4.7 Article

Exploring the Optimal Feature Combination of Tree Species Classification by Fusing Multi-Feature and Multi-Temporal Sentinel-2 Data in Changbai Mountain

期刊

FORESTS
卷 13, 期 7, 页码 -

出版社

MDPI
DOI: 10.3390/f13071058

关键词

multi-feature; random forest; topographic factors; tree species classification; spatial distribution

类别

资金

  1. National Natural Science Foundation of China [42171407, 42077242]
  2. Natural Science Foundation of Jilin province [20210101098JC]
  3. Open Fund of Key Laboratory of Urban Land Resources Monitoring and Simulation, Ministry of Natural Resources [KF-2020-05-024]
  4. National Key R&D Program of China [2021YFD1500100]

向作者/读者索取更多资源

Tree species classification is crucial for forest resource investigation and management. In this study, we fused multiple features including spectral, texture, phenological, and topographic features to classify tree species in Changbai Mountain. The results indicate that topographic features play an important role in tree species classification.
Tree species classification is crucial for forest resource investigation and management. Remote sensing images can provide monitoring information on the spatial distribution of tree species and multi-feature fusion can improve the classification accuracy of tree species. However, different features will play their own unique role. Therefore, considering various related factors about the growth of tree species such as spectrum information, texture structure, vegetation phenology, and topography environment, we fused multi-feature and multi-temporal Sentinel-2 data, which combines spectral features with three other types of features. We combined different feature-combinations with the random forest method to classify Changbai Mountain tree species. Results indicate that topographic features participate in tree species classification with higher accuracy and more efficiency than phenological features and texture features, and the elevation factor possesses the highest importance through the Mean Decrease in Gini (MDG) method. Finally, we estimated the area of the target tree species and analyzed the spatial distribution characteristics by overlay analysis of the Classification 3 result and topographic features (elevation, slope, and aspect). Our findings emphasize that topographic factors have a great influence on the distribution of forest resources and provide the basis for forest resource investigation.

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