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Semivariance analysis of forest structure and remote sensing data to determine an optimal sample plot size

机译:森林结构和遥感数据的半动物分析,以确定最佳样本绘图尺寸

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In a preliminary study to determine the optimal sample plot size for a mixed forest region, semivariograms have yielded useful statistics from analysis of forest structural data and remotely sensed imagery. Omni-directional semivariograms were extracted from forest structure attributes; DBH, tree height and average crown diameter, to derive second moment statistics describing the spatial dependence of each data set. The variables were then related using the relative nugget effect (ratio of nugget: sill) to determine the most reliable range value to use for the determination of an optimal ground sample resolution. The same analysis was performed on a series of degraded red and near infrared images covering the same study site. The results show that each data set usually characterizes spatial dependence between 8 and 10m.
机译:在初步研究中确定混合林地区的最佳样品绘图尺寸,半血管仪从森林结构数据分析和远程感测图像中产生了有用的统计数据。从森林结构属性中提取全方位半导体函数; DBH,树高和平均冠直径,从而导出描述每个数据集的空间依赖性的第二矩统计。然后,变量使用相对核对效果(核数的比率:窗台)来确定用于确定最佳地样本分辨率的最可靠的范围值。对覆盖同一研究现场的一系列退化的红色和近红外图像进行了相同的分析。结果表明,每个数据集通常表征在8到10米之间的空间依赖性。

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