Improving Satellite Leaf Area Index Estimation Based On Various Integration Methods

Leaf Area Index (LAI) is an important land surface biophysical variable that is used to characterize vegetation amount and activity. Current satellite LAI products, however, do not satisfy the requirements of the modeling community due to their large uncertainties and frequent missing values. Each LAI product is currently generated from only one satellite sensor data. There is an urgent need for advanced methods to integrate multiple LAI products to improve the product's accuracy and integrality for various applications. To meet this need, this study proposes four methods, including the Optimal Interpolation (OI), Bayesian Maximum Entropy (BME), Multi-Resolution Tree (MRT) and Empirical Orthogonal Function (EOF), to integrate multiple LAI products. Three LAI products have been considered in this study: Moderate Resolution Imaging Spectroradiometer (MODIS), Multi-angle Imaging SpectroRadiometer (MISR) and Carbon cYcle and Change in Land Observational Products from an Ensemble of Satellites (CYCLOPES) LAI. As the basis of data integration, this dissertation first validates and intercompares MODIS and CYCLOPES LAI products and also evaluates their geometric accuracies. The CYCLOPES LAI product has smoother temporal profiles and fewer spatial variations, but tends to produce spurious large errors in winter. The Locally Adjusted Cubic-spline Capping algorithm is revised to smooth multiple years' average and variance. Although OI, BME and MRT based methods have been used in other fields, this is the first research to employ them in integrating multiple LAI products. This dissertation also presents a new integration method based on EOF to solve the problem of large data volume and inconsistent temporal resolution of different datasets. High resolution LAI reference maps generated with ground measurements are used to validate these algorithms. Validation results show that all of these four methods can fill data gaps and reduce the errors of the existing LAI products. The data gaps are filled with information from adjacent pixels and background. These algorithms remove the spurious large temporal and spatial variation of the original LAI products. The combination of multiple satellite products significantly reduces bias. OI and BME can reduce the RMSE from 1.0 (MODIS) to 0.7 and reduce the bias from +0.3 (MODIS) and -0.2 (CYCLOPES) to -0.1. MRT can produce similar results with OI but with significantly improved efficiency. EOF also generates the results with the RMSE of 0.7 but zero bias. Limited ground measurement data hardly prove which methods outperform the others. OI and BME theoretically produce statistically optimal results. BME relaxes OI's linear and Gaussian assumption and explicitly considers data error, but bears a much higher computational burden. MRT has improved efficiency but needs strict assumptions on the scale transfer function. EOF requires simpler model identification, while it is more "empirical" than "statistical". The original contributions of this study mainly include: 1) a new application of several different integration methods to incorporate multiple satellite LAI products to reduce uncertainties and improve integrality, 2) an enhancement of the Locally Adjusted Cubic-spline Capping by revising the end condition, 3) a novel comprehensive comparison of MODIS C5 LAI product with other satellite products, 4) the development of a new LAI normalization scheme by assuming the linear relationship between measurement error and LAI natural variance to account for the inconsistency between products, and finally, 5) the creation of a new data integration method based on EOF

Training Images-Based Stochastic Simulation on Many-Core Architectures

In the past decades, multiple-point geostatistical methods (MPS) are increasing in popularity in various fields. Compared with the traditional techniques, MPS techniques have the ability to characterize geological reality that commonly has complex structures such as curvilinear and long-range channels by using high-order statistics for pattern reconstruction. As a result, the computational burden is heavy, and sometimes, the current algorithms are unable to be applied to large-scale simulations. With the continuous development of hardware architectures, the parallelism implementation of MPS methods is an alternative to improve the performance. In this chapter, we overview the basic elements for MPS methods and provide several parallel strategies on many-core architectures. The GPU-based parallel implementation of two efficient MPS methods known as SNESIM and Direct Sampling is detailed as examples

Multiple-point statistical simulation for hydrogeological models: 3D training image development and conditioning strategies

Most studies about the application of geostatistical simulations based on multiple-point statistics (MPS) to hydrogeological modelling focus on relatively fine-scale models and concentrate on the estimation of facies-level, structural uncertainty. Much less attention is paid to the use of input data and optimal construction of training images. For instance, even though the training image should capture a set of spatial geological characteristics to guide the simulations, the majority of the research still relies on 2D or quasi-3D training images. In the present study, we demonstrate a novel strategy for 3D MPS modelling characterized by: (i) realistic 3D training images, and (ii) an effective workflow for incorporating a diverse group of geological and geophysical data sets. The study covers an area of 2810 km2 in the southern part of Denmark. MPS simulations are performed on a subset of the geological succession (the lower to middle Miocene sediments) which is characterized by relatively uniform structures and dominated by sand and clay. The simulated domain is large and each of the geostatistical realizations contains approximately 45 million voxels with size 100 m × 100 m × 5 m. Data used for the modelling include water well logs, high-resolution seismic data, and a previously published 3D geological model. We apply a series of different strategies for the simulations based on data quality, and develop a novel method to effectively create observed sand/clay spatial trends. The training image is constructed as a small 3D voxel model covering an area of 90 km2. We use an iterative training image development strategy and find that even slight modifications in the training image create significant changes in simulations. Thus, the study underlines that it is important to consider both the geological environment, and the type and quality of input information in order to achieve optimal results from MPS modelling. In this study we present a possible workflow to build the training image and effectively handle different types of input information to perform large-scale geostatistical modellin

