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

我们的目标是开发图像和信号处理算法的一种新型的空气或空间，遥感，三维成像激光雷达系统，旨在测量森林光合作用的活动在三维空间。我们希望进行全波形、多光谱信号分析，对森林生态系统进行详细的结构和生理测量。要求更好地解释描述地球表面以上树木和植被的几何形状（森林树冠高度、高度剖面和覆盖率）和生理特征（光合作用、蒸腾作用、体细胞反应）的数据。通过更好地了解从空中和卫星成像收集的森林生态系统的数据，拟议的研究将使我们能够更好地监测景观动态和碳循环，这是预测气候变化的关键因素。我们的项目是一个学科'跳'，有5个阶段阶段1：建立一个跨学科的计划：这是一个熟悉的时期，因为申请人与爱丁堡地球天文台（EEO）的研究人员合作，以更好地了解仪器和使用现有的测量和解释软件。第二阶段：开发贝叶斯技术处理多光谱独木舟激光雷达（MCSL）数据：然后，我们将扩展和应用现有的处理技术的遥感激光雷达图像，看看我们是否可以获得显着改善结构图像。与此相关，我们应该研究使用更好的结构模型的森林冠层的情况下，因此开发的算法。任务三：EEO仪器中使用的编码算法：我们需要在重建更好的MCSL图像时将几个波长中固有的互信息结合起来。空间结构的重建和反射率分析（分类）成为对数据进行后验推理的一种方法。考虑到我们提出的数学模型，一个重要的活动将是开发结构良好和有文档记录的代码来处理LiDAR数据。随着项目的进行，我们需要对项目中开发的原始软件进行编码和记录，以便其他研究人员可以随时使用。任务4：评估和试验：在开发方法时，我们需要评估EEO提供的数据的有效性。在结构上，我们需要评估我们是否可以在存在明显的视觉“混乱”和其他混乱因素的情况下创建更准确的3D森林树冠和地面结构。在光谱方面，我们必须超越目前的做法，从一系列光谱剖面中提取有用的数据。在整个拟议计划中，EOI将使用现有和新的MCSL仪器进行实验室和现场调查。将在0.4 - 2.5微米范围内的广泛波长范围内进行测量，使用不同生长阶段的对比植被类型，在不同水文条件下随时间进行检查，以观察高光谱后向散射。在HWU和ERPem池倡议（www.example.com）内，我们有许多工作人员研究信号处理的理论在一个单一的和几个维度，传感器和场景的表示和建模，创新的图像和信号处理技术，图像和信号控制的自主系统，以及模拟人类技术合作的系统。我们会就主要问题举办研讨会，邀请内部人员和特邀讲者主讲，然后举行分组会议，制定研究和技术转移建议。申请人将承担其组织的主要责任，在与家庭和东道国机构的学术人员协商。

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.