Developing full waveform, Bayesian analysis for Multi-Spectral Canopy LiDAR (MSCL) images

We aim to develop image and signal processing algorithms for a new type of air or space borne, remote sensing, 3D imaging LiDAR system designed to measure forest photosynthetic activity in three dimensions. We want to perform full waveform, multi-spectral signal analysis to conduct detailed structural and physiological measurements on forest ecosystems. The requirement is to better interpret data describing the geometry (forest canopy height, height profile, and fractional cover) and physiological signature (photosynthesis, transpiration, somatal response) of trees and vegetation above the earth's surface. By providing better understanding of the data collected from aerial and satellite imaging of forest ecosystems, the proposed research will allow us to better monitor landscape dynamics and the carbon cycle, which is a key factor in the prediction of climate change.Our project is a discipline 'hop' and has 5 phasesPhase 1: Establishing an Inter-Disciplinary Programme: This is a period of familiarisation as the applicant works with researchers at the Edinburgh Earth Observatory (EEO) to better understand the instruments and use of existing software for measurement and interpretation. Phase 2: Developing Bayesian techniques for processing Multi-spectral Canoy LiDAR (MCSL) data: we shall then extend and apply existing processing techniques to the remotely sensed LiDAR imagery to see whether we can gain significant improvement in structural imagery. Allied to this, we should investigate the use of better structural models for the forest canopy scenario, and so develop the algorithms. Task 3: Encoding the algorithms for use within EEO instruments: We need to incorporate the mutual information inherent in several wavelengths in reconstructing better MCSL imagery. The reconstruction of spatial structure and the reflectance analysis (classification) becomes one of drawing posterior inferences from data. Given the mathematical model that we propose, a significant activity will be the development of well structured and documented code to process the LiDAR data. As the project proceeds, we need to encode and document the original software developed in this project so it can be readily used by other researchers.Task 4: Evaluation and trials: As we develop the methodology, we need to assess its effectiveness on data provided by EEO. Structurally, we need to assess whether we can create more accurate 3D forest canopy and ground structure in the presence of significant visual 'clutter' and other confusing factors. Spectrally, we must go beyond current practice in extracting useful data from a series of spectral profiles. Throughout the proposed programme, EOl will be carrying out laboratory and field investigations with MCSL instruments, both existing and new. Measurements will be carried out over an extensive wavelength range in the range 0.4-2.5um using contrasting vegetation types at different growth stages, to be examined over time with different hydrological conditions to observe hyperspectral backscatter.Task 5: Pump-priming and collaboration: A key task will be to bring together the signal processing and geoscience communities to develop further cross-disciplinary activities. At HWU and within the ERPem pooling inititiative (www.erp.ac.uk) we have many staff studying the theory of signal processing in a single and several dimensions, the representation and modelling of sensors and scenes, innovative image and signal processing technologies, image and signal controlled autonomous systems, and systems that model the human-technology collaboration. We would organise pump-priming workshops on key problems with in-house and invited speakers, followed by break-out sessions to develop research and technology transfer proposals. The applicant would assume primary responsibility for their organisation, in consultation with academic staff at the home and host institutions.

The main beneficiaries would be the public and private sectors interested in applications for forestry inventory; wood industry; environmental monitoring; biomass measurements; habitat quality assessments; ecosystem damage and degradation assessments; mapping; surface topography and mapping beneath vegetation and; 3-D surface details of flood plains and wetlands. Goverment policy is informed by detailed knowledge of climate change. For example, the 2008 Eliasch Review on Financing Global Forests was commissioned by the Prime Minister in preparation for the UNFCCC climate talks scheduled in Copenhagen in December 2009. 1. Environmental Impact The Earth is a complex system of interactions between the oceans, the atmosphere, the biosphere and the solid earth. Deforestation (and reforestation) is, with fossil fuel burning, one of the major contributors to an increase in the levels of atmospheric CO2. The effects are not fully known, but there is a strong possibility of global warming, significant climatic shifts including flood and droughts, and rises in sea levels leading to coastal recession. The global economic cost of climate change caused by deforestation alone could reach $1 trillion a year by 2100. The potential economic and social impact of improved understanding of the terrestrial carbon cycle and the role of forests is considerable. One of the hosts (Woodhouse) has recently been appointed as a Linking Innovation in NERC Knowledge Exchange Fellow working with industry and service providers in the field of environmental sensing, with a focus on forestry. Results of this study will therefore link directly into this network and has the potential for immediate impact on a number of commercial companies within the UK, including Ecometrica Ltd and LTS International. 2. Better Exploitation of MCSL Data MSCL Lidar data can provide more accurate measurements of structural information for input to the Forestry Commission's timber quality models, mapping entire forest districts from an airborne platform. This would provide much needed data to improve models of timber quality within the UK forestry sector. Combining a multi-spectral capability with structural data means that one can characterise reflectance and structure with a single instrument and data processing stream, rather than using separate active and passive sensors, with all the associated problems of registration and data fusion. This could significantly reduce the costs of MCSL in future. 3. Impact on existing work The host institution want to analyse data from the launch of an orbiting Lidar system akin to the VCL, the Carbon 3-D and the DESDynI Lidar missions for the monitoring of forests globally. By providing means for estimating forest parameters such as canopy height, health and biomass simultaneously, this project impacts on the understanding of the role of forests in the global carbon cycle, as pools of biodiversity and as renewable resources. 4. Industrial Links: HWU have been developing time-correlated single photon counting LiDARs for several years and have patented the technology. The main applications have been in industrial metrology and defence, with collaborators including BAE Systems, Thales and Qinetiq. The EEO have already organised a meeting attended by a number of industrial stakeholders, including AEL Consultants, Clyde Space, CST Global, Ecometrica, Gilden Photonics, ITI Life Sciences and Techmedia, Optocap, Scottish Enterprise, the Scottish Universities Environmental Research Centre. As described in the letter of support and impact plan, we have an explicit route to exploitation with SELEX GALILEO. 5. Widening Impact: A key impact is to establish a much wider inter-disciplinary, future programme. Andrew Wallace would assume responsibility to organise as series of workshops on the role of improved computational processing on LiDAR, RADAR and Distributed Sensor Networks broadening the scope of the existing proposal.