کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
8866514 | 1621187 | 2018 | 11 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Forest species diversity mapping using airborne LiDAR and hyperspectral data in a subtropical forest in China
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کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
علوم زمین و سیارات
کامپیوتر در علوم زمین
پیش نمایش صفحه اول مقاله
![عکس صفحه اول مقاله: Forest species diversity mapping using airborne LiDAR and hyperspectral data in a subtropical forest in China Forest species diversity mapping using airborne LiDAR and hyperspectral data in a subtropical forest in China](/preview/png/8866514.png)
چکیده انگلیسی
Monitoring biodiversity is essential for the conservation and management of forest resources. A method called “spectranomics” that maps the diversity of forest species based on species-driven leaf optical traits using imaging spectroscopy has been developed for tropical forests in earlier studies. In this study we applied the “spectranomics” method in combination with airborne hyperspectral (PHI-3 sensor with 1â¯m spatial resolution) and LiDAR (>4 points/m2) data to first identify interspecies variations in biochemical and structural properties of trees and then estimate the tree species diversity within the Shennongjia Forest Nature Reserve in China. Firstly, we used the watershed algorithm based on morphological crown control to isolate individual tree crowns (ITCs) from the LiDAR data. For each ITC, we then calculated seven vegetation indices (VIs) representing key biochemical properties from the hyperspectral data and additionally derived the LiDAR-based tree height which was identified to support the discrimination of the tree species in a preceding analysis. Finally we utilized the combination of the seven selected VIs and tree height as input to a self-adaptive Fuzzy C-Means (FCM) clustering algorithm. The FCM algorithm was applied to fixed subsets of 30â¯mâ¯Ãâ¯30â¯m and it was assumed that the number of clusters identified within a subset represents the number of occurring species. The species richness and Shannon-Wiener diversity index calculated from the clustering outputs correlated well with the field reference data (R2â¯=â¯0.83, RMSEâ¯=â¯0.25). The results show that forest species diversity can be directly predicted using the suggested clustering method based on crown-by-crown variations in biochemical and structural properties in the examined subtropical forest without the need to distinguish the individual tree species.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Remote Sensing of Environment - Volume 213, August 2018, Pages 104-114
Journal: Remote Sensing of Environment - Volume 213, August 2018, Pages 104-114
نویسندگان
Yujin Zhao, Yuan Zeng, Zhaoju Zheng, Wenxue Dong, Dan Zhao, Bingfang Wu, Qianjun Zhao,