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首页> 外文期刊>Silva Fennica >Local models for forest canopy cover with beta regression.
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Local models for forest canopy cover with beta regression.

机译：森林冠层的局部模型具有beta回归。

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摘要

Accurate field measurement of the forest canopy cover is too laborious to be used in extensive forest inventories. A possible alternative to the separate canopy cover measurements is to utilize the correlations between the percent canopy cover and easier-to-measure forest variables, especially the basal area. A fairly new analysis technique, the beta regression, is specially designed for modelling percentages. As an extension to the generalized linear models, the beta regression takes into account the distribution of the model residuals, and uses a logistic link function to ensure logical predictions. In this study, the beta regression method was found to perform well in conifer dominated study area located in central Finland. The same model shape, with basal area, tree height and an additional predictor (Scots pine: site fertility, Norway spruce: percentage of hardwoods) as independent variables, produced good results for both pine and spruce dominated sites. The models had reasonably high pseudo R-squared values (pine: 0.91, spruce: 0.87) and low standard errors (pine: 6.3%, spruce: 5.9%) for the fitting data, and also performed well in a cross validation test. The models were also tested on separate test plots located in a different geographical area, where the prediction errors were slightly larger (pine: 8.8%, spruce: 7.4%). In pine plots, the model fit was further improved by introducing additional predictors such as stand age and density. This improved also the performance of the models in the cross validation test, but weakened the results for the external data set. Our results indicated that the beta regression method offers a noteworthy alternative to separate canopy cover measurements, especially if time is limited and the models can be applied in the same region where the modelling data were collected.
机译：对林冠层进行准确的野外测量非常费力，无法用于大量的森林资源清查。单独的冠层覆盖率测量的一种可能替代方法是利用冠层覆盖率百分比与更易于测量的森林变量（尤其是基础面积）之间的相关性。 Beta回归是一种相当新的分析技术，专门用于建模百分比。作为广义线性模型的扩展，β回归考虑了模型残差的分布，并使用逻辑链接函数来确保逻辑预测。在这项研究中，发现β回归方法在位于芬兰中部的针叶树为主的研究区域中表现良好。相同的模型形状具有基础面积，树高和其他预测变量（苏格兰松树：站点肥力，挪威云杉：阔叶树的百分比）作为自变量，对于松树和云杉为主的站点均产生了良好的结果。这些模型的拟合数据具有相当高的伪R平方值（松木：0.91，云杉：0.87）和低标准误差（松木：6.3％，云杉：5.9％），并且在交叉验证测试中也表现良好。还在不同地理区域的单独测试地块上对模型进行了测试，预测误差略大（松木：8.8％，云杉：7.4％）。在松树地带，通过引入其他预测因子（例如林分年龄和密度）进一步改善了模型拟合。这也提高了交叉验证测试中模型的性能，但削弱了外部数据集的结果。我们的结果表明，β回归方法为单独的冠层覆盖测量提供了一个值得注意的替代方法，尤其是在时间有限且模型可以应用于收集建模数据的同一区域的情况下。

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