Doctor of Philosophy

dissertationWildfire is a multifaceted, global phenomenon with ecological, environmental, climatic and socioeconomic impacts. Live fuel moisture content (LFMC) is a critical fuel property for determining fire danger. Previous research has used meteorological data and remote sensing to estimate LFMC with the goal of extending direct ground measurement. A fundemental understanding of plant physiology and spectral response toLFMC variation is needed to advance use of LFMC for fire risk management and remote sensing applications. This study integrates field samples of three species, lab measurements, remote sensing dataand statistical analysis to construct a more complete knowledge of the physical foundations of LFMC seasonalityfrom three perspectives: 1)relationships between soil moisture and LFMC; 2) spectroscopic analysis of seasonal changes in LFMC and leaf dry mass; 3) relationships between LFMC and leaf net heat content, and between leaf net heat content and remotely sensed indices. This study is the first to demonstrate a relationship between in situ soil moisture and LFMC. It also challengesthe current asumption of changing water content and stable dry matter content over time in remote sensing esimation of LFMC, showing the dominant contribution of dry matter in LFMC variation in some conifer species. The resultsdemonstrate the combination of spectroscopic data and partial least squares regression can improve modeling accuray for LFMC temporal variation, but the spectral response to changing LFMC and dry mass is difficult to seperate from broader spectral trends due to temporal change in chlorophyll, leaf structure, water and covaried biochemical components. Lastly it introducesa new vegetation variable, leaf net heat content, and demostrates its relationship with LFMC and potential for remote sensing estimation.This study will improve present capabilities of remote sensing for monitoring vegetation water stress and physiological properties. It will also advance understanding of seasonal changes in LFMC to better estimate fire danger and potential impacts of fire on ecosystems and the carbon cycle

Determination of the Water Content of Coffee Leaves Using Infrared Spectroscopy

M.S. University of Hawaii at Manoa 2016.Includes bibliographical references.Infrared (IR) spectroscopy and flat plate capacitors were examined as potential methods for the determination of leaf water content as alternatives to the current pressure bomb method. Flat plate capacitors were found to be a poor solution. IR spectroscopy was found to provide a good estimate of the leaf water content when using broad spectrum spectroscopy with partial least squares regression fitting (R2=0.95). Normalized indices comparing reflectance between 1080 nm and 1200 nm (R1080R1200) and between 1250 nm and 1450 nm (R1250R1450) were found to provide strong correlations (R2=0.90) with the commonly measured equivalent water thickness (EWT) and to provide reasonable (R2= 0.70) correlation with the leaf water pressure as measured by pressure bomb

Noise-Resistant Spectral Features for Retrieving Foliar Chemical Parameters

Foliar chemical constituents are important indicators for understanding vegetation growing status and ecosystem functionality. Provided the noncontact and nondestructive traits, the hyperspectral analysis is a superior and efficient method for deriving these parameters. In practice, thespectral noise issue significantly impacts the performance of the hyperspectral retrieving system. To systematically investigate this issue, by introducing varying levels of noise to spectral signals, an assessment on noiseresistant capability of spectral features and models for retrieving concentrations of chlorophyll, carotenoids, and leaf water content was conducted. Given the continuous waveletanalysis (CWA) showed superior performance in extracting critical information associating plants biophysical and biochemical status in recent years, both wavelet features (WFs) and some conventional features (CFs) were chosen for the test. Two datasets including a leaf optical properties experiment dataset (n = 330), and a corn leaf spectral experiment dataset (n = 213) were used for analysis and modeling. The results suggested that the WFs had stronger correlations with all leaf chemical parameters than the CFs. According to an evaluation by decay rate of retrieving error that indicates noise-resistant capability, both WFs and CFs exhibited strong resistance to spectral noise. Particularly for WFs, the noise-resistant capability is relevant to the scale of the features. Based on the identified spectral features, both univariate and multivariate retrieving models were established and achieved satisfactory accuracies. Synthesizing the retrieving accuracy, noise resistivity, and model’s complexity, the optimal univariate WF-models were recommended in practice for retrieving leaf chemical parameters

Remote Sensing for Precision Nitrogen Management

This book focuses on the fundamental and applied research of the non-destructive estimation and diagnosis of crop leaf and plant nitrogen status and in-season nitrogen management strategies based on leaf sensors, proximal canopy sensors, unmanned aerial vehicle remote sensing, manned aerial remote sensing and satellite remote sensing technologies. Statistical and machine learning methods are used to predict plant-nitrogen-related parameters with sensor data or sensor data together with soil, landscape, weather and/or management information. Different sensing technologies or different modelling approaches are compared and evaluated. Strategies are developed to use crop sensing data for in-season nitrogen recommendations to improve nitrogen use efficiency and protect the environment

Commercial forest species discrimination and mapping using image texture computed from WorldView-2 pan sharpened imagery in KwaZulu-Natal, South Africa.

