Search

The escape fraction of Lyman-continuum (LyC) photons (f(esc)) is a key parameter for determining the sources of cosmic reionization at z >= 6. At these redshifts, owing to the opacity of the intergalactic medium, the LyC emission cannot be measured directly. However, LyC leakers during the epoch of reionization could be identified using indirect indicators that have been extensively tested at low and intermediate redshifts. These include a high [OIII]/[OII] flux ratio, high star-formation surface density, and compact sizes. In this work, we present observations of 29 4.5 = 6 have provided a substantial contribution to cosmic reionization.

The escape fraction of Lyman-continuum (LyC) photons (f(esc)) is a key parameter for determining the sources of cosmic reionization at z >= 6. At these redshifts, owing to the opacity of the intergalactic medium, the LyC emission cannot be measured directly. However, LyC leakers during the epoch of reionization could be identified using indirect indicators that have been extensively tested at low and intermediate redshifts. These include a high [OIII]/[OII] flux ratio, high star-formation surface density, and compact sizes. In this work, we present observations of 29 4.5 = 6 have provided a substantial contribution to cosmic reionization.

The demand for iron ore has increased recently and employing geochemical and hyperspectral remote sensing techniques for discovering new ore and mineral resources have primarily been concentrated on the economic phases. The present study aimed to characterize the hyperspectral spectral signatures of iron ores of field samples to map the deposits that occurred in the Tirthamalai hill region, which lies in the parts of Harur Taluk, Dharmapuri district of Tamil Nadu state, India The measurement and study of spectral signatures of the different samples of the deposits showed strong spectral absorptions near 500 nm, 900 nm and 2400 nm wavelength regions and were confirmed with the Fourier Transform Infrared (FTIR) spectroscopy method. The spectral absorption characteristics of the samples were evaluated by the study of the physical, optical, and chemical characteristics of the samples. The study of hyperspectral and FTIR spectral signatures with petrographic and major chemical elements revealed the best absorption characteristics of the iron ore deposits of the study region and can be used elsewhere in the world. This report presents preliminary findings on the use of hyperspectral imaging to characterize iron ore. The mineralogical products produced from hyperspectral images may improve in situ grade control on an iron ore mine face. It will be extremely useful for businesses in measuring large numbers of commodities quickly and objectively.

Soil study plays a significant role in the cultivation of crops. To increase the productivity of any crop, one must know the soil type and properties of that soil. The conventional soil type identification, grid sampling and hydrometer method require expert intervention, more time and extensive laboratory experimentation. Digital soil mapping, while applying remote sensing, offers soil type information and has rapidity, low cost, and spatial resolution advantages. This study proposes a model to identify the soil type using remote sensing data. Spectral data of the Upper Indus Plain of Pakistan Pothwar region and Doabs were acquired using fifteen Landsat eight images dated between June 2020 to August 2020. Bare soil images were obtained to identify the soil type classes Silt Loam, Loam, Sandy Loam, Silty Clay Loam and Clay Loam. Spectral data of band values, reflectance band values, corrective reflectance band values and vegetation indices are practiced studying the reflectance factor of soil type. Regarding multi-class classification, Random Forest and Support Vector Machine are two popular techniques used in the research community. In the present work, we used these two techniques aided with Logistic Model Tree with 10-fold cross-validation. The classification with the best performance is achieved using the spectral data, with an overall accuracy of 86.61% and 84.41% for the Random Forest and Logistic Model Tree classification, respectively. These results may be applied for crop cultivation in specific areas and assist decision-makers in better agricultural planning., Soil study plays a significant role in the cultivation of crops. To increase the productivity of any crop, one must know the soil type and properties of that soil. The conventional soil type identification, grid sampling and hydrometer method require expert intervention, more time and extensive laboratory experimentation. Digital soil mapping, while applying remote sensing, offers soil type information and has rapidity, low cost, and spatial resolution advantages. This study proposes a model to identify the soil type using remote sensing data. Spectral data of the Upper Indus Plain of Pakistan Pothwar region and Doabs were acquired using fifteen Landsat eight images dated between June 2020 to August 2020. Bare soil images were obtained to identify the soil type classes Silt Loam, Loam, Sandy Loam, Silty Clay Loam and Clay Loam. Spectral data of band values, reflectance band values, corrective reflectance band values and vegetation indices are practiced studying the reflectance factor of soil type. Regarding multi-class classification, Random Forest and Support Vector Machine are two popular techniques used in the research community. In the present work, we used these two techniques aided with Logistic Model Tree with 10-fold cross-validation. The classification with the best performance is achieved using the spectral data, with an overall accuracy of 86.61% and 84.41% for the Random Forest and Logistic Model Tree classification, respectively. These results may be applied for crop cultivation in specific areas and assist decision-makers in better agricultural planning.

