Landsat approaches to map agro-pastoral farming in the wetlands of southern Sudan
Landsat approaches to map agro-pastoral farming in the wetlands of southern Sudan
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DOI：
10.1080/01431161.2017.1392634
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发表时间：
2018-02
影响因子：
3.4
通讯作者：
E. Prins
中科院分区：
文献类型：
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作者：
E. Prins
摘要在卫星遥感(SRS)领域，机器学习算法(MLA)正在走向成熟。这项研究比较了几个工作重点与更传统的指数和算法的表现，以绘制苏丹南部年度农牧耕作活动图。对2014年和2015年旱季早期的两幅陆地卫星图像进行了深入分析，并通过谷歌地球(GE)上甚高分辨率(VHR)图像的农业覆盖解译进行了评估。与使用蓝色和短波红外(SWIR)波段相比，用于监测牧场的基于红色和近红外(NIR)波段的传统SRS指数在潮湿的牧场条件下表现不佳。物种分布模型程序MaxEnt被用来生成仅使用Landsat衍生变量的连续耕作活动指数。与其他SRS分类方法相比，最大熵(MaxEnt)在绘制耕作活动图方面表现出最好的综合性能，其次是分类树分析(CTA)。大多数方法的总体测绘协议>；95.0%，其中MaxEnt显示两年的测绘协议非常高(≥98.5%)。当MaxEnt的良好表现综合在2014-15年或1999-2002年的变化检测情景中时，它证实了关于苏丹南部联合州发生的大规模侵犯人权行为的地面报告。
ABSTRACT Machine-learning algorithms (MLA) are coming of age within satellite remote sensing (SRS). This study compares the performance of a number of MLAs with more traditional indices and algorithms to map annual agro-pastoralist farming activity in southern Sudan. Two Landsat images from the early dry season 2014 and 2015 were analysed thoroughly and evaluated by interpretation of farming cover from very high resolution (VHR) images on Google Earth (GE). Traditional SRS indices based upon red and near infrared (NIR) bands used for monitoring rangelands did not perform well for the wet rangeland conditions compared to the use of blue and shortwave infrared (SWIR) bands. The species distribution model programme, MaxEnt, was used to produce a continuous farming activity indices using only Landsat-derived variables. Compared to other SRS classification approaches, maximum entropy (MaxEnt) showed the best overall performance to map farming activity followed by classification tree analysis (CTA). Overall mapping agreement >95.0% was reached for most methodologies, with MaxEnt showing very high mapping agreement (≥98.5%) for both years. When the result of MaxEnt’s good performance is put together in a 2014–15 or a 1999–2002 change detection scenario, it corroborates ground reports on massive human abuses that have taken place in Unity state of southern Sudan.