Propagation of uncertainty in atmospheric parameters to hyperspectral unmixing

Atmospheric correction (AC) is important in pre-processing of airborne hyperspectral imagery. AC requires knowledge on the atmospheric state expressed by atmospheric condition parameters. Their values are affected by uncertainties that propagate to the application level. This study investigates the propagation of uncertainty from column water vapor (CWV) and aerosol optical depth (AOD) towards abundance maps obtained by means of spectral unmixing. Both Fully Constrained Least Squares (FCLS) and FCLS with Total Variation (FCLS-TV) are applied. We use five simulated datasets contaminated by various noise levels. Three datasets cover two spectral scenarios with different endmembers. A univariate and a bivariate analysis are carried out on CWV and AOD. The other two datasets are used to analyze the effect of surface albedo. The analysis identifies trends in performance degradation caused by the gradual shift in parameter values from their true value. The maximum achievable performance depends upon spectral characteristics of the datasets, noise level, and surface albedo. As expected, under noisy conditions FCLS-TV performs better than FCLS. Our research opens new perspectives for applications where estimation of reflectance is so far considered to be deterministic

Identification of urban surface materials using high-resolution hyperspectral aerial imagery

La connaissance des matériaux de surface est essentielle pour l’aménagement et la gestion des villes. Avec les avancées en télédétection, particulièrement en imagerie de haute résolution spatiale et spectrale, l’identification et la cartographie détaillée des matériaux de surface en milieu urbain sont maintenant envisageables. Les signatures spectrales décrivent les interactions entre les objets au sol et le rayonnement solaire, et elles sont supposées uniques pour chaque type de matériau de surface. Dans ce projet de recherche nous avons utilisé des images hyperspectrales aériennes du capteur CASI, avec une résolution de 1 m2 et 96 bandes contigües entre 380nm et 1040nm. Ces images couvrant l’île de Montréal (QC, Canada), acquises en 2016, ont été analysées pour identifier les matériaux de surfaces. Pour atteindre ces objectifs, notre méthode d’analyse est fondée sur la comparaison des signatures spectrales d’un pixel quelconque à celles des objets typiques contenues dans des bibliothèques spectrales (matériaux inertes et végétation). Pour mesurer la correspondance entre la signature spectrale d’un objet et la signature spectrale de référence nous avons utilisé deux métriques. La première métrique tient compte de la forme d’une signature spectrale et la seconde, de la différence des valeurs de réflectance entre la signature spectrale observée et celle de référence. Un classificateur flou utilisant ces deux métriques est alors appliqué afin de reconnaître le type de matériau de surface sur la base du pixel. Des signatures spectrales typiques ont été extraites des deux librairies spectrales (ASTER et HYPERCUBE). Des signatures spectrales des objets typiques à Montréal mesurées sur le terrain (spectroradiomètre ASD) ont été aussi utilisées comme références. Trois grandes catégories de matériaux ont été identifiées dans les images pour faciliter la comparaison entre les classifications par source de références spectrales : l’asphalte, le béton et la végétation. La classification utilisant ASTER comme bibliothèque de référence a eu le plus grand taux de réussite avec 92%, suivi par ASD à 88% et finalement HYPERCUBE avec 80%. Nous 5 n’avons pas trouvé de différences significatives entre les trois résultats, ce qui indique que la classification est indépendante de la source des signatures spectrales de référence.Knowledge of surface cover materials is crucial for urban planning and management. With advances in remote sensing, especially in high spatial and spectral resolution imagery, the identification and detailed mapping of surface materials in urban areas based on spectral signatures are now feasible. Spectral signatures describe the interactions between ground objects and solar radiation and are assumed unique for each type of material. In this research, we use airborne CASI images with 1 m2 spatial resolution, with 96 contiguous bands in a spectral range between 367 nm and 1044 nm. These images covering the island of Montreal (Quebec, Canada), obtained in 2016, were analyzed to identify urban surface materials. The objectives of the project were first to find a correspondence between the physical and chemical characteristic of typical surface materials, present in the Montreal scenes, and the spectral signatures within the images. Second, to develop a sound methodology for identifying these surface materials in urban landscapes. To reach these objectives, our method of analysis is based on a comparison of pixel spectral signatures to those contained in a reference spectral library that describe typical surface covering materials (inert materials and vegetation). Two metrics were used in order to measure the correspondence of pixel spectral signatures and reference spectral signature. The first metric considers the shape of a spectral signature and the second the difference of reflectance values between the observed and reference spectral signature. A fuzzy classifier using these two metrics is then applied to recognize the type of material on a pixel basis. Typical spectral signatures were extracted from two spectral libraries (ASTER and HYPERCUBE). Spectral signatures of typical objects in Montreal measured on the ground (ASD spectroradiometer) were also used as reference spectra. Three general types of surface materials (asphalt, concrete, and vegetation) were used to ease the comparison between classifications using these spectral libraries. The classification using ASTER as a reference library had the highest success rate reaching 92%, followed by the field spectra at 88%, and finally with HYPERCUBE at 80%. There were no significant differences in the classification results indicating that the methodology works independently of the source of reference spectral signatures