Masters Degree. University of KwaZulu-Natal, Pietermaritzburg.Forest species discrimination is vital for precise and dependable information, essential for commercial forest management and monitoring. Recently, the adoption of remote sensing approaches has become an important source of information in commercial forest management. However, previous studies have utilized spectral data or vegetation indices to detect and map commercial forest species, with less focus on the spatial elements. Therefore, this study using image texture aims to discriminate commercial forest plantations (i.e. A. mearnsii, E. dunnii, E. grandis and P. patula) computed from a 0.5m WorldView-2 pan-sharpened image in KwaZuluNatal, South Africa. The first objective of the study was to discriminate commercial forest species using image texture computed from a 0.5m WorldView-2 pan-sharpened image and the Partial Least Squares Discriminate Analysis (PLS-DA) algorithm. The results indicated that the image texture model (overall accuracy (OA) = 77%, kappa = 0.69) outperformed both the vegetation indices model (OA = 69%, kappa = 0.59) and raw spectral bands model (OA = 64%, kappa = 0.52). The most successful texture parameters selected by PLS-DA were mean, correlation, and homogeneity, which were primarily computed from the red-edge, NIR1 and NIR2 bands. Lastly, the 7x7 moving window was commonly selected by the PLS-DA model when compared to the 3x3 and 5x5 moving windows. The second objective of the study was to explore the utility of texture combinations computed from a fused 0.5m WorldView-2 image in discriminating commercial forest species in conjunction with the PLS-DA and Sparse Partial Least Squares Discriminate Analysis (SPLS-DA) algorithm. The accuracies achieved using SPLS-DA model, which performed variable selection and dimension reduction simultaneously yielded an overall accuracy of 86%. In contrast, the PLS-DA and variable importance in the projection (VIP) produced an overall classification accuracy of 81%. Generally, the finding of this study demonstrated the ability of image texture to precisely provide adequate information that is essential for tree species mapping and monitoring

The data concept behind the data: From metadata models and labelling schemes towards a generic spectral library

Spectral libraries play a major role in imaging spectroscopy. They are commonly used to store end-member and spectrally pure material spectra, which are primarily used for mapping or unmixing purposes. However, the development of spectral libraries is time consuming and usually sensor and site dependent. Spectral libraries are therefore often developed, used and tailored only for a specific case study and only for one sensor. Multi-sensor and multi-site use of spectral libraries is difficult and requires technical effort for adaptation, transformation, and data harmonization steps. Especially the huge amount of urban material specifications and its spectral variations hamper the setup of a complete spectral library consisting of all available urban material spectra. By a combined use of different urban spectral libraries, besides the improvement of spectral inter- and intra-class variability, missing material spectra could be considered with respect to a multi-sensor/ -site use. Publicly available spectral libraries mostly lack the metadata information that is essential for describing spectra acquisition and sampling background, and can serve to some extent as a measure of quality and reliability of the spectra and the entire library itself. In the GenLib project, a concept for a generic, multi-site and multi-sensor usable spectral library for image spectra on the urban focus was developed. This presentation will introduce a 1) unified, easy-to-understand hierarchical labeling scheme combined with 2) a comprehensive metadata concept that is 3) implemented in the SPECCHIO spectral information system to promote the setup and usability of a generic urban spectral library (GUSL). The labelling scheme was developed to ensure the translation of individual spectral libraries with their own labelling schemes and their usually varying level of details into the GUSL framework. It is based on a modified version of the EAGLE classification concept by combining land use, land cover, land characteristics and spectral characteristics. The metadata concept consists of 59 mandatory and optional attributes that are intended to specify the spatial context, spectral library information, references, accessibility, calibration, preprocessing steps, and spectra specific information describing library spectra implemented in the GUSL. It was developed on the basis of existing metadata concepts and was subject of an expert survey. The metadata concept and the labelling scheme are implemented in the spectral information system SPECCHIO, which is used for sharing and holding GUSL spectra. It allows easy implementation of spectra as well as their specification with the proposed metadata information to extend the GUSL. Therefore, the proposed data model represents a first fundamental step towards a generic usable and continuously expandable spectral library for urban areas. The metadata concept and the labelling scheme also build the basis for the necessary adaptation and transformation steps of the GUSL in order to use it entirely or in excerpts for further multi-site and multi-sensor applications

