Advances in Waveform and Photon Counting Lidar Processing for Forest Vegetation Applications

Full waveform (FW) and photon counting LiDAR (PCL) data have garnered greater attention due to increasing data availability, a wealth of information they contain and promising prospects for large scale vegetation mapping. However, many factors such as complex processing steps and scarce non-proprietary tools preclude extensive and practical uses of these data for vegetation characterization. Therefore, the overall goal of this study is to develop algorithms to process FW and PCL data and to explore their potential in real-world applications. Study I explored classical waveform decomposition methods such as the Gaussian decomposition, Richardson–Lucy (RL) deconvolution and a newly introduced optimized Gold deconvolution to process FW LiDAR data. Results demonstrated the advantages of the deconvolution and decomposition method, and the three approaches generated satisfactory results, while the best performances varied when different criteria were used. Built upon Study I, Study II applied the Bayesian non-linear modeling concepts for waveform decomposition and quantified the propagation of error and uncertainty along the processing steps. The performance evaluation and uncertainty analysis at the parameter, derived point cloud and surface model levels showed that the Bayesian decomposition could enhance the credibility of decomposition results in a probabilistic sense to capture the true error of estimates and trace the uncertainty propagation along the processing steps. In study III, we exploited FW LiDAR data to classify tree species through integrating machine learning methods (the Random forests (RF) and Conditional inference forests (CF)) and Bayesian inference method. Results of classification accuracy highlighted that the Bayesian method was a superior alternative to machine learning methods, and rendered users with more confidence for interpreting and applying classification results to real-world tasks such as forest inventory. Study IV focused on developing a framework to derive terrain elevation and vegetation canopy height from test-bed sensor data and to pre-validate the capacity of the upcoming Ice, Cloud and Land Elevation Satellite-2 (ICESat-2) mission. The methodology developed in this study illustrates plausible ways of processing the data that are structurally similar to expected ICESat-2 data and holds the potential to be a benchmark for further method adjustment once genuine ICESat-2 are available

Bayesian Learning of Coupled Biogeochemical-Physical Models

Predictive dynamical models for marine ecosystems are used for a variety of needs. Due to sparse measurements and limited understanding of the myriad of ocean processes, there is however significant uncertainty. There is model uncertainty in the parameter values, functional forms with diverse parameterizations, level of complexity needed, and thus in the state fields. We develop a Bayesian model learning methodology that allows interpolation in the space of candidate models and discovery of new models from noisy, sparse, and indirect observations, all while estimating state fields and parameter values, as well as the joint PDFs of all learned quantities. We address the challenges of high-dimensional and multidisciplinary dynamics governed by PDEs by using state augmentation and the computationally efficient GMM-DO filter. Our innovations include stochastic formulation and complexity parameters to unify candidate models into a single general model as well as stochastic expansion parameters within piecewise function approximations to generate dense candidate model spaces. These innovations allow handling many compatible and embedded candidate models, possibly none of which are accurate, and learning elusive unknown functional forms. Our new methodology is generalizable, interpretable, and extrapolates out of the space of models to discover new ones. We perform a series of twin experiments based on flows past a ridge coupled with three-to-five component ecosystem models, including flows with chaotic advection. The probabilities of known, uncertain, and unknown model formulations, and of state fields and parameters, are updated jointly using Bayes' law. Non-Gaussian statistics, ambiguity, and biases are captured. The parameter values and model formulations that best explain the data are identified. When observations are sufficiently informative, model complexity and functions are discovered.Comment: 45 pages; 18 figures; 2 table

Quantifying Vegetation Biophysical Variables from Imaging Spectroscopy Data: A Review on Retrieval Methods

