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使用机器学习算法对发展中国家的土地利用/土地覆盖变化的定量评估:伊拉克库尔德斯坦地区的案例研究

Abdulqadeer Rash^1,2, Yaseen Mustafa³, Rahel Hamad^1,2

¹Dept. of Petroleum Geosciences, Faculty of Science, Soran University, 44008, Soran, Erbil, Iraq.

|November 13, 2023

概括

本研究使用Landsat图像评估了库尔德斯坦土地使用/土地覆盖面变化检测的机器学习算法. 随机森林 (RF) 证明是最准确的,揭示了三十年来土地覆盖面的重大变化.

科学领域:

环境科学环境科学
遥感遥感遥感遥感
地理信息系统 (GIS) 是指地理信息系统.

背景情况:

土地使用/土地覆盖 (LULC) 变化监测对于自然资源管理至关重要.
在库尔德斯坦地区,有限的研究评估了用于LULC分类和变化检测的机器学习算法 (MLA).
这项研究通过分析三十年来LULC动态来解决这个差距.

研究的目的:

从1991年到2021年,监测和分析库尔德斯坦地区的LULC变化.
为了评估五种不同的机器学习算法对LULC分类的有效性.
确定LULC变化的关键驱动因素和模式.

主要方法:

利用了从1991年到2021年的Landsat多时间图像.
应用并比较了五种机器学习算法:支持矢量机器 (SVM),随机森林 (RF),人工神经网络 (ANN),K-最近邻居 (KNN) 和极端梯度增强 (XGBoost).
进行了定量变化检测分析,以评估LULC转移.

主要成果:

随机森林 (RF) 算法证明了LULC分类的最高准确性 (卡帕系数0.93-0.97).
观察到显著的LULC变化:牧场和荒地减少,而农业用地,森林和建筑面积增加.
具体变化包括牧场减少11.33%,荒地减少6.68%,农田增加13.54%,森林增加3.43%,建筑区增加0.71%.

结论:

随机森林是库尔德斯坦地区LULC变化检测的高效算法.
该研究强调了由社会经济变化带来的土地覆盖面的重大变化.
结果为发展中国家的可持续土地利用规划和环境保护提供了宝贵的见解.

关键词:

变更检测检测改变的检测.这是Landsat卫星的图像.遥感是一种远程传感.社会经济学社会经济学监督的分类是监督的分类.

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