Soil study plays a significant role in the cultivation of crops. To increase the productivity of any crop, one must know the soil type and properties of that soil. The conventional soil type identification, grid sampling and hydrometer method require expert intervention, more time and extensive laboratory experimentation. Digital soil mapping, while applying remote sensing, offers soil type information and has rapidity, low cost, and spatial resolution advantages. This study proposes a model to identify the soil type using remote sensing data. Spectral data of the Upper Indus Plain of Pakistan Pothwar region and Doabs were acquired using fifteen Landsat eight images dated between June 2020 to August 2020. Bare soil images were obtained to identify the soil type classes Silt Loam, Loam, Sandy Loam, Silty Clay Loam and Clay Loam. Spectral data of band values, reflectance band values, corrective reflectance band values and vegetation indices are practiced studying the reflectance factor of soil type. Regarding multi-class classification, Random Forest and Support Vector Machine are two popular techniques used in the research community. In the present work, we used these two techniques aided with Logistic Model Tree with 10-fold cross-validation. The classification with the best performance is achieved using the spectral data, with an overall accuracy of 86.61% and 84.41% for the Random Forest and Logistic Model Tree classification, respectively. These results may be applied for crop cultivation in specific areas and assist decision-makers in better agricultural planning., Soil study plays a significant role in the cultivation of crops. To increase the productivity of any crop, one must know the soil type and properties of that soil. The conventional soil type identification, grid sampling and hydrometer method require expert intervention, more time and extensive laboratory experimentation. Digital soil mapping, while applying remote sensing, offers soil type information and has rapidity, low cost, and spatial resolution advantages. This study proposes a model to identify the soil type using remote sensing data. Spectral data of the Upper Indus Plain of Pakistan Pothwar region and Doabs were acquired using fifteen Landsat eight images dated between June 2020 to August 2020. Bare soil images were obtained to identify the soil type classes Silt Loam, Loam, Sandy Loam, Silty Clay Loam and Clay Loam. Spectral data of band values, reflectance band values, corrective reflectance band values and vegetation indices are practiced studying the reflectance factor of soil type. Regarding multi-class classification, Random Forest and Support Vector Machine are two popular techniques used in the research community. In the present work, we used these two techniques aided with Logistic Model Tree with 10-fold cross-validation. The classification with the best performance is achieved using the spectral data, with an overall accuracy of 86.61% and 84.41% for the Random Forest and Logistic Model Tree classification, respectively. These results may be applied for crop cultivation in specific areas and assist decision-makers in better agricultural planning.

We conducted a systematic review and inventory of recent research achievements related to spaceborne and aerial Earth Observation (EO) data-driven monitoring in support of soil-related strategic goals for a three-year period (2019–2021). Scaling, resolution, data characteristics, and modelling approaches were summarized, after reviewing 46 peer-reviewed articles in international journals. Inherent limitations associated with an EO-based soil mapping approach that hinder its wider adoption were recognized and divided into four categories: (i) area covered and data to be shared; (ii) thresholds for bare soil detection; (iii) soil surface conditions; and (iv) infrastructure capabilities. Accordingly, we tried to redefine the meaning of what is expected in the next years for EO data-driven topsoil monitoring by performing a thorough analysis driven by the upcoming technological waves. The review concludes that the best practices for the advancement of an EO data-driven soil mapping include: (i) a further leverage of recent artificial intelligence techniques to achieve the desired representativeness and reliability; (ii) a continued effort to share harmonized labelled datasets; (iii) data fusion with in situ sensing systems; (iv) a continued effort to overcome the current limitations in terms of sensor resolution and processing limitations of this wealth of EO data; and (v) political and administrative issues (e.g., funding, sustainability). This paper may help to pave the way for further interdisciplinary research and multi-actor coordination activities and to generate EO-based benefits for policy and economy.

