Lead supervisor: Emily Lines, Geography
Co-supervisor: Niall Origo, National Physical Laboratory; Phil Wilkes, RBG Kew
This is a CASE project with the NPL
Brief summary:
Monitoring structural change in forests using cutting-edge autonomous remote sensing.
Importance of the area of research concerned:
Ecosystem structure is a key determinant of habitat quality, carbon storage and sequestration, microclimate and ecosystem-climate interactions, and is an Essential Biodiversity Variable (EBV). Options for measuring structure and structural dynamics are, however, limited: traditional forestry techniques measure only simple structural elements, whilst Earth Observation (EO) approaches lack detailed information and require many simplifying assumptions to be interpreted. High resolution remote sensing, such as terrestrial and UAV-based LiDAR (TLS and UAV-LS) offer new methods to observe forest structure in detail, but are usually single surveys and not repeated through time, limiting their ability to help us to understand how forests change. New, static TLS sensors can measure forest structural dynamics at both high spatial and temporal resolution, so offering the opportunity to determine changes in key properties such as leaf angle and leaf area, and to test the impact of realistic structural representation within radiative transfer models for EO information retrieval.
Project summary :
This project will leverage existing and newly collected novel high resolution Terrestrial Laser Scanning (TLS) data to determine how fine scale structural properties change through time. The project will quantify temporal change in different aspects of forest structure typically modelled using simplifying assumptions within radiative transfer and ecosystem models. These may include leaf area, leaf angle, leaf arrangement and canopy woody area, which are known to be dynamic through the day and year. The project will produce new findings on the magnitude of structural dynamics in forests, and the importance of these for information retrieval from EO data using radiative transfer and virtual forest models (Calders et al. 2018). Depending on student interests, the project may include further work to understand how structural dynamics influence microclimate and light conditions.
What will the student do?:
The student will undertake fieldwork, computational data analysis and modelling, using data collected by autonomous LEAF sensors in the StructNet network, supplemented by data they collect using a high-resolution portable TLS. They will develop/apply processing approaches to an existing multi-year time series from a temperate woodland in the UK (Wytham Woods), and use 3D radiative transfer modelling and a virtual forest model (Calders et al. 2018) to quantify structural change. The student will determine how the structural dynamics information from the LEAF sensors impacts EO information retrieval using data from multiple satellites, including NASA’s GEDI and ESA’s Sentinel1. Further work may include using data from other sensors in the StructNet community to broaden the findings to other ecosystem types, and installing sensors to quantify the impact of structural dynamics on abiotic conditions in forests. The student should have strong programming skills and some familiarity with the physics of radiative transfer, and must be willing to undertake fieldwork. The student will join a multi-institutional group working on related projects, and be able to tailor the research focus.
References - references should provide further reading about the project:
Calders et al. (2018). Realistic Forest Stand Reconstruction from Terrestrial LiDAR for Radiative Transfer Modelling, Remote Sens.10(6), 933; https://doi.org/10.3390/rs10060933
Calders et al. (2023) StrucNet: a global network for automated vegetation structure monitoring, Remote Sensing in Ecology and Conservation, https://doi.org/10.1002/rse2.333
Lines et al. (2022) The shape of trees: reimagining forest ecology in three dimensions with remote sensing, Journal of Ecology, https://doi.org/10.1111/1365-2745.13944
Applying
You can find out about applying for this project on the Department of Geography page.