利用多光谱卫星图像、土壤图和随机森林算法构建爱沙尼亚树种组成图

Q4 Agricultural and Biological Sciences
Mait Lang, Mihkel Kaha, D. Laarmann, A. Sims
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引用次数: 10

摘要

利用2015年和2016年的Landsat-8 OLI和Sentinel-2 MSI图像、1:10 000数字土壤图和大量参考样本,结合GRASS GIS中的随机森林机器学习实现,构建了爱沙尼亚整个领土(42,755 km2)的树种地图。为每个像元分配了7种主要树种的分类概率、其他树种的额外分类概率和不符合森林定义的森林覆盖概率。优势种在县域的面积分布验证结果表明,无论是国有林(R2 = 0.98)还是自营林(R2 = 0.93),优势种在县域的分布都具有很强的相关性。利用2045个更新采伐区的采伐机测量数据验证树种组成与针叶树比例的测量值也有很强的相关性(R2 = 0.75)。常见物种的比例有低估的趋势,而比例较小的物种的比例有高估的趋势。在数量较少的参考观测中出现的落叶物种比例的准确性要小得多。通过使用爱沙尼亚森林研究样地网络数据库中的659个大样地和国家森林清查数据库中的3002个小样地的数据对结果进行验证,证实了基于采集者数据的发现。NFI数据还显示,估算误差随林龄的增加而减小。NFI样地主种比例大于或等于75%的主要种的Cohen’s kappa一致性指数在20年以下的林龄下由0.69下降到0.66。总的来说,绘制的地图为没有最新盘存数据的森林或需要在整个爱沙尼亚持续覆盖已知质量的树种数据的项目提供了关于树种组成的宝贵数据。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Construction of tree species composition map of Estonia using multispectral satellite images, soil map and a random forest algorithm
Abstract Landsat-8 OLI and Sentinel-2 MSI images from years 2015 and 2016, a 1:10,000 digital soil map and a large number of reference samples were used with a random forest machine learning implementation in GRASS GIS to construct a tree species map for the entire territory of Estonia (42,755 km2). Class probabilities for seven main tree species, an extra class for other species and probability of the forest cover not conforming to the forest definition were assigned for each pixel. Validation of dominant species distribution by area showed very strong correlation at county level both in state forests (R2 = 0.98) and in private forests (R2 = 0.93). Validation of tree species composition using harvester measurement data from 2,045 regeneration felling areas showed also very strong correlation (R2 = 0.75) with the measured values of the proportion of coniferous trees. There was some tendency to underestimate the proportion of more common species and overestimation was found for the species with smaller proportion in the mixture. The accuracy for the proportion of deciduous species that were present in a smaller number of reference observations was substantially smaller. Validation of the results by using data from 659 large sample plots from the database of the Estonian Network of Forest Research Plots and 3,002 small sample plots from the National Forest Inventory (NFI) data base confirmed the findings based on harvester data. The NFI data revealed also a decrease of estimation error with the increase of forest age. Cohen’s kappa index of agreement for main species for NFI sample plots with main species proportion equal to or greater than 75% decreased from 0.69 to 0.66 when observations with forests younger than 20 years were included in the comparison. Overall, the constructed map provides valuable data about tree species composition for the forests where no up to date inventory data are available or for the projects that require continuous cover of tree species data of known quality over the entire Estonia.
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来源期刊
Forestry Studies
Forestry Studies Agricultural and Biological Sciences-Forestry
CiteScore
0.70
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0.00%
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