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

NASA SensorWeb and OGC Standards for Disaster Management

I. Goal: Enable user to cost-effectively find and create customized data products to help manage disasters; a) On-demand; b) Low cost and non-specialized tools such as Google Earth and browsers; c) Access via open network but with sufficient security. II. Use standards to interface various sensors and resultant data: a) Wrap sensors in Open Geospatial Consortium (OGC) standards; b) Wrap data processing algorithms and servers with OGC standards c) Use standardized workflows to orchestrate and script the creation of these data; products. III. Target Web 2.0 mass market: a) Make it simple and easy to use; b) Leverage new capabilities and tools that are emerging; c) Improve speed and responsiveness

Sensor capability and atmospheric correction in ocean colour remote sensing

© 2015 by the authors; licensee MDPI, Basel, Switzerland. Accurate correction of the corrupting effects of the atmosphere and the water's surface are essential in order to obtain the optical, biological and biogeochemical properties of the water from satellite-based multi-and hyper-spectral sensors. The major challenges now for atmospheric correction are the conditions of turbid coastal and inland waters and areas in which there are strongly-absorbing aerosols. Here, we outline how these issues can be addressed, with a focus on the potential of new sensor technologies and the opportunities for the development of novel algorithms and aerosol models. We review hardware developments, which will provide qualitative and quantitative increases in spectral, spatial, radiometric and temporal data of the Earth, as well as measurements from other sources, such as the Aerosol Robotic Network for Ocean Color (AERONET-OC) stations, bio-optical sensors on Argo (Bio-Argo) floats and polarimeters. We provide an overview of the state of the art in atmospheric correction algorithms, highlight recent advances and discuss the possible potential for hyperspectral data to address the current challenges

The HYSPIRI Decadal Survey Mission: Update on the Mission Concept and Science Objectives for Global Imaging Spectroscopy and Multi-Spectral Thermal Measurements

The NASA HyspIRI mission is planned to provide global solar reflected energy spectroscopic measurement of the terrestrial and shallow water regions of the Earth every 19 days will all measurements downlinked. In addition, HyspIRI will provide multi-spectral thermal measurements with a single band in the 4 micron region and seven bands in the 8 to 12 micron region with 5 day day/night coverage. A direct broadcast capability for measurement subsets is also planned. This HyspIRI mission is one of those designated in the 2007 National Research Council (NRC) Decadal Survey: Earth Science and Applications from Space. In the Decadal Survey, HyspIRI was recognized as relevant to a range of Earth science and science applications, including climate: "A hyperspectral sensor (e.g., FLORA) combined with a multispectral thermal sensor (e.g., SAVII) in low Earth orbit (LEO) is part of an integrated mission concept [described in Parts I and II] that is relevant to several panels, especially the climate variability panel." The HyspIRI science study group was formed in 2008 to evaluate and refine the mission concept. This group has developed a series of HyspIRI science objectives: (1) Climate: Ecosystem biochemistry, condition & feedback; spectral albedo; carbon/dust on snow/ice; biomass burning; evapotranspiration (2) Ecosystems: Global plant functional types, physiological condition, and biochemistry including agricultural lands (3) Fires: Fuel status, fire frequency, severity, emissions, and patterns of recovery globally (4) Coral reef and coastal habitats: Global composition and status (5) Volcanoes: Eruptions, emissions, regional and global impact (6) Geology and resources: Global distributions of surface mineral resources and improved understanding of geology and related hazards These objectives are achieved with the following measurement capabilities. The HyspIRI imaging spectrometer provides: full spectral coverage from 380 to 2500 at 10 nm sampling; 60 m spatial sampling with a 150 km swath; and fully downlinked coverage of the Earth's terrestrial and shallow water regions every 19 days to provide seasonal cloud-free coverage of the terrestrial surface. The HyspIRI Multi-Spectral Thermal instrument provides: 8 spectral bands from 4 to 12 microns; 60 m spatial sampling with a 600 km swath; and fully downlinked coverage of the Earth's terrestrial shallow water regions every 5 days (day/night) to provide nominally cloud-free monthly coverage. The HyspIRI mission also includes an on-board processing and direct broadcast capability, referred to as the Intelligent Payload Module (IPM), which will allow users with the appropriate antenna to download a subset of the HyspIRI data stream to a local ground station. These science and science application objectives are critical today and uniquely addressed by the combined imaging spectroscopy, thermal infrared measurements, and IPM direct broadcast capability of HyspIRI. Two key objectives are: (1) The global HyspIRI spectroscopic measurements of the terrestrial biosphere including vegetation composition and function to constrain and reduce the uncertainty in climate-carbon interactions and terrestrial biosphere feedback. (2) The global 8 band thermal measurements to provide improved constraint of fire related emissions. In this paper the current HyspIRI mission concept that has been reviewed and refined to its current level of maturity with a Data Products Symposium, Science Workshop and NASA HWorkshop is presented including traceability between the measurements and the science and science application objectives

