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首页> 外文期刊>The Forestry Chronicle >Operational implementation of a LiDAR inventory in Boreal Ontario
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Operational implementation of a LiDAR inventory in Boreal Ontario

机译：安大略省北部的LiDAR库存操作实施

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An existing Light Detection and Ranging (LiDAR) data set captured on the Romeo Malette Forest near Timmins, Ontario, was used to explore and demonstrate the feasibility of such data to enrich existing strategic forest-level resource inventory data. Despite suboptimal calibration data, stand inventory variables such as top height, average height, basal area, gross total volume, gross merchantable volume, and above-ground biomass were estimated from 136 calibration plots and validated on 138 independent plots, with root mean square errors generally less than 20% of mean values. Stand densities (trees per ha) were estimated with less precision (30%). These relationships were used as regression estimators to predict the suite of variables for each 400-m~2 tile on the 630 000-ha forest, with predictions capable of being aggregated in any user-defined manner - for a stand, block, or forest - with appropriate estimates of statistical precision. This pilot study demonstrated that LiDAR data may satisfy growing needs for inventory data to scale operational/tactical, through strategic needs, as well as provide spatial detail for planning and the optimization of forest management activities.
机译：在安大略省Timmins附近的Romeo Malette森林中捕获的现有光探测与测距（LiDAR）数据集被用于探索和证明此类数据丰富现有森林战略资源清单数据的可行性。尽管校准数据欠佳，但仍从136个校准图中估算了林分存货变量，如最高高度，平均高度，基础面积，总体积，可销售总量和地上生物量，并在138个独立图中进行了验证，均方根误差通常小于平均值的20％。林分密度（每公顷树木）的估算精度较低（30％）。这些关系用作回归估计量，以预测630000公顷森林中每个400-m〜2瓦片的变量集，并且可以以任何用户定义的方式对预测进行汇总-对于林分，块或森林-具有适当的统计精度估计。这项初步研究表明，LiDAR数据可以通过战略需求满足清单数据不断增长的需求，以扩展运营/战术范围，并为森林管理活动的规划和优化提供空间细节。

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