Abstract
The spatial distribution of forest stands is one of the fundamental properties of forests. Timely and accurately obtained stand distribution can help people better understand, manage, and utilize forests. The development of remote sensing technology has made it possible to map the distribution of tree species in a timely and accurate manner. At present, a large amount of remote sensing data have been accumulated, including high-spatial-resolution images, time-series images, light detection and ranging (LiDAR) data, etc. However, these data have not been fully utilized. To accurately identify the tree species of forest stands, various and complementary data need to be synthesized for classification. A curve matching based method called the fusion of spectral image and point data (FSP) algorithm was developed to fuse high-spatial-resolution images, time-series images, and LiDAR data for forest stand classification. In this method, the multispectral Sentinel-2 image and high-spatial-resolution aerial images were first fused. Then, the fused images were segmented to derive forest stands, which are the basic unit for classification. To extract features from forest stands, the gray histogram of each band was extracted from the aerial images. The average reflectance in each stand was calculated and stacked for the time-series images. The profile curve of forest structure was generated from the LiDAR data. Finally, the features of forest stands were compared with training samples using curve matching methods to derive the tree species. The developed method was tested in a forest farm to classify 11 tree species. The average accuracy of the FSP method for ten performances was between 0.900 and 0.913, and the maximum accuracy was 0.945. The experiments demonstrate that the FSP method is more accurate and stable than traditional machine learning classification methods.
Highlights
IntroductionAn important type of land cover and a key part of ecosystems, have a decisive influence on maintaining carbon dioxide balance, biodiversity, and ecological balance
A high-resolution aerial image was fused with a single-time Sentinel-2 image, and the forest stand was obtained by image was fused with a single-time Sentinel-2 image, and the forest stand was obtained the fractal net evolution approach (FNEA) segmentation
This paper proposed an FSP method to synthesize high-spatial-resolution multispectral images, time-series images, and light detection and ranging (LiDAR) data
Summary
An important type of land cover and a key part of ecosystems, have a decisive influence on maintaining carbon dioxide balance, biodiversity, and ecological balance. According to the report by the Food and Agriculture Organization (FAO) of the United Nations, forest ecosystems cover approximately one-third of the earth’s land surface [1]. The composition and spatial distribution of forest tree species have a great impact on the forest ecological environment, biodiversity, resource utilization efficiency, production and carbon storage capacity, and nutrition cycle [2,3,4,5,6,7,8]. The basic unit for the forest inventory is the forest stands, which is a large forested area of homogeneous tree species composition [9]
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