We conducted a systematic review and inventory of recent research achievements related to spaceborne and aerial Earth Observation (EO) data-driven monitoring in support of soil-related strategic goals for a three-year period (2019–2021). Scaling, resolution, data characteristics, and modelling approaches were summarized, after reviewing 46 peer-reviewed articles in international journals. Inherent limitations associated with an EO-based soil mapping approach that hinder its wider adoption were recognized and divided into four categories: (i) area covered and data to be shared; (ii) thresholds for bare soil detection; (iii) soil surface conditions; and (iv) infrastructure capabilities. Accordingly, we tried to redefine the meaning of what is expected in the next years for EO data-driven topsoil monitoring by performing a thorough analysis driven by the upcoming technological waves. The review concludes that the best practices for the advancement of an EO data-driven soil mapping include: (i) a further leverage of recent artificial intelligence techniques to achieve the desired representativeness and reliability; (ii) a continued effort to share harmonized labelled datasets; (iii) data fusion with in situ sensing systems; (iv) a continued effort to overcome the current limitations in terms of sensor resolution and processing limitations of this wealth of EO data; and (v) political and administrative issues (e.g., funding, sustainability). This paper may help to pave the way for further interdisciplinary research and multi-actor coordination activities and to generate EO-based benefits for policy and economy.

The assessment of the eco-environmental vulnerability allows to identify land cover patterns and to establish risk areas. This research aims to assess the eco-environmental vulnerability of the Sauce Grande river basin (Argentina) from the delimitation of the land cover obtained from two matrices: one resulting from a spatial simulation model (optimal scenario), and another, derived from fieldwork (in situ scenario). The methodology included the analyses of climatic, geomorphologic, topographic, hydric and edaphic data in order to design a modeling land cover map. The in situ land cover map was the result of the obtention of spectral signatures and the digital processing of Landsat images. As a result, we identified five areas with different levels of eco-environmental vulnerability. The most common level was low (27.7 %), following by high (22.5 %). These analyses permitted to design sustainable management strategies orientated to reduce negative environmental impacts and motivate the permanence of local and regional economic activities., La evaluación de la vulnerabilidad eco-ambiental permite identificar patrones de uso de la tierra y establecer áreas o recursos en riesgo. El objetivo de este estudio es evaluar la vulnerabilidad ecoambiental de la cuenca del río Sauce Grande (Argentina) a partir de la delimitación de las coberturas del suelo obtenidas de dos matrices: una resultante de un modelo de simulación espacial (escenario óptimo) y otra a campo (escenario in situ). La metodología incluyó el análisis de información climática, geomorfológica, topográfica, hídrica y edáfica, para realizar el primer modelo y obtención de firmas espectrales y procesamiento de imágenes Landsat para el segundo. La información resultante, permitió definir cinco áreas con diferente nivel de vulnerabilidad eco-ambiental. El nivel más frecuente fue el bajo (27,7 %), seguido del alto (22,5 %). Esta información permitió diseñar estrategias de manejo sustentable que estuvieron destinadas a reducir los impactos ambientales negativos y fomentar la permanencia de las actividades económicas locales y regionales., L’évaluation de la vulnérabilité éco-environnementale permet d’identifier les caractéristiques de la couverture terrestre et d’établir des zones à risque. Cette recherche vise à évaluer la vulnérabilité éco-environnementale du bassin de la Sauce Grande (Argentine) à partir de la délimitation de la couverture végétale obtenue à partir de deux matrices: l’une issue d’un modèle de simulation spatiale (scénario optimal) et l’autre, dérivée du travail de terrain ( scénario in situ). La méthodologie comprenait des analyses de données climatiques, géomorphologiques, topographiques, hydriques et édaphiques afin de concevoir une carte de modélisation de la couverture terrestre. La carte de la couverture terrestre in situ est le résultat de l’obtention de signatures spectrales et du traitement numérique d’images Landsat. En conséquence, nous avons identifié cinq zones présentant différents niveaux de vulnérabilité écoenvironnementale. Le niveau le plus commun était faible (27,7 %), suivi par élevé (22,5 %). Ces analyses ont permis de concevoir des stratégies de gestion durable visant à réduire les impacts environnementaux négatifs et à motiver la permanence des activités économiques locales et régionales.