NASA's surface biology and geology designated observable: A perspective on surface imaging algorithms

The 2017–2027 National Academies' Decadal Survey, Thriving on Our Changing Planet, recommended Surface Biology and Geology (SBG) as a “Designated Targeted Observable” (DO). The SBG DO is based on the need for capabilities to acquire global, high spatial resolution, visible to shortwave infrared (VSWIR; 380–2500 nm; ~30 m pixel resolution) hyperspectral (imaging spectroscopy) and multispectral midwave and thermal infrared (MWIR: 3–5 μm; TIR: 8–12 μm; ~60 m pixel resolution) measurements with sub-monthly temporal revisits over terrestrial, freshwater, and coastal marine habitats. To address the various mission design needs, an SBG Algorithms Working Group of multidisciplinary researchers has been formed to review and evaluate the algorithms applicable to the SBG DO across a wide range of Earth science disciplines, including terrestrial and aquatic ecology, atmospheric science, geology, and hydrology. Here, we summarize current state-of-the-practice VSWIR and TIR algorithms that use airborne or orbital spectral imaging observations to address the SBG DO priorities identified by the Decadal Survey: (i) terrestrial vegetation physiology, functional traits, and health; (ii) inland and coastal aquatic ecosystems physiology, functional traits, and health; (iii) snow and ice accumulation, melting, and albedo; (iv) active surface composition (eruptions, landslides, evolving landscapes, hazard risks); (v) effects of changing land use on surface energy, water, momentum, and carbon fluxes; and (vi) managing agriculture, natural habitats, water use/quality, and urban development. We review existing algorithms in the following categories: snow/ice, aquatic environments, geology, and terrestrial vegetation, and summarize the community-state-of-practice in each category. This effort synthesizes the findings of more than 130 scientists

Estimation of Power Plant Emissions with Unscented Kalman Filter

© 2008-2012 IEEE. Emissions from power plants constitute a major part of air pollution and should be adequately estimated. In this paper, we consider the problem of estimating nitrogen dioxide (NO-X ) emission of power plants by developing an inverse method to integrate satellite observations of atmospheric pollutant column concentrations with species concentrations and direct sensitivities predicted by a regional air quality model, in order to discern biases in the emissions of the pollutant precursors. Using this method, the emission fields are analyzed using a 'bottom-up' approach, with an inversion performed by an unscented Kalman filter (UKF) to improve estimation profiles from emissions inventories data for the Sydney metropolitan area. The idea is to integrate information from the original inventories with tropospheric nitrogen dioxide (NO-2) emissions estimated during one month from the air pollution model-chemical transport model, and then, for validation, to compare the resulting model with satellite retrievals from the ozone monitoring instrument (OMI) above the region. The UKF-based estimation of NO-2 emissions shows better agreement with OMI observations, implying a significant improvement in accuracy as compared with the original inventories. Therefore, the proposed method is a promising tool for estimation of air emissions in urban areas

Caracterização e estudo comparativo de exsudações de hidrocarbonetos e plays petrolíferos em bacias terrestres das regiões central do Irã e sudeste do Brasil usando sensoriamento remoto espectral

