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评估机器学习算法的性能,用于分析伊朗赫尔卡尼亚森林的土地使用变化

Zeinab Aminzadeh¹, Abazar Esmali Ouri², Raoof Mostafazadeh³

¹Department of Natural Resources, Faculty of Agriculture and Natural Resources, University of Mohaghegh Ardabili, Ardabil, Iran.

Environmental science and pollution research international

|November 30, 2024

概括

机器学习算法有效地分析了伊朗东北部的土地使用变化. 随机森林和支持矢量机模型在预测从牧场转向农业和森林衰退方面表现出很高的准确性.

科学领域:

环境科学环境科学
遥感遥感遥感遥感
地理信息系统地理信息系统

背景情况:

土地利用变化对于环境可持续性和资源管理至关重要.
机器学习算法 (MLA) 为分析复杂的土地使用模式提供了先进的功能.
了解历史的土地使用动态对于未来的规划至关重要.

研究的目的:

评估各种立法机关在分析伊朗东北部土地使用土地覆盖面 (LULC) 变化方面的效率.
在27年的时间内确定最准确的LULC变化检测的MLA.
在研究区域量化LULC轮班及其驱动因素.

主要方法:

利用谷歌地球引擎 (GEE) 来处理Landsat卫星图像从1994年到2021年.
应用了多个MLA,包括支持矢量机 (SVM),K-最近邻居 (KNN),分类和回归树 (CART),天真湾和随机森林.
使用ArcGIS 10.8软件进行了分类后分析.

主要成果:

随机森林在LULC变化分析中普遍优于其他算法.
支持矢量机 (SVM) 在2014年和2021年实现了最高准确率 (85%).
观察到牧场 (34.04%) 和森林面积的显著减少,农业用地大幅增加 (高达275.83%).

结论:

法律法官是监测和分析LULC动态的有效工具.
该研究强调了伊朗东北部的农业和远离牧场和森林的重大转变.
这些发现需要明智的土地利用规划和保护策略,以应对观察到的环境变化.

关键词:

环境变化环境变化土地使用分类土地使用分类土地利用转型土地使用转型景观变化变化的景观变化随机的森林随机的森林

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