Derivation of forest stand metrics through the use of LiDAR and 4-band multi-spectral digital imagery

遥感数据已成功地与地面清查数据相结合，以便得出林分计量，特别是对于相当同质的森林类型。不列颠哥伦比亚省拥有加拿大最复杂的沿海、干带、内陆湿带和亚寒带森林。不列颠哥伦比亚省的森林工业目前正面临着在山松甲虫大流行之后寻找经济木材供应的挑战。因此，关键是要更好地利用遥感技术和现有地面数据来确定林分指标，如林分种类、体积和原木尺寸。该项目将演示如何将激光雷达数据和4波段多光谱图像与标准巡航数据相结合，以识别林分物种、体积和原木尺寸，从而更可靠地估计林分木材库存，然后将其整合到关键决策中。一旦实施，开发的方法将使森林管理者拥有先进的工具，通过确保将正确的原木剖面送入正确的加工设施，以优化木材生产，从而制定更好的战略和业务决策。如果提议的方法在不列颠哥伦比亚省的不同森林中被证明是有效的，那么在加拿大其他地方应用它可能会更容易。

Remote-sensing data have successfully been combined with ground inventory data in order to derive forest stand metrics, especially for fairly homogeneous forest types. British Columbia has some of the most complex costal, dry belt, interior wet belt, and sub boreal forests in Canada. Forest industry in British Columbia is currently facing challenges of finding economical wood supplies in the aftermath of a major Mountain Pine Beetle epidemic. Therefore, it is critical to make better use of remote-sensing technology and existing ground data to identify forest stand metrics such as forest stand species, volume, and log piece sizes. This project will demonstrate how LiDAR data and 4-band multi-spectral imagery can be combined with standard cruise data to identify forest stand species, volume, and log piece size resulting in more reliable estimates of standing timber inventories, which will then be integrated into key decision making. Once implemented, the developed methodology will empower forest managers with advanced tools to make better strategic and operational decisions by ensuring the right log profiles are going to the right processing facilities for optimization of lumber production. If the proposed approach is proven to work in the diverse forests of BC, it will likely be easier to apply elsewhere in Canada.