生态模型是研究生态系统碳循环的重要工具之一。然而，大多数生态模型参数众多，很多参数（如植被结构参数）无法在大尺度上通过观测获取，带来模拟的不确定性。激光雷达作为一种新兴的主动遥感技术，可以穿透植被覆盖获得三维结构参数。本项目拟以激光雷达作为技术依托，通过优化生态模型中的植被参数，改进模型模拟精度：（1）以典型流域为研究对象，利用激光雷达技术获取局地景观尺度上的植被参数，包括叶面积指数、树高和生物量；（2）探索获取的植被参数与光学遥感指数之间的关系，将观测的植被参数推绎到整个研究区域；（3）选取主流生态模型，利用数据同化的方法，将计算的参数融合到模型中，优化模型相应参数；（4）利用观测数据验证模型模拟结果，评价模型改进精度。优化模型参数，改进模拟精度对准确预测森林在全球气候变化背景下的响应具有重要意义。另外，通过本项目的执行，也可使激光雷达技术在森林生态学领域得到进一步推广与应用。

Ecological models are important tools to study ecosystem-scale carbon cycle. However, most models are with many parameters and many of which, such as vegetation structural parameters, cannot be obtained in large spatial scale. The missing of accurate measurements of these parameters can bring significant uncertainty to modelling results. Light Detection and Ranging (Lidar), known as a new active remote sensing technology, can penetrate vegetation and derive three-dimensional structural parameters. Therefore, using Lidar as a basis, this project intends to improve the simulation accuracy of ecosystem models by optimizing vegetation parameters. Specifically, this project has four objectives listed as follows: (1) taken typical forest ecosystem basin as study area, use Lidar to extract vegetation parameters at landscape scale, including leaf area index, forest height, and biomass; (2) explore the relationship between Lidar-derived vegetation parameters and various types of optical remote sensing vegetation index, and then scale up the derived landscape-scale vegetation parameters to the entire study area; (3) optimize the corresponding parameters in the selected ecosystem model by assimilating the derived vegetation parameters; (4) use field observations to calibrate the model simulation results and evaluate the simulation accuracy. The improvement of the model simulation is of great significance to the prediction of how forests response to the global climate change. Furthermore, the proposed project can also contribute to the promotion and application of Lidar technology in the field of forest ecology.