Details of Award
NERC Reference : NE/J016926/1
[ENVIRONMENT] Quantifying forest state and degradation: exploiting new lidar measurements
Training Grant Award
- Lead Supervisor:
- Professor MI Disney, University College London, Geography
- Grant held at:
- University College London, Geography
- Science Area:
- Terrestrial
- Overall Classification:
- Terrestrial
- ENRIs:
- Global Change
- Natural Resource Management
- Science Topics:
- None
- Abstract:
- This project will develop methods to exploit new lidar scanning systems for quantifying forest biomass (state and change). The project aims to establish how well airborne laser scanning (ALS) can estimate biomass via measurement of key canopy structural properties (height, cover, structure) over contrasting forest biomes (temperate, tropical). Quantifying forest biomass is increasingly important for a range of applications including forestry, terrestrial C-cycle studies and resource management, particularly in the tropics. While satellite optical and radar remote sensing can provide largescale coverage they can be limited by cloud cover, lack of sensitivity to high biomass, and the difficulty of relating biomass to rather indirect measurements. Lidar is an extremely promising alternative, providing much more direct measurement of canopy properties. Newer waveform systems provide a rich source of distance-resolved information on canopy structure (gappiness, leaf area density), potentially allowing better estimates of biomass using models relating lidar scattering to canopy structure, particularly height. Waveform ALS estimates of height can be related to biomass through field-scale calibration of allometric (height to mass) relationships. ALS thus holds great promise for rapid estimation of biomass, which is of great interest both commercially and scientifically. Terrestrial laser scanning (TLS) systems are much more limited spatially than ALS, but are cheap, easy to deploy and provide complementary information on tree diameter and crown shape. TLS data therefore provide a direct route for testing and validating ALS measurements. Used in combination, the two sources of lidar can provide a much better picture of canopy state and change, particularly important for monitoring applications, such as the impacts of degradation. This can be particularly difficult to monitor in tropical regions, as the impact is far less dramatic than pure deforestation. A key requirement for improved ALS parameter estimation is retrieval methods that are robust, and require as few ground measurements for calibration and validation as possible. This would make them more general, increasing costeffectiveness and reliability. The way to achieve this is to understand the key (structural) variables affecting the lidar signal. Isolating these would reduce reliance on local calibration, as well as allowing calibration information to be used as efficiently as possible. Models provide a powerful framework for interpreting lidar, enabling simulation of ALS and TLS data over different canopy types and states, while at the same time controlling external factors such as observation characteristics. Much lidar modelling tends to be statistical/empirical, or results in 'effective' parameters which cannot be validated directly. This project will use detailed 3D models that avoid the necessity for assumptions about canopy structure which are often not met in practice. This project will combine modelling and fieldwork at temperate and tropical field sites to test the hypotheses that: biomass can be estimated from canopy height and cover via ALS; canopy cover can be derived from ALS independent of structural assumptions; ALS-derived biomass estimates can be tested using stem density and diameter measurements obtained from TLS. The CASE partner is specifically interested in the operational potential of ALS for the mapping and estimation of woody biomass, given an expanding market for commercial services in forestry, both nationally and internationally. Their wish for the project would be to see improved relationships of lidar to tree/forest characteristics to improve accuracy, robustness and customer confidence. The student would benefit from exposure to, and training in a wide range of modelling and measurement skills.
- NERC Reference:
- NE/J016926/1
- Grant Stage:
- Completed
- Scheme:
- DTG - directed
- Grant Status:
- Closed
- Programme:
- Open CASE
This training grant award has a total value of £76,678
FDAB - Financial Details (Award breakdown by headings)
| Total - Fees | Total - Student Stipend | Total - RTSG |
|---|---|---|
| £13,812 | £55,285 | £7,582 |
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