A Comparison of Six Forest Mapping Products in Southeast Asia, Aided by Field Validation Data
文献类型：期刊论文
| 作者 | Liu, Bin1,2; Yang, Xiaomei1,2; Wang, Zhihua1,2; Ding, Yaxin1,2; Zhang, Junyao1,2; Meng, Dan1,2 |
| 刊名 | REMOTE SENSING
 |
| 出版日期 | 2023-09-01 |
| 卷号 | 15期号:18页码:26 |
| 关键词 | forest mapping products FROM-GLC10 ESA WorldCover 10 m 2020 ESRI 2020 Land Cover Hansen TreeCover2010 GFC30_2020 FNF spatial consistency field validation accuracy assessment |
| DOI | 10.3390/rs15184584 |
| 通讯作者 | Wang, Zhihua(zhwang@lreis.ac.cn) |
| 英文摘要 | Currently, many globally accessible forest mapping products can be utilized to monitor and assess the status of and changes in forests. However, substantial disparities exist among these products due to variations in forest definitions, classification methods, and remote sensing data sources. This becomes particularly conspicuous in regions characterized by significant deforestation, like Southeast Asia, where forest mapping uncertainty is more pronounced, presenting users with challenges in selecting appropriate datasets across diverse regions. Moreover, this situation impedes the further enhancement of accuracy for forest mapping products. The aim of this research is to assess the consistency and accuracy of six recently produced forest mapping products in Southeast Asia. These products include three 10 m land cover products (Finer Resolution Observation and Monitoring Global LC (FROM-GLC10), ESA WorldCover 10 m 2020 (ESA2020), and ESRI 2020 Land Cover (ESRI2020)) and three forest thematic mapping products (Global PALSAR-2 Forest/Non-Forest map (JAXA FNF2020), global 30 m spatial distribution of forest cover in 2020 (GFC30_2020), and Generated_Hansen2020, which was synthesized based on Hansen TreeCover2010 (Hansen2010) and Hansen Global Forest Change (Hansen GFC) for the year 2020). Firstly, the research compared the area and spatial consistency. Next, accuracy was assessed using field validation points and manual densification points. Finally, the research analyzed the geographical environmental and biophysical factors influencing consistency. The results show that ESRI2020 had the highest overall accuracy for forest, followed by ESA2020, FROM-GLC10, and Generated_Hansen2020. Regions with elevations ranging from 200 to 3000 m and slopes below 15 degrees or above 25 degrees showed high spatial consistency, whereas other regions showed low consistency. Inconsistent regions showed complex landscapes heavily influenced by human activities; these regions are prone to being confused with shrubs and cropland and are also impacted by rubber and oil palm plantations, significantly affecting the accuracy of forest mapping. Based on the research findings, ESRI2020 is recommended for mountainous areas and abundant forest regions. However, in areas significantly affected by human activities, such as forest and non-forest edges and mixed areas of plantations and natural forests, caution should be taken with product selection. The research has identified areas of forest inconsistency that require attention in future forest mapping. To enhance our understanding of forest mapping and generate high-precision forest cover maps, it is recommended to incorporate multi-source data, subdivide forest types, and increase the number of sample points. |
| WOS关键词 | RUBBER PLANTATION ; ESTIMATING AREA ; TIME-SERIES ; ACCURACY ; COVER ; DYNAMICS ; LANDSAT |
| 资助项目 | We thank the anonymous reviewers for improving the quality of the paper. We would also like to thank the other project team members who provided help: Yueming Liu, Xiaoliang Liu, Ku Gao, Guo Yu, QingyangZhang, Junmei Kang, and Jun Wang. |
| WOS研究方向 | Environmental Sciences & Ecology ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology |
| 语种 | 英语 |
| WOS记录号 | WOS:001075863800001 |
| 出版者 | MDPI |
| 资助机构 | We thank the anonymous reviewers for improving the quality of the paper. We would also like to thank the other project team members who provided help: Yueming Liu, Xiaoliang Liu, Ku Gao, Guo Yu, QingyangZhang, Junmei Kang, and Jun Wang. |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/198383]  |
| 专题 | 中国科学院地理科学与资源研究所 |
| 通讯作者 | Wang, Zhihua |
| 作者单位 | 1.Chinese Acad Sci, State Key Lab Resources & Environm Informat Syst, Inst Geog Sci & Nat Resources Res, Beijing 100101, Peoples R China 2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China |
| 推荐引用方式 GB/T 7714 | Liu, Bin,Yang, Xiaomei,Wang, Zhihua,et al. A Comparison of Six Forest Mapping Products in Southeast Asia, Aided by Field Validation Data[J]. REMOTE SENSING,2023,15(18):26. |
| APA | Liu, Bin,Yang, Xiaomei,Wang, Zhihua,Ding, Yaxin,Zhang, Junyao,&Meng, Dan.(2023).A Comparison of Six Forest Mapping Products in Southeast Asia, Aided by Field Validation Data.REMOTE SENSING,15(18),26. |
| MLA | Liu, Bin,et al."A Comparison of Six Forest Mapping Products in Southeast Asia, Aided by Field Validation Data".REMOTE SENSING 15.18(2023):26. |
入库方式： OAI收割
来源：地理科学与资源研究所
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