Detecting tropical freshly-opened swidden fields using a combined algorithm of continuous change detection and support vector machine
文献类型：期刊论文
| 作者 | Jiang, Ningsang1,2; Li, Peng1,2; Feng, Zhiming1,2 |
| 刊名 | INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION
 |
| 出版日期 | 2025-02-01 |
| 卷号 | 136页码:104403 |
| 关键词 | Swidden agriculture Continuous change detection Support Vector Machine Landsat Active fires Laos |
| ISSN号 | 1569-8432 |
| DOI | 10.1016/j.jag.2025.104403 |
| 产权排序 | 1 |
| 文献子类 | Article |
| 英文摘要 | Swidden agriculture, widely practiced by impoverished ethnic groups, continues to undergo rapid transition and transformation in tropical highlands. Exploring universal approaches for accurate mapping of newly-opened swiddens and fallows of different ages has not yet been stopped. The development of data-, information-, and knowledge-based algorithms for monitoring swidden agriculture requires integration of multi-dimensional features. The first part of the Continuous Change Detection and Classification (CCDC) algorithm holds promising potential in capturing abrupt changes. However, the CCD-derived temporal attributes and other multi-dimension features are seldom utilized to monitor swidden agriculture. Here, a combined algorithm integrating CCD and Support Vector Machine (SVM) is firstly developed to comprehensively highlight fundamental characteristics of swidden agriculture for maximumly and effectively mapping freshly opened swiddens. Local experimental results demonstrate that the CCD-SVM algorithm significantly enhances the performance of SVM in newly-opened swidden identification, with an average accuracy of over 85% (around a 10-20% improvement) under different land cover conditions. Next, CCD-SVM is applied to generate the 2019 map of newly-opened swidden in Laos using Landsat-8 dry-season (February to April) imagery. Comparisons with the same year results obtained from the CCDC-Spectral Mixture Analysis (SMA) show that CCD-SVM (94.69%) outperforms CCDC-SMA (87.52%) primarily due to less commission errors. Features inclusion of terrain and fire greatly improves classification accuracy. Additionally, over 60% of Laotian swiddens cross-validated by the 375-meter Visible Infrared Imaging Radiometer Suite active fires demonstrate CCD-SVM's reliability and fidelity. The integration CCDC with SVM represents a novelty in combining time series analysis and machine learning techniques and helps monitor annual swidden agriculture in the tropics. |
| URL标识 | 查看原文 |
| WOS关键词 | LAND-USE ; AGRICULTURE ; CLASSIFICATION ; FOREST ; SEGMENTATION ; CULTIVATION ; DYNAMICS ; HISTORY ; SYSTEM |
| WOS研究方向 | Remote Sensing |
| 语种 | 英语 |
| WOS记录号 | WOS:001423058400001 |
| 出版者 | ELSEVIER |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/212341]  |
| 专题 | 资源利用与环境修复重点实验室_外文论文 |
| 通讯作者 | Li, Peng |
| 作者单位 | 1.Univ Chinese Acad Sci, Coll Resources & Environm, Beijing 100049, Peoples R China 2.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Beijing 100101, Peoples R China; |
| 推荐引用方式 GB/T 7714 | Jiang, Ningsang,Li, Peng,Feng, Zhiming. Detecting tropical freshly-opened swidden fields using a combined algorithm of continuous change detection and support vector machine[J]. INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION,2025,136:104403. |
| APA | Jiang, Ningsang,Li, Peng,&Feng, Zhiming.(2025).Detecting tropical freshly-opened swidden fields using a combined algorithm of continuous change detection and support vector machine.INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION,136,104403. |
| MLA | Jiang, Ningsang,et al."Detecting tropical freshly-opened swidden fields using a combined algorithm of continuous change detection and support vector machine".INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION 136(2025):104403. |
入库方式： OAI收割
来源：地理科学与资源研究所
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