基于机器学习的评估和模拟土地利用变化对季节性和年度土地表面温度变化的影响
Mudassir Khan1, Muhammad Qasim2, Adnan Ahmad Tahir3
1Department of Environmental Sciences, Quaid-i-Azam University, Islamabad, Pakistan.
Heliyon
|December 21, 2023
概括
伊斯兰堡的城市扩张从1990年到2020年显著增加了建筑区和土地表面温度 (LST). 未来的预测表明,LST的持续增长,特别是在未规划的地区,突出了城市绿化的需要.
科学领域:
- 环境科学 环境科学
- 城市规划 城市规划
- 遥感 遥感 遥感 遥感
背景情况:
- 城市迅速扩张会对当地气候和生态系统产生负面影响.
- 伊斯兰堡已经发生了重大的土地使用土地覆盖面 (LULC) 变化,增加了土地表面温度 (LST).
研究的目的:
- 量化计划与非计划城市地区的LULC和LST波动 (1990-2020年).
- 预计未来的LULC和LST变化为2035年和2050年.
- 评估城市发展对温度变化的影响.
主要方法:
- 利用了Landsat数据和机器学习算法,包括支持矢量机器 (SVM).
- 采用混合细胞自动机-马尔科夫 (CA-Markov) 和人工神经网络 (ANN) 模型进行未来预测.
- 分析了不同土地覆盖类型的季节性和年度LST变化.
主要成果:
- 建筑面积增加了约22%,而植被和赤裸的土壤减少了.
- 建筑用地表现出最高的年平均LST,夏季月份显示最高的温度.
- 未来的预测显示,建筑区和高LST区的数量将大幅增加,特别是在未规划区域.
结论:
- 规划中的城市区域由于绿色空间和控制的基础设施,显示出更好的温度调节.
- 建议包括控制建筑区的扩张,促进城市森林以减轻城市热岛 (UHI) 效应.
- 有效的城市规划对于管理LST和缓解快速发展的城市的气候影响至关重要.
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