Uniting Forest Inventory and Remote Sensing Data to Assess Forest Composition and Structure in Tropical Mountain Regions

要量化和预测森林分布对持续气候变化的反应，需要关于物种分布和森林构成的准确数据。这种数据通常是从以地块为基础的森林调查中收集的。然而，这种方法在交通条件较差或地形复杂的地区极其有限。因此，数据可得性很差，因此对热带山区系统将如何应对气候变化知之甚少。这一重大知识差距对我们预测未来环境变化从全球到地方范围的影响以及从生物群分布和碳经济到当地生物多样性和生态系统服务的各种因素的影响具有重大影响。该项目将把地块级别的森林调查数据与航空照片和高分辨率遥感数据结合起来，得出在较难进入的地区进行和解释森林评估的新方法。该项目将把现有数据与实地研究相结合，适合来自不同背景的学生，从地理到生态再到环境科学。然而，必须热衷于森林和山区的实地考察，以及了解不同空间尺度的模式和过程。目标：1)利用现有森林调查数据，对山地热带森林组成的空间变化与整个中部山脉的环境变化进行分类；2)确定样地组成数据与来自卫星图像和航空摄影的遥感数据之间的关系；3)利用地面调查和三维激光扫描，确定样地子集的结构特征。4)结合上述目标1-3的结果，制定遥感森林结构和组成评估方法。

Quantifying and predicting the response of forest distribution to on-going climatic changes requires accurate data on species distribution and forest composition. Such data are typically gathered from plot-based forest inventory surveys. However, this approach is extremely limited in areas with poor access or difficult terrain. Consequently, there is poor data availability and hence little understanding, of how tropical mountain systems will respond to climate change. This significant knowledge gap has major implications for our ability to predict future impacts of environmental change from global to local scales and for factors spanning from biome distribution and carbon economy to local biodiversity and ecosystem services.This project will combine plot-level forest inventory data with aerial photographs and high-resolution remote sensing data to derive new methods for conducting and interpreting forest assessments in less accessible regions. The project will integrate existing data with field-based research and would suit students from a wide range of backgrounds, spanning from geography, through ecology to environmental science. An enthusiasm for fieldwork in forests and mountain terrain and for understanding pattern and process at contrasting spatial scales is a must, however. Objectives:1) To classify spatial variation in montane tropical forest composition associated with environmental variation across the central mountain range using existing forest inventory data.2) To identify relationships between plot compositional data and remote sensing data from satellite imagery and aerial photography.3) To determine structural characteristics of a subset of plots using ground based inventory augmented with 3D laser scanning.4) To develop methods for remote forest structural and compositional assessment by combining outcomes of objectives 1-3, above.