Investigating the use of a leaf trait-based approach to remotely measure tropical forest productivity

研究使用基于叶子性状的方法来远程测量热带森林生产力

• 批准号: 1942356
• 金额: --
• 依托单位: University of Oxford
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2017
• 资助国家: 英国
• 起止时间: 2017 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-1942356
• 关键词: Investigating; use; leaf; trait; based

Tropical forests and savannahs account for 60% of terrestrial carbon cycling. Thus, understanding rates of carbon sequestration in these areas, along with projected changes, is vital for predicting future climate. MODIS satellites offer two products to measure gross primary productivity (GPP) and net primary productivity (NPP) across vegetated areas. Yet significant doubt has been cast on the ability of these products to accurately estimate the magnitude and spatial patterns of GPP and NPP over tropical forests (Cleveland et al. 2015). It is clear that in these regions a new methodology is required. This proposal aims to investigate the use of a novel leaf trait-based approach to remotely sense tropical forest productivity. Using leaf spectroscopy, airborne hyperspectral data and partial least square regression analysis (PLSR), it will build on existing literature to show that carbon cycling processes, such as leaf photosynthesis, respiration and woody productivity, can be accurately estimated by calibrating SENTINEL satellite data on field-collected values. Ultimately, it intends to create maps of tropical forest productivity in Google Earth Engine (a cloud computing platform for processing satellite imagery) for public and scientific use. Initial research carried out for my MSc dissertation in this area was very promising, and, following a year-long position as Research Scholar at Northern Arizona University, where I collaborated with Prof. Chris Doughty to expand this work, I believe I am uniquely placed to undertake this research and advance this novel and emerging field.

热带森林和草原占陆地碳循环的 60%。因此，了解这些地区的碳固存率以及预计的变化对于预测未来气候至关重要。 MODIS 卫星提供两种产品来测量植被区域的总初级生产力 (GPP) 和净初级生产力 (NPP)。然而，人们对这些产品准确估计热带森林 GPP 和 NPP 的幅度和空间模式的能力提出了重大质疑（Cleveland 等人，2015）。显然，这些地区需要新的方法。该提案旨在研究使用一种新颖的基于叶子性状的方法来遥感热带森林生产力。它将利用叶子光谱、机载高光谱数据和偏最小二乘回归分析（PLSR），以现有文献为基础，表明通过根据现场收集的值校准SENTINEL卫星数据，可以准确估计叶子光合作用、呼吸和木质生产力等碳循环过程。最终，它打算在谷歌地球引擎（一个用于处理卫星图像的云计算平台）中创建热带森林生产力地图，供公众和科学使用。我在该领域的硕士学位论文所进行的初步研究非常有前途，在北亚利桑那大学担任研究学者一年后，我与 Chris Doughty 教授合作扩展了这项工作，我相信我处于独特的地位来开展这项研究并推进这一新颖的新兴领域。