机器学习和空间时间分析用于评估数十亿树木植林项目的生态影响
Kaleem Mehmood1,2,3, Shoaib Ahmad Anees4, Sultan Muhammad3
1College of Forestry Beijing Forestry University Beijing China.
Ecology and evolution
|February 20, 2025
概括
巴基斯坦 巴基斯坦 巴基斯坦.
科学领域:
- 环境科学 环境科学
- 遥感 遥感 遥感 遥感
- 机器学习 机器学习
背景情况:
- 巴基斯坦Khyber Pakhtunkhwa (KPK) 省的十亿树林项目 (BTAP) 旨在增加森林覆盖面.
- 评估项目的成功对于可持续的环境管理至关重要.
研究的目的:
- 评估巴基斯坦Khyber Pakhtunkhwa (KPK) 的十亿树林项目 (BTAP) 的有效性.
- 用遥感和机器学习技术分析树木覆盖和荒地变化.
- 确定影响植被生长的关键环境驱动因素.
主要方法:
- 随机森林 (RF) 分类适用于Sentinel-2卫星图像从2015年到2023年.
- 热点和空间聚类分析以确定植被恢复模式.
- 使用人工神经网络 (ANN) 和SHAP分析开发了标准化差异植被指数 (NDVI) 的预测模型.
主要成果:
- 树木覆盖率从25.02%增加到29.99%,而贫土地在2015年至2023年期间从20.64%减少到16.81%,分类准确度超过85%.
- 观察到显著的植被恢复,高信心的恢复热点从36.76%增加到42.56%.
- 土壤水分和降水被确定为植被生长的主要驱动因素,ANN模型实现了0.8556.6的R2.
结论:
- 机器学习和遥感的整合为评估植树项目提供了一个强大的框架.
- 在增加树木覆盖和促进KPK的植被恢复方面,BTAP已经证明了其有效性.
- 这项研究的数据驱动洞察力可以为未来的可持续环境管理和植树战略提供信息.
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