Temporal transferability of LiDAR-based imputation of forest inventory attributes
Temporal transferability of LiDAR-based imputation of forest inventory attributes
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基于 LiDAR 的森林清查属性插补的时间可转移性
DOI：
10.1139/cjfr-2014-0405
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发表时间：
2015
影响因子：
2.2
通讯作者：
A. Hudak
中科院分区：
文献类型：
--
作者：
P. Fekety;M. Falkowski;A. Hudak
LiDAR数据经常为森林清查和规划决策提供信息。重复的LiDAR采集提供了更新森林资源清单的机会，并有可能随着时间的推移提高森林资源清单的估计。我们对美国北方爱达荷州的一个研究区域进行了重复的激光雷达和地面测量，预测（通过插补）-跨越空间和时间-四个森林资源清查属性：地上碳（AGC），断面积（BA），林分密度指数（SDI），和总干体积（SDI）。模型是从2003年和2009年的LiDAR数据集独立开发的，用于在两个时间段对响应变量进行空间预测。通过比较两个集合之间的响应变量来计算年度变化率。此外，通过合并两个年份的参考观察结果构建合并模型，以检验是否可以在测量日期之间进行插补。合并模型的R2值分别为0.87、0.90、0.89和0.87（AGC、BA、SDI和DBP）。映射响应变量...
Forest inventory and planning decisions are frequently informed by LiDAR data. Repeated LiDAR acquisitions offer an opportunity to update forest inventories and potentially improve forest inventory estimates through time. We leveraged repeated LiDAR and ground measures for a study area in northern Idaho, U.S.A., to predict (via imputation) — across both space and time — four forest inventory attributes: aboveground carbon (AGC), basal area (BA), stand density index (SDI), and total stem volume (Vol). Models were independently developed from 2003 and 2009 LiDAR datasets to spatially predict response variables at both times. Annual rates of change were calculated by comparing response variables between the two collections. Additionally, a pooled model was built by combining reference observations from both years to test if imputation can be performed across measurement dates. The R2 values for the pooled model were 0.87, 0.90, 0.89, and 0.87 for AGC, BA, SDI, and Vol, respectively. Mapping response variable...