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Freshly-opened swidden mapping using Support Vector Machine （SVM） and spatial characteristics in Phongsaly Province， Laos

  • role： First author第一作者
  • Affiliation：

    Institute of Geographic Sciences and Natural Resources, Chinese Academy of Sciences, Beijing 100101, China

    College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China

  • Email：lip@igsnrr.ac.cn
  • Introduction：李鹏， 研究方向为资源遥感与边境地理。E-mail： lip@igsnrr.ac.cn
LI Peng12,  
  • Affiliation：

    Institute of Geographic Sciences and Natural Resources, Chinese Academy of Sciences, Beijing 100101, China

    College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China

JIANG Ningsang12,  
  • Affiliation：

    Institute of Geographic Sciences and Natural Resources, Chinese Academy of Sciences, Beijing 100101, China

    College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China

FENG Zhiming12,  
  • Affiliation：

    Institute of Geographic Sciences and Natural Resources, Chinese Academy of Sciences, Beijing 100101, China

XIAO Chiwei1

résumé

Swidden agriculture is a widespread but controversial traditional land-use type in the tropics, especially in mountainous Laos with high percentage of forest cover. Driven by population growth, forestry policies, and climate change, swidden agriculture has been experiencing rapid evolution itself and drastic transformations into commercial plantations, such as rubber plantation. However, the remote sensing monitoring of tropical swidden agriculture has always been challenged, primarily because of the spatial and temporal dynamics in agricultural and forest cover, marginal feature compared with modern agriculture, and fragmentation and random distribution of swidden patches, hence with many unsettled issues and very limited information on its involved population, exact distribution and spatio-temporal dynamics. To explore the application potentials of machine learning algorithms in monitoring swidden agriculture, with two Landsat Operational Land Imager (OLI) images acquired in April, or the peak of the 2016 dry season, a support machine algorithm (SVM) was modified by masking out the information of construction land to improve the classification accuracy, or an overall accuracy of 95% and a Kappa coefficient of 0.81, followed by the examination of spatial (e.g., district-level) differences of freshly-opened swidden in Phongsaly Province, Laos, and their characteristics to local settlements and varied-level roads as well as topographical features. The results showed that: (1) Swidden agriculture remains an important land use type in Phongsaly because the newly-opened swidden was about 987.93 km2 (6.10% of the province) in 2016. More swidden patches were detected in the south and west parts of the province, with a fragmented distribution. (2) The area of newly-opened swiddens at district level ranged between 100—210 km2, with Samphanh District ranking the first (1/5) and Boonneua District the last (1/10). (3) Approximately 90% of newly-opened swiddens were concentrated within five km to residential points, particularly within four km. Similarly, these swiddens exhibited a distance decay pattern along the minor roads, tracks and major roads, in particular within a distance of five km of minor roads. (4) The newly-opened swiddens were mainly distributed in low mountain area (500—1000 m) with slope gradients of 15°—25° and aspects of southeast, showing slight variations among the districts of Phongsaly Province. This study provides reference for exploring machine learning algorithms in remote sensing monitoring of swidden agriculture in transition in the tropics.

mots-clés

swidden agriculture;Support Vector Machine (SVM);Landsat;accessibility analysis;topographic features;Laos

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