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

我们的目标是为一种新型的空中或太空机载、遥感、3D成像激光雷达系统开发图像和信号处理算法，该系统旨在三维测量森林光合活性。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。要求是更好地解释描述地球表面以上树木和植被的几何形状（森林冠层高度、高度剖面和覆盖度）和生理特征（光合作用、蒸腾作用、躯体反应）的数据。通过更好地了解从森林生态系统的航空和卫星成像中收集的数据，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“跳跃”，分为5个阶段：第一阶段：建立跨学科计划：这是申请人与爱丁堡地球天文台（EEO）的研究人员合作的熟悉期，以更好地了解仪器和现有软件的测量和解释使用。第二阶段：开发处理多光谱独木舟激光雷达（MCSL）数据的贝叶斯技术：然后我们将扩展和应用现有的处理技术到遥感激光雷达图像，看看我们是否可以在结构图像上获得显着的改进。与此相关的是，我们应该研究使用更好的森林冠层结构模型，从而开发算法。任务3：编码用于EEO仪器的算法：在重建更好的MCSL图像时，我们需要结合几个波长固有的相互信息。空间结构重构和反射率分析（分类）成为数据后验推理的一种方法。考虑到我们提出的数学模型，一项重要的活动将是开发结构良好、文档化的代码来处理激光雷达数据。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轻松地使用它。任务4：评估和试验：当我们开发方法时，我们需要根据平等就业机会提供的数据评估其有效性。在结构上，我们需要评估在存在明显的视觉“杂乱”和其他混淆因素的情况下，我们是否可以创建更准确的三维森林冠层和地面结构。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。在整个拟议方案中，EOl将使用现有和新的MCSL仪器进行实验室和实地调查。测量将在0.4-2.5um的广泛波长范围内进行，使用不同生长阶段的对比植被类型，随着时间的推移，在不同的水文条件下进行检查，以观察高光谱背散射。任务5：泵启动和合作：一项关键任务将是将信号处理和地球科学社区聚集在一起，进一步开展跨学科活动。在HWU和ERPem联合倡议（www.erp.ac.uk）中，我们有许多工作人员研究单一和多个维度的信号处理理论，传感器和场景的表示和建模，创新的图像和信号处理技术，图像和信号控制的自主系统，以及模拟人类技术协作的系统。我们将组织由内部和特邀演讲者参与的关于关键问题的激励研讨会，随后举行分组会议，以制定研究和技术转让建议。申请人将与本地及接收院校的学术人员协商，承担其组织的主要责任。

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.