Hyper spectral and Geochemical Signatures Study on Late Archeaen of Indian High Grade Manganese Deposits in Halekal Band of Bhahaddurghatta-Hosahatty Village, Chitradurga Schist Belt (Csb), Karnataka, India

Manganese is one of the most widely distributed elements in the earth’s crust and mapping of these deposit are of high economic interest. Manganese occurs as diverse genetic types that is vital for iron and steel production. It occurs usually in the form of oxide, hydroxide, carbonate and silicate which is an important raw material for iron, steel industry, chief manufacturer of chemicals and dry cells in the form of manganese dioxides. Advanced analysis of hyperspectral signatures and GPS applications have opened a newest approach in exploration and systematic mapping of economic ore deposits. The present study aims to integrate the hyperspectral signatures with major elements of manganese ore deposits of Halekal band in Bhahaddurghatta-Hosahatty village of Chitradurga Schist Belt. The samples collected from field are studied in the laboratory using thin and polished sections under microscope and X-Ray Diffraction (XRD) analysis. Efforts are created to assess the spectral signatures of four representative random ore samples collected and analyzed through ASD Spectro-radiometer instrument operative in Visible and InfraRed (325 to 2500 nm) region with concentration of major elements. This study clearly demonstrated and documented the spectral absorption features of the selected rock samples in the study area mainly depend on the optical and physico-chemical characters of the rock and major elemental composition as well as mineral constituents of the samples.

SUGARCANE PRODUCTIVITY ESTIMATION THROUGH PROCESSING HYPERSPECTRAL SIGNATURES USING ARTIFICIAL NEURAL NETWORKS

This project uses an artificial neural network to calculate the net primary productivity of an organic sugarcane crop in Hatico’s farm, in Cerrito, Valle del Cauca. The pilot scheme used in this project is composed by 6 treatments of nitrogen fertilization based on green manures (poultry manure and cowpea). During the last two crops’ phenological phases, the artificial neural network was provided with hyperspectral data collected in the field. In addition, an exploratory data study was implemented in order to identify anomalous signs related to the light saturation and the curvature geometry. The first network applied was Autoencoder, in order to reduce the dimensionality of the radiometric resolution of the data. The second network applied was Multilayer Perceptron (MLP), to calculate the productivity values of the patches. After having compared the actual productivity values provided by Cenicaña, this project obtained an accuracy of 91.23% in the productivity predictions.

Assessing Earthquake-Induced Urban Rubble by Means of Multiplatform Remotely Sensed Data

Earthquake-induced rubble in urbanized areas must be mapped and characterized. Location, volume, weight and constituents are key information in order to support emergency activities and optimize rubble management. A procedure to work out the geometric characteristics of the rubble heaps has already been reported in a previous work, whereas here an original methodology for retrieving the rubble’s constituents by means of active and passive remote sensing techniques, based on airborne (LiDAR and RGB aero-photogrammetric) and satellite (WorldView-3) Very High Resolution (VHR) sensors, is presented. Due to the high spectral heterogeneity of seismic rubble, Spectral Mixture Analysis, through the Sequential Maximum Angle Convex Cone algorithm, was adopted to derive the linear mixed model distribution of remotely sensed spectral responses of pure materials (endmembers). These endmembers were then mapped on the hyperspectral signatures of various materials acquired on site, testing different machine learning classifiers in order to assess their relative abundances. The best results were provided by the C-Support Vector Machine, which allowed us to work out the characterization of the main rubble constituents with an accuracy up to 88.8% for less mixed pixels and the Random Forest, which was the only one able to detect the likely presence of asbestos.

Hyperspectral remote sensing monitoring of cyanobacteria blooms in a large South American reservoir: high- and medium-spatial resolution satellite algorithm simulation

We used hyperspectral remote sensing with the aim of establishing a monitoring program for cyanobacteria in a South American reservoir. We sampled at a wide temporal (2012–16; 10 seasons) and spatial (30km) gradient, and retrieved 111 field hyperspectral signatures, chlorophyll-a, cyanobacteria densities and total suspended solids. The hyperspectral signatures for cyanobacteria-dominated situations (n=75) were used to select the most suitable spectral bands in seven high- and medium-spatial resolution satellites (Sentinel 2, Landsat 5, 7 and 8, SPOT-4/5 and -6/7, WorldView 2), and for the development of chlorophyll and cyanobacteria cell abundance algorithms (λ550 – λ650+λ800) ÷ (λ550+λ650+λ800). The best-performing chlorophyll algorithm was Sentinel 2 ((λ560 – λ660+λ703) ÷ (λ560+λ660+λ703); R2=0.80), followed by WorldView 2 ((λ550 – λ660+λ720) ÷ (λ550+λ660+λ720); R2=0.78), Landsat and the SPOT series ((λ550 – λ650+λ800) ÷ (λ550+λ650+λ800); R2=0.67–0.74). When these models were run for cyanobacteria abundance, the coefficient of determination remained similar, but the root mean square error increased. This could affect the estimate of cyanobacteria cell abundance by ~20%, yet it still enable assessment of the alert level categories for risk assessment. The results of this study highlight the importance of the red and near-infrared region for identifying cyanobacteria in hypereutrophic waters, demonstrating coherence with field cyanobacteria abundance and enabling assessment of bloom distribution in this ecosystem.

Obtaining Hyperspectral Signatures for Seafloor Massive Sulphide Exploration

Seafloor massive sulphide (SMS) deposits are hosts to a wide range of economic minerals, and may become an important resource in the future. The exploitation of these resources is associated with considerable expenses, and a return on investment may depend on the availability of multiple deposits. Therefore, efficient exploration methodologies for base metal deposits are important for future deep sea mining endeavours. Underwater hyperspectral imaging (UHI) has been demonstrated to be able to differentiate between different types of materials on the seafloor. The identification of possible end-members from field data requires prior information in the form of representative signatures for distinct materials. This work presents hyperspectral imaging applied to a selection of materials from the Loki’s Castle active hydrothermal vent site in a laboratory setting. A methodology for compensating for systematic effects and producing the reflectance spectra is detailed, and applied to recover the spectral signatures from the samples. The materials investigated were found to be distinguishable using unsupervised dimensionality reduction methods, and may be used as a reference for future field application.