Coastal and Inland Aquatic Data Products for the Hyperspectral Infrared Imager (HyspIRI)

The HyspIRI Aquatic Studies Group (HASG) has developed a conceptual list of data products for the HyspIRI mission to support aquatic remote sensing of coastal and inland waters. These data products were based on mission capabilities, characteristics, and expected performance. The topic of coastal and inland water remote sensing is very broad. Thus, this report focuses on aquatic data products to keep the scope of this document manageable. The HyspIRI mission requirements already include the global production of surface reflectance and temperature. Atmospheric correction and surface temperature algorithms, which are critical to aquatic remote sensing, are covered in other mission documents. Hence, these algorithms and their products were not evaluated in this report. In addition, terrestrial products (e.g., land use land cover, dune vegetation, and beach replenishment) were not considered. It is recognized that coastal studies are inherently interdisciplinary across aquatic and terrestrial disciplines. However, products supporting the latter are expected to already be evaluated by other components of the mission. The coastal and inland water data products that were identified by the HASG, covered six major environmental and ecological areas for scientific research and applications: wetlands, shoreline processes, the water surface, the water column, bathymetry and benthic cover types. Accordingly, each candidate product was evaluated for feasibility based on the HyspIRI mission characteristics and whether it was unique and relevant to the HyspIRI science objectives

Detecting soil erosion in semi-arid Mediterranean environments using simulated EnMAP data

Soil is an essential nature resource. Management of this resource is vital for sustainability and the continued functioning of earths atmospheric, hydrospheric and lithospheric functioning. The assessment and continued monitoring of surface soil state provides the information required to effectively manage this resource. This research used a simulated Environmental Mapping and Analysis Program (EnMAP) hyperspectral image cube of an agricultural region in semi- arid Mediterranean Spain to classify soil erosion states. Multiple Endmember Spectral Mixture Analysis (MESMA) was used to derive within pixel fractions of eroded and accumulated soils. A Classification of the soil erosion states using the scene fraction outputs and digital terrain information. The information products generated in this research provided an optimistic outlook for the applicability of the future EnMAP sensor for soil erosion investigations in semi-arid Mediterranean environments. Additionally, this research verifies that the launch of the EnMAP satellite sensor in 2018 will provide the opportunity to further improve the monitoring of earth finite soil resources.NSERC create AMETHYST , Alberta Terrestrial Imaging Centre

The Use of Hyperspectral Imaging for Remote Sensing, and the Development of a Novel Hyperspectral Imager

This thesis determines the potential uses of a novel technology in hyperspectral remote sensing, by testing the capabilities of a prototype imaging spectrometer that was built using microslice technology. These capabilities are compared to those of current hyperspectral remote sensing instruments in the context of the requirements for various remote sensing applications. Due to the wide variety of potential applications for hyperspectral imaging, any unique capability of a new instrument is likely to improve a current application, or even develop a new one. The use of microslice technology allows a 2-dimensional eld of view (FoV) to be imaged simultane ously with a wide spectral range. Modelling of the remote sensing performance of the spectrometer shows that this enables it to achieve a signal to noise ratio (SNR) an order of magnitude higher than conventional hyperspectral instruments. The prototype microslice spectrometer images in the 475-650 nm wavelength range at 7 nm spectral resolution. It also images an instantaneous eld of view (IFoV) of 260 x 52 mrad, at a spatial resolution of 2.6 mrad. Classication techniques are used on ground based laboratory and eld test data from the instrument to demonstrate that it can accurately identify some mineral, vegetation, and water pollutant samples. Various trade-os can theoretically be performed on the prototype specications to develop an instru ment with particular capabilities for a specic application. This novel design means that a greater detector area is required than for conventional designs; but the 2-dimentsional FoV gives greater trade-o exibility, in particular allowing the SNR to enter into the trade-o equation. This unique capability was found to lend itself to two applications in particular: detecting water pollutants in rivers, and detecting hydrocarbons contamination of ecosystems

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

An investigation in the use of advanced remote sensing and geographic information system techniques for post-fire impact assessment on vegetation.