An unprecedented spectroscopic data stream will soon become available with forthcoming Earth-observing satellite missions equipped with imaging spectroradiometers. This data stream will open up a vast array of opportunities to quantify a diversity of biochemical and structural vegetation properties. The processing requirements for such large data streams require reliable retrieval techniques enabling the spatiotemporally explicit quantification of biophysical variables. With the aim of preparing for this new era of Earth observation, this review summarizes the state-of-the-art retrieval methods that have been applied in experimental imaging spectroscopy studies inferring all kinds of vegetation biophysical variables. Identified retrieval methods are categorized into: (1) parametric regression, including vegetation indices, shape indices and spectral transformations; (2) nonparametric regression, including linear and nonlinear machine learning regression algorithms; (3) physically based, including inversion of radiative transfer models (RTMs) using numerical optimization and look-up table approaches; and (4) hybrid regression methods, which combine RTM simulations with machine learning regression methods. For each of these categories, an overview of widely applied methods with application to mapping vegetation properties is given. In view of processing imaging spectroscopy data, a critical aspect involves the challenge of dealing with spectral multicollinearity. The ability to provide robust estimates, retrieval uncertainties and acceptable retrieval processing speed are other important aspects in view of operational processing. Recommendations towards new-generation spectroscopy-based processing chains for operational production of biophysical variables are given

Advances in Waveform and Photon Counting Lidar Processing for Forest Vegetation Applications

Full waveform (FW) and photon counting LiDAR (PCL) data have garnered greater attention due to increasing data availability, a wealth of information they contain and promising prospects for large scale vegetation mapping. However, many factors such as complex processing steps and scarce non-proprietary tools preclude extensive and practical uses of these data for vegetation characterization. Therefore, the overall goal of this study is to develop algorithms to process FW and PCL data and to explore their potential in real-world applications. Study I explored classical waveform decomposition methods such as the Gaussian decomposition, Richardson–Lucy (RL) deconvolution and a newly introduced optimized Gold deconvolution to process FW LiDAR data. Results demonstrated the advantages of the deconvolution and decomposition method, and the three approaches generated satisfactory results, while the best performances varied when different criteria were used. Built upon Study I, Study II applied the Bayesian non-linear modeling concepts for waveform decomposition and quantified the propagation of error and uncertainty along the processing steps. The performance evaluation and uncertainty analysis at the parameter, derived point cloud and surface model levels showed that the Bayesian decomposition could enhance the credibility of decomposition results in a probabilistic sense to capture the true error of estimates and trace the uncertainty propagation along the processing steps. In study III, we exploited FW LiDAR data to classify tree species through integrating machine learning methods (the Random forests (RF) and Conditional inference forests (CF)) and Bayesian inference method. Results of classification accuracy highlighted that the Bayesian method was a superior alternative to machine learning methods, and rendered users with more confidence for interpreting and applying classification results to real-world tasks such as forest inventory. Study IV focused on developing a framework to derive terrain elevation and vegetation canopy height from test-bed sensor data and to pre-validate the capacity of the upcoming Ice, Cloud and Land Elevation Satellite-2 (ICESat-2) mission. The methodology developed in this study illustrates plausible ways of processing the data that are structurally similar to expected ICESat-2 data and holds the potential to be a benchmark for further method adjustment once genuine ICESat-2 are available

Investigating summer thermal stratification in Lake Ontario

Summer thermal stratification in Lake Ontario is simulated using the 3D hydrodynamic model Environmental Fluid Dynamics Code (EFDC). Summer temperature differences establish strong vertical density gradients (thermocline) between the epilimnion and hypolimnion. Capturing the stratification and thermocline formation has been a challenge in modeling Great Lakes. Deviating from EFDC's original Mellor-Yamada (1982) vertical mixing scheme, we have implemented an unidimensional vertical model that uses different eddy diffusivity formulations above and below the thermocline (Vincon-Leite, 1991; Vincon-Leite et al., 2014). The model is forced with the hourly meteorological data from weather stations around the lake, flow data for Niagara and St. Lawrence rivers; and lake bathymetry is interpolated on a 2-km grid. The model has 20 vertical layers following sigma vertical coordinates. Sensitivity of the model to vertical layers' spacing is thoroughly investigated. The model has been calibrated for appropriate solar radiation coefficients and horizontal mixing coefficients. Overall the new implemented diffusivity algorithm shows some successes in capturing the thermal stratification with RMSE values between 2-3°C. Calibration of vertical mixing coefficients is under investigation to capture the improved thermal stratification