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21世纪瑞士的高分辨率土地使用/覆盖预测
Luca Bütikofer1,2, Antoine Adde3, Davnah Urbach4,5
1Centre alpien de phytogéographie CAP, Fondation Aubert, Route de l'Adray 27, CH-1938, Champex-Lac, Switzerland. luca.butikofer@unil.ch.
Scientific data
|February 23, 2024
概括
用先进的神经网络和CLUE-S算法预测了瑞士未来的土地使用/土地覆盖 (LULC) 变化. 这为规划和资源管理提供了准确的LULC预测.
科学领域:
- 环境科学 环境科学
- 地理空间分析是什么
- 气候变化影响评估
背景情况:
- 准确的土地使用/土地覆盖 (LULC) 数据对于有效的环境规划和资源管理至关重要.
- 现有的瑞士LULC地图缺乏可比分辨率的未来预测.
- 了解未来的LULC动态对于适应气候变化和政策转变至关重要.
研究的目的:
- 为整个21世纪为瑞士生成高分辨率的LULC变化预测.
- 通过使用一个神经网络 (NN) 和200多个预测器来建模LULC适合性.
- 将气候和政策场景 (RCP 4.5 和 8.5) 纳入LULC预测.
主要方法:
- 利用神经网络 (NN) 模拟14个LULC类的空间适应性.
- 采用数据增强算法来提高代表性不足的阶级的模型性能.
- 集成的NN适用性图与CLUE-S算法,考虑未来的LULC需求和过渡规则.
主要成果:
- 实现了高预测准确度,数量分歧为0.053和分配分歧为0.15.
- 产生的LULC预测在100m空间分辨率为未来的三个时间步骤.
- 开发了瑞士的第一个LULC预测,其主题分辨率与历史LULC地图相提并论.
结论:
- 产生的LULC预测为瑞士的土地利用规划和资源管理提供了宝贵的工具.
- 该方法为不同气候场景下的未来LULC变化建模提供了一个强大的框架.
- 这些预测支持生态和液压建模,息地恢复和保护工作中的应用.
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