Orientador: Carlos Roberto de Souza FilhoTese (doutorado) - Universidade Estadual de Campinas, Instituto de GeociênciasResumo: O objetivo desta pesquisa foi explorar as assinaturas de exsudações de hidrocarbonetos na superfície usando a tecnologia de detecção remota espectral. Isso foi alcançado primeiro, realizando uma revisão abrangente das capacidades e potenciais técnicas de detecção direta e indireta. Em seguida, a técnica foi aplicada para investigar dois locais de teste localizados no Irã e no Brasil, conhecidos por hospedar sistemas ativos de micro-exsudações e afloramentos betuminosos, respectivamente. A primeira área de estudo está localizada perto da cidade de Qom (Irã), e está inserida no campo petrolífero Alborz, enterrado sob sedimentos datados do Oligoceno da Formação Upper Red. O segundo local está localizado perto da cidade de Anhembi (SP), na margem oriental da bacia do Paraná, no Brasil, e inclui acumulações de betume em arenitos triássicos da Formação Pirambóia. O trabalho na área de Qom integrou evidências de (i) estudos petrográficos e geoquímicos em laboratório, (ii) investigações de afloramentos em campo, e (iii) mapeamento de anomalia em larga escala através de conjuntos de dados multi-espectrais ASTER e Sentinel-2. O resultado deste estudo se trata de novos indicadores mineralógicos e geoquímicos para a exploração de micro-exsudações e um modelo de micro-exsudações atualizado. Durante este trabalho, conseguimos desenvolver novas metodologias para análise de dados espectroscópicos. Através da utilização de dados simulados, indicamos que o instrumento de satélite WorldView-3 tem potencial para detecção direta de hidrocarbonetos. Na sequência do estudo, dados reais sobre afloramentos de arenitos e óleo na área de Anhembi foram investigados. A área foi fotografada novamente no chão e usando o sistema de imagem hiperespectral AisaFENIX. Seguiu-se estudos e amostragem no campo,incluindo espectroscopia de alcance fechado das amostras no laboratório usando instrumentos de imagem (ou seja, sisuCHEMA) e não-imagem (ou seja, FieldSpec-4). O estudo demonstrou que uma abordagem espectroscópica multi-escala poderia fornecer uma imagem completa das variações no conteúdo e composição do betume e minerais de alteração que acompanham. A assinatura de hidrocarbonetos, especialmente a centrada em 2300 nm, mostrou-se consistente e comparável entre as escalas e capaz de estimar o teor de betume de areias de petróleo em todas as escalas de imagemAbstract: The objective of this research was to explore for the signatures of seeping hydrocarbons on the surface using spectral remote sensing technology. It was achieved firstly by conducting a comprehensive review of the capacities and potentials of the technique for direct and indirect seepage detection. Next, the technique was applied to investigate two distinctive test sites located in Iran and Brazil known to retain active microseepage systems and bituminous outcrops, respectively. The first study area is located near the city of Qom in Iran, and consists of Alborz oilfield buried under Oligocene sediments of the Upper-Red Formation. The second site is located near the town of Anhembi on the eastern edge of the Paraná Basin in Brazil and includes bitumen accumulations in the Triassic sandstones of the Pirambóia Formation. Our work in Qom area integrated evidence from (i) petrographic, spectroscopic, and geochemical studies in the laboratory, (ii) outcrop investigations in the field, and (iii) broad-scale anomaly mapping via orbital remote sensing data. The outcomes of this study was novel mineralogical and geochemical indicators for microseepage characterization and a classification scheme for the microseepage-induced alterations. Our study indicated that active microseepage systems occur in large parts of the lithofacies in Qom area, implying that the extent of the petroleum reservoir is much larger than previously thought. During this work, we also developed new methodologies for spectroscopic data analysis and processing. On the other side, by using simulated data, we indicated that WorldView-3 satellite instrument has the potential for direct hydrocarbon detection. Following this demonstration, real datasets were acquired over oil-sand outcrops of the Anhembi area. The area was further imaged on the ground and from the air by using an AisaFENIX hyperspectral imaging system. This was followed by outcrop studies and sampling in the field and close-range spectroscopy in the laboratory using both imaging (i.e. sisuCHEMA) and nonimaging instruments. The study demonstrated that a multi-scale spectroscopic approach could provide a complete picture of the variations in the content and composition of bitumen and associated alteration mineralogy. The oil signature, especially the one centered at 2300 nm, was shown to be consistent and comparable among scales, and capable of estimating the bitumen content of oil-sands at all imaging scalesDoutoradoGeologia e Recursos NaturaisDoutor em Geociências2015/06663-7FAPES

