首页> 外文期刊>The Science of the Total Environment >Species discrimination and individual tree detection for predicting main dendrometric characteristics in mixed temperate forests by use of airborne laser scanning and ultra-high-resolution imagery
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Species discrimination and individual tree detection for predicting main dendrometric characteristics in mixed temperate forests by use of airborne laser scanning and ultra-high-resolution imagery

机译:利用机载激光扫描和超高分辨率图像,通过树种鉴别和单棵树检测来预测温带混交林的主要测树特征

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This study aims to investigate the combined use of two types of remote sensing data - ALS derived and digital aerial photogrammetry data (based on imagery collected by airborne UAV sensors) - along with intensive field measurements for extracting and predicting tree and stand parameters in even-aged mixed forests. The study is located in South West Romania and analyzes data collected from mixed-species plots. The main tree species within each plot are Norway spruce (Picea abies L Karst.) and Beech (Fops sylvatica L).The ALS data were used to extract the digital terrain model (UM), digital surface model (DSM) and normalized canopy height model (CHM). Object-Based Image Analysis (OBIA) dassification was performed to automatically detect and separate the main tree species. A local filtering algorithm with a canopy-height based variable window size was applied to identify the position, height and crown diameter of the main tree species within each plot. The filter was separately applied for each of the plots and for the areas covered with Norway spruce and beech trees, respectively (i.e. as resulted from OBIA classification).The dbh was predicted based on ALS data by statistical Monte Carlo simulations and a linear regression model that relates field dbh for each tree species with their corresponding ALS-derived tree height and crown diameter. The overall RMSE for each of the tree species within all the plots was 5.8 cm for the Norway spruce trees, respectively 5.9 cm for the beech trees. The results indicate a higher individual tree detection rate and subsequently a more precise estimation of dendrometric parameters for Norway spruce compared to beech trees located in spruce-beech even-aged mixed stands. Further investigations are required, particularly in the case of choosing the best method for individual tree detection of beech trees located in temperate even-aged mixed stands. (C) 2019 Elsevier B.V. All rights reserved.
机译:这项研究的目的是调查两种遥感数据的结合使用-ALS衍生数据和数字航空摄影测量数据(基于机载UAV传感器收集的图像)-以及密集现场测量,以提取和预测均匀的树木和林分参数。古老的混交林。该研究位于罗马尼亚西南部,分析了从混合物种地块收集的数据。每个样地中的主要树种是挪威云杉(Picea abies L Karst。)和比奇(Fops sylvatica L).ALS数据用于提取数字地形模型(UM),数字表面模型(DSM)和归一化冠层高度模型(CHM)。进行了基于对象的图像分析(OBIA)简化处理,以自动检测和分离主要树种。应用具有基于树冠高度的可变窗口大小的局部过滤算法,以识别每个图中主要树种的位置,高度和树冠直径。该过滤器分别应用于每个样地以及挪威云杉和山毛榉树覆盖的区域(即,根据OBIA分类得出的结果)。基于统计蒙特卡洛模拟和线性回归模型基于ALS数据预测dbh将每个树种的字段dbh与它们对应的ALS衍生的树高和树冠直径相关联。在所有样地中,每种树种的总RMSE对于挪威云杉树为5.8厘米,对于山毛榉树为5.9厘米。结果表明,与位于云杉-山毛榉平均年龄的混交林中的山毛榉树相比,挪威云杉的单棵树检出率更高,并且随后更精确地估算树木密度参数。需要进一步的研究,特别是在选择最佳方法以检测位于温带平均年龄的混交林中的山毛榉树的最佳方法的情况下。 (C)2019 Elsevier B.V.保留所有权利。

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