Applications of Satellite Earth Observations section - NEODAAS: Providing satellite data for efficient research

The NERC Earth Observation Data Acquisition and Analysis Service (NEODAAS) provides a central point of Earth Observation (EO) satellite data access and expertise for UK researchers. The service is tailored to individual users’ requirements to ensure that researchers can focus effort on their science, rather than struggling with correct use of unfamiliar satellite data

Satellite monitoring of harmful algal blooms (HABs) to protect the aquaculture industry

Harmful algal blooms (HABs) can cause sudden and considerable losses to fish farms, for example 500,000 salmon during one bloom in Shetland, and also present a threat to human health. Early warning allows the industry to take protective measures. PML's satellite monitoring of HABs is now funded by the Scottish aquaculture industry. The service involves processing EO ocean colour data from NASA and ESA in near-real time, and applying novel techniques for discriminating certain harmful blooms from harmless algae. Within the AQUA-USERS project we are extending this capability to further HAB species within several European countries

Satellite remote sensing of active wildfires in Alaska's boreal forest

Thesis (Ph.D.) University of Alaska Fairbanks, 2017This research addresses improvements to the detection and characterization of active wildfires in Alaska with satellite-based sensors. The VIIRS I-band Fire Detection Algorithm for High Latitudes (VIFDAHL) was developed and evaluated against existing active fire products from the Visible Infrared Imaging Radiometer Suite (VIIRS) and the Moderate Resolution Imaging Spectroradiometer (MODIS). This new algorithm is based on VIIRS 375 m spatial resolution imagery and was tuned using fires in Alaska's boreal forest. It provides improved fire detection of low-intensity fires, especially during daytime and at sensor zenith angles smaller than approximately 50° off nadir. Low-intensity active fires, which represent residual combustion present after the passage of a high-intensity fire front, are not very well detected by existing active fire products. A second topic was fire remote sensing with ~30 m resolution imaging spectrometer (or hyperspectral instrument), the Hyperion sensor on NASA's EO-1 spacecraft, which was in use from 2000 to 2016. Hyperion had a much higher spectral resolution than VIIRS or MODIS, but no repeat imagery of the same active fire was available in Alaska. The investigation relied on absorption and emission features in the radiance spectra acquired at every pixel location. Three fire detection methods were evaluated using archived Hyperion data from three fires in interior Alaska from 2004 and 2009: A version of the Hyperspectral Fire Detection Algorithm (HFDI) produced excellent active fire maps; an approach that relies on a shortwave infrared carbon dioxide absorption feature and associated Continuum Interpolated Band Ratio (CO₂ CIBR) proved to be useful, but was affected by sensor noise and clouds; finally, a potassium emission feature from biomass burning was not detectable in the Hyperion data. Fire temperatures were determined using the Hyperion shortwave infrared spectra between 1400 nm and 2400 nm. The temperatures of active fire, the corresponding partial pixel areas, and the pixel areas occupied by unburned and already-burned vegetation, respectively, were modeled within each fire pixel. A model with two reflected background components and two temperature endmembers, applied to the same three study scenes, yielded an excellent fit to Hyperion spectral radiance data. Fire temperatures ranged from approximately 500-600 K to approximately 800-900 K. The retrieved lower fire temperatures are within the range of smoldering combustion; high-temperature