UAV based hyperspectral grassland monitoring in an alpine shallow erosion area Recent research in the Alps found that a reduction in grassland management is correlated to an increase in a certain type of shallow erosion areas called blaiken. This change also entails changes to the dominant grassland vegetation. New developments in hyperspectral technology have produced cameras which are sufficiently light for Unmanned Aerial Vehicles (UAVs). This thesis explores whether UAV mounted hyperspectral cameras (RIKOLA (SENOP Optronics, Lievestuore, Finnland)) can detect the spectral signatures of managed vs. blaiken-related unmanaged grasslands accurately, and whether their signatures are so distinct as to enable grassland classification on an aerial image. Accurate mapping of Alpine grasslands can guide erosion mitigation measures to manage grassland types that are more susceptible to erosion. A field study was undertaken at a blaiken hotspot at the Schluter lodge, located in the Dolomite ¨ mountain range within the Italian Alps, in order to evaluate the accuracy of the RIKOLA camera. Its spectral signatures taken on the ground were compared to those of a high precision spectroradiometer (Spectra Vista Corporation, Poughkeepsie, USA). The study also evaluates whether the spectral signatures of different grassland types were sufficiently unique and the quality of the Rikola images taken from the UAV system was high enough to permit the characterization of grassland types by a maximum likelihood classification algorithm. The spectral separability analysis demonstrated that the spectral signatures of designated grassland classes separate in Rikola images taken on the ground. However, an orthomosaic created from aerial UAV-Rikola images displayed low precision and accuracy. These errors stem from unstable lighting conditions throughout the flight and the NIR sensor malfunction. The malfunction caused the NIR bands not to be recorded, a wavelength range which was shown to be h, Hyperspectral grassland and erosion monitoring by UAV in the Italian Alps Recent research in the Alps finds that decreased management of grasslands is correlated to increased soil erosion. These developments also cause changes to the dominant grassland vegetation. Different types of vegetation have been shown to be distinguishable by hyperspectral cameras. However, these cameras, mounted to aeroplanes and satellites, are expensive, inflexible in their use and have a relatively low ground resolution. Newly developed, cheaper, miniaturized hyperspectral cameras, have a more detailed ground resolution and can be deployed more flexible when mounted to Unmanned Aerial Vehicles (UAVs). This thesis explores whether these, UAV based, cameras can be used to detect differences between highly similar grassland vegetation of managed and unmanaged grasslands. Classification maps derived from hyperspectral images can be used for the mapping of alpine grasslands types and can support erosion mitigation projects by locating areas with vegetation which is more susceptible to erosion. A field study was undertaken at an erosion hotspot near the Schluter lodge, located in the Dolomite ¨ mountain range within the Italian Alps, in order to evaluate the accuracy of the UAV based hyperspectral camera. Measurements taken on the ground were compared to those of a high precision reference measuring device (Spectrometer). The study also evaluated whether the image quality of such an approach is high enough, and the grassland types are separable when using a image classification algorithm. Analysis of the hyperspectral data shows that grassland types could be distinguished by the new lightweight hyperspectral camera. However, the aerial image created by stitching together UAV images (orthomosaic) displayed unsatisfactory precision and accuracy. These errors stem from a partial sensor malfunction and unstable lighting conditions throughout the flight. The malfunction caused the Near Infra Red NI

