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Geostatistical estimation of forest biomass in interior Alaska combining Landsat-derived tree cover, sampled airborne lidar and field observations

机译:阿拉斯加内陆森林生物量的地统计学估算   Landsat衍生的树木覆盖,采样机载激光雷达和现场观测

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摘要

The goal of this research was to develop and examine the performance of ageostatistical coregionalization modeling approach for combining fieldinventory measurements, strip samples of airborne lidar and Landsat-basedremote sensing data products to predict aboveground biomass (AGB) in interiorAlaska's Tanana Valley. The proposed modeling strategy facilitates pixel-levelmapping of AGB density predictions across the entire spatial domain.Additionally, the coregionalization framework allows for statistically soundestimation of total AGB for arbitrary areal units within the study area---a keyadvance to support diverse management objectives in interior Alaska. Thisresearch focuses on appropriate characterization of prediction uncertainty inthe form of posterior predictive coverage intervals and standard deviations.Using the framework detailed here, it is possible to quantify estimationuncertainty for any spatial extent, ranging from pixel-level predictions of AGBdensity to estimates of AGB stocks for the full domain. The lidar-informedcoregionalization models consistently outperformed their counterpart lidar-freemodels in terms of point-level predictive performance and total AGB precision.Additionally, the inclusion of Landsat-derived forest cover as a covariatefurther improved estimation precision in regions with lower lidar samplingintensity. Our findings also demonstrate that model-based approaches that donot explicitly account for residual spatial dependence can grosslyunderestimate uncertainty, resulting in falsely precise estimates of AGB. Onthe other hand, in a geostatistical setting, residual spatial structure can bemodeled within a Bayesian hierarchical framework to obtain statisticallydefensible assessments of uncertainty for AGB estimates.
机译:这项研究的目的是开发和检验年龄统计共区域化建模方法的性能,该方法可结合现场清单测量,机载激光雷达的带状样本和基于Landsat的遥感数据产品来预测阿拉斯加塔纳纳河谷内部的地上生物量(AGB)。拟议的建模策略有助于在整个空间域内对AGB密度预测进行像素级映射。此外,共区域化框架允许对研究区域内任意区域单位的AGB进行统计合理估计-这是支持内部各种管理目标的一项关键举措阿拉斯加州。本研究侧重于以后预测覆盖区间和标准偏差的形式对预测不确定性进行适当表征。使用此处详述的框架,可以量化任何空间范围内的估计不确定性,范围从像素级的AGB密度预测到AGB股票的估计完整域。激光雷达信息共区域化模型在点级预测性能和总AGB精度方面一直优于其同类无激光雷达模型。我们的发现还表明,没有明确考虑剩余空间依赖性的基于模型的方法可能会严重低估不确定性,从而导致对AGB的错误精确估算。另一方面,在地统计环境中,可以在贝叶斯层次框架内对剩余空间结构进行建模,以获得AGB估计不确定性的统计上可辩驳的评估。

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