values are limited by Hyperion's saturation behavior. High-temperature fire occupying 0.2% of a pixel (2 m²) was detectable. Sub-pixel fire area and temperature were also retrieved using VIIRS 750 m (M-band) data, with comparable results. Uncertainties were evaluated using a Monte Carlo simulation. This work offers insight into the sensitivity of fire detection products to time of day (largely due to overpass timing), spatial distribution over the study area (largely due to orbital properties) and sensor zenith angle. The results are relevant for sensor and algorithm design regarding the use of new multi- and hyperspectral sensors for fire science in the northern high latitudes. Data products resulting from this research were designed to be suitable for supporting fire management with an emphasis on real-time applications and also address less time-sensitive questions such as retrievals of fire temperature and time series of fire evolution.Chapter 1: General Introduction -- 1.1 Fires in the boreal forest -- 1.2 Satellite remote sensing of active fires -- 1.3 Objectives and structure of this dissertation -- References. Chapter 2: Detecting high and low-intensity fires in Alaska using VIIRS I-band data: An improved operational approach for high latitudes -- Abstract -- 2.1 Introduction -- 2.2 Global active fire products: a brief review -- 2.3 Wildfire study areas -- 2.3.1 Willow: Sockeye fire, June 2015 -- 2.3.2 Yukon-Koyukuk: multiple wildfires, July 2015 -- 2.3.3 Eagle: early-season wildfires, May 2015 -- 2.3.4 Northern Koyukuk: multiple large fires, July 2016 -- 2.4 Data -- 2.4.1 Global MODIS and VIIRS I-band products -- 2.4.2 VIIRS Sensor Data Record (SDR) data -- 2.4.3 Fire location and perimeter data -- 2.4.4 Landsat 8 imagery -- 2.4.5 Evaluation of operational MODIS and VIIRS I-band products -- 2.4.6 VIIRS I-band Fire Detection Algorithm for High Latitudes (VIFDAHL) -- 2.4.7 Validation using Landsat -- 2.5 Results -- 2.5.1 Exploratory data analysis of operational MODIS and VIIRS I-band fire detection datasets -- 2.5.2 VIIRS I-band Fire Detection Algorithm for High Latitudes (VIFDAHL) -- 2.6 Discussion and conclusions -- 2.7 Acknowledgements -- References. Chapter 3: Fire detection and temperature retrieval using EO-1 Hyperion data over selected Alaskan boreal fires -- Abstract -- 3.1 Introduction -- 3.2 Study Areas -- 3.3 Data -- 3.3.1 The Hyperion sensor on EO-1 -- 3.3.2 Hyperion scenes -- 3.4 Methods -- 3.4.1 Fire-related feature extraction -- 3.4.2 Fire detection -- 3.4.3 MODTRAN for atmospheric correction -- 3.4.4 Temperature retrieval -- 3.5 Results -- 3.5.1 Fire detection and comparative analysis -- 3.5.2 Temperature retrieval -- 3.6 Discussion -- 3.7 Conclusions, recommendations, and future work -- 3.7 Conclusions, recommendations, and future work -- 3.8 Acknowledgements -- References. Chapter 4: Sensitivity considerations in fire detection and sub-pixel fire temperature retrieval with Suomi-NPP VIIRS -- Abstract -- 4.1 Introduction -- 4.2 Study area and data used -- 4.3 Methods -- 4.3.1 Fire detection -- 4.3.2 Sensor angle statistics -- 4.3.3 Temperature retrieval -- 4.3.4 Atmospheric correction -- 4.3.5 Uncertainty estimation -- 4.4 Results and discussion -- 4.4.1 Zenith angle dependency of fire detection -- 4.4.2 Fire temperature and partial pixel area retrieval -- 4.5 Conclusions -- References. Chapter 5: General Conclusion -- References. Appendix A: Coal-Fire Hazard Mapping in High-Latitude Coal Basins: A Case Study from Interior Alaska -- A.1 High latitude coal fires -- A.1.1 Introduction -- A.1.2 Alaskan Context -- A.2 Case Study from Interior Alaska -- A.2.1 Introduction -- A.2.2 Study Area -- A.2.3 Data -- A.2.4 Data Processing -- A.2.5 Results -- A.2.6 Discussion -- A.2.7 Conclusions -- A.2.8 Acknowledgements -- A.2.9 Important Terms -- References

