1994年至2024年的中部里奥格兰德的同时作物和土地覆盖的土地使用地图
Saman Ebrahimi1, Mahdis Khorram1, Raquel Neri Barranco1
1School of Sustainable Engineering and the Built Environment, Arizona State University, 660 S College Ave, Tempe, AZ, 85281, USA.
Scientific data
|August 21, 2025
概括
一个新的数据集每年为中部里约格兰德地区从1994年到2024年绘制30米分辨率的作物和土地覆盖土地使用 (CLCLU) 地图. 这项资源有助于了解美国和墨西哥地区的土地使用变化.
科学领域:
- 环境科学
- 遥感技术
- 地理空间分析
背景情况:
- 准确的土地覆盖和土地利用数据对于环境监测和资源管理至关重要.
- 现有的数据集往往缺乏详细分析所需的空间分辨率或时间覆盖,特别是在跨境地区.
研究的目的:
- 为中部里约格兰德 (MRG) 地区引入全新的高分辨率 (30米) 年度作物和土地覆盖土地利用 (CLCLU) 数据集.
- 为MRG的美国和墨西哥部分提供全面的30年 (1994-2024) CLCLU绘图产品.
- 在数据稀缺的跨境环境中评估CLCLU分类的深度学习模型的性能.
主要方法:
- 从1994年到2024年使用Landsat复合材料开发了年度CLCLU数据集.
- 使用一个MANet语义细分模型与ResNeXt-101编码器,使用美国农田数据层 (CDL) 进行训练.
- 使用四种不同的训练策略来缓解传感器和时间变化,增强模型的概括性.
主要成果:
- 在持有CDL子集上实现了高分类准确度,总准确度为97.10%,平均交叉度为78.85%.
- 与国家土地覆盖数据库 (NLCD) 和MODIS土地覆盖类型 (MCD12Q1-UMD) 验证了数据集,证实了强烈的同意.
- 确定了特定的挑战,包括数据可用性差异,墨西哥有限的地面真相,以及影响年初分类的云文物.
结论:
- 推出的CLCLU数据集为研究MRG地区的土地使用动态提供了有价值的高分辨率资源.
- 采用的深度学习方法在各种环境条件和数据可用性方面产生一致的CLCLU地图.
- 未来的努力应集中在改善墨西哥的地面真相数据和完善处理传感器噪声和云层覆盖的方法,以提高准确性.
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