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

Lead Research Organisation: Heriot-Watt University
Department Name: Sch of Engineering and Physical Science

Abstract

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.

Planned Impact

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.

Publications

10 25 50
publication icon
Andrew M. Wallace (Author) (2010) Full Waveform Analysis for Long-Range 3D Imaging Laser Radar in EURASIP JOURNAL ON ADVANCES IN SIGNAL PROCESSING

publication icon
Ren, X (2014) Multispectral single-photon detection in time-of-flight depth profiling in Institute of Physics Conference on Photonics

publication icon
Wallace A (2014) Design and Evaluation of Multispectral LiDAR for the Recovery of Arboreal Parameters in IEEE Transactions on Geoscience and Remote Sensing

publication icon
Woodhouse I (2011) A Multispectral Canopy LiDAR Demonstrator Project in IEEE Geoscience and Remote Sensing Letters

publication icon
Ye J (2013) Parallel Bayesian inference of range and reflectance from LaDAR profiles in Journal of Parallel and Distributed Computing

 
Description Multi-spectral LiDAR has the potential to recover physiological and structural data from arboreal samples, and by extension from forest canopies when deployed on aerial or space platforms.

We have designed and evaluated a multi-spectral LiDAR system to measure tree height, leaf area, abundance profiles and leaf physiological parameters, using a series of experiments on tree samples 'viewed from above', by tilting living conifers such that the apex is directed on the viewing axis.



To evaluate the effectiveness of the method, and to constrain the parameter inversion process, we also conducted laboratory measurements to determine full leaf and bark spectra and structural distribution.

Our work shows the very fine detail that can be resolved by use of a time-correlated single photon counting technique and highlights some of the key areas for development for field deployment. Ultimately, aerial or space survey by multi- or hyper-spectral LiDAR could provide improved estimates of carbon sequestration and existing forest stocks, allowing us to better understand and predict the impact of climate change, and the seasonal dynamics of ecosystem carbon uptake in response to environmental drivers such as water, temperature, light and nutrient availability.
Exploitation Route Air borne and space borne LIDAR systems, particularly for earth observation. Currently we are looking at how we might deploy our sensors and field analyses on an air borne platform.
Sectors Aerospace, Defence and Marine,Environment

 
Description The main commercial use has been in forest mapping, through the spin-out company, Carbomap. Using UAV LiDAR, the company provide services in assessing forest health in 3D which is vitally important for fire risk mapping, species identification (including monitoring of invasive species), and for better quantification of carbon fluxes within the canopy, rather than just the surface of the canopy.
First Year Of Impact 2013
Sector Digital/Communication/Information Technologies (including Software),Environment
Impact Types Societal,Economic

 
Description Demonstrating the quantitative recovery of structural and biochemical parameters from forest canopies using a new hyper-spectral lidar
Amount £46,705 (GBP)
Funding ID Contract No. 4500134278 
Organisation Airbus Group 
Department Airbus Defence & Space
Sector Private
Country Germany
Start 09/2011 
End 05/2012
 
Description Quantum detection techniques for underwater imaging
Amount £219,684 (GBP)
Organisation Defence Science & Technology Laboratory (DSTL) 
Sector Public
Country United Kingdom
Start 04/2013 
End 04/2016
 
Description Airborne Sensing of Forest Canopies 
Organisation University of Edinburgh
Department School of Geosciences Edinburgh
Country United Kingdom 
Sector Academic/University 
PI Contribution Jointly, we have developed new algorithms to analyse new and existing LiDAR data on trees and forest canopies to detect anomalous returns and extract parameters of interest to Geo-scientists, for example tree heights, leaf area indices and chlorophyll content
Collaborator Contribution Making in situ measurements to establish ground truth.for algorithmic evaluation. Conducting flight trials using airborne LiDAR sensors.
Impact A number of publications have resulted from this collaboration.
Start Year 2010