Remote-sensing support for the Estonian National Forest Inventory, facilitating the construction of maps for forest height, standing-wood volume, and tree species composition

Q4 Agricultural and Biological Sciences
Mait Lang, A. Sims, K. Pärna, R. Kangro, M. Möls, Marta Mõistus, A. Kiviste, Mati Tee, Toivo Vajakas, Mattias Rennel
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引用次数: 3

Abstract

Abstract Since 1999, Estonia has conducted the National Forest Inventory (NFI) on the basis of sample plots. This paper presents a new module, incorporating remote-sensing feature variables from airborne laser scanning (ALS) and from multispectral satellite images, for the construction of maps of forest height, standing-wood volume, and tree species composition for the entire country. The models for sparse ALS point clouds yield coefficients of determination of 89.5–94.8% for stand height and 84.2–91.7% for wood volume. For the tree species prediction, the models yield Cohen's kappa values (taking 95% confidence intervals) of 0.69–0.72 upon comparing model results against a previous map, and values of 0.51–0.54 upon comparing model results against NFI sample plots. This paper additionally examines the influence of foliage phenology on the predictions and discusses options for further enhancement of the system.
为爱沙尼亚国家森林清查提供遥感支助,促进编制森林高度、立木量和树种组成地图
自1999年以来,爱沙尼亚在样地的基础上进行了国家森林清查(NFI)。本文提出了一个结合机载激光扫描(ALS)和多光谱卫星影像遥感特征变量的新模块,用于构建全国森林高度、活立木量和树种组成图。稀疏ALS点云模型对林分高度和材积的屈服系数分别为89.5 ~ 94.8%和84.2 ~ 91.7%。对于树种预测,在将模型结果与之前的地图进行比较时,模型得出的Cohen’s kappa值(取95%置信区间)为0.69-0.72,在将模型结果与NFI样地进行比较时,模型得出的值为0.51-0.54。此外,本文还探讨了叶片物候对预测的影响,并讨论了进一步增强系统的选择。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Forestry Studies
Forestry Studies Agricultural and Biological Sciences-Forestry
CiteScore
0.70
自引率
0.00%
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0
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