Novel High-Resolution Three-Dimensional Mapping of Vegetation Using Unmanned Aerial Vehicles (UAV) and Structure from Motion Photogrammetry (SfM).

森林储存了陆地生态系统中发现的大约一半的碳，并且是大气中不断上升的二氧化碳的重要汇，但是监测表明气候变化和干扰对森林长期动态影响的精细尺度结构变化是昂贵的，而且在大尺度上是不切实际的。陆地和航空遥感的最新进展首次产生了植被结构的高分辨率三维测量，这有可能彻底改变我们对生物多样性、物种分布和生态系统功能的理解。然而，实现这些技术的高成本和不切实际限制了它们的应用。例如，植被的机载遥感通常需要安装在飞机上的光探测和测距（LiDAR）和高光谱传感器，这意味着研究通常只在大面积（100万公顷）上具有成本效益，而地面遥感则需要昂贵的激光扫描仪（TLS），并且仅限于小型，易于访问的地块（通常为100万公顷）。从无人机上采集的运动摄影测量（SfM）数据中获取结构是一种很有前途的新方法，可以廉价、方便地采集大面积的高分辨率三维数据。尽管SfM已经在地貌学和考古学领域应用了十多年，但它在森林生态学中的应用还处于起步阶段，在数据采集和处理的最佳实践方面存在许多未解决的不确定性。该项目将建立一个强有力的方法，从森林结构特征中提取森林结构特征，并确定在不同生态系统中的准确性和最佳做法。将开发预测模型，以便从绘制的单个树冠中准确估计森林结构、功能和碳储量，这是森林动态的关键组成部分。这种方法将应用于一系列森林类型，展示一种低成本、多时间森林监测的新方法，能够量化单个树木和整个森林对干旱和疾病等环境压力因素的反应。在这里开发的方法广泛适用，提供了一个新的框架，以克服地球上一些最缺乏研究的地区的常见数据获取问题；例如，热带森林是地球上生物多样性最高的地区之一。

Forests store around half of the carbon found in terrestrial ecosystems and are a significant sink for rising atmospheric CO2, but monitoring the fine-scale structural changes that are indicative of impacts of climate change and disturbances on long-term forest dynamics is expensive and impractical at large scales. Recent advances in terrestrial and airborne remote sensing are producing, for the first time, high resolution three-dimensional measurements of vegetation structure that have the potential to revolutionise our understanding of biodiversity, species distributions, and ecosystem function. However, the high cost and impracticalities of implementing these technologies limits their application. For example, airborne remote sensing of vegetation usually requires Light Detection and Ranging (LiDAR) and hyperspectral sensors mounted on a plane, meaning studies are often only cost effective over large areas (> 100,000 ha), whilst terrestrial remote sensing, requires expensive laser scanners (TLS), and is limited to small, easily accessible plots (typically > 1ha). Structure from Motion Photogrammetry (SfM) data collected on unmanned aerial vehicles (UAVs) is a promising new method to cheaply and easily collect high resolution 3D data over large areas. Although SfM has been applied in the fields of geomorphology and archaeology for over a decade, it's use in forest ecology is in its infancy with many unresolved uncertainties around best practice for data acquisition and processing. This project will establish a robust methodology for extracting forest structural traits from SfM and determine accuracy and best-practice in different ecosystems. Predictive models will be developed for accurately estimating forest structure, function and carbon storage from mapped individual tree crowns, a key component of forest dynamics. This methodology will be applied to a range of forest types, demonstrating a new method of low-cost, multi-temporal forest monitoring, capable of quantifying individual tree and whole-forest responses to environmental stressors such as drought and disease. Methods that are developed here are widely applicable, delivering a new framework to overcome common data acquisition issues in some of the most critically understudied regions on the planet; for example, tropical forests which are areas with some of the highest biodiversity on Earth.