Vegetation characterization for precision forestry and for environment protection

准确描述植被冠层的空间分布和动态特征，对于森林资源的可持续管理和环境保护具有重要意义。遥感技术的进步为我们提供了令人难以置信的高质量和高空间分辨率的数据，这使得确定单个树干水平的植被冠层特征和进行战术管理是可行的。然而，新的遥感技术在开发用于数据处理和分析的先进算法方面也提出了巨大的挑战。在这项研究中，将开发算法和软件工具，根据从激光雷达数据和光学图像获得的结构、空间和光谱信息，准确识别单个树种。还将开发创新技术和算法，以量化植被生长和健康。

Accurate characterization of the spatial distribution and dynamic of vegetation canopies is very important for sustainable management of forest resources and protection of our environment. Advances in remote sensing technologies provide us unbelievable high-quality and high spatial resolution data, which makes it feasible to characterize vegetation canopies at individual stem levels and for tactical management. However, new remote sensing technologies also pose great challenges in developing advanced algorithms for their data processing and analysis. In this study, algorithms and software tools will be developed to accurately identify individual tree species based on the structural, spatial and spectral information derived from lidar data and optical imagery. Innovative technologies and algorithms will also be developed to quantify vegetation growth and health.