使用人工神经网络和蜂自动机马尔科夫链算法对土地使用/土地覆盖动态进行建模,位于埃塞俄比亚的戈安流域的马尔科夫链算法
Getahun Sisay1, Berehan Gesesse2, Christine Fürst3,4
1Department of Geography and Environmental Studies, University of Gondar, P. O. Box 196, Gondar, Ethiopia.
Heliyon
|October 9, 2023
概括
戈安流域的土地利用变化显示了农田和建筑区的大幅增加,影响了自然景观. 迫切需要进行保护,以确保生态系统健康和可持续发展.
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) 是指地理信息系统.
背景情况:
- 土地使用/土地覆盖 (LULC) 的变化影响气候,生物多样性和生态系统服务,阻碍了可持续发展.
- 了解LULC历史和未来的动态对于有效的自然资源管理至关重要.
研究的目的:
- 为了建模和描述1984年至2060年高安流域的时空景观变化轨迹.
- 评估历史的LULC趋势,并模拟未来的景观动态在一个商业常规场景下.
主要方法:
- 采用混合方法,将卫星图像光谱识别和支持矢量机 (SVM) 结合起来,将LULC分为七个类别.
- 在TerrSet软件中使用土地变化建模器 (LCM) 进行历史趋势分析和未来LULC模拟.
- 使用多层感知器 (MLP) 神经网络算法生成过渡潜力图,以预测未来的景观变化.
主要成果:
- 戈安流域经历了显著的LULC变化,在1984年至2022年期间,农田 (64.3公里2/年) 和建筑面积 (2公里2/年) 大幅增加.
- 农田和建筑区的扩张主要是以牺牲森林,灌木和草地覆盖为代价.
- 未来的预测 (2022-2060年) 表明,农田扩张将继续,森林,灌木和草原面积将迅速减少.
结论:
- 农田和建筑区的快速扩张对高流域的生物多样性和生态系统服务构成重大威胁.
- 实施环保的保护战略对于维护生态系统健康和促进可持续发展至关重要.
- 需要积极地进行土地管理,以减轻LULC变化的负面影响,并确保长期生态平衡.
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