Remote sensing-based fire frequency mapping in a savannah rangeland

Burnt area mapping and fire frequency analysis were carried out in Hwange National Park, Zimbabwe. Hwange National Park typifies a savannah ecosystem which is semi-arid and fire-prone. This paper presents a geospatial analysis to quantify the spatial distribution and fire frequency from 2000 to 2006. Moderate Resolution Imaging Spectroradiometer (MODIS) images from 2000 to 2006 were obtained and classified for burnt area mapping. Linear pixel unmixing was used for image classification and subsequent mapping of burnt areas. The results showed that it was feasible to have discrimination of burnt areas and ‘un-burnt’ areas as well as generating a six year fire frequency map of the study area. Accuracy assessment of the classified images was carried out using field obtained information on fire occurrence to validate the classification results. An error matrix quantified accuracy of classified maps through producer's accuracy, user's accuracy and overall accuracy. High overall accuracy rates of appromately 96%, in turne, justify use of linear pixel unmixing in identifying and mapping burnt areas. Thus pixel unmixing offers a viable mapping tool for fire monitoring and management in protected areas

Summaries of the Sixth Annual JPL Airborne Earth Science Workshop

This publication contains the summaries for the Sixth Annual JPL Airborne Earth Science Workshop, held in Pasadena, California, on March 4-8, 1996. The main workshop is divided into two smaller workshops as follows: (1) The Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) workshop, on March 4-6. The summaries for this workshop appear in Volume 1; (2) The Airborne Synthetic Aperture Radar (AIRSAR) workshop, on March 6-8. The summaries for this workshop appear in Volume 2

Modeling wildland fire radiance in synthetic remote sensing scenes

This thesis develops a framework for implementing radiometric modeling and visualization of wildland fire. The ability to accurately model physical and op- tical properties of wildfire and burn area in an infrared remote sensing system will assist efforts in phenomenology studies, algorithm development, and sensor evaluation. Synthetic scenes are also needed for a Wildland Fire Dynamic Data Driven Applications Systems (DDDAS) for model feedback and update. A fast approach is presented to predict 3D flame geometry based on real time measured heat flux, fuel loading, and wind speed. 3D flame geometry could realize more realistic radiometry simulation. A Coupled Atmosphere-Fire Model is used to de- rive the parameters of the motion field and simulate fire dynamics and evolution. Broad band target (fire, smoke, and burn scar) spectra are synthesized based on ground measurements and MODTRAN runs. Combining the temporal and spa- tial distribution of fire parameters, along with the target spectra, a physics based model is used to generate radiance scenes depicting what the target might look like as seen by the airborne sensor. Radiance scene rendering of the 3D flame includes 2D hot ground and burn scar cooling, 3D flame direct radiation, and 3D indirect reflected radiation. Fire Radiative Energy (FRE) is a parameter defined from infrared remote sensing data that is applied to determine the radiative energy released during a wildland fire. FRE derived with the Bi-spectral method and the MIR radiance method are applied to verify the fire radiance scene synthesized in this research. The results for the synthetic scenes agree well with published values derived from wildland fire images

Statistical atmospheric parameter retrieval largely benefits from spatial-spectral image compression

The infrared atmospheric sounding interferometer (IASI) is flying on board of the Metop satellite series, which is part of the EUMETSAT Polar System. Products obtained from IASI data represent a significant improvement in the accuracy and quality of the measurements used for meteorological models. Notably, the IASI collects rich spectral information to derive temperature and moisture profiles, among other relevant trace gases, essential for atmospheric forecasts and for the understanding of weather. Here, we investigate the impact of near-lossless and lossy compression on IASI L1C data when statistical retrieval algorithms are later applied. We search for those compression ratios that yield a positive impact on the accuracy of the statistical retrievals. The compression techniques help reduce certain amount of noise on the original data and, at the same time, incorporate spatial-spectral feature relations in an indirect way without increasing the computational complexity. We observed that compressing images, at relatively low bit rates, improves results in predicting temperature and dew point temperature, and we advocate that some amount of compression prior to model inversion is beneficial. This research can benefit the development of current and upcoming retrieval chains in infrared sounding and hyperspectral sensors