Advancing coastal wetland management: a combined UAV-satellite approach for Spartina alterniflora aboveground biomass estimation using interpretable machine learning
文献类型：期刊论文
| 作者 | Zhang, Xiaotong5; Jia, Mingming1; Yan, Fengqin4; Zhang, Yongbin5; Man, Weidong3,5; Li, Fuping3,5; Liu, Mingyue2,5; Wu, Fenghua5; Yin, Xuan5; Duan, Jihang5 |
| 刊名 | INTERNATIONAL JOURNAL OF DIGITAL EARTH
 |
| 出版日期 | 2025-12-31 |
| 卷号 | 18期号:1页码:2525383 |
| 关键词 | Salt marsh vegetation random forest Shapley additive interpretations space-for-time substitution upscaling |
| ISSN号 | 1753-8947 |
| DOI | 10.1080/17538947.2025.2525383 |
| 产权排序 | 3 |
| 文献子类 | Article |
| 英文摘要 | Aboveground biomass (AGB) is a key indicator for carbon storage and ecological health in wetland ecosystems. Spartina alterniflora (S.alterniflora), an invasive species in coastal wetlands, threatens biodiversity and blue carbon stocks. This process impacts blue carbon stocks in coastal marshes, making their assessment and management during ecological restoration crucial. While satellite remote sensing is useful for large-scale AGB monitoring, its coarse resolution limits accuracy. UAV technology provides high-resolution plot-scale data, complementing satellite imagery for more precise estimates. This study proposes a combined UAV and satellite remote sensing framework for AGB estimation in coastal wetlands. An AGB inversion map for S.alterniflora was developed at the plot scale using UAV imagery and a Random Forest (RF) model, achieving an R-2 of 0.64 and an RMSE of 0.321 kg/m(2). This map was then scaled up to the landscape level by integrating satellite imagery, with an R-2 of 0.68 and an RMSE of 0.300 kg/m(2). SHAP analysis was conducted to interpret the model, and the statistical analysis of AGB under different invasion stages was performed using long-term classification data and space-for-time substitution. This approach highlights the potential of combining UAV and satellite data for large-scale, accurate AGB estimation to inform wetland conservation and management efforts. |
| URL标识 | 查看原文 |
| WOS关键词 | CHLOROPHYLL ; INVERSION ; VARIABLES ; FORESTS ; MODEL ; LEAF |
| WOS研究方向 | Physical Geography ; Remote Sensing |
| 语种 | 英语 |
| WOS记录号 | WOS:001522214100001 |
| 出版者 | TAYLOR & FRANCIS LTD |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/215301]  |
| 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 | Liu, Mingyue |
| 作者单位 | 1.Chinese Acad Sci, Northeast Inst Geog & Agroecol, Key Lab Wetland Ecol & Environm, Changchun, Peoples R China; 2.Collaborat Innovat Ctr Green Dev & Ecol Restorat M, Tangshan, Peoples R China 3.Hebei Ind Technol Inst Mine Ecol Remediat, Tangshan, Peoples R China; 4.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing, Peoples R China; 5.North China Univ Sci & Technol, Coll Min Engn, Tangshan 063210, Peoples R China; |
| 推荐引用方式 GB/T 7714 | Zhang, Xiaotong,Jia, Mingming,Yan, Fengqin,et al. Advancing coastal wetland management: a combined UAV-satellite approach for Spartina alterniflora aboveground biomass estimation using interpretable machine learning[J]. INTERNATIONAL JOURNAL OF DIGITAL EARTH,2025,18(1):2525383. |
| APA | Zhang, Xiaotong.,Jia, Mingming.,Yan, Fengqin.,Zhang, Yongbin.,Man, Weidong.,...&Duan, Jihang.(2025).Advancing coastal wetland management: a combined UAV-satellite approach for Spartina alterniflora aboveground biomass estimation using interpretable machine learning.INTERNATIONAL JOURNAL OF DIGITAL EARTH,18(1),2525383. |
| MLA | Zhang, Xiaotong,et al."Advancing coastal wetland management: a combined UAV-satellite approach for Spartina alterniflora aboveground biomass estimation using interpretable machine learning".INTERNATIONAL JOURNAL OF DIGITAL EARTH 18.1(2025):2525383. |
入库方式： OAI收割
来源：地理科学与资源研究所
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