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

为多光谱冠层 LiDAR (MSCL) 图像开发全波形贝叶斯分析

• 批准号: EP/H022414/1
• 负责人: Andrew Wallace
• 金额: $ 16.08万
• 依托单位: Heriot-Watt University
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2010
• 资助国家: 英国
• 起止时间: 2010 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FH022414%2F1
• 关键词: Developing; full; waveform; Bayesian; analysis

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.

我们旨在为新型的空气或空间，遥感，3D成像激光痛系统开发图像和信号处理算法，旨在在三个维度中测量森林光合作用。我们希望执行完整的波形，多光谱信号分析，以对森林生态系统进行详细的结构和生理测量。要求是更好地解释描述地球表面上方的树木和植被的生理特征（光合作用，蒸腾，蒸腾，释放，躯体反应）的几何形状（森林冠层高度，高度盖和分数覆盖率）的数据。通过更好地了解从森林生态系统的空中和卫星成像收集的数据，拟议的研究将使我们能够更好地监控景观动态和碳周期，这是预测气候变化的关键因素。 （EEO）更好地了解现有软件进行测量和解释的工具和使用。第2阶段：开发用于处理多光谱牙齿轮（MCSL）数据的贝叶斯技术：然后，我们将扩展并应用现有的处理技术到遥感的激光雷达成像，以查看我们是否可以在结构图像中获得显着改善。与此相关，我们应该研究对森林冠层方案的更好结构模型的使用，然后开发算法。任务3：编码用于在EEO仪器中使用的算法：我们需要在重建更好的MCSL图像中纳入几个波长中固有的相互信息。空间结构的重建和反射分析（分类）成为从数据中绘制后验推断之一。鉴于我们提出的数学模型，重大的活动将是结构化良好和记录的代码来处理LiDAR数据。随着项目的进行，我们需要编码和记录该项目中开发的原始软件，以便其他研究人员很容易使用。任务4：评估和试验：随着我们开发方法，我们需要评估其对EEO提供的数据的有效性。从结构上讲，我们需要评估是否可以在存在明显的视觉“混乱”和其他令人困惑的因素的情况下创建更准确的3D森林冠层和地面结构。从光谱上讲，我们必须超越当前的实践，从一系列光谱曲线中提取有用的数据。在整个拟议的计划中，EOL将使用现有和新的MCSL工具进行实验室和现场调查。测量将在0.4-2.5um的广泛波长范围内使用不同的生长阶段的对比植被类型进行，随着时间的流逝，将在不同的水文条件下进行检查，以观察高光谱的反向划线。Task5：泵浦和协作：一个关键任务将是一项关键任务，将是一项信号处理和地球科学社区，以开发进一步的跨学科活动。在HWU和ERPEM合并攻击性（www.erp.ac.uk）中，我们有许多员工在单个和几个维度中研究信号处理的理论，传感器和场景的表示和建模，创新的图像和信号处理技术，图像和信号处理技术，图像和信号控制技术，图像和信号控制的自治系统以及建模人类与人为合作的系统。我们将组织有关内部和受邀演讲者的关键问题的泵送研讨会，然后进行突破性的会议，以开发研究和技术转移建议。申请人将与家庭和东道机构的学术人员协商，对其组织承担主要责任。

项目成果

Design and Evaluation of Multispectral LiDAR for the Recovery of Arboreal Parameters

• DOI: 10.1109/tgrs.2013.2285942
• 发表时间: 2014-08-01
• 期刊: IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
• 影响因子: 8.2
• 作者: Wallace, Andrew M.;McCarthy, Aongus;Buller, Gerald S.
• 通讯作者: Buller, Gerald S.

Multispectral single-photon detection in time-of-flight depth profiling

飞行时间深度分析中的多光谱单光子检测

• 发表时间: 2014
• 期刊: Institute of Physics Conference on Photonics
• 作者: Ren, X

Full Waveform Analysis for Long-Range 3D Imaging Laser Radar

• DOI: 10.1155/2010/896708
• 发表时间: 2010-01-01
• 期刊: EURASIP JOURNAL ON ADVANCES IN SIGNAL PROCESSING
• 影响因子: 1.9
• 作者: Wallace, Andrew M.;Ye, Jing;Buller, Gerald S.
• 通讯作者: Buller, Gerald S.

Developing Hyperspectral LiDAR for Structural and Biochemical Analysis of Forest Data

• 发表时间: 2012-11
• 作者: D. Martinez-Ramirez;G. Buller;A. Mccarthy;Ximing Ren;Andrew M. Wallace;S. Morak;Caroline Nichol;Iain H. Woodhouse

Recovery of Forest Canopy Parameters by Inversion of Multispectral LiDAR Data

• DOI: 10.3390/rs4020509
• 发表时间: 2012-02
• 期刊: Remote. Sens.
• 作者: Andrew M. Wallace;C. Nichol;Iain H. Woodhouse