Integrating eco-biogeographical knowledge into deep learning: An approach for fine-grained mountain vegetation classification
文献类型：期刊论文
| 作者 | Yao, Yonghui2; Liu, Yulian1,2 |
| 刊名 | REMOTE SENSING OF ENVIRONMENT
 |
| 出版日期 | 2026-05-15 |
| 卷号 | 338页码:115346 |
| 关键词 | Deep learning Eco-biogeographical knowledge Mountain vegetation knowledge graph Similarity calculation Vegetation formation classification |
| ISSN号 | 0034-4257 |
| DOI | 10.1016/j.rse.2026.115346 |
| 产权排序 | 1 |
| 文献子类 | Article |
| 英文摘要 | Fine-grained mountain vegetation classification remains a critical challenge in remote sensing due to spectral similarities among vegetation canopies and the complexity of mountainous ecosystems. Although deep learning (DL) has demonstrated superior performance in generic land cover classification, its application to vegetation formation classification suffers from three inherent limitations: (1) poor generalizability across heterogeneous terrains, (2) constrained accuracy when handling ecologically similar taxa, and (3) lack of interpretability due to the black-box nature. To address these challenges, this study develops a DL vegetation classification framework enhanced by elevation-dependent eco-biogeographical knowledge computation. The framework comprises three key steps: (1) Extracting computable knowledge rules from the Mountain Vegetation Knowledge Graph; (2) Designing an eco-biogeography-informed loss function to integrate these rules to train the DL model, thereby enhancing its interpretability and generalizability; (3) Performing post-classification refinement through ecobiogeographical knowledge-based similarity calculations, which further improves model accuracy. Experimental results with FCN8s-ResNet50 show significant improvements, achieving 77%-87% precision, 70%-88% recall, 74%-92% F1-score, and 82% overall accuracy for the training model. The framework achieves an additional 21.67% accuracy boost (from 69.84% of Exp.1 to 91.51% of Exp.4) for vegetation formation classification. This approach systematically enhances the accuracy, interpretability, and generalizability of vegetation classification models. Additionally, it provides an efficient framework for automated vegetation mapping and monitoring. |
| URL标识 | 查看原文 |
| WOS关键词 | LAND-USE CHANGE ; CLIMATE ; CHINA ; MAPS ; CO2 |
| WOS研究方向 | Environmental Sciences & Ecology ; Remote Sensing ; Imaging Science & Photographic Technology |
| 语种 | 英语 |
| WOS记录号 | WOS:001717880900001 |
| 出版者 | ELSEVIER SCIENCE INC |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/221379]  |
| 专题 | 资源与环境信息系统国家重点实验室_外文论文 |
| 通讯作者 | Yao, Yonghui |
| 作者单位 | 1.Univ Chinese Acad Sci, Beijing 100049, Peoples R China 2.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China; |
| 推荐引用方式 GB/T 7714 | Yao, Yonghui,Liu, Yulian. Integrating eco-biogeographical knowledge into deep learning: An approach for fine-grained mountain vegetation classification[J]. REMOTE SENSING OF ENVIRONMENT,2026,338:115346. |
| APA | Yao, Yonghui,&Liu, Yulian.(2026).Integrating eco-biogeographical knowledge into deep learning: An approach for fine-grained mountain vegetation classification.REMOTE SENSING OF ENVIRONMENT,338,115346. |
| MLA | Yao, Yonghui,et al."Integrating eco-biogeographical knowledge into deep learning: An approach for fine-grained mountain vegetation classification".REMOTE SENSING OF ENVIRONMENT 338(2026):115346. |
入库方式： OAI收割
来源：地理科学与资源研究所
浏览0
下载0
收藏0
其他版本
除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。