Mapping Impervious Surface Using Phenology-Integrated and Fisher Transformed Linear Spectral Mixture Analysis

The impervious surface area (ISA) is a key indicator of urbanization, which brings out serious adverse environmental and ecological consequences. The ISA is often estimated from remotely sensed data via spectral mixture analysis (SMA). However, accurate extraction of ISA using SMA is compromised by two major factors, endmember spectral variability and plant phenology. This study developed a novel approach that incorporates phenology with Fisher transformation into a conventional linear spectral mixture analysis (PF-LSMA) to address these challenges. Four endmembers, high albedo, low albedo, evergreen vegetation, and seasonally exposed soil (H-L-EV-SS) were identified for PF-LSMA, considering the phenological characteristic of Shanghai. Our study demonstrated that the PF-LSMA effectively reduced the within-endmember spectral signature variation and accounted for the endmember phenology effects, and thus well-discriminated impervious surface from seasonally exposed soil, enhancing the accuracy of ISA extraction. The ISA fraction map produced by PF-LSMA (RMSE = 0.1112) outperforms the single-date image Fisher transformed unmixing method (F-LSMA) (RMSE = 0.1327) and the other existing major global ISA products. The PF-LSMA was implemented on the Google Earth Engine platform and thus can be easily adapted to extract ISA in other places with similar climate conditions.Peer Reviewe

Mapping Impervious Surface Using Phenology-Integrated and Fisher Transformed Linear Spectral Mixture Analysis

The impervious surface area (ISA) is a key indicator of urbanization, which brings out serious adverse environmental and ecological consequences. The ISA is often estimated from remotely sensed data via spectral mixture analysis (SMA). However, accurate extraction of ISA using SMA is compromised by two major factors, endmember spectral variability and plant phenology. This study developed a novel approach that incorporates phenology with Fisher transformation into a conventional linear spectral mixture analysis (PF-LSMA) to address these challenges. Four endmembers, high albedo, low albedo, evergreen vegetation, and seasonally exposed soil (H-L-EV-SS) were identified for PF-LSMA, considering the phenological characteristic of Shanghai. Our study demonstrated that the PF-LSMA effectively reduced the within-endmember spectral signature variation and accounted for the endmember phenology effects, and thus well-discriminated impervious surface from seasonally exposed soil, enhancing the accuracy of ISA extraction. The ISA fraction map produced by PF-LSMA (RMSE = 0.1112) outperforms the single-date image Fisher transformed unmixing method (F-LSMA) (RMSE = 0.1327) and the other existing major global ISA products. The PF-LSMA was implemented on the Google Earth Engine platform and thus can be easily adapted to extract ISA in other places with similar climate conditions