Mineral identification using data-mining in hyperspectral infrared imagery

Les applications de l’imagerie infrarouge dans le domaine de la géologie sont principalement des applications hyperspectrales. Elles permettent entre autre l’identification minérale, la cartographie, ainsi que l’estimation de la portée. Le plus souvent, ces acquisitions sont réalisées in-situ soit à l’aide de capteurs aéroportés, soit à l’aide de dispositifs portatifs. La découverte de minéraux indicateurs a permis d’améliorer grandement l’exploration minérale. Ceci est en partie dû à l’utilisation d’instruments portatifs. Dans ce contexte le développement de systèmes automatisés permettrait d’augmenter à la fois la qualité de l’exploration et la précision de la détection des indicateurs. C’est dans ce cadre que s’inscrit le travail mené dans ce doctorat. Le sujet consistait en l’utilisation de méthodes d’apprentissage automatique appliquées à l’analyse (au traitement) d’images hyperspectrales prises dans les longueurs d’onde infrarouge. L’objectif recherché étant l’identification de grains minéraux de petites tailles utilisés comme indicateurs minéral -ogiques. Une application potentielle de cette recherche serait le développement d’un outil logiciel d’assistance pour l’analyse des échantillons lors de l’exploration minérale. Les expériences ont été menées en laboratoire dans la gamme relative à l’infrarouge thermique (Long Wave InfraRed, LWIR) de 7.7m à 11.8 m. Ces essais ont permis de proposer une méthode pour calculer l’annulation du continuum. La méthode utilisée lors de ces essais utilise la factorisation matricielle non négative (NMF). En utlisant une factorisation du premier ordre on peut déduire le rayonnement de pénétration, lequel peut ensuite être comparé et analysé par rapport à d’autres méthodes plus communes. L’analyse des résultats spectraux en comparaison avec plusieurs bibliothèques existantes de données a permis de mettre en évidence la suppression du continuum. Les expérience ayant menés à ce résultat ont été conduites en utilisant une plaque Infragold ainsi qu’un objectif macro LWIR. L’identification automatique de grains de différents matériaux tels que la pyrope, l’olivine et le quartz a commencé. Lors d’une phase de comparaison entre des approches supervisées et non supervisées, cette dernière s’est montrée plus approprié en raison du comportement indépendant par rapport à l’étape d’entraînement. Afin de confirmer la qualité de ces résultats quatre expériences ont été menées. Lors d’une première expérience deux algorithmes ont été évalués pour application de regroupements en utilisant l’approche FCC (False Colour Composite). Cet essai a permis d’observer une vitesse de convergence, jusqu’a vingt fois plus rapide, ainsi qu’une efficacité significativement accrue concernant l’identification en comparaison des résultats de la littérature. Cependant des essais effectués sur des données LWIR ont montré un manque de prédiction de la surface du grain lorsque les grains étaient irréguliers avec présence d’agrégats minéraux. La seconde expérience a consisté, en une analyse quantitaive comparative entre deux bases de données de Ground Truth (GT), nommée rigid-GT et observed-GT (rigide-GT: étiquet manuel de la région, observée-GT:étiquetage manuel les pixels). La précision des résultats était 1.5 fois meilleur lorsque l’on a utlisé la base de données observed-GT que rigid-GT. Pour les deux dernières epxérience, des données venant d’un MEB (Microscope Électronique à Balayage) ainsi que d’un microscopie à fluorescence (XRF) ont été ajoutées. Ces données ont permis d’introduire des informations relatives tant aux agrégats minéraux qu’à la surface des grains. Les résultats ont été comparés par des techniques d’identification automatique des minéraux, utilisant ArcGIS. Cette dernière a montré une performance prometteuse quand à l’identification automatique et à aussi été utilisée pour la GT de validation. Dans l’ensemble, les quatre méthodes de cette thèse représentent des méthodologies bénéfiques pour l’identification des minéraux. Ces méthodes présentent l’avantage d’être non-destructives, relativement précises et d’avoir un faible coût en temps calcul ce qui pourrait les qualifier pour être utilisée dans des conditions de laboratoire