This MURI program progress report covers the fifteen months from 1 August 2007 to 30 September 2008. The overall purpose of this program is a study of the science underlying the signatures generated by land targets (both natural and manmade). Specifically, the objective is to understand the dependency of hyperspectral and hyperspectral/polarimetric signatures on variations in environmental conditions. An additional objective involves algorithm development and focuses on the identification of new target discriminants and the evaluation of their utility for target detection and sensor fusion. The phenomenology research continued to focus on spectroscopic soil measurements, optical property analyses, field data analysis, physics-based signature modeling, and synthetic scene generation for concealed targets. The algorithm work extended the signature-based detection research and conducted investigations into patterned based detection methods. Additional algorithm work exploiting local spatial-spectral background characteristics was conducted. Two endmember selection algorithms were developed based on a sparsity promoting approach. Fusion research continued to investigate methods for fusing SAR and hyperspectral data through the Choquet Integral approach. Target detection performance improved using this data fusion approach. Clutter complexity research continued to investigate metrics to tie image complexity to detection algorithm performance and continued work on methods for generating diverse sample images., Prepared in cooperation with University of Hawaii, University of Florida, University of Maryland, Rochester Institute of Technology, Clark Atlanta Univesity and Army Research Laboratory. Report consists of briefing charts and multiple doctoral dissertations.

This work describes the current status of remote and proximal (on-ground) weed detection systems for site-specific weed management and discusses the limitations and opportunities of these technologies. Remote sensing based on multispectral aerial imagery can provide accurate weed maps, especially at late weed phenological stages, whereas images from high spatial resolution satellite and unmanned aerial vehicles must still be analysed. Hyperspectral images produce highly accurate maps at early and late phenological stages at a farm scale or medium spatial scale. However, this technology is not profitable, because of current operating costs, which are prohibitive. In studies of on-ground weed seedling detection, accurate results can be obtained at a medium farm scale. Despite numerous efforts, a powerful and flexible classifier of soil, weeds and crops in a number of situations, remains the greatest challenge of this technology. The main limitations of remote and proximal sensing may be summarised in the following two points: (i) the time and education required for applying new technological advances and (ii) the high cost of the technology and the lack of compatibility of the machinery. Possible solutions might include: (i) offering an advisory service that provides technical support, agronomic knowledge and specific training courses, (ii) the development and implementation of uniform and cheaper standards, (iii) increased research of both high resolution satellite imagery exploring object-based image analysis and pan-sharpened imagery and unmanned aerial vehicles (UAV) and (iv) enabling the development of current prototypes of robotic weeding into commercial products. The general lack of multidisciplinary research groups can be a disadvantage when comparing the economic feasibility of site-specific weed management with conventional systems. © 2010 CSIC. Weed Research © 2010 European Weed Research Society.

Researchers use eigendecomposition to leverage MIT's Wi-Fi network activity data and analyze to the physical environment. We proposed a method to analyze and categorize wireless access points based on common usage characteristics that reflect real-world, placed-based behaviors. It uses eigendecomposition to study the Wi-Fi network at the Massahusetts Institute of Technology (MIT), correlating data generated as a byproduct of network activity with the physical environment. Our approach provides an instant survey of building use across the entire campus at a surprisingly fine-grained level. The resulting eigenplaces have implications for reseach across a range of wireless technology as well as potential applications in network planning, traffic and tourism management, and even marketing.

Researchers use eigendecomposition to leverage MIT's Wi-Fi network activity data and analyze to the physical environment. We proposed a method to analyze and categorize wireless access points based on common usage characteristics that reflect real-world, placed-based behaviors. It uses eigendecomposition to study the Wi-Fi network at the Massahusetts Institute of Technology (MIT), correlating data generated as a byproduct of network activity with the physical environment. Our approach provides an instant survey of building use across the entire campus at a surprisingly fine-grained level. The resulting eigenplaces have implications for reseach across a range of wireless technology as well as potential applications in network planning, traffic and tourism management, and even marketing.

Salinity, waterlogging and drought are major causes of land degradation and serious threats to sustainable agricultural productivity in the south-west agricultural region of Western Australia (WA). The spectral signatures of pasture plants under drought, waterlogging and nutrient deficiency were examined in a greenhouse study using both a field and an analytical laboratory spectrometer (400 to 2,500 nm wavelength). Drought stress group (RWC, 39.9%) has the highest reflectance of 48.2%, waterlogging group (RWC, 49.7%) with a magnitude of 43.1% reflectance and control group (RWC, 61.5%) has the lowest of 41.9%. The highest separability based on magnitude among control, waterlogging and drought stress groups is located at reflection band at 1,666 nm, 1,818 nm and 2,216 nm and at 1,450 nm absorption bands. Remote sensing and GIS techniques were used to predict risks of soil salinity and waterlogging in the study area. Time-sequenced Landsat TM satellite data and groundwater data were analysed to delineate areas where major changes in soil salinity, waterlogging have taken place before and after engineering interventions of deep drains.The rainfall data analysis of all cities in the south-west of Western Australia indicate that annual rainfall has been decreasing since 1969 for some cities in the region and rainfall is decreasing in some cities since 1975. The winter season rainfall shows a downward trend and summer season rainfall shows an upward trend linked to an increase in the frequency of summer storm events in the south-west, in the Wheatbelt and in the east of Western Australia. The annual rainfall and summer season rainfalls have been increasing in the north of Western Australia and both annual rainfall and summer season rainfalls show an increasing trend. Climate change was studied for northern, eastern, Wheatbelt and south-west of WA and its impacts of on surface runoff, groundwater recharge, and land degradation were studied.Deep open drains were monitored in

Bacterial spores, or endospores, such as those of Bacillus anthracis, are an asymmetrical threat. Decontamination following endospore release is costly and can be difficult and slow to confirm. Part of the constraint is because rapid and accurate means to differentiate live (viable) from dead (non-viable) bacterial endospores are lacking. Endospores have minimal metabolic activity and withstand many sterilization techniques, yet they can germinate and grow rapidly in favorable conditions, increasing the likelihood of disease. In scenarios such as post-infrastructure decontamination, a rapid and quantitative assessment of endospore viability is prerequisite to establishing health risks. Current and accepted methods for determining endospore viability require culturing, which takes several days. Scatter and depolarization signatures from polarized laser sources and micro- and nano-sensors show promise as a means of optically confirming endospore presence, but have not been shown to have the ability to distinguish viable from non-viable endospores. In the laboratory, we have exploited the oxidative alteration of the spore coat caused by decontamination solutions and used hyperspectral reflectance and appropriate post-processing to differentiate viable from non-viable endospores following endospore decontamination by oxidants., See also ADM002187. Presented at the Army Science Conference (26th) held in Orlando, FL on 1-4 December 2008. The original document contains color images.

Field studies were conducted to determine the potential of multispectral classification of late-season grass weeds in wheat. Several classification techniques have been used to discriminate differences in reflectance between wheat and Avena sterilis, Phalaris brachystachys, Lolium rigidum and Polypogon monspeliensis in the 400–900 nm spectrum, and to evaluate the accuracy of performance for a spectral signature classification into the plant species or group to which it belongs. Fisher's linear discriminant analysis, nonparametric functional discriminant analysis and several neural networks have been applied, either with a preliminary principal component analysis (PCA) or not and in different scenarios. Fisher's linear discriminant analysis, feedforward neural networks and one-layer neural network, all showed classification percentages between 90% and 100% with PCA. Generally, a preliminary computation of the most relevant principal components considerably improves the correct classification percentage. These results are promising because A. sterilis and L. rigidum, two of the most problematic, clearly patchy and expensive-to-control weeds in wheat, could be successfully discriminated from wheat in the 400–900 nm range. Our results suggest that mapping grass weed patches in wheat could be feasible with analysis of real-time and high-resolution satellite imagery acquired in mid-May under these conditions.

The dominant physics affecting spherical and cylindrical plasma implosions is studied. Detailed modeling of opacity sources, line and continuum emission, and coupled hydrodynamics is presented. Simulations of actual experimental spectra, plasma mixture experiments, and improved photon transport methods are described. Z-pinch code benchmarks, and output predictions for various elements and mass loads are given.(Author)