Multiscale Imaging of Evapotranspiration

Evapotranspiration (ET; evaporation + transpiration) is central to a wide range of biological, chemical, and physical processes in the Earth system. Accurate remote sensing of ET is challenging due to the interrelated and generally scale dependent nature of the physical factors which contribute to the process. The evaporation of water from porous media like sands and soils is an important subset of the complete ET problem. Chapter 1 presents a laboratory investigation into this question, examining the effects of grain size and composition on the evolution of drying sands. The effects of composition are found to be 2-5x greater than the effects of grain size, indicating that differences in heating caused by differences in reflectance may dominate hydrologic differences caused by grain size variation. In order to relate the results of Chapter 1 to the satellite image archive, however, the question of information loss between hyperspectral (measurements at 100s of wavelength intervals) laboratory measurements and multispectral (≤ 12 wavelength intervals) satellite images must be addressed. Chapter 2 focuses on this question as applied to substrate materials such as sediment, soil, rock, and non-photosynthetic vegetation. The results indicate that the continuum that is resolved by multispectral sensors is sufficient to resolve the gradient between sand-rich and clay-rich soils, and that this gradient is also a dominant feature in hyperspectral mixing spaces where the actual absorptions can be resolved. Multispectral measurements can be converted to biogeophysically relevant quantities using spectral mixture analysis (SMA). However, retrospective multitemporal analysis first requires cross-sensor calibration of the mixture model. Chapter 3 presents this calibration, allowing multispectral image data to be used interchangeably throughout the Landsat 4-8 archive. In addition, a theoretical explanation is advanced for the observed superior scaling properties of SMA-derived fraction images over spectral indices. The physical quantities estimated by the spectral mixture model are then compared to simultaneously imaged surface temperature, as well as to the derived parameters of ET Fraction and Moisture Availability. SMA-derived vegetation abundance is found to produce substantially more informative ET maps, and SMA-derived substrate fraction is found to yield a surprisingly strong linear relationship with surface temperature. These results provide context for agricultural applications. Chapter 5 investigates the question of mapping and monitoring rice agricultural using optical and thermal satellite image time series. Thermal image time series are found to produce more accurate maps of rice presence/absence, but optical image time series are found to produce more accurate maps of rice crop timing. Chapter 6 takes a more global approach, investigating the spatial structure of agricultural networks for a diverse set of landscapes. Surprisingly consistent scaling relations are found. These relations are assessed in the context of a network-based approach to land cover analysis, with potential implications for the scale dependence of ET estimates. In sum, this thesis present a novel approach to improving ET estimation based on a synthesis of complementary laboratory measurements, satellite image analysis, and field observations. Alone, each of these independent sources of information provides novel insights. Viewed together, these insights form the basis of a more accurate and complete geophysical understanding of the ET phenomenon

Modeling quaternary geomorphic surfaces using laboratory, field, and imaging spectrometry in the lower Colorado Sonoran Desert : the Chameleon concept

Chameleon is a physics-based landscape modeling software system designed for modeling and simulations applications. Hyperspectral laboratory, field, and imaging spectrometer measurements are collected as empirical foundation data. Linear spectral unmixing is performed to decompose each image pixel into spectral endmembers. Mathematical manipulation of these fractional abundances and introduction of new spectral information is accomplished with spectral editing tools. Image spectra are modified on a sub-pixel, per-pixel, or neighborhood basis, or the entire hypercube can be customized at once. Chameleon then regenerates synthetic, but spectrally accurate, terrain models using linear spectral remixing algorithms. By incorporating elevation, sun angle, and weather data, the landscape becomes a "Chameleon"- able to change hyperspectral properties based on multitemporal spectral measurements, requirements for developmental tests and operational training, or as required by specific simulation scenarios. Advanced knowledge of natural environments to be modeled is prerequisite to generating useful synthetic terrains. Our spectral research on and Quaternary geomorphic surfaces suggests that deserts (often assumed to be less difficult to study remotely than humid, temperate, and cold environments) are more complex than is generally accepted. A variety of rock coatings can significantly alter reflectance in the solar reflected spectrum. Weathering rinds and carbonate deposits inhibit lithologic reflectance altogether. However, manganese-rich rock varnish obscures rock reflectance in the visible and near infrared wavelengths, but transmits lithologic information in the 2,000 to 2,500 nanometer (nm) wavelengths. Surface soils on desert pavements consist of a layer of eolian dust that overlies an accreting vesicular (Av) horizon. These soils have same structure and chemistry, and, therefore, the same hyperspectral signature, regardless of landform age, geomorphic process, or parent material. From a remote sensing perspective, this has a normalizing effect on reflectance across the landscape. Spectral Mixture Analysis is a proven hyperspectral technique for mapping composition and abundance of surface materials characteristic of volcanic landforms that exhibit diagnostic absorption features. We found that desert pavement spectra are featureless in that they exhibit few distinct spectral features related to rock varnish, clast lithology, or soil. Image spectra of these surfaces are the result of intimate mixtures of heterogeneous materials, requiring nonlinear spectral unmixing solutions