ou sur le terrain.The geological applications of hyperspectral infrared imagery mainly consist in mineral identification, mapping, airborne or portable instruments, and core logging. Finding the mineral indicators offer considerable benefits in terms of mineralogy and mineral exploration which usually involves application of portable instrument and core logging. Moreover, faster and more mechanized systems development increases the precision of identifying mineral indicators and avoid any possible mis-classification. Therefore, the objective of this thesis was to create a tool to using hyperspectral infrared imagery and process the data through image analysis and machine learning methods to identify small size mineral grains used as mineral indicators. This system would be applied for different circumstances to provide an assistant for geological analysis and mineralogy exploration. The experiments were conducted in laboratory conditions in the long-wave infrared (7.7μm to 11.8μm - LWIR), with a LWIR-macro lens (to improve spatial resolution), an Infragold plate, and a heating source. The process began with a method to calculate the continuum removal. The approach is the application of Non-negative Matrix Factorization (NMF) to extract Rank-1 NMF and estimate the down-welling radiance and then compare it with other conventional methods. The results indicate successful suppression of the continuum from the spectra and enable the spectra to be compared with spectral libraries. Afterwards, to have an automated system, supervised and unsupervised approaches have been tested for identification of pyrope, olivine and quartz grains. The results indicated that the unsupervised approach was more suitable due to independent behavior against training stage. Once these results obtained, two algorithms were tested to create False Color Composites (FCC) applying a clustering approach. The results of this comparison indicate significant computational efficiency (more than 20 times faster) and promising performance for mineral identification. Finally, the reliability of the automated LWIR hyperspectral infrared mineral identification has been tested and the difficulty for identification of the irregular grain’s surface along with the mineral aggregates has been verified. The results were compared to two different Ground Truth(GT) (i.e. rigid-GT and observed-GT) for quantitative calculation. Observed-GT increased the accuracy up to 1.5 times than rigid-GT. The samples were also examined by Micro X-ray Fluorescence (XRF) and Scanning Electron Microscope (SEM) in order to retrieve information for the mineral aggregates and the grain’s surface (biotite, epidote, goethite, diopside, smithsonite, tourmaline, kyanite, scheelite, pyrope, olivine, and quartz). The results of XRF imagery compared with automatic mineral identification techniques, using ArcGIS, and represented a promising performance for automatic identification and have been used for GT validation. In overall, the four methods (i.e. 1.Continuum removal methods; 2. Classification or clustering methods for mineral identification; 3. Two algorithms for clustering of mineral spectra; 4. Reliability verification) in this thesis represent beneficial methodologies to identify minerals. These methods have the advantages to be a non-destructive, relatively accurate and have low computational complexity that might be used to identify and assess mineral grains in the laboratory conditions or in the field

PyrSat - Prevention and response to wild fires with an intelligent Earth observation CubeSat

Forest fires are a pervasive and serious problem. Besides loss of life and extensive environmental damage, fires also result in substantial economic losses, not to mention property damage, injuries, displacements and hardships experienced by the affected citizens. This project proposes a low-cost intelligent hyperspectral 3U CubeSat for the production of fire risk and burnt area maps. It applies Machine Learning algorithms to autonomously process images and obtain final data products on-board the satellite for direct transmission to users on the ground. Used in combination with other services such as EFFIS or AFIS, the system could considerably reduce the extent and consequences of forest fires