2006/2007Gli incendi boschivi rappresentano uno dei maggiori problemi ambientali nella regione Mediterranea con vaste superfici colpite ogni estate. Una stima dell’impatto ambientale degli incendi (a breve e a lungo termine) richiede la raccolta di informazioni accurate post-incendio relative al tipo di incendio, all’intensità, alla rigenerazione forestale ed al ripristino della vegetazione. L’utilizzo di tecniche avanzate di telerilevamento può fornire un valido strumento per lo studio di questi fenomeni. L’importanza di queste ricerche è stata più volte sottolineata dalla Commissione Europea che si è concentrata sullo studio degli incendi boschivi ed il loro effetto sulla vegetazione attraverso lo sviluppo di adeguati metodi di stima dell’impatto e di mitigazione. Scopo di questo lavoro è la stima dell’impatto post-incendio sulla vegetazione in ambiente Mediterraneo per mezzo di immagini satellitari ad alta risoluzione, di rilievi a terra e mediante tecniche avanzate di analisi dei dati. Il lavoro ha riguardato lo sviluppo di un sistema per l’integrazione di dati telerilevati ad altissima risoluzione spaziale e spettrale. Per la stima dell’impatto a breve termine, un modello di classificazione ad oggetti è stato sviluppato utilizzando immagini Ikonos ad altissima risoluzione spaziale per cartografare il tipo di incendio, differenziando l’incendio radente dall’incendio di chioma. I risultati mostrano che la classificazione ad oggetti potrebbe essere utilizzata per distinguere con elevata accuratezza (87% di accuratezza complessiva) le due tipologie di incendio, in particolare nei boschi Mediterranei aperti. È stata inoltre valutata la capacità della classificazione ad oggetti di distinguere e cartografare tre livelli di intensità del fuoco utilizzando le immagini Ikonos e l’accuratezza del risultato è stimata all’ 83%. Per la stima dell’impatto a lungo termine, la mappatura della rigenerazione post-incendio (pino) e la ripresa della vegetazione arbustiva sono state valutate mediante tre approcci: 1) la classificazione ad oggetti di immagini ad altissima risoluzione QuickBird che ha permesso di mappare la ripresa della vegetazione e l’impatto sulla copertura a seguito dell’incendio distinguendo due livelli di intensità dell’incendio (accuratezza della classificazione 86%). 2) l’analisi statistica di dati iperspettrali rilevati in campo che ha permesso una riduzione del 97% del volume di dati e la selezione delle migliori 14 bande per discriminare l’età e le specie di pino e le 18 migliori bande per la caratterizzazione delle specie arbustive. Successivamente, i dati iperspettrali Hyperion sono stati utlizzati per mappare la rigenerazione forestale e la ripresa della vegetazione. L’accuratezza complessiva della classificazione è stata del 75.1% considerando due diverse specie di pino ed altre specie vegetali. 3) una classificazione ad oggetti che ha combinato l’analisi dei dati QuickBird ed Hyperion. Si è registrato un aumento dell’accuratezza della classificazione pari all’8.06% rispetto all’utilizzo dei soli dati Hyperion. Complessivamente, si osserva che strumenti avanzati di telerilevamento consentono di raccogliere le informazioni relative alle aree incendiate, la rigenerazione forestale e la ripresa della vegetazione in modo accurato e vantaggioso in termini di costi e tempi.Forest fires are a major environmental problem in the Mediterranean region, where large areas are affected each summer. An assessment of the environmental impact of forest fires (in the short-term and in the long-term) requires the collection of accurate and detailed post-fire information related to fire type, fire severity, forest regeneration and vegetation recovery. Advanced tools in remote sensing provide a powerful tool for the study of this phenomenon. The importance of this work was often emphasized by the European Commission, which focused on the studying of forest fires and their effect on vegetation through the development of appropriate impact assessment and mitigation methods. The aim of this study was to assess the post-fire impact on vegetation in a Mediterranean environment by employing high quality satellite and field data and by using advanced data processing techniques. The work entailed the development of a whole system integrating very high spatial and spectral resolution remotely sensed data. For short-term impact assessment, an object-oriented model was developed using very high spatial resolution Ikonos imagery to map the type of fire, namely, canopy fire and surface fire. The results showed that object-oriented classification could be used to accurately distinguish and map areas of surface and crown fire spread (overall accuracy of 87%), especially that occurring in open Mediterranean forests. Also, the performance of object-based classification in mapping three levels of fire severity by employing high spatial resolution Ikonos imagery was evaluated, and accuracy of the obtained results was estimated to be 83%. As for long-term impact assessment, the mapping of post-fire forest regeneration (pine) and vegetation recovery (shrub) was performed by following three different approaches. First, the developed object-based classification of QuickBird (very high spatial resolution) allowed post-fire vegetation recovery and survival mapping of canopy within two different fire severity levels (86% of classification accuracy). The main effect of fire has been to create a more homogeneous landscape. Second, statistical analysis of field hyperspectral data allowed a 97% reduction in data volume and recommended 14 best narrowbands to discriminate among pine trees (age and species) and 18 bands that best characterize the different shrub species. Then, hyperspectral Hyperion was employed for mapping post-fire forest regeneration and vegetation recovery. The overall classification accuracy was found to be 75.81% when mapping two different regenerated pine species and other species of vegetation recovery. Third, an object-oriented combined analysis of QuickBird and Hyperion was investigated for the same objective. An improvement in classification accuracy of 8.06% was recorded when combining both Hyperion and QuickBird imageries than by using only the Hyperion image. Overall, it was observed that advanced tools in remote sensing provided the necessary means for gathering information about the burned areas, the regenerated forests and the recovered vegetations in a successful and a timely/cost effective manner.XX Ciclo197

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

UAVs for the Environmental Sciences

This book gives an overview of the usage of UAVs in environmental sciences covering technical basics, data acquisition with different sensors, data processing schemes and illustrating various examples of application