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Implementing a growth pattern of climax community as post-processing filters core to improve tree species classification accuracy

文献类型： 外文期刊

作者： Zhang, Biao 1 ; Dong, Liguo 3 ; Wang, Zhichao 1 ; Feng, Zebang 4 ; Shao, Yakui 1 ; Ma, Tiantian 1 ; Chen, Xuanchi 5 ; Li, Hao 1 ; Wang, Bin 6 ; Feng, Zhongke 1 ;

作者机构： 1.Beijing Forestry Univ, Precis Forestry Key Lab Beijing, Beijing 100083, Peoples R China

2.Hainan Univ, Hainan, Peoples R China

3.Ningxia Acad Agr & Forestry, Ningxia, Peoples R China

4.NavInfo Co Ltd, Beijing, Peoples R China

5.Zhengzhou Univ, Zhengzhou, Peoples R China

6.Tianjin Beidagang Wetland Nat Reserve Management C, Tianjin, Peoples R China

关键词： Tree species classification; filter; sentinel-2; post-processing algorithm; timelines and spatial neighboring

期刊名称：INTERNATIONAL JOURNAL OF DIGITAL EARTH （ 影响因子：4.9； 五年影响因子：4.7 ）

ISSN： 1753-8947

年卷期： 2025 年 18 卷 1 期

页码：

收录情况： SCI

摘要： Improving tree species classification accuracy often involves complex workflows, constrained by high computational costs, extensive data requirements, and sensitivity to spatiotemporal variations. This study introduces the Change Resistance Filter (CR-Filter), inspired by the stable growth patterns of the Climax Community. The CR-Filter, applied as a post-processing tool, integrates Change Resistance on Timelines and Change Resistance on Spatial Neighboring into a unified framework, enhancing classification precision by mitigating spatiotemporal fluctuations. Liupan Mountain Nature Reserve was selected as the study area for its ecological stability. Multi-temporal Sentinel-2 data spanning several years were used to extract and correct phenological indices, which were combined with Sentinel-1 and terrain data to generate interannual tree species classification maps. These maps were subsequently refined using the CR-Filter. Compared to traditional methods, robustness in highly heterogeneous regions was improved by leveraging interannual map integration, yielding species distribution maps with greater spatial consistency and temporal stability. Overall accuracy increased by 8.44%, from 85.85% to 93.10%, effectively reducing misclassification from noise or transient changes. This approach highlights the CR-Filter's efficacy with limited samples and medium-to-low resolution, providing strong technical support for remote sensing-based species mapping and ecological research.

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