest stands by combining Hyperion data and panchromatic QuickBird data. Improvements of neural network method using additional remote sensing information for retrieval OU-LAI are discussed.
Hyperion Panchromatic QuickBird Neural Network Leaf Area Index Overstory and Understory
Jianxi Huang Yuan Zeng Wenbin Wu Kebiao Mao Jingyu Xu Wei Su
College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, Ch State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing Applications,Chinese Aca Key Laboratory of Resources Remote Sensing and Digital Agriculture, MOA, Institute of Agricultural R School of Geoscience and Environmental Engineering, Central South University, Changsha 410083, China
国际会议
南昌
英文
964-973
2010-10-22（万方平台首次上网日期，不代表论文的发表时间）