Urban population explosion may increase ecological environment discomfort,thereby affecting negatively humans’mental and physical performance.Therefore,it is important to detect and monitor vegetation and predict its...Urban population explosion may increase ecological environment discomfort,thereby affecting negatively humans’mental and physical performance.Therefore,it is important to detect and monitor vegetation and predict its ecological benefits.The complex composition of urban environment ground objects,such as steel roofs,plastic courts,and building shadows,significantly interferes with vegetation detection and monitoring.The optimized hyperspectral image-based vegetation index(OHSVI)constructed in this study effectively solves this problem.However,it is difficult to accurately predict the ecological benefits of vegetation based on the two-dimensional vegetation information extracted based on remote sensing images;this is related to the three-dimensional(3D)structure of vegetation and the 3D pattern of buildings.Therefore,wefirst proposed the vegetation ecological benefits index(VEBI)based on the 3D structure of vegetation to reveal how vegetation acts on its 3D surroundings.The method was tested in a playground,an academic building,and a parking space.The results showed that the vegetation extraction accuracy of the OHSVI exceeded 93%,which is better than that of the existing indices.Ourfindings suggest that VEBI may be efficient in predicting 3D vegetation ecological benefits combined with remote sensing and lidar datasets.展开更多
基金supported by Independent innovation project-strategic special:[Grant Number 24720221004A-3]National Natural Science Foundation of China:[Grant Number 42106172].
文摘Urban population explosion may increase ecological environment discomfort,thereby affecting negatively humans’mental and physical performance.Therefore,it is important to detect and monitor vegetation and predict its ecological benefits.The complex composition of urban environment ground objects,such as steel roofs,plastic courts,and building shadows,significantly interferes with vegetation detection and monitoring.The optimized hyperspectral image-based vegetation index(OHSVI)constructed in this study effectively solves this problem.However,it is difficult to accurately predict the ecological benefits of vegetation based on the two-dimensional vegetation information extracted based on remote sensing images;this is related to the three-dimensional(3D)structure of vegetation and the 3D pattern of buildings.Therefore,wefirst proposed the vegetation ecological benefits index(VEBI)based on the 3D structure of vegetation to reveal how vegetation acts on its 3D surroundings.The method was tested in a playground,an academic building,and a parking space.The results showed that the vegetation extraction accuracy of the OHSVI exceeded 93%,which is better than that of the existing indices.Ourfindings suggest that VEBI may be efficient in predicting 3D vegetation ecological benefits combined with remote sensing and lidar datasets.
