Although airborne hyperspectral data with detailed spatial and spectral information has demonstrated significant potential for tree species classification,it has not been widely used over large areas.A comprehensive p...Although airborne hyperspectral data with detailed spatial and spectral information has demonstrated significant potential for tree species classification,it has not been widely used over large areas.A comprehensive process based on multi-flightline airborne hyperspectral data is lacking over large,forested areas influenced by both the effects of bidirectional reflectance distribution function(BRDF)and cloud shadow contamination.In this study,hyperspectral data were collected over the Mengjiagang Forest Farm in Northeast China in the summer of 2017 using the Chinese Academy of Forestry's LiDAR,CCD,and hyperspectral systems(CAF-LiCHy).After BRDF correction and cloud shadow detection processing,a tree species classification workflow was developed for sunlit and cloud-shaded forest areas with input features of minimum noise fraction reduced bands,spectral vegetation indices,and texture information.Results indicate that BRDF-corrected sunlit hyperspectral data can provide a stable and high classification accuracy based on representative training data.Cloud-shaded pixels also have good spectral separability for species classification.The red-edge spectral information and ratio-based spectral indices with high importance scores are recommended as input features for species classification under varying light conditions.According to the classification accuracies through field survey data at multiple spatial scales,it was found that species classification within an extensive forest area using airborne hyperspectral data under various illuminations can be successfully carried out using the effective radiometric consistency process and feature selection strategy.展开更多
Based on radiative transfer theory in vegetation and geometric-optical principles, an analytical physi-cal mode] for calculating multiangular, multispectral reflectance over a non-random, multiple component vegetation...Based on radiative transfer theory in vegetation and geometric-optical principles, an analytical physi-cal mode] for calculating multiangular, multispectral reflectance over a non-random, multiple component vegetation canopy is developed. This model is derived by taking advantages of the previous leaf canopy and multicomponent canopy BRF models. It quantitatively accounts for both the impact of foliage elements’ orientation on the canopy hotspot through an innovative algorithm to estimate the hotspot function for any arbitrarily oriented foliage element and contributions of all foliage elements to the reflectance by multiple scattering. Thus, it is characterized by more com-pletely considering the integrative influence of spatial variations in optical and structural properties of all foliage ele-ments on canopy reflectance than any previous analytical BRF models. Simulation results from this model demonstrate that canopy hotspot becomes strongest when the mean inclination angle of foliage elements is展开更多
基金supported by the National Natural Science Foundation of China (Grant No.42101403)the National Key Researchand Development Program of China (Grant No.2017YFD0600404)。
文摘Although airborne hyperspectral data with detailed spatial and spectral information has demonstrated significant potential for tree species classification,it has not been widely used over large areas.A comprehensive process based on multi-flightline airborne hyperspectral data is lacking over large,forested areas influenced by both the effects of bidirectional reflectance distribution function(BRDF)and cloud shadow contamination.In this study,hyperspectral data were collected over the Mengjiagang Forest Farm in Northeast China in the summer of 2017 using the Chinese Academy of Forestry's LiDAR,CCD,and hyperspectral systems(CAF-LiCHy).After BRDF correction and cloud shadow detection processing,a tree species classification workflow was developed for sunlit and cloud-shaded forest areas with input features of minimum noise fraction reduced bands,spectral vegetation indices,and texture information.Results indicate that BRDF-corrected sunlit hyperspectral data can provide a stable and high classification accuracy based on representative training data.Cloud-shaded pixels also have good spectral separability for species classification.The red-edge spectral information and ratio-based spectral indices with high importance scores are recommended as input features for species classification under varying light conditions.According to the classification accuracies through field survey data at multiple spatial scales,it was found that species classification within an extensive forest area using airborne hyperspectral data under various illuminations can be successfully carried out using the effective radiometric consistency process and feature selection strategy.
基金Project supported by the National Natural Science Foundation of China and the President Fund of the Chinese Academy of Sciences.
文摘Based on radiative transfer theory in vegetation and geometric-optical principles, an analytical physi-cal mode] for calculating multiangular, multispectral reflectance over a non-random, multiple component vegetation canopy is developed. This model is derived by taking advantages of the previous leaf canopy and multicomponent canopy BRF models. It quantitatively accounts for both the impact of foliage elements’ orientation on the canopy hotspot through an innovative algorithm to estimate the hotspot function for any arbitrarily oriented foliage element and contributions of all foliage elements to the reflectance by multiple scattering. Thus, it is characterized by more com-pletely considering the integrative influence of spatial variations in optical and structural properties of all foliage ele-ments on canopy reflectance than any previous analytical BRF models. Simulation results from this model demonstrate that canopy hotspot becomes strongest when the mean inclination angle of foliage elements is
