Shridhar holds a Master’s and a doctoral degree in Geoinformatics, with a primary research focus on Earth’s cryosphere. With over 14 years of experience in Earth observation (EO), remote sensing (RS), and Geoinformation science, he has been actively involved in addressing environmental changes in the Arctic, Antarctic, and Himalayas, collaborating with research institutes, academia, and international organizations. Shridhar was honoured with the Indian National Geospatial Award and Young Geospatial Medal in 2017 for his noteworthy contributions to Earth observation research in polar regions.
Current research study emphasizes the importance of advanced digital image processing methods in ... more Current research study emphasizes the importance of advanced digital image processing methods in order to delineate between various LULC features. In the case of the Antarctica, the present LC (snow/ice, landmass, water, vegetation etc.) and the present LU (research stations of various nations) needs to be mapped accurately for the hassle free routine activities. Geo-location has become the most important part of geosciences studies. In this paper we have tried to locate three most important features (snow/ice, landmass, and water) and also have extracted the extent of the same using the multisource classification (image fusion/pansharpening) and pattern recognition (supervised/unsupervised methods, index ratio methods). Innovation in developing spectral index ratios has led us to come up with an unique ratio named Normalized Difference Landmass Index (NDLI) which performed better (Avg. Bias: 51.99m) than other ratios such as Normalized Difference Snow/Ice Index (NDSII) (Avg. Bias: -1572.11m) and Normalized Difference Water Index (NDWI) (Avg. Bias: 1886.60m). The practiced trial and error methodology quantifies the productivity of not only the classification methods over one other but also that of the fusion methods. In present study, classifiers used (Mahalanobis and Winner Takes All) performed better (Avg. Bias: 122.16 m) than spectral index ratios (Avg. Bias: 620.16 m). The study also revealed that newly introduced bands in WorldView-2, band 1 (Coastal Blue), 4 (Yellow), 6 (Red-edge) and 8 (Near Infrared-2) along with traditional bands have the capacity to mine the polar geospatial information with utmost accuracy and efficiency.
Abstract Significant changes in the interannual variation of Arctic snow and sea ice are connecte... more Abstract Significant changes in the interannual variation of Arctic snow and sea ice are connected to changes in the global climate. Retreat of ice sheets/glaciers is due to increased melting in many regions of the cryosphere. Active microwave sensors are frequently used to detect surface melting because of their sensitivity to the liquid water presence in snow/ice. We mapped the annual melt duration and summer melt onset for the Svalbard archipelago using microwave scatterometers flown on QuikSCAT, OSCAT, ASCAT, and OSCAT-2, providing one of the longest continuous records of radar backscatter to estimate snowmelt onset and melt duration on Svalbard spanning 2000–17. A single threshold-based model was used to detect the timing of snowmelt; the threshold was calculated using meteorological data from manned weather stations. The results capture the timing and extent of melt events caused by warm air temperature and precipitation, as a consequence of the influx of moist, mild air from the Norwegian and Barents seas. The highest melt duration and earlier melt onset occurred in southernmost and western Svalbard, in response to the influence of the warm West Spitsbergen Current. Compared to earlier studies, we found considerable interannual variability and regional differences. Though the record is short, there is an indication of an increasing trend in total days of melt duration and earlier summer melt onset date, possibly linked to the general warming trend. Climate indices such as Interdecadal Pacific Oscillation and Pacific Decadal Oscillation are well correlated with onset melt and duration across Svalbard. With the reported year-after-year decrease in sea ice cover over the Arctic Ocean, the trend toward longer snowmelt duration inferred from this study is expected to enhance the Arctic amplification.
Digital elevation model (DEM) is indispensable for analysis such as topographic feature extractio... more 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.
Svalbard Integrated Arctic Earth Observing System (SIOS) is an international collaboration of 28 ... more Svalbard Integrated Arctic Earth Observing System (SIOS) is an international collaboration of 28 scientific institutions from 10 countries to build a collaborative research infrastructure that will enable better estimates of future environmental and climate changes in the Arctic. SIOS' mission is to develop an efficient observing system in Svalbard, share technology and data using FAIR principles, fill knowledge gaps in Earth system science and reduce the environmental footprint of science in the Arctic. This study presents SIOS' efforts to strengthen science, international collaboration and capacity building in the high Arctic archipelago of Svalbard through its airborne research infrastructure. SIOS supports the coordinated usage of its airborne remote sensing resources such as the Dornier aircraft and uncrewed aerial vehicles (UAVs) for improved research activities in Svalbard, complementing in situ and space-borne measurements and reducing the environmental footprint of research in Svalbard. Since 2019, SIOS in collaboration with its member institution Norwegian Research Centre (NORCE) installed, tested, and operationalised optical imaging sensors in the Lufttransport Dornier (DO228) passenger aircraft stationed in Longyearbyen under the SIOS-InfraNor project making it compatible with research use in Svalbard. Two optical sensors are installed onboard the Dornier aircraft; (1) the PhaseOne IXU-150 RGB camera and (2) the HySpex VNIR-1800 hyperspectral sensor. The aircraft with these cameras is configured to acquire aerial RGB imagery and hyperspectral remote sensing data in addition to its regular logistics and transport operation in Svalbard. Since 2020, SIOS has supported and coordinated around 50 flight hours to acquire airborne data using the Dornier aircraft and UAVs in Svalbard supporting around 20 scientific projects. The use of airborne imaging sensors in these projects enabled a variety of applications within glaciology, biology, hydrology, and other fields of Earth system science: Mapping glacier crevasses, generating DEMs for glaciological applications, mapping and characterising earth (e.g., minerals, vegetation), ice (e.g., sea ice, icebergs, glaciers and snow cover) and ocean surface features (e.g., colour, chlorophyll). The use of passenger aircraft warrants the following benefits: (1) regular logistics and research activities are optimally coordinated to reduce flight hours in carrying scientific observations, (2) project proposals for the usage of aircraft-based measurements facilitate international collaboration, (3) measurements conducted during 2020-21 are useful in filling the gaps in field based observations occurred due to the Covid-19 pandemic, (4) airborne data are used to train polar scientists as a part of the annual SIOS training course and upcoming data usability contest, (5) data is also useful for Arctic field safety as it can be used to make products such as high-resolution maps of crevassed areas on glaciers. In short, SIOS airborne remote sensing activities represent optimized use of infrastructure, promote capacity building, Arctic safety and facilitate international cooperation.
<p>This study provides an overview of the Earth observation and rem... more <p>This study provides an overview of the Earth observation and remote sensing activities of Svalbard Integrated Arctic Earth Observing System (SIOS) undertaken when building an observing system for sustained measurements in and around Svalbard to address Earth System Science (ESS) questions. SIOS research infrastructures are distributed across and around Svalbard for acquiring long-term in situ observations. These in situ measurements are not only useful for various ground-based studies, but also applicable for calibration and validation (Cal/Val) of current and future satellite missions e.g. Copernicus Imaging Microwave Radiometer (CIMR), Radar Observing System for Europe - L-Band (ROSE-L ) or Sentinel-1,2, Copernicus Polar Ice and Snow Topography Altimeter (CRISTAL), Sentinel-5 Precursor, and Copernicus Hyperspectral Imaging Mission for Environment (CHIME). Better integration of in situ and satellite-based measurements is crucial for building a coherent network of observations to fill observational gaps. Additionally. complementing in situ measurements with satellite data is a prime necessity to generate operational reliable geoinformation products using traditional and advanced methods, for example, mapping vegetation extent in Svalbard using Sentinel-2 data complemented with in situ measurements of spectral reflectance collected by SIOS infrastructure. SIOS’s remote sensing activities are developed in SIOS knowledge centre (SIOS-KC) under the direction of the remote sensing working group (RSWG). This study highlights our current activities, goals for the next five years (2022-2026) and future activities with the intention of attracting potential collaborations to support achieving these goals. The study discusses SIOS’s present activities, including (1) capacity building e.g., webinar series, online conference, and training courses on EO and