Identification and mapping of high-potential iron ore alteration zone across Joda, Odisha using ASTER and EO-1 hyperion data

Sengupta, Arnab; Adhikari, Manik Das; Maiti, Sabyasachi; Maiti, Soumya Kanti; Mahanta, Pankajini; Bhaumick, Siddhartha

doi:10.1080/14498596.2018.1485120

Cited by 8 publications

(6 citation statements)

References 56 publications

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“…Multiple algorithms have been proposed to preprocess Hyperion data, and various commercial software exist to perform AC and retrieve surface reflectance from the images. Previous studies have often employed FLAASH to perform AC when processing Hyperion imagery [ 1 , 20 , 21 , 22 , 46 ]. FLAASH is based on the AC model MODTRAN [ 47 ], retrieves water vapor and aerosol concentrations from the hyperspectral image, and does a flat terrain AC.…”

Section: Discussionmentioning

confidence: 99%

“…More recently, Pal et al [ 13 ] presented a new method based on local statistics to reduce striping in Hyperion images. To estimate surface reflectance, most AC methods used with Hyperion images have relied on processors using radiative transfer models (RTM), such as ATCOR [ 14 ], FLAASH [ 15 ], or other specific algorithms [ 16 , 17 , 18 , 19 , 20 , 21 , 22 ]. However, empirical or analytical methods can also be used: Prieto-Amparan et al [ 23 ] demonstrated that the empirical line [ 24 ] could be sufficient for biomass estimation of grasslands, and Katkovsky et al [ 25 ] proposed an algorithm based on the solutions of radiative transfer equations, and demonstrated that its results were in agreement with the FLAASH outputs when used over Hyperion images.…”

Section: Introductionmentioning

confidence: 99%

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SUREHYP: An Open Source Python Package for Preprocessing Hyperion Radiance Data and Retrieving Surface Reflectance

Miraglio

Coops

2022

Sensors

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Surface reflectance is an essential product from remote sensing Earth observations critical for a wide variety of applications, including consistent land cover mapping and change, and estimation of vegetation attributes. From 2000 to 2017 the Earth Observing-1 Hyperion instrument acquired the first satellite based hyperspectral image archive from space resulting in over 83,138 publicly available images. Hyperion imagery however requires significant preprocessing to derive surface reflectance. SUREHYP is a Python package designed to process batches of Hyperion images, bringing together a number of published algorithms and methods to correct at sensor radiance and derive surface reflectance. In this paper, we present the SUREHYP workflow and demonstrate its application on Hyperion imagery. Results indicate SUREHYP produces flat terrain surface reflectance results comparable to commercially available software, with reflectance values for the whole spectral range almost entirely within 10% of the software’s over a reference target, yet it is publicly available and open source, allowing the exploitation of this valuable hyperspectral archive on a global scale.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

SUREHYP: An Open Source Python Package for Preprocessing Hyperion Radiance Data and Retrieving Surface Reflectance

Miraglio

Coops

2022

Sensors

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“…4.1.3 Remote sensing. Remote sensing (Sengupta et al, 2019) technologies, including satellite and airborne sensors, are used to identify changes in the Earth's surface and to detect anomalies that might indicate the presence of valuable mineral resources.…”

Section: Explorationmentioning

confidence: 99%

“…4.1.8 Feasibility studies. Feasibility studies (Sengupta et al, 2019) assess the economic viability of developing a mining project based on the information gathered during exploration. These studies consider factors such as market conditions, infrastructure requirements and the cost of extraction.…”

Section: Source(s): Authormentioning

confidence: 99%

Unearthing narratives: an ethnographic lens on the organizational tapestry of massive iron ore mining operations

Khatua,

Dash,

Mishra

2024

JOE

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PurposeOres and minerals are extracted from the earth’s crust depending on the type of deposit. Iron ore mines come under massive deposit patterns and have their own mine development and life cycles. This study aims to depict the development and life cycle of large open-pit iron ore mines and the intertwined organizational design of the departments/sections operated within the industry.Design/methodology/approachPrimary data were collected on the site by participant observation, in-depth interviews of the field staff and executives, and field notes. Secondary data were collected from the literature review to compare and cite similar or previous studies on each mining activity. Finally, interactions were conducted with academic experts and top field executives to validate the findings. An organizational ethnography methodology was employed to study and analyse four large-scale iron ore mines of India’s largest iron-producing state, Odisha, from January to April 2023.FindingsSix stages were observed for development and life cycle, and the operations have been depicted in a schematic diagram for ease of understanding. The intertwined functioning of organizational set-up is also discovered.Originality/valueThe paper will benefit entrepreneurs, mining and geology students, new recruits, and professionals in allied services linked to large iron ore mines. It offers valuable insights for knowledge enhancement, operational manual preparation and further research endeavours.

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“…Several authοrs have used data frοm Landsat TM, ETM+ (Sabin, 1999;Abrams et al, 1988) and ASTER (Gad and Kusky, 2007;Safari et al, 2018;Pοur et al, 2019;Küçük Matçı and Avdan, 2019) fοr the explοratiοn οf mineral resοurces and mapping οf geοlοgical fοrmatiοns in the arid and semi-arid zοnes. Many studies have proved the pοtential οf remοte sensing in the mapping οf irοn οre using Landsat (Abrams et al, 1983;Sabins, 1997, Ducart et al, 2016 and ASTER (Rajendran, 2011;Huang et al, 2018;Sengupta et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

Discriminatiοn of Irοn Deposits Using Feature Οriented Principal Cοmpοnent Selectiοn and Band Ratiο Methοds: Eastern Taurus /Turkey

Traoré

Çan

Tekin

2020

International Journal of Environment and Geoinformatics

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Identification and mapping of high-potential iron ore alteration zone across Joda, Odisha using ASTER and EO-1 hyperion data

Cited by 8 publications

References 56 publications

SUREHYP: An Open Source Python Package for Preprocessing Hyperion Radiance Data and Retrieving Surface Reflectance

SUREHYP: An Open Source Python Package for Preprocessing Hyperion Radiance Data and Retrieving Surface Reflectance

Unearthing narratives: an ethnographic lens on the organizational tapestry of massive iron ore mining operations

Discriminatiοn of Irοn Deposits Using Feature Οriented Principal Cοmpοnent Selectiοn and Band Ratiο Methοds: Eastern Taurus /Turkey

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