1. Inversion of Mangrove Canopy Leaf Functional Traits on the Qi'ao Island Based on UAV Hyperspectral Remote Sensing
- Author
-
Wang Meng, Sun Zhengzheng, He Zhidong, Wang Zhihui, Geng Shoubao, Zhao Xinfeng, Yang Long, and Sun Zhongyu
- Subjects
leaf functional traits ,uav hyperspectral remote sensing ,functional trait mapping ,plsr ,mangrove forest ,qi'ao island ,Geography (General) ,G1-922 - Abstract
Quantitative studies of mangrove leaf functional traits will help us understand the adaptive evolutionary strategies of mangrove plants and the relationship between mangrove biodiversity and ecosystem functions. Because of the special environment of the intertidal zone where mangroves are located, it is very difficult to obtain the functional traits of mangrove canopies from the ground, and relevant studies are lacking. The maturity of Unmanned Aerial Vehicle (UAV) hyperspectral remote sensing technology provides a new means of conducting such research. This study considered mangroves on Qi'ao Island, Zhuhai, as the research object. Based on UAV hyperspectral data, two UAV hyperspectral data processing methods, which combined Partial Least Squares Regression with Normalized Difference Vegetation Index (PLSR+NDVI) and Partial Least Squares Regression with Continuous Wavelet Transform (PLSR+CWT), were used to estimate the 10 canopy leaf functional traits of mangroves on Qi'ao Island. The results showed that the PLSR + NDVI method was more suitable for the inversion of mangrove canopy-specific leaf weight (LMA), phosphorus content per unit mass (Pmass), and nitrogen content per unit area (Narea), whereas the PLSR + CWT method was more suitable for the estimation of the nitrogen/phosphorus ratio (N/P), chlorophyll content (Cab), and carotenoid content (Cxc). However, the results of the above two methods for retrieving the nitrogen content per unit mass (Nmass), potassium content per unit mass (Kmass), phosphorus content per unit area (Parea), and potassium content per unit area (Karea) were not ideal (R2
- Published
- 2023
- Full Text
- View/download PDF