Detecting tropical freshly-opened swidden fields using a combined algorithm of continuous change detection and support vector machine
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...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-02-01
|
| Series: | International Journal of Applied Earth Observations and Geoinformation |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1569843225000500 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1825206919812349952 |
|---|---|
| author | Ningsang Jiang Peng Li Zhiming Feng |
| author_facet | Ningsang Jiang Peng Li Zhiming Feng |
| author_sort | Ningsang Jiang |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-04c53cdfac0843bc888de8cb5647d509 |
| institution | Kabale University |
| issn | 1569-8432 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Elsevier |
| record_format | Article |
| series | International Journal of Applied Earth Observations and Geoinformation |
| spelling | doaj-art-04c53cdfac0843bc888de8cb5647d5092025-02-07T04:47:20ZengElsevierInternational Journal of Applied Earth Observations and Geoinformation1569-84322025-02-01136104403Detecting tropical freshly-opened swidden fields using a combined algorithm of continuous change detection and support vector machineNingsang Jiang0Peng Li1Zhiming Feng2Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101 China; College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049 ChinaInstitute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101 China; College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049 China; Corresponding author.Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101 China; College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049 ChinaSwidden 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.http://www.sciencedirect.com/science/article/pii/S1569843225000500Swidden agricultureContinuous change detectionSupport Vector MachineLandsatActive firesLaos |
| spellingShingle | Ningsang Jiang Peng Li Zhiming Feng Detecting tropical freshly-opened swidden fields using a combined algorithm of continuous change detection and support vector machine International Journal of Applied Earth Observations and Geoinformation Swidden agriculture Continuous change detection Support Vector Machine Landsat Active fires Laos |
| title | Detecting tropical freshly-opened swidden fields using a combined algorithm of continuous change detection and support vector machine |
| title_full | Detecting tropical freshly-opened swidden fields using a combined algorithm of continuous change detection and support vector machine |
| title_fullStr | Detecting tropical freshly-opened swidden fields using a combined algorithm of continuous change detection and support vector machine |
| title_full_unstemmed | Detecting tropical freshly-opened swidden fields using a combined algorithm of continuous change detection and support vector machine |
| title_short | Detecting tropical freshly-opened swidden fields using a combined algorithm of continuous change detection and support vector machine |
| title_sort | detecting tropical freshly opened swidden fields using a combined algorithm of continuous change detection and support vector machine |
| topic | Swidden agriculture Continuous change detection Support Vector Machine Landsat Active fires Laos |
| url | http://www.sciencedirect.com/science/article/pii/S1569843225000500 |
| work_keys_str_mv | AT ningsangjiang detectingtropicalfreshlyopenedswiddenfieldsusingacombinedalgorithmofcontinuouschangedetectionandsupportvectormachine AT pengli detectingtropicalfreshlyopenedswiddenfieldsusingacombinedalgorithmofcontinuouschangedetectionandsupportvectormachine AT zhimingfeng detectingtropicalfreshlyopenedswiddenfieldsusingacombinedalgorithmofcontinuouschangedetectionandsupportvectormachine |