Remote Sensing of Plant Biodiversity

This Open Access volume aims to methodologically improve our understanding of biodiversity by linking disciplines that incorporate remote sensing, and uniting data and perspectives in the fields of biology, landscape ecology, and geography. The book provides a framework for how biodiversity can be detected and evaluated—focusing particularly on plants—using proximal and remotely sensed hyperspectral data and other tools such as LiDAR. The volume, whose chapters bring together a large cross-section of the biodiversity community engaged in these methods, attempts to establish a common language across disciplines for understanding and implementing remote sensing of biodiversity across scales. The first part of the book offers a potential basis for remote detection of biodiversity. An overview of the nature of biodiversity is described, along with ways for determining traits of plant biodiversity through spectral analyses across spatial scales and linking spectral data to the tree of life. The second part details what can be detected spectrally and remotely. Specific instrumentation and technologies are described, as well as the technical challenges of detection and data synthesis, collection and processing. The third part discusses spatial resolution and integration across scales and ends with a vision for developing a global biodiversity monitoring system. Topics include spectral and functional variation across habitats and biomes, biodiversity variables for global scale assessment, and the prospects and pitfalls in remote sensing of biodiversity at the global scale

Remote Sensing of Plant Biodiversity

At last, here it is. For some time now, the world has needed a text providing both a new theoretical foundation and practical guidance on how to approach the challenge of biodiversity decline in the Anthropocene. This is a global challenge demanding global approaches to understand its scope and implications. Until recently, we have simply lacked the tools to do so. We are now entering an era in which we can realistically begin to understand and monitor the multidimensional phenomenon of biodiversity at a planetary scale. This era builds upon three centuries of scientific research on biodiversity at site to landscape levels, augmented over the past two decades by airborne research platforms carrying spectrometers, lidars, and radars for larger-scale observations. Emerging international networks of fine-grain in-situ biodiversity observations complemented by space-based sensors offering coarser-grain imagery—but global coverage—of ecosystem composition, function, and structure together provide the information necessary to monitor and track change in biodiversity globally. This book is a road map on how to observe and interpret terrestrial biodiversity across scales through plants—primary producers and the foundation of the trophic pyramid. It honors the fact that biodiversity exists across different dimensions, including both phylogenetic and functional. Then, it relates these aspects of biodiversity to another dimension, the spectral diversity captured by remote sensing instruments operating at scales from leaf to canopy to biome. The biodiversity community has needed a Rosetta Stone to translate between the language of satellite remote sensing and its resulting spectral diversity and the languages of those exploring the phylogenetic diversity and functional trait diversity of life on Earth. By assembling the vital translation, this volume has globalized our ability to track biodiversity state and change. Thus, a global problem meets a key component of the global solution. The editors have cleverly built the book in three parts. Part 1 addresses the theory behind the remote sensing of terrestrial plant biodiversity: why spectral diversity relates to plant functional traits and phylogenetic diversity. Starting with first principles, it connects plant biochemistry, physiology, and macroecology to remotely sensed spectra and explores the processes behind the patterns we observe. Examples from the field demonstrate the rising synthesis of multiple disciplines to create a new cross-spatial and spectral science of biodiversity. Part 2 discusses how to implement this evolving science. It focuses on the plethora of novel in-situ, airborne, and spaceborne Earth observation tools currently and soon to be available while also incorporating the ways of actually making biodiversity measurements with these tools. It includes instructions for organizing and conducting a field campaign. Throughout, there is a focus on the burgeoning field of imaging spectroscopy, which is revolutionizing our ability to characterize life remotely. Part 3 takes on an overarching issue for any effort to globalize biodiversity observations, the issue of scale. It addresses scale from two perspectives. The first is that of combining observations across varying spatial, temporal, and spectral resolutions for better understanding—that is, what scales and how. This is an area of ongoing research driven by a confluence of innovations in observation systems and rising computational capacity. The second is the organizational side of the scaling challenge. It explores existing frameworks for integrating multi-scale observations within global networks. The focus here is on what practical steps can be taken to organize multi-scale data and what is already happening in this regard. These frameworks include essential biodiversity variables and the Group on Earth Observations Biodiversity Observation Network (GEO BON). This book constitutes an end-to-end guide uniting the latest in research and techniques to cover the theory and practice of the remote sensing of plant biodiversity. In putting it together, the editors and their coauthors, all preeminent in their fields, have done a great service for those seeking to understand and conserve life on Earth—just when we need it most. For if the world is ever to construct a coordinated response to the planetwide crisis of biodiversity loss, it must first assemble adequate—and global—measures of what we are losing

Artificial Neural Networks in Agriculture

Modern agriculture needs to have high production efficiency combined with a high quality of obtained products. This applies to both crop and livestock production. To meet these requirements, advanced methods of data analysis are more and more frequently used, including those derived from artificial intelligence methods. Artificial neural networks (ANNs) are one of the most popular tools of this kind. They are widely used in solving various classification and prediction tasks, for some time also in the broadly defined field of agriculture. They can form part of precision farming and decision support systems. Artificial neural networks can replace the classical methods of modelling many issues, and are one of the main alternatives to classical mathematical models. The spectrum of applications of artificial neural networks is very wide. For a long time now, researchers from all over the world have been using these tools to support agricultural production, making it more efficient and providing the highest-quality products possible