在喀拉拉邦使用遥感和机器学习方法对土地使用和土地覆盖的时空分类及其变化
1Department of Water Resources & Ocean Engineering, National Institute of Technology Karnataka, Surathkal Mangalore, 575 025, India. anjalivijay21@gmail.com.
Environmental monitoring and assessment
|April 18, 2024
概括
这项研究分析了喀拉拉邦.
科学领域:
- 遥感和地理空间分析
- 环境科学与土地利用规划 规划
背景情况:
- 印度喀拉拉邦拥有充满活力的土地使用历史,但缺乏全面的LULC变化调查.
- 了解LULC演变对于区域规划和环境管理至关重要.
研究的目的:
- 分析喀拉拉邦1990年至2020年的土地使用和土地覆盖 (LULC) 变化.
- 为了比较LULC分类的随机森林 (RF) 和分类和回归树 (CART) 机器学习算法的有效性.
- 评估LULC变化对植被,建筑区,水体和荒地的影响.
主要方法:
- 在谷歌地球引擎 (GEE) 平台上利用了从1990年到2020年的Landsat卫星图像.
- 使用随机森林 (RF) 和分类和回归树 (CART) 来进行LULC分类.
- 纳入规范差异植被指数 (NDVI),规范差异构建指数 (NDBI),修改规范差异水指数 (MNDWI) 和裸土指数 (BSI) 以提高分类准确性.
主要成果:
- 随机森林 (RF) 算法表现出比CART.优越的性能.
- 在过去的三十年中,城市扩张 (158.2%) 和农业面积减少 (15.52%) 显著.
- 增加的NDBI和BSI表明建筑和荒土地的增长,而减少的MNDWI则表明水体的缩小. 由于城市扩张,NDVI变化证实了植被丧失.
结论:
- 该研究强调了喀拉拉邦的快速城市化和农业衰退,需要可持续的土地利用规划.
- 射频算法对复杂地形中的LULC变化检测非常有效.
- 调查结果为决策者,规划者和非政府组织提供了关键数据,以实施可持续的土地利用实践.
关键词:
BSI BSI BSI BSI BSI BSI BSI BSI BSI BSI BSI BSI BSI BSI BSI BSI BSI BSI BSI BSI BSI BSI BSI BSI BSI BSI BSI BSI BSI BSI BSI BSI BSI BSI BSI BSI BSI BSI BSI BSI BSI B分类树和回归树.谷歌的地球引擎.喀拉拉邦 喀拉拉邦 喀拉拉邦土地使用和土地覆盖.在MNDWIWI中,我们可以看到印度国家开发银行 (NDBI)NDVI NDVI 在线阅读随机的森林随机的森林更多相关视频
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