Key message

We compared (lognormal) universal kriging with the area-based approach for estimation of forest inventory variables using LiDAR data as auxiliary information and showed that universal kriging could be an accurate alternative when there is spatial autocorrelation.

Context

Forest inventories supported by geospatial technologies are essential to achieve a spatially informed assessment of forest structure. LiDAR technology supplies comprehensive and spatially explicit data enabling the estimation of wide-scale forest variables.

Aims

To compare the area-based approach with universal kriging for estimation of the stem density, basal area, and quadratic mean diameter using LiDAR data as auxiliary information.

Methods

We used data from 202 inventory plots, distributed in four Forest Management Units with differences in structure and management, and a 6-points/m² resolution LiDAR dataset from a Pinus sylvestris L. forest in Spain to test the accuracy of the (lognormal) universal kriging and the area-based approaches.

Results

In those Forest Management Units where the analyzed variables showed spatial autocorrelation, kriging showed better results than the area-based approach in terms of RMSE and Pearson coefficient between observed and estimated values, although lognormal universal kriging provided slightly biased estimations (up to 2%).

Conclusion

Universal kriging is an accurate method for estimation of forest inventory variables with LiDAR data as auxiliary information for those variable exhibiting spatial autocorrelation.

中文翻译：

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森林清单中野外采样和LiDAR数据的集成：基于区域的方法和（对数正态）通用克里金法的比较

关键信息

我们将（对数正态）通用克里金法与基于面积的方法（使用LiDAR数据作为辅助信息来估算森林清单变量）进行了比较，结果表明，当存在空间自相关时，通用克里金法可能是一种准确的选择。

语境

地理空间技术支持的森林清单对于实现对森林结构的空间知情评估至关重要。LiDAR技术可提供全面的空间明确数据，从而可以估算大范围的森林变量。

目的

使用LiDAR数据作为辅助信息，将基于区域的方法与通用克里金法进行比较，以估算茎密度，基面积和二次平均直径。

方法

我们使用来自202个清单样地的数据，分布在四个结构和管理差异的森林管理单位中以及西班牙Pinus sylvestris L.森林的6点/ m ²分辨率LiDAR数据集来测试（对数正态）通用克里格法和基于区域的方法。

结果

在分析变量显示空间自相关的那些森林经营单位中，就对数值进行估计和估计值之间的均方根误差和皮尔逊系数而言，克里格方法显示出比基于区域的方法更好的结果，尽管对数正态通用克里格方法提供了略有偏差的估计（高达2％） 。

结论

通用克里金法是一种利用LiDAR数据作为森林空间变量估计值的准确方法，这些数据是那些具有空间自相关性的变量的辅助信息。