A Novel Pixel-based Multiple-Point Geostatistical Simulation Method for Stochastic Modeling of Earth Resources

Uncertainty is an integral part of modeling Earth\u27s resources and environmental processes. Geostatistical simulation technique is a well-established tool for uncertainty quantification of earth systems modeling. Multiple-point statistical (MPS) algorithms are specifically advantageous when dealing with the complexity and heterogeneity of geological data. MPS algorithms take advantage of using training images to mimic physical reality. This research presents a novel and efficient pixel-based multiple-point geostatistical simulation method for mineral resource modeling. Pixel-based simulation implies the sequential modeling of individual points on the simulation grid by borrowing spatial information from the training image and honoring conditioning data points. The developed method borrows information by integrating multiple machine learning algorithms, including Principal Component Analysis (PCA), t-Distributed Stochastic Neighbor Embedding (t-SNE), and Density-based Spatial Clustering of Applications with Noise (DBSCAN) algorithms. For automation and to ensure high-quality realizations, multiple optimizations, and parameter tuning strategies were introduced. The proposed methodology proved its applicability by accurate reproduction of complex geological features honoring conditioning data while maintaining reasonable computational time. The model is validated by simulating a variety of categorical and continuous variables for both two and three-dimensional cases and conditional and unconditional simulations. As a three-dimensional case study for categorical stochastic modeling, the proposed method is applied to a gold deposit for orebody modeling. The proposed algorithm can be applied to a variety of contexts, including but not limited to petroleum reservoir characterization, seismic inversion, mineral resources modeling, gap-filling in remote sensing, and climate modeling. The developed model can be extended for spatio-temporal modeling, multivariate simulation, non-stationary modeling, and super-resolution realizations

Evaluating covariance-based geostatistical methods with bed-scale outcrop statistics conditioning for reproduction of intra-point bar facies architecture, Cretaceous Horseshoe Canyon Formation, Alberta, Canada

2022 Summer.Includes bibliographical references.Geostatistical characterization of petroleum reservoirs typically suffers from problems of sparse data, and modelers often draw key parameters from analogous outcrop, numerical, and experimental studies to improve predictions. While quantitative information (bed-scale statistical distributions) from outcrop studies is available, translating the data from outcrop to models and generating geologically-realistic realizations with available geostatistical algorithms is often problematic. The overarching goal of this thesis is to test the capacity of covariance-based geostatistical methods to reproduce intra-point bar facies architecture while guiding those algorithms with bed-scale outcrop statistics from the Late Cretaceous Horseshoe Canyon Formation in southeastern Alberta. First, general facies architecture reproduction is tested with 2- and 3-facies synthetic and outcrop-based experiments with variable hard data, soft data weight, and soft data reliability. Next, 3-D sector models compare performance of different geostatistical simulation methods: sequential / co-sequential indicator, plurigaussian, and nested truncated gaussian. Findings show that despite integration of outcrop statistics, all conventional covariance-based geostatistical algorithms struggle to reproduce complex facies architecture that is observed in outcrop. Specifically, problems arise with: 1) low-proportion facies and 2) a weak statistical relationship between hard data (measured sections) and soft data (probability models). Nested modeling partially mitigates low-proportion issues and performs better as a result

Analog-based meandering channel simulation

Characterizing the complex geometries and the heterogeneity of the deposits in meandering river systems is a long-standing issue for the 3-D modeling of alluvial formations. Such deposits are important sources of accessible groundwater in alluvial aquifers throughout the world and also play a major role as hydrocarbons reservoirs. In this paper, we present a method to generate meandering river centerlines that are stochastic, geologically realistic, connected, and conditioned to local observations or global geomorphological characteristics. The method is based on fast 1-D multiple-point statistics in a transformed curvilinear domain: the succession in directions observed in a real-world meandering river (the analog) is considered as statistical model for multiple-point statistics simulation. The integration of local data is accomplished by an inverse procedure ensuring that the channels pass through a given set of locations while conserving the high-order spatial characteristics of an analog. The methodology is applied on seven real-world case studies. This work demonstrates the flexibility and the applicability of multiplepoint statistics outside the standard paradigm that considers the simulation of a 2-D or 3-D variable with spatial coordinates