基于支持向量机的老挝丰沙里省新开辟刀耕火种遥感监测及其空间特征

李鹏; 蒋宁桑; 封志明; 肖池伟

doi:10.11834/jrs.20211113

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基于支持向量机的老挝丰沙里省新开辟刀耕火种遥感监测及其空间特征
Freshly-opened swidden mapping using Support Vector Machine （SVM） and spatial characteristics in Phongsaly Province， Laos
2022年26卷第11期页码：2329-2343
纸质出版日期： 2022-11-07 ，
DOI： 10.11834/jrs.20211113

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李鹏，蒋宁桑，封志明，肖池伟.2022.基于支持向量机的老挝丰沙里省新开辟刀耕火种遥感监测及其空间特征.遥感学报，26（11）： 2329-2343

Li P，Jiang N S， Feng Z M and Xiao C W. 2022. Freshly-opened swidden mapping using Support Vector Machine （SVM） and spatial characteristics in Phongsaly Province， Laos. National Remote Sensing Bulletin， 26（11）：2329-2343
李鹏，蒋宁桑，封志明，肖池伟.2022.基于支持向量机的老挝丰沙里省新开辟刀耕火种遥感监测及其空间特征.遥感学报，26（11）： 2329-2343 DOI： 10.11834/jrs.20211113.

Li P，Jiang N S， Feng Z M and Xiao C W. 2022. Freshly-opened swidden mapping using Support Vector Machine （SVM） and spatial characteristics in Phongsaly Province， Laos. National Remote Sensing Bulletin， 26（11）：2329-2343 DOI： 10.11834/jrs.20211113.

摘要

刀耕火种农业是热带广泛存在且备受争议的传统土地利用类型，在多山且森林覆盖率高的老挝尤甚。人口增长、林业政策与气候变化等正在加剧刀耕火种农业自身演变及其向商品化种植园（如橡胶林）转变。因农林时空转变动态性、相较于现代农业的边缘性及其斑块破碎且分布随机等，热带刀耕火种农业遥感监测历来受到挑战，有关其从业人口、确切分布及其时空动态等问题仍悬而未决，且资料匮乏。为探究机器学习算法在刀耕火种监测中的应用潜力，利用2016年旱季（4月）两景Landsat OLI 影像，基于支持向量机算法，并通过消除建设用地噪声以提高新开辟刀耕火种提取精度（总体精度95%，Kappa系数0.81），据此揭示了老挝丰沙里省新开辟刀耕火种县域差异、与居民点‒道路距离以及地形特征。结果表明：（1）当年新开辟刀耕火种约为987.93 km

（全省占比6.10%），刀耕火种仍是该省重要土地利用类型，斑块南多北少、西多东少且呈破碎化分布。（2）各县新开辟刀耕火种面积介于100—210 km

，桑潘县最大（占该省面积的1/5），本讷县最小（1/10）。（3）近九成新开辟刀耕火种集中分布在距居民点5 km范围内，且距不同等级道路（次要公路

山路

主要公路）表现出距离衰减规律，次要公路两侧5 km内尤甚。（4）新开辟刀耕火种常见于低山（500—1000 m）斜坡（15°—25°）地带并以东南坡为主，县域变化差异小。本研究可为探索机器学习算法在热带刀耕火种演变遥感监测提供借鉴。

Abstract

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 km

(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 km

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.

关键词

刀耕火种支持向量机Landsat可达性分析地形特征老挝

Keywords

swidden agricultureSupport Vector Machine (SVM)Landsataccessibility analysistopographic featuresLaos

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