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每像素森林属性映射和错误估计:谷歌地球引擎和R数据驱动工具
Saverio Francini1,2, Agnese Marcelli3, Gherardo Chirici1,4
1Department of Agriculture, Food, Environment and Forestry, University of Firenze, 50145 Firenze, Italy.
Sensors (Basel, Switzerland)
|June 27, 2024
概括
本研究介绍了dataDriven,这是一个开放式访问工具,用于估计远程传感地图中的每像素不确定性. 这项创新通过为每个地图位置提供可靠的错误估计来增强森林监测.
科学领域:
- 遥感 遥感 遥感 遥感
- 林业林业 林业 林业 林业
- 地理空间分析的研究.
背景情况:
- 遥感产品往往缺乏每像素准确性评估,限制了它们的实用性.
- 在不同的地图区域和类别中存在显著的精度差异.
- 估计每像素的不确定性对于提高遥感数据可用性至关重要.
研究的目的:
- 介绍数据驱动的开放访问工具,用于估计远程传感地图中的每像素不确定性.
- 解决遥感产品中可变精度的挑战.
- 通过不确定性估计,提高遥感数据的可用性和潜力.
主要方法:
- 使用基于统计设计的方法与引导重新抽样.
- 它利用了Sentinel-2遥感数据和谷歌地球引擎.
- 应用R编程语言进行数据分析.
主要成果:
- 在意大利托斯卡纳的一个森林庄园中测试了数据驱动工具,专注于体积密度.
- 对体积密度的估计像素误差在每公93到979立方米之间,平均每公285立方米.
- 证明了该工具能够提供空间显式错误估计的能力.
结论:
- 产生每像素误差估计的能力是遥感和森林监测的新.
- 数据驱动工具通过识别可靠和不可靠的地图区域来支持森林管理.
- 旨在促进遥感衍生产品的空间全面使用和验证.
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