Dr. Shridhar D. Jawak Profile

Dr. Shridhar D. Jawak

Scientist and Project Leader at Climate and Environmental Research Institute NILU

SPIE Involvement:

Author

Area of Expertise:

Remote Sensing , Earth Observation , Photgrammetry , High resolution satellites , Calibration and validation , Polar science

Websites:

Personal Website | Company Website

Dr. Shridhar D. Jawak boasts over 14 years of experience in Earth observation (EO) and remote sensing (RS) applications, specializing in the Earth's cryosphere. From 2018 to 2023, he served as a Remote Sensing Officer at the Svalbard Integrated Arctic Earth Observing System (SIOS), Norway, holding an academic equivalent position to Senior Advisor/Associate Professor. Since April 2023, Dr. Jawak has taken on the role of adjunct faculty/visiting faculty at the Manipal Academy of Higher Education (MAHE), India. In 2022-23, he contributed to the EEA-funded Cryosphere Integrated Observatory Network on Svalbard (CRIOS) project as a steering committee member and project participant. His extensive involvement includes serving on the project advisory board (PAB) of the EU Horizon 2020-KEPLER Project, the Expert Advisory Board (EAB) of the EU Horizon-2020 OCRE Project, and the project evaluation board of the EU Horizon 2020-CoRDiNet Project. Over the past 14 years, Dr. Jawak has chaired more than 14 sessions at international conferences, published over 50 peer-reviewed articles, and delivered over 100 conference presentations. Notably, he has mentored around 90 master's students in the field of Earth observation and Remote Sensing in Polar Regions. Dr. Jawak has been recognized with five prestigious awards, including the Emerging Leadership Award-2022 by Arctic Frontiers, the International Mentorship Award-2021 by the Association of Polar Early Career Scientists (APECS), and the Indian National Geo-spatial Award-2018 by the Indian Society of Remote Sensing (ISRS). His research focuses on remote sensing of the cryosphere, particularly utilizing multi-satellite (SAR/Optical/LiDAR) and airborne data for spatiotemporal changes in the cryosphere of the Arctic, Antarctic, and Himalayas. During his doctoral research, he participated in three summer expeditions to Antarctica and one field campaign to Svalbard.

Proceedings Article | 19 October 2023 Presentation + Paper

How precisely can we map blue ice in Antarctica?

Shridhar Jawak, Sagar Wankhede, Alvarinho Luis, Prashant Pandit, Peter Convey, Peter Fretwell

Proceedings Volume 12734, 1273404 (2023) https://doi.org/10.1117/12.2680318

KEYWORDS: Support vector machines, Convolutional neural networks, Ice, Satellites, Climatology, Earth observing sensors, Satellite imaging, Landsat, Neural networks, Mahalanobis distance, Education and training

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Precise mapping of Blue Ice Regions (BIRs) in Antarctica can facilitate resource management during expeditions and help plan field investigations. Satellite-based BIR mapping was traditionally performed with large-swath, coarse to medium-resolution imagery. This study presents a comprehensive evaluation of four classification methods for mapping blue ice regions: Neural Network Classifier (NNC), Support Vector Machine (SVM), Maximum Likelihood (MXL), and Mahalanobis Distance (MD). Each method was trained and tested using a labeled dataset, and their performance was assessed based on Overall Accuracy (OA), Bias, and Average Root Mean Squared Error (RMSe). Among the methods, SVM stood out with exceptional performance, achieving an impressive OA of 92% and demonstrating a low bias of 1.27%. The SVM method also exhibited superior spatial accuracy, as indicated by an Average RMSe of 1978 m2, making it well-suited for precise delineation of blue ice regions. On the other hand, NNC and MXL demonstrated reasonable overall accuracy but exhibited higher spatial errors. MD showed potential for improvement in spatial accuracy, recording an Average RMSe of 3031 m2. Based on the evaluation, SVM emerged as the most effective technique for mapping blue ice regions, providing valuable insights for glaciological research and climate change studies. MODIS, Landsat, Sentinel, and Very High Resolution (VHR) data, such as WorldView-2 (WV-2), are recommended for mapping large, moderate-sized, and small-sized BIR patches, respectively. The inclusion of WV-2 data allows for accurate estimation of the maximum summer extent of BIRs across Antarctica, facilitating comprehensive assessments of their spatial distribution and dynamics. This research contributes to advancing the understanding of BIRs and their significance in climate and glaciological studies. The refined VHR blue ice map from the current study will be useful for future studies of mass balance, radiation budget, and regional climate changes in the study region.