Enviromentální aplikace obrazové spektroskopie

The main purpose of this thesis is to use Image Spectroscopy as a tool to monitor the environmental conditions in a region affected by anthropogenic activities via estimating both geochemical and biochemical parameters on a regional scale. The research has been carried on the Sokolov lignite mine, NW Bohemia, a region affected by long-term extensive mining. The thesis is divided into two thematic parts. First part is devoted to applications of Image Spectroscopy into Acid Mine Drainage mapping and its related issues (chapters 2 and 3). In chapter 2 the equivalent mineral end-members were successfully derived from the ASTER image data (Advanced Space-borne Thermal Emission and Reflection Radiometer satellite data). In the chapter 3 the pH was estimated on the basis of mineral and image spectroscopy. The Multi Range Spectral Feature Fitting (MRSFF) technique was utilized for mineral mapping and the multiple regression model using the fit images, the results of MRSFF, as inputs was constructed to estimate the surface pH and statistical significant accuracy was attained. In the second thematic part (chapters 4-6) Image Spectroscopy is applied into monitoring of vegetation stress. A new statistical method was developed to assess the physiological status of macroscopically undamaged foliage of Norway...Předložená disertační práce se věnuje aplikaci metod obrazové spektroskopie jako moderního nástroje pro environmentální monitoring, přičemž se zaměřuje na modelování vybraných geochemických a biochemických parametrů Disertační práce je členěna do dvou tematických celků. První z nich (kapitoly 2 a 3) je věnován aplikaci minerální a obrazové spektroskopie pro vymezení plošného výskytu povrchové acidifikace (anglický termín: AMD - Acid Mine Drainage) a modelování povrchového pH. Druhá tematická část (kapitoly 4, 5 a 6) se věnuje zhodnocení fyziologického stavu smrkových porostů. V kapitole 2 jsou s využitím satelitních dat ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer satellite data) plošně vymezeny kyselé zvětralinové povrchy (pH<4), jež charakterizuje výskyt jarositu a lignitu (hnědé uhlí). Kapitola 3 se věnuje vytvoření modelu pro odhad povrchového pH odkrytých substrátů s využitím leteckých hyperspektrálních dat HyMap (07/2009). Tato studie je jednou z prvních, jež aplikuje metody obrazové spektroskopie pro kvantitativní modelování pH v prostředí povrchových dolů vyznačující se vysokou heterogenitou. V druhé tematické části je obrazová spektroskopie aplikována do oblasti monitoringu zdravotního stavu lesních smrkových porostů, které se vyskytují v bezprostředním okolí...Department of Applied Geoinformatics and CartographyKatedra aplikované geoinformatiky a kartografieFaculty of SciencePřírodovědecká fakult

Multispectral and Hyperspectral Remote Sensing Data for Mineral Exploration and Environmental Monitoring of Mined Areas

In recent decades, remote sensing technology has been incorporated in numerous mineral exploration projects in metallogenic provinces around the world. Multispectral and hyperspectral sensors play a significant role in affording unique data for mineral exploration and environmental hazard monitoring. This book covers the advances of remote sensing data processing algorithms in mineral exploration, and the technology can be used in monitoring and decision-making in relation to environmental mining hazard. This book presents state-of-the-art approaches on recent remote sensing and GIS-based mineral prospectivity modeling, offering excellent information to professional earth scientists, researchers, mineral exploration communities and mining companies

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

Using Remote Sensing Techniques to Improve Hydrological Predictions in a Rapidly Changing World

Remotely sensed geophysical datasets are being produced at increasingly fast rates to monitor various aspects of the Earth system in a rapidly changing world. The efficient and innovative use of these datasets to understand hydrological processes in various climatic and vegetation regimes under anthropogenic impacts has become an important challenge, but with a wide range of research opportunities. The ten contributions in this Special Issue have addressed the following four research topics: (1) Evapotranspiration estimation; (2) rainfall monitoring and prediction; (3) flood simulations and predictions; and (4) monitoring of ecohydrological processes using remote sensing techniques. Moreover, the authors have provided broader discussions on how to capitalize on state-of-the-art remote sensing techniques to improve hydrological model simulations and predictions, to enhance their skills in reproducing processes for the fast-changing world

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