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基于数据的多来源估计地表生物质密度:牧场监测的基线

Zerihun Chere^1,2, Berhan Gessesse^3,4, Abebe Mohammed Ali⁵

¹Department of Geography and Environmental Studies, Dire Dawa University, Dire Dawa, Ethiopia. zerihunchere@gmail.com.

Environmental monitoring and assessment

|March 14, 2026

概括

这项研究利用卫星数据和人工智能绘制了埃塞俄比亚牧场的地面生物质密度 (AGBD),为可持续管理和牧场性提供了关键的见解. 高分辨率的AGBD地图有助于识别退化热点,并为目标恢复工作提供信息.

科学领域:

生态生态学生态生态学
遥感遥感遥感遥感
地理空间分析的研究.

背景情况:

可持续的牧场管理需要监测生态系统的健康状况,以地面生物质密度 (AGBD) 为关键指标.
在许多牧场地区,准确的高分辨率的AGBD数据是有限的,这阻碍了有效的管理和气候变化减缓战略.
埃塞俄比亚的牧场面临着季节性AGBD评估的挑战,影响牧民的生计和生态系统的弹性.

研究的目的:

为埃塞俄比亚一个重要的牧民地区生成空间连续,高分辨率的地面生物质密度 (AGBD) 地图.
应用和比较卷积神经网络 (CNN) 和随机森林 (RF) 模型,以使用地球观测数据进行AGBD估计.
为AGBD监测建立一个可扩展的框架,以支持可持续的牧场管理和牧场性.

主要方法:

利用开放访问的地球观测数据,包括Sentinel-1/2和太空全球生态系统动态调查LiDAR (GEDI L4A) 测量.
使用递归特征消除与交叉验证 (RFECV) 和随机森林 (RF) 进行变量选择和超参数调整.
应用CNN和RF回归模型,在季节性和年度数据集中以50米分辨率估计AGBD.

主要成果:

在AGBD估计中,CNN模型的表现始终优于RF模型,在综合年度分析中达到0.93的确定系数 (R2).
在CNN模型中,每年AGBD估计的根平均平方误差 (RMSE) 为4.77t/ha,平均绝对误差 (MAE) 为2.55t/ha.
与主要雨季 (RMSE 6.99 t/ha,MAE 3.69 t/ha) 相比,在短雨季 (RMSE 4.88 t/ha,MAE 1.90 t/ha) 中,AgedBD估计误差较低.

结论:

开发了一种新的,可扩展的框架,用于使用开放式卫星数据和先进的机器学习进行高分辨率的AGBD绘图.
生成的AGBD基线为检测牧场退化热点提供了关键数据.
该研究支持在埃塞俄比亚制定季节性战略,以实现可持续的牧场,恢复和增强牧场的弹性.

关键词:

阿法尔地区阿法尔地区生物质是生物质中的一部分.卷积神经网络是一种卷积神经网络.吉迪 (GEDI) 是一个人.随机的森林随机的森林一个哨兵,一个哨兵.

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