Author(s)

Herman R. Mason

Affiliation(s)

Department of Environmental Science and Policy, University of California, Davis.

Corresponding Author

Herman R. Mason

ABSTRACT

Tree species classification is an important application field in forestry remote sensing. It has widespread application scenarios in the fields of sustainable forestry management, biodiversity research and ecological environment protection. Combining the research results in this field after 2000 and the application practice in the forestry production process in recent years, the application of multi-source data in tree species classification is summarized. From the perspectives of workflow and corresponding algorithms, this problem was analyzed and compared based on image classification and mathematical statistics. Facing the problems and challenges encountered in the application of forestry remote sensing in tree species classification, different working ideas based on semantic segmentation and instance segmentation were proposed, and the development prospects of future multi-source remote sensing data fusion acquisition and hardware processing equipment were prospected.

KEYWORDS

Forestry remote sensing; Tree species classification; Lidar data; Hyperspectral data; Theoretical application

CITE THIS PAPER

Herman R. Mason. Application analysis and prospects of tree species classification based on forestry remote sensing. Frontiers in Environmental Research. 2024, 2(1): 1-4. DOI: 10.61784/fer240113.

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