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[基于土地类型的土地利用变化预测模型的全面比较分析]

Song-Jie Qu¹, Ling Han¹, Liang-Zhi Li¹

¹School of Land Engineering, Chang'an University, Xi'an 710054, China.

Huan jing ke xue= Huanjing kexue

|March 14, 2026

概括

像PLUS这样的土地使用模型在预测土地使用变化方面显示出更高的准确性,这是由生态土地的自然因素和建筑土地的社会经济因素所驱动的. 这项研究有助于可持续的土地管理.

科学领域:

环境科学环境科学
地理信息科学地理信息科学
空间建模空间建模

背景情况:

由于人类活动和气候变化,土地利用变化正在加速,使土地利用建模成为一个关键的研究领域.
了解和预测这些变化对于有效的空间规划和可持续发展至关重要.

研究的目的:

综合比较四种土地使用模型的预测性能:CA-Markov,CLUES,FLUS和PLUS.
用LightGBM模型分析各种驱动因素对土地使用模式的贡献.
评估模型适用性和预测2017年至2022年山西省土地使用变化的差异.

主要方法:

对 CA-Markov,CLUES,FLUS 和 PLUS 模型进行土地使用预测的比较分析.
利用LightGBM模型来识别和量化自然和社会经济驱动因素的影响.
使用2017年至2022年的土地使用数据验证模型准确性,评估整体准确性 (OA) 和卡帕系数.

主要成果:

山西省经历了森林,耕地和建筑用地增加,草原面积大幅减少 (4,085.63平方公里).
自然因素主要影响了生态土地分配,而社会经济因素显著影响了建筑土地的变化.
PLUS模型显示了最高的预测准确性 (OA:0.93,卡帕:0.90),FLUS显示了土地类型中最小的总偏差.

结论:

对于关键的土地类型,PLUS模型提供了卓越的预测准确性和性能,而FLUS模型则表现出最小的偏差.
不同的模型具有不同的空间分布模式 (例如,PLUS碎片化,FLUS基于补丁的扩展) 和不同的操作要求.
这项比较研究为土地利用变化预测提供了关键的方法见解和数据参考,支持可持续的空间管理.

关键词:

轻GBMM 轻GBM 轻GBM 轻GBM 准确性验证验证的准确性土地转让是土地的转让.土地使用变化模型景观模式指数景观模式指数

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