Abstract
Urban vegetation extraction is very important for urban biodiversity assessment and protection. However, due to the diversity of vegetation types and vertical structure, it is still challenging to extract vertical information of urban vegetation accurately with single remotely sensed data. Airborne light detection and ranging (LiDAR) can provide elevation information with high-precision, whereas hyperspectral data can provide abundant spectral information on ground objects. The complementary advantages of LiDAR and hyperspectral data could extract urban vegetation much more accurately. Therefore, a three-dimensional (3D) vegetation extraction workflow is proposed to extract urban grasses and trees at individual tree level in urban areas using airborne LiDAR and hyperspectral data. The specific steps are as follows: (1) airborne hyperspectral and LiDAR data were processed to extract spectral and elevation parameters, (2) random forest classification method and object-based classification method were used to extract the two-dimensional distribution map of urban vegetation, (3) individual tree segmentation was conducted on a canopy height model (CHM) and point cloud data separately to obtain three-dimensional characteristics of urban trees, and (4) the spatial distribution of urban vegetation and the individual tree delineation were assessed by validation samples and manual delineation results. The results showed that (1) both the random forest classification method and object-based classification method could extract urban vegetation accurately, with accuracies above 99%; (2) the watershed segmentation method based on the CHM could extract individual trees correctly, except for the small trees and the large tree groups; and (3) the individual tree segmentation based on point cloud data could delineate individual trees in three-dimensional space, which is much better than CHM segmentation as it can preserve the understory trees. All the results suggest that two- and three-dimensional urban vegetation extraction could play a significant role in spatial layout optimization and scientific management of urban vegetation.
Highlights
With the increasing urbanization, urban areas are facing more and more environmental problems, such as air pollution, urban heat island effect and ecological destruction [1]
The purposes of this study are to (1) evaluate the performance of two classification methods in urban vegetation extraction, including random forest classification method and object based classification method; and (2) evaluate the performance of two segmentation methods in individual tree extraction, including segmentation based on canopy height model (CHM) and point cloud data
The results indicated that the overall accuracy and kappa coefficient of random forest classification method was 95.87% and 0.9550, respectively
Summary
Urban areas are facing more and more environmental problems, such as air pollution, urban heat island effect and ecological destruction [1]. Among them, improving environmental quality and ecological conditions are the most important problems to be solved. As an important element of the urban ecosystem, urban vegetation has important ecological functions in improving air quality, reducing urban heat island effect [2], reducing CO2 emissions [3], decreasing street noise [4], regulating microclimate, maintaining urban ecological balance, and protecting biodiversity [5,6,7]. With the continuous improvement of living standards, residents’ requirements for urban vegetation are constantly improving. Urban vegetation has become one of the important standards to measure the livability of a city.
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