WorldView-2 Satellite Imagery and Airborne LiDAR Data for Object-Based Forest Species Classification in a Cool Temperate Rainforest Environment

2013 ◽  
pp. 103-122 ◽  
Author(s):  
Zhenyu Zhang ◽  
Xiaoye Liu
Keyword(s):  
Satellite Imagery ◽  
Airborne Lidar ◽  
Lidar Data ◽  
Forest Species ◽  
Species Classification ◽  
Temperate Rainforest ◽  
Object Based ◽  
Cool Temperate ◽  
Airborne Lidar Data

Cool temperate rainforest and adjacent forests classification using airborne LiDAR data

Area ◽  
2011 ◽  
Vol 43 (4) ◽  
pp. 438-448 ◽  
Author(s):  
Zhenyu Zhang ◽  
Xiaoye Liu ◽  
Jim Peterson ◽  
Wendy Wright
Keyword(s):  
Airborne Lidar ◽  
Lidar Data ◽  
Temperate Rainforest ◽  
Cool Temperate ◽  
Airborne Lidar Data

scholarly journals FOREST SPECIES CLASSIFICATION BASED ON THREE-DIMENSIONAL COORDINATE AND INTENSITY INFORMATION OF AIRBORNE LIDAR DATA WITH RANDOM FOREST METHOD

2020 ◽  
Vol XLII-3/W10 ◽  
pp. 117-123
Author(s):  
H. T. You ◽  
P. Lei ◽  
M. S. Li ◽  
F. Q. Ruan
Keyword(s):  
Classification Accuracy ◽  
Three Dimensional ◽  
Airborne Lidar ◽  
Lidar Data ◽  
Forest Species ◽  
Species Classification ◽  
Combined Application ◽  
Intensity Information ◽  
Three Dimensional Coordinate ◽  
Airborne Lidar Data

Abstract. Forest species is a basic parameter of forest ecosystem. The accurate identification of forest species can not only improve the estimation accuracy of other forest structural parameters, but also have important significance for forest resource monitoring and management. As an active remote sensing technology, the LiDAR could not only acquire the three-dimensional coordinate information of the object, but also acquire the intensity information. The airborne LiDAR data have been successfully used in forest species classification research. However, most of the research is based on the three-dimensional coordinate information of LiDAR data. It's fact that the parameters derived from the intensity data are closely related to the spectral reflection of forest species and could be beneficial for forest species classification, but the research with LiDAR intensity data is fewer. Therefore, it is necessary to explore the potential of LiDAR intensity data on forest species classification and test if the combined application of the three-dimensional coordinate and intensity information can improve the forest species classification accuracy. In this paper, the Moon Lake National Forest Park located in Changchun is selected as the study area, which planted with Scotch pine, Larch pine, Mongolian oak, aspen and other tree species. Two kinds of parameters are separately derived from the three-dimensional coordinate and intensity information of airborne LiDAR data. Then Random Forest is used to classify the forest species based on the above parameters. The main purposes of this study are: (1) to test if the parameters derived from the three-dimensional coordinate information of LiDAR data can be used to identify the forest species; (2) to test if the parameters derived from the intensity information of LiDAR data can be used to identify the forest species; (3) to test if the combined application of the three-dimensional coordinate and the intensity information can improve the accuracy of forest tree species identification. It was found that the classification accuracy of forest species based on structural parameters derived from the three-dimensional coordinate information was 87.54% and Kappa coefficient was 0.81. The classification accuracy based on the parameters derived from LiDAR intensity information was 89.23% and Kappa coefficient was 0.83. And the classification accuracy based on three-dimensional coordinate and intensity information was 92.35% and Kappa coefficient was 0.88. The results demonstrated that both the parameters derived from LiDAR three-dimensional coordinate and intensity information can identify forest species. The results based on LiDAR intensity information are better than that of three-dimensional coordinate information. And the combined application of the two information can improve the classification accuracy of forest species. Therefore, further research should make use of the three-dimensional coordinates and intensity information of LiDAR data to improve the accuracy of results.

scholarly journals Hierarchical Geographic Object-Based Vegetation Type Extraction Based on Multi-Source Remote Sensing Data

Forests ◽  
2020 ◽  
Vol 11 (12) ◽  
pp. 1271
Author(s):  
Xuegang Mao ◽  
Yueqing Deng ◽  
Liang Zhu ◽  
Yao Yao
Keyword(s):  
Remote Sensing ◽  
Forest Type ◽  
Vegetation Type ◽  
Remote Sensing Data ◽  
Airborne Lidar ◽  
Lidar Data ◽  
Sensing Data ◽  
Object Based ◽  
Four Levels ◽  
Airborne Lidar Data

Providing vegetation type information with accurate surface distribution is one of the important tasks of remote sensing of the ecological environment. Many studies have explored ecosystem structure information at specific spatial scales based on specific remote sensing data, but it is still rare to extract vegetation information at various landscape levels from a variety of remote sensing data. Based on Gaofen-1 satellite (GF-1) Wide-Field-View (WFV) data (16 m), Ziyuan-3 satellite (ZY-3) and airborne LiDAR data, this study comparatively analyzed the four levels of vegetation information by using the geographic object-based image analysis method (GEOBIA) on the typical natural secondary forest in Northeast China. The four levels of vegetation information include vegetation/non-vegetation (L1), vegetation type (L2), forest type (L3) and canopy and canopy gap (L4). The results showed that vegetation height and density provided by airborne LiDAR data could extract vegetation features and categories more effectively than the spectral information provided by GF-1 and ZY-3 images. Only 0.5 m LiDAR data can extract four levels of vegetation information (L1–L4); and from L1 to L4, the total accuracy of the classification decreased orderly 98%, 93%, 80% and 69%. Comparing with 2.1 m ZY-3, the total classification accuracy of L1, L2 and L3 extracted by 2.1 m LiDAR data increased by 3%, 17% and 43%, respectively. At the vegetation/non-vegetation level, the spatial resolution of data plays a leading role, and the data types used at the vegetation type and forest type level become the main influencing factors. This study will provide reference for data selection and mapping strategies for hierarchical multi-scale vegetation type extraction.

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