基于2017年,2020年,2021年和2023年的卫星遥感数据的欧洲生物质密度地图
Maurizio Santoro1, Oliver Cartus1, Arnan Araza2,3
1Gamma Remote Sensing, Worbstrasse 225, 3073 Gümligen, Switzerland.
Data in brief
|February 24, 2026
概括
新的卫星地图提供了欧洲森林生物质的年度估计,这对于政策监测至关重要. 虽然像素级准确性有限,但聚合数据显示出良好的协议,有助于森林管理和碳核算.
科学领域:
- 遥感和地球观测 遥感和地球观测
- 林业与生态 林业与生态
- 地理空间分析的研究.
背景情况:
- 欧洲政策需要详细的森林结构和生物质数据进行监测和报告.
- 卫星遥感为绘制和监测森林资源提供了一个可扩展的解决方案.
- 现有的方法缺乏足够的空间细节和年度更新,以实现有效的森林管理.
研究的目的:
- 通过使用卫星数据,每年生成欧洲森林生物质变量 (GSV,AGB,BGB) 的空间显式地图.
- 为各种用户需求提供多个分辨率 (20m和100m) 的产品.
- 评估生成的生物质地图的准确性和一致性.
主要方法:
- 利用了 2017-2023 年的 Sentinel-1 和 ALOS-2 PALSAR-2 合成孔径雷达 (SAR) 数据.
- 开发了一个涉及SAR数据预处理和对物理模型的反转的处理链.
- 估计的地面生物量 (AGB) 和地下生物量 (BGB) 使用与生长库存量 (GSV) 的全米关系.
- 传播的不确定性用于计算生物质变量的每像素标准偏差.
主要成果:
- 以20米分辨率生成年度森林生物质地图 (GSV,AGB,BGB),反映预期的空间模式.
- 与国家森林库存 (NFIs) 相比,像素级验证显示出高误差 (>50%).
- 综合数据 (例如,行政单位) 显示了可以忽略不计的偏差和<30%的MSE的改进准确性.
- 在ALOS-2 PALSAR-2观测有限的地区,年间地图的一致性减少.
结论:
- 生成的基于卫星的森林生物质地图为欧洲森林动态提供了宝贵的见解.
- 虽然像素级准确度是一个局限性,但聚合数据适用于与政策相关的报告.
- 该方法为改善森林监测提供了基础,尽管数据采集频率会影响一致性.
关键词:
这是AGB AGB AGB.在ALOS-2 PALSAR-2中使用ALOS-2.这是BGB BGB BGB.森林的森林森林的森林.这是GSV的GSV.李达尔 (LiDAR) 是一种激光雷达.哨兵一号 - 哨兵一号更多相关视频
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