Individual tree and plot measurements using close-range digital photography and visible laser

良好的森林管理需要良好的管理工具和良好的森林调查数据。高效的设计 森林清单需要就样地的数量和空间安排以及 所使用的采样单元的类型。对于加拿大的大多数森林清单，Bitterlich的角度计数抽样 (Acs)被使用。结合对树木进行有效的次采样以进行详细的树干测量的ACS通常 性价比高，与其他常用方法相比，不易产生测量偏差和误差。在这 建议使用数码相机与可见光激光技术相结合来获取ACS样本和茎 直径测量。ACS适用于360度全景照片。图像分析算法有 建议将树木的检测和测量自动化，并采用现场采样技术来纠正 用于因隐藏的树木而产生的计数偏差。加拿大森林的固碳潜力是一个重要的 作为全球碳预算的组成部分，所提出的方法将提高实地效率和准确性 与加拿大的森林调查工作有关。改进的库存估计将使更好的 加拿大森林中碳含量的核算。

Good forest management requires good management tools and good forest inventory data. Efficient design of forest inventories requires decisions regarding the number and spatial arrangement of sample plots as well as the type of sampling unit utilized. For most forest inventories in Canada, Bitterlich's angle count sampling (ACS) is used. ACS coupled with efficient subsampling of trees for detailed stem measurements is generally cost-effective and less prone to measurement bias and error than other commonly employed methods. In this proposal, a digital camera coupled with visible laser technology are used to obtain ACS samples and stem diameter measurements. ACS is applied to 360-degree panoramic photographs. Image analysis algorithms are proposed that will automate the detection and measurement of trees and field sampling techniques to correct for count biases due to hidden trees. The carbon sequestration potential of Canadian forests is an important component of the global carbon budget, the methods proposed will increase field efficiency and accuracy associated with forest inventory work in Canada. The improved inventory estimates will enable better accounting of carbon content within Canadian forests.