Proceedings Article | 19 October 2023 Paper

Modulating spectral reflectance and its impact on thematic mapping: an analysis on glacier facies using very high-resolution satellite imagery

Sagar Wankhede, Prashant Pandit, Shridhar Jawak, Alvarinho Luis, Keshava Balakrishna

Proceedings Volume 12734, 127341F (2023) https://doi.org/10.1117/12.2680310

KEYWORDS: Glaciers, Atmospheric corrections, Reflectivity, Image processing, Image fusion, Short wave infrared radiation, Machine learning, Image analysis

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Proceedings Article | 7 May 2016 Paper

High-resolution multispectral satellite imagery for extracting bathymetric information of Antarctic shallow lakes

Shridhar Jawak, Alvarinho Luis

Proceedings Volume 9878, 987819 (2016) https://doi.org/10.1117/12.2222769

KEYWORDS: Satellites, Multispectral imaging, Satellite imaging, Earth observing sensors, Image resolution, Associative arrays, Feature extraction, Image retrieval, Reflectivity, Remote sensing, Data modeling, Water, Solar radiation models

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Proceedings Article | 7 May 2016 Paper

Exploratory normalized difference water indices for semi-automated extraction of Antarctic lake features

Shridhar Jawak, Alvarinho Luis

Proceedings Volume 9878, 987818 (2016) https://doi.org/10.1117/12.2222766

KEYWORDS: Satellites, Satellite imaging, Earth observing sensors, Image resolution, Feature extraction, Target detection, Sensors, Remote sensing, Atmospheric corrections, Visualization, Accuracy assessment

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Proceedings Article | 5 May 2016 Paper

Object-oriented feature extraction approach for mapping supraglacial debris in Schirmacher Oasis using very high-resolution satellite data

Shridhar Jawak, Ajay Jadhav, Alvarinho Luis

Proceedings Volume 9877, 98772L (2016) https://doi.org/10.1117/12.2223012

KEYWORDS: Feature extraction, Satellites, Atmospheric optics, Associative arrays, Image analysis, Image resolution, Optical resolution, Remote sensing, Calibration, Atmospheric sensing, Image classification, Earth observing sensors, Satellite imaging, Error analysis, Atmospheric corrections, Spatial resolution

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Proceedings Article | 5 May 2016 Paper

Application of high-resolution multispectral data for mapping blue ice areas in the Antarctic environment

Shridhar Jawak, Alvarinho Luis

Proceedings Volume 9877, 98772M (2016) https://doi.org/10.1117/12.2222770

KEYWORDS: Earth observing sensors, Feature extraction, Associative arrays, Image resolution, Calibration, Image analysis, Target detection, Accuracy assessment, Visible radiation, Infrared radiation, Remote sensing, Satellites, Error analysis, Satellite imaging, Atmospheric corrections

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Proceedings Article | 5 May 2016 Paper

Generation of a precise DEM by interactive synthesis of multi-temporal elevation datasets: a case study of Schirmacher Oasis, East Antarctica

Shridhar Jawak, Alvarinho Luis

Proceedings Volume 9877, 98772E (2016) https://doi.org/10.1117/12.2223609

KEYWORDS: Data modeling, Feature extraction, Radar, Databases, Satellites, Earth sciences, Laser systems engineering, Associative arrays, Reflection, Radiometry, Global Positioning System, Analytical research, Error analysis, Accuracy assessment, Spatial resolution, Data conversion, Remote sensing

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Digital elevation model (DEM) is indispensable for analysis such as topographic feature extraction, ice sheet melting, slope stability analysis, landscape analysis and so on. Such analysis requires a highly accurate DEM. Available DEMs of Antarctic region compiled by using radar altimetry and the Antarctic digital database indicate elevation variations of up to hundreds of meters, which necessitates the generation of local improved DEM. An improved DEM of the Schirmacher Oasis, East Antarctica has been generated by synergistically fusing satellite-derived laser altimetry data from Geoscience Laser Altimetry System (GLAS), Radarsat Antarctic Mapping Project (RAMP) elevation data and Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) global elevation data (GDEM). This is a characteristic attempt to generate a DEM of any part of Antarctica by fusing multiple elevation datasets, which is essential to model the ice elevation change and address the ice mass balance. We analyzed a suite of interpolation techniques for constructing a DEM from GLAS, RAMP and ASTER DEM-based point elevation datasets, in order to determine the level of confidence with which the interpolation techniques can generate a better interpolated continuous surface, and eventually improve the elevation accuracy of DEM from synergistically fused RAMP, GLAS and ASTER point elevation datasets. The DEM presented in this work has a vertical accuracy (≈ 23 m) better than RAMP DEM (≈ 57 m) and ASTER DEM (≈ 64 m) individually. The RAMP DEM and ASTER DEM elevations were corrected using differential GPS elevations as ground reference data, and the accuracy obtained after fusing multitemporal datasets is found to be improved than that of existing DEMs constructed by using RAMP or ASTER alone. This is our second attempt of fusing multitemporal, multisensory and multisource elevation data to generate a DEM of Antarctica, in order to address the ice elevation change and address the ice mass balance. Our approach focuses on the strengths of each elevation data source to produce an accurate elevation model.

Proceedings Article | 5 May 2016 Paper

Geospatial mapping of vegetation in the Antarctic environment using very high-resolution WorldView-2 imagery

Shridhar Jawak, Udhayaraj A. D., Alvarinho Luis

Proceedings Volume 9877, 98772N (2016) https://doi.org/10.1117/12.2222751

KEYWORDS: Vegetation, Multispectral imaging, Associative arrays, Remote sensing, Spatial resolution, High resolution satellite images, Satellites, Sensors, Carbon, Photosynthesis, Target detection, Image resolution, Near infrared, Reflectivity

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Proceedings Article | 2 May 2016 Paper

C-band RISAT-1 imagery for geospatial mapping of cryospheric surface features in the Antarctic environment

Shridhar Jawak, Satej Panditrao, Alvarinho Luis

Proceedings Volume 9881, 98811R (2016) https://doi.org/10.1117/12.2222782

KEYWORDS: Synthetic aperture radar, Polarimetry, Polarization, Spatial resolution, Calibration, Analytical research, Sensor performance, Sensor calibration, Scattering, Remote sensing, Fuzzy logic, Image classification, Associative arrays, Accuracy assessment, Statistical analysis

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Proceedings Article | 30 April 2016 Paper

Semi-automatic extraction of supra-glacial features using fuzzy logic approach for object-oriented classification on WorldView-2 imagery

Shridhar Jawak, Yogesh Palanivel V., Alvarinho Luis

Proceedings Volume 9880, 98801S (2016) https://doi.org/10.1117/12.2223024

KEYWORDS: Feature extraction, Fuzzy logic, Image analysis, Image classification, Spatial resolution, Satellites, High resolution satellite images, Image segmentation, Spectral resolution, Image resolution, Near infrared, Remote sensing

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Proceedings Article | 30 April 2016 Paper

Geospatial mapping of Antarctic coastal oasis using geographic object-based image analysis and high resolution satellite imagery

Shridhar Jawak, Alvarinho Luis

Proceedings Volume 9880, 98801Q (2016) https://doi.org/10.1117/12.2222767

KEYWORDS: Image segmentation, Image analysis, Satellite imaging, Earth observing sensors, Satellites, Image classification, Image resolution, Image processing, Remote sensing, Associative arrays

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Proceedings Article | 30 April 2016 Paper

Multisource classification and pattern recognition methods for polar geospatial information mining using WorldView-2 data

Parag Khopkar, Shridhar Jawak, Alvarinho Luis

Proceedings Volume 9880, 98801R (2016) https://doi.org/10.1117/12.2222984

KEYWORDS: Earth observing sensors, Pattern recognition, Image classification, Feature extraction, Error analysis, Mining, Remote sensing, Satellites, Satellite imaging, Data mining

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SPIE Journal Paper | 17 December 2013

Very high-resolution satellite data for improved land cover extraction of Larsemann Hills, Eastern Antarctica

Shridhar Jawak, Alvarinho Luis

JARS, Vol. 7, Issue 01, 073460, (December 2013) https://doi.org/10.1117/12.10.1117/1.JRS.7.073460

KEYWORDS: Satellites, Image classification, Satellite imaging, Photography, Error analysis, Earth observing sensors, Image processing, Remote sensing, Associative arrays, Spatial resolution

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SPIE Journal Paper | 24 April 2013

Improved land cover mapping using high resolution multiangle 8-band WorldView-2 satellite remote sensing data

Shridhar Jawak, Alvarinho Luis

JARS, Vol. 7, Issue 01, 073573, (April 2013) https://doi.org/10.1117/12.10.1117/1.JRS.7.073573

KEYWORDS: Vegetation, Satellites, Associative arrays, Multispectral imaging, Geographic information systems, Remote sensing, Near infrared, Reflectivity, Image fusion, Data fusion

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