印度米佐拉姆的半十年土地使用土地覆盖面变化分析,使用机器学习算法进行地形校正
Priyanka Gupta1, Dericks Praise Shukla2
1DExtER Lab, School of Civil and Environmental Engineering, A-11 Building, North Campus, IIT Mandi, Mandi, Himachal Pradesh, India, 175075.
Environmental science and pollution research international
|April 12, 2024
概括
这项研究利用卫星图像和机器学习绘制了2000年至2020年间印度米佐拉姆的土地利用和土地覆盖面变化. 随机森林分类器在检测从密集森林到开放森林,农业和定居点的转移方面取得了最高的准确性.
科学领域:
- 遥感 遥感 遥感 遥感
- 地理信息系统 (GIS) 是指地理信息系统.
- 环境科学 环境科学
背景情况:
- 印度的生物多样性热点米佐拉姆,由于部落转移农业,经常经历土地使用土地覆盖面 (LULC) 变化.
- 了解这些LULC动态对于该地区的保护和可持续土地管理至关重要.
研究的目的:
- 使用卫星数据生成2000年至2020年米佐拉姆的准确LULC地图.
- 为了评估不同的机器学习算法对LULC分类的性能.
- 评估地形校正方法对分类准确性的影响.
主要方法:
- 陆地卫星5号和8号卫星图像 (2000-2020年) 被分为六个LULC类别:水体,农田,定居点,开放森林,密集森林和裸露土地.
- 四个机器学习算法 (随机森林,CART,最小距离,SVM) 应用并使用80%的训练和20%的测试数据进行评估.
- 使用SCS+C地形校正来减轻影子效应,提高分类准确度.
主要成果:
- 随机森林 (RF) 分类器表现出卓越的性能,平均整体精度 (OA) 为84.00%,卡帕系数 (KC) 为0.79.
- SCS+C地形校正显著改善了结果,减少了错误分类.
- 分析显示,密集森林覆盖面的减少,与2000年至2020年间开放森林,定居点和农业的同时增加.
结论:
- 射频分类器与SCS+C地形校正相结合,为在米佐拉姆准确地绘制LULC提供了强大的方法.
- 观察到的LULC变化凸显了需要有针对性的土地管理战略,以平衡农业需求与森林保护.
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