Department of Organismic and Evolutionary Biology, Harvard University, 26 Oxford Street, Cambridge, Massachusetts 02138, USA.
Ecol Appl. 2011 Jun;21(4):1120-37. doi: 10.1890/10-0274.1.
Insights into vegetation and aboveground biomass dynamics within terrestrial ecosystems have come almost exclusively from ground-based forest inventories that are limited in their spatial extent. Lidar and synthetic-aperture Radar are promising remote-sensing-based techniques for obtaining comprehensive measurements of forest structure at regional to global scales. In this study we investigate how Lidar-derived forest heights and Radar-derived aboveground biomass can be used to constrain the dynamics of the ED2 terrestrial biosphere model. Four-year simulations initialized with Lidar and Radar structure variables were compared against simulations initialized from forest-inventory data and output from a long-term potential-vegtation simulation. Both height and biomass initializations from Lidar and Radar measurements significantly improved the representation of forest structure within the model, eliminating the bias of too many large trees that arose in the potential-vegtation-initialized simulation. The Lidar and Radar initializations decreased the proportion of larger trees estimated by the potential vegetation by approximately 20-30%, matching the forest inventory. This resulted in improved predictions of ecosystem-scale carbon fluxes and structural dynamics compared to predictions from the potential-vegtation simulation. The Radar initialization produced biomass values that were 75% closer to the forest inventory, with Lidar initializations producing canopy height values closest to the forest inventory. Net primary production values for the Radar and Lidar initializations were around 6-8% closer to the forest inventory. Correcting the Lidar and Radar initializations for forest composition resulted in improved biomass and basal-area dynamics as well as leaf-area index. Correcting the Lidar and Radar initializations for forest composition and fine-scale structure by combining the remote-sensing measurements with ground-based inventory data further improved predictions, suggesting that further improvements of structural and carbon-flux metrics will also depend on obtaining reliable estimates of forest composition and accurate representation of the fine-scale vertical and horizontal structure of plant canopies.
关于陆地生态系统中植被和地上生物量动态的深入了解几乎完全来自地面森林清查，而这些清查在空间范围上存在局限性。激光雷达和合成孔径雷达是很有前途的基于遥感的技术，可以在区域到全球范围内综合测量森林结构。在本研究中，我们研究了如何利用激光雷达衍生的森林高度和雷达衍生的地上生物量来约束 ED2 陆地生物圈模型的动态。将四年的模拟初始化为激光雷达和雷达结构变量，然后与从森林清查数据初始化的模拟以及从长期潜在植被模拟输出的模拟进行比较。来自激光雷达和雷达测量的高度和生物量初始化显著改善了模型中森林结构的表示，消除了潜在植被初始化模拟中出现的过多大树的偏差。激光雷达和雷达初始化将潜在植被模拟中估计的较大树木的比例降低了约 20-30%，与森林清查结果相匹配。与潜在植被模拟相比，这导致了对生态系统尺度碳通量和结构动态的预测的改善。雷达初始化产生的生物量值与森林清查最为接近，接近度达到 75%，而激光雷达初始化产生的冠层高度值与森林清查最为接近。雷达和激光雷达初始化的净初级生产力值与森林清查的接近度约为 6-8%。通过将遥感测量与地面清查数据相结合来校正激光雷达和雷达的初始值，从而改善了生物量和基面积动态以及叶面积指数。通过将遥感测量与地面清查数据相结合来校正激光雷达和雷达的初始值以改善生物量和基面积动态以及叶面积指数，同时校正激光雷达和雷达的初始值以改善生物量和基面积动态以及叶面积指数。通过将遥感测量与地面清查数据相结合来校正激光雷达和雷达的初始值以改善生物量和基面积动态以及叶面积指数，同时校正森林组成和精细结构，可以进一步提高预测结果，这表明结构和碳通量指标的进一步改进还将取决于获得可靠的森林组成估计和对植物冠层精细垂直和水平结构的准确表示。
