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

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.