Hyper Spectral Analysis of Soil Iron Oxide using PLSR Method: A Review

Spectroscopy is a rapid, simple, non-destructive and analytical technique, which provides a good alternative that may be used to replace conventional methods of soil analysis. Soil iron oxides occur in almost all type�s soils and they re?ect different environmental conditions by the high variability of their mineralogy and concentration. Soil iron oxide, being an important pedogenic indicator of the soil, measurement of Iron Oxide content can be used as an index of soil fertility. Analytical Spectral Device (ASD) Field Spec 4 Spectroradiometer is used which has 350-2500 nm spectral wavelength range to estimate iron oxide content from the soil sample. The Vis-NIR reflectance spectroscopy requires less effort and it is quick innovation to predict the soil iron oxide content. For collecting the soil iron oxide content from spectral data we are utilizing PLSR which is statistical regression method. This paper states the work that is done on different soil types at different places to observe the iron oxide content in soil

A Bespoke Workflow Management System for Data-Driven Urgent HPC

In this paper we present a workflow management system which permits the kinds of data-driven workflows required by urgent computing, namely where new data is integrated into the workflow as a disaster progresses in order refine the predictions as time goes on. This allows the workflow toadapt to new data at runtime, a capability that most workflow management systems do not possess. The workflow management system was developed for the EU-funded VESTEC project, which aims to fuse HPC with real-time data for supporting urgent decision making. We first describe an example workflow from the VESTEC project, and show why existing workflow technologies do not meet the needs of the project. We then go on to present the design of our Workflow Management System, describe how it is implemented into the VESTEC system, and provide an example of the workflow system in use for a test case

Ensuring Agricultural Sustainability through Remote Sensing in the Era of Agriculture 5.0

This work was supported by the projects: "VIRTUOUS" funded by the European Union's Horizon 2020 Project H2020-MSCA-RISE-2019. Ref. 872181, "SUSTAINABLE" funded by the European Union's Horizon 2020 Project H2020-MSCA-RISE-2020. Ref. 101007702 and the "Project of Excellence" from Junta de Andalucia 2020. Ref. P18-H0-4700. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.Timely and reliable information about crop management, production, and yield is considered of great utility by stakeholders (e.g., national and international authorities, farmers, commercial units, etc.) to ensure food safety and security. By 2050, according to Food and Agriculture Organization (FAO) estimates, around 70% more production of agricultural products will be needed to fulfil the demands of the world population. Likewise, to meet the Sustainable Development Goals (SDGs), especially the second goal of “zero hunger”, potential technologies like remote sensing (RS) need to be efficiently integrated into agriculture. The application of RS is indispensable today for a highly productive and sustainable agriculture. Therefore, the present study draws a general overview of RS technology with a special focus on the principal platforms of this technology, i.e., satellites and remotely piloted aircrafts (RPAs), and the sensors used, in relation to the 5th industrial revolution. Nevertheless, since 1957, RS technology has found applications, through the use of satellite imagery, in agriculture, which was later enriched by the incorporation of remotely piloted aircrafts (RPAs), which is further pushing the boundaries of proficiency through the upgrading of sensors capable of higher spectral, spatial, and temporal resolutions. More prominently, wireless sensor technologies (WST) have streamlined real time information acquisition and programming for respective measures. Improved algorithms and sensors can, not only add significant value to crop data acquisition, but can also devise simulations on yield, harvesting and irrigation periods, metrological data, etc., by making use of cloud computing. The RS technology generates huge sets of data that necessitate the incorporation of artificial intelligence (AI) and big data to extract useful products, thereby augmenting the adeptness and efficiency of agriculture to ensure its sustainability. These technologies have made the orientation of current research towards the estimation of plant physiological traits rather than the structural parameters possible. Futuristic approaches for benefiting from these cutting-edge technologies are discussed in this study. This study can be helpful for researchers, academics, and young students aspiring to play a role in the achievement of sustainable agriculture.European Commission 101007702 872181Junta de Andalucia P18-H0-470

Characterization of coastal environment by means of hyper- and multispectral techniques

The management of the coastal environment is a complex issue, which needs for appropriate methodologies. Erosional processes and longshore currents present in the submerged beach represent a serious danger for both people and human infrastructures. A proper integration between traditional and innovative techniques can help in the characterization and management of the beach environment. Several different multispectral and hyperspectral techniques were used to retrieve information about the hydro and morphodynamic settings of the Pisa province coast (Tuscany, Italy). Results were validated using about 130 samples collected along the study area, between the mouths of the Serchio river and the Scolmatore canal. The composition of sand samples was evaluated by means of petrographic microscopy and grain size analyses. The same samples were analyzed using an Analytical Spectral Device (ASD) Fieldspec. The obtained sediment spectral library was used to evaluate the differences in mineralogical composition, which can be related to different source areas. Results coming from spectroscopy were compared to those obtained from the petrographic and grain size analysis. Furthermore a multispectral aerial image was used to evaluate sediment distribution along the submerged beach, to map the geomorphic features and to detect the presence of longshore and rip currents. This works suggests that optical remote sensing technique can be profitably used in order to reduce the need for expensive and time consuming conventional analysis

NASA Tech Briefs, September 2008

Topics covered include: Nanotip Carpets as Antireflection Surfaces; Nano-Engineered Catalysts for Direct Methanol Fuel Cells; Capillography of Mats of Nanofibers; Directed Growth of Carbon Nanotubes Across Gaps; High-Voltage, Asymmetric-Waveform Generator; Magic-T Junction Using Microstrip/Slotline Transitions; On-Wafer Measurement of a Silicon-Based CMOS VCO at 324 GHz; Group-III Nitride Field Emitters; HEMT Amplifiers and Equipment for their On-Wafer Testing; Thermal Spray Formation of Polymer Coatings; Improved Gas Filling and Sealing of an HC-PCF; Making More-Complex Molecules Using Superthermal Atom/Molecule Collisions; Nematic Cells for Digital Light Deflection; Improved Silica Aerogel Composite Materials; Microgravity, Mesh-Crawling Legged Robots; Advanced Active-Magnetic-Bearing Thrust- Measurement System; Thermally Actuated Hydraulic Pumps; A New, Highly Improved Two-Cycle Engine; Flexible Structural-Health-Monitoring Sheets; Alignment Pins for Assembling and Disassembling Structures; Purifying Nucleic Acids from Samples of Extremely Low Biomass; Adjustable-Viewing-Angle Endoscopic Tool for Skull Base and Brain Surgery; UV-Resistant Non-Spore-Forming Bacteria From Spacecraft-Assembly Facilities; Hard-X-Ray/Soft-Gamma-Ray Imaging Sensor Assembly for Astronomy; Simplified Modeling of Oxidation of Hydrocarbons; Near-Field Spectroscopy with Nanoparticles Deposited by AFM; Light Collimator and Monitor for a Spectroradiometer; Hyperspectral Fluorescence and Reflectance Imaging Instrument; Improving the Optical Quality Factor of the WGM Resonator; Ultra-Stable Beacon Source for Laboratory Testing of Optical Tracking; Transmissive Diffractive Optical Element Solar Concentrators; Delaying Trains of Short Light Pulses in WGM Resonators; Toward Better Modeling of Supercritical Turbulent Mixing; JPEG 2000 Encoding with Perceptual Distortion Control; Intelligent Integrated Health Management for a System of Systems; Delay Banking for Managing Air Traffic; and Spline-Based Smoothing of Airfoil Curvatures

FIREMAP: Cloud-based software to automate the estimation of wildfire-induced ecological impacts and recovery processes using remote sensing techniques

[EN] The formulation and planning of integrated fire management strategies must be strengthened by decision support systems about fire-induced ecological impacts and ecosystem recovery processes, particularly in the context of extreme wildfire events that challenge land management initiatives. Wildfire data collection and analysis through remote sensing earth observations is of utmost importance for this purpose. However, the needs of land managers are not always met because the exploitation of the full potential of remote sensing techniques requires a high level of technical expertise. In addition, data acquisition and storage, database management, networking, and computing requirements may present technical difficulties. Here, we present FIREMAP software, which leverages the potential of Google Earth Engine (GEE) cloud-based platform, an intuitive graphical user interface (GUI), and the European Forest Fire Information System (EFFIS) wildfire database for wildfire analyses through remote sensing techniques and data collections. FIREMAP software allows automatic computing of (i) machine learning-based burned area (BA) detection algorithms to facilitate the mapping of (historical) fire perimeters, (ii) fire severity spectral indices, and (iii) post-fire recovery trajectories through the inversion of physically-based radiative transfer models. We introduce (i) the FIREMAP platform architecture and the GUI, (ii) the implementation of well-established algorithms for wildfire science and management in GEE, (iii) the validation of the algorithm implementation in fifteen case-study wildfires across the western Mediterranean Basin, and (iv) the near-future and long-term planned expansion of FIREMAP featuresSIThis study was financially supported by the Spanish Ministry of Science and Innovation in the framework of LANDSUSFIRE project (PID2022-139156OB-C21) within the National Program for the Promotion of Scientific-Technical Research (2021-2023), and with Next-Generation Funds of the European Union (EU) in the framework of the FIREMAP project (TED2021-130925B-I00); and by the Regional Government of Castile and León in the framework of the IA-FIREXTCyL project (LE081P23). Víctor Fernández-García was supported by a Margarita Salas post-doctoral fellowship from the Ministry of Universities of Spain, financed with European Union-NextGenerationEU and Ministerio de Universidades Fund

A Spatio-temporal Model of African Animal Trypanosomosis Risk

[b]Background[/b]African animal trypanosomosis (AAT) is a major constraint to sustainable development of cattle farming in sub-Saharan Africa. The habitat of the tsetse fly vector is increasingly fragmented owing to demographic pressure and shifts in climate, which leads to heterogeneous risk of cyclical transmission both in space and time. In Burkina Faso and Ghana, the most important vectors are riverine species, namely Glossina palpalis gambiensis and G. tachinoides, which are more resilient to human-induced changes than the savannah and forest species. Although many authors studied the distribution of AAT risk both in space and time, spatio-temporal models allowing predictions of it are lacking.[b]Methodology/Principal Findings[/b]We used datasets generated by various projects, including two baseline surveys conducted in Burkina Faso and Ghana within PATTEC (Pan African Tsetse and Trypanosomosis Eradication Campaign) national initiatives. We computed the entomological inoculation rate (EIR) or tsetse challenge using a range of environmental data. The tsetse apparent density and their infection rate were separately estimated and subsequently combined to derive the EIR using a "one layer-one model" approach. The estimated EIR was then projected into suitable habitat. This risk index was finally validated against data on bovine trypanosomosis. It allowed a good prediction of the parasitological status (r(2) = 67%), showed a positive correlation but less predictive power with serological status (r(2) = 22%) aggregated at the village level but was not related to the illness status (r(2) = 2%).[b]Conclusions/Significance[/b]The presented spatio-temporal model provides a fine-scale picture of the dynamics of AAT risk in sub-humid areas of West Africa. The estimated EIR was high in the proximity of rivers during the dry season and more widespread during the rainy season. The present analysis is a first step in a broader framework for an efficient risk management of climate-sensitive vector-borne diseases

A ground based investigation of snow metamorphism using an energy flux model and hyperspectral imaging across cropland, grassland and barren surface in northeast Iowa

Snow is unstable under natural environmental conditions; it undergoes metamorphism that can be measurable with an energy flux model. The demand for snow research is increasing due to its importance for maintaining Earth’s energy balance and hydrological applications. Snow metamorphism is a process of transformation of snow particles with an expense of surface free energy. Very few studies have been completed on snow metamorphism in grassland, cropland and barren surfaces that needs farther investigation. In this study, an attempt was made to measure winter snow metamorphism with physical based model and detecting snow metamorphism by using hyperspectral imaging spectroradiameter in grassland, cropland and barren surfaces. The questions proposed in this research were: Can snow metamorphism be estimated from a ground based investigation using physical properties of snow across cropland, grassland and barren surface? Do the characteristics of snow metamorphism vary between cropland, grassland and barren surface?Can ground based hyperspectral imaging detect differences in snow metamorphism characteristics between cropland, grassland and barren surface? The instruments used to collect field data on snow physical properties in this research were ASD FieldSpec 3 Spectroradiometer, Fluke 561 IR Thermometer, gridded mesh cards of various size, ruler, portable weighing scale, graduated measuring cylinder and magnifying glass respectively. The study indicated that by considering physical properties of snow such as, snow grain size; snow surface temperature; snow depth and snow volume coupled with meteorological parameters like, wind speed; air temperature; atmospheric pressure and temperature at dew point derived from weather station on different days at specific time period used in an energy flux model, it is possible to measure snow metamorphism across cropland, grassland and barren surfaces. The results further showed that snow metamorphism characteristics such as: estimated amounts of snowmelt and snow grain size varied during different field days in different land types which indicate an increase – decrease in snowpack cold content respectively. Mean estimated amounts of snowmelt in grassland, cropland and barren surface found were 0.065 mm/day, 0.066 mm/day and 0.061 mm/day respectively. Diverse weather phenomenon altered the characteristics of snow metamorphism in different land types observed from this study. Results of statistical analysis with different methods showed there was no significant difference in the amounts of snow melt between three different land types under study. Reflectance of snow from hyperspectral imaging device showed difference in spectral signatures from different land types for different snow grain size on particular field day in specific time. After comparing all the snow reflectance spectra from three land types, the grassland showed the highest snow reflectance followed by cropland and barren surface which had the lowest snow reflectance according to different grain size on particular field day during specific time. Beside this, when PCA was applied to the combined datasets collected during different dates with ASD FIeldSpec 3 spectroradiometer it revealed components 1 and 2 with the following hyperspectral bands with component loading \u3e 0.9: 1014.5, 1024.5, 1034.5 and 355, 356, 374.5 and 394.5 nm at wavelength range between 350nm –1039nm bands were important for identifying snow metamorphism characteristics . Additional instances of snow metamorphism were observed from this study such as, icy structures in three different land classes on a particular day with varied amounts of snow melt and grain sizes that require further investigation. During icy condition the estimated amounts of snow melt found in three different land types as, cropland: 0.0536 (mm/day); grassland: 0.0544 (mm/day) and barren surface: 0.0491(mm/day) having snow grain size .5mm, 1mm and 2mm respectively. This study provided valuable insights about snow surface energy balance regulation and its effect on seasonal snow metamorphism in cropland, grassland and barren surfaces respectively. The importance of hyper spectral imaging spectroradiometer in detecting snow metamorphism was determined. Snow spectral libraries were created according to grain size, which can serve as future references for corresponding studies on snow metamorphismin three different land classes under study respectivel