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In order to better assess the spatial variability in subtropical forest biomass, the goal of our study was to use small-footprint, discrete-return Light Detection and Ranging (LiDAR) data to accurately estimate and map above-and below-ground biomass components of subtropical forests. Foliage, branch, trunk, root, above-ground and total biomass of 53 plots(More)
An accurate estimation of total biomass and its components is critical for understanding the carbon cycle in forest ecosystems. The objectives of this study were to explore the performances of forest canopy structure characterization from a single small-footprint Light Detection and Ranging (LiDAR) dataset using two different techniques focusing on (i) 3-D(More)
Accurate estimation of forest biomass is critical in the study of global carbon balance and climate change. This research was undertaken in the Yushan forest, in southeast china. We used metrics extracted from hyperspectral data as predictor variables to establish three types of biomass prediction models, and then the models are verified by cross-validation(More)
Accurate classification of tree species provides key information for mapping species diversity, managing forest ecosystems and modeling individual tree growth. While airborne Light Detection and Ranging (LiDAR) technology offers significant potential to estimate forest structural attributes, the capacity of this new tool to classify species is less well(More)
This paper focuses on the estimation of green land variation with similarity theory by using two temporal spatial data in Shenzhen. The location, shape and areas of green land units have been used as the similarity elements. Thus the similarity coefficients can be defined. The ratio of overlapping number of green patches to the intersecting number of green(More)
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