使用集成蜂自动机模型,在布拉曼尼和拜塔尼盆地进行LULC的空间和时间分类和预测
Gorantla Indraja1, Agarwal Aashi1, Vamsi Krishna Vema2
1Department of Civil Engineering, National Institute of Technology Warangal, Warangal, 506004, Telangana, India.
Environmental monitoring and assessment
|January 6, 2024
概括
布拉姆尼和拜塔尼盆地的土地利用变化是由人口增长和靠近建筑区的因素驱动的,而不仅仅是物理因素. 这种快速的城市化导致森林和水覆盖面大幅减少,影响生态系统服务.
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) 是一个地理信息系统.
背景情况:
- 由于气候变化和城市化,土地使用和土地覆盖 (LULC) 动态在全球范围内至关重要.
- LULC的变化对流域水文有很大的影响.
- 之前的研究往往忽略了LULC预测模型中的社会经济和气候因素.
研究的目的:
- 分析物理,社会经济和气候因素对布拉曼尼和拜塔尼 (BB) 盆地LULC预测准确性的影响.
- 使用机器学习算法对最近几年 (2007年,2014年,2021年) 的LULC进行分类.
- 预测BB盆地未来的LULC变化.
主要方法:
- 使用灵敏度分析方法来评估各种驱动因素.
- 在Google地球引擎平台上应用了三个机器学习算法 (随机森林,CART,SVM).
- 历史LULC数据和相关的地理空间数据集被用于分类和预测.
主要成果:
- 随机森林 (RF) 在LULC分类中表现优于CART和SVM,特别是在建筑区.
- 发现,靠近建筑区和人口密度是LULC变化的主要驱动因素,而不是物理因素.
- 建筑面积增加了351% (2007-2021年),而森林和水覆盖面分别减少了12%和36%.
结论:
- 贝贝盆地正在经历快速的城市化,侵占农业和森林土地.
- 未来的LULC预测表明,建筑区的持续扩张和森林覆盖面的下降.
- 这些LULC变化对区域生态系统服务和可持续性产生重大影响.
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