Detecting tropical freshly-opened swidden fields using a combined algorithm of continuous change detection and support vector machine

• A Landsat CCD-SVM combined algorithm is developed to identify newly opened swiddens. • Integrating multidimensional features reduces erroneous estimates in swidden mapping. • Landsat CCD-derived temporal features are critical for mapping newly opened swiddens. • Support Vector Machine effectively handles multidimensional features of swiddening. • Over 60% of CCD-SVM derived newly-open swiddens are validated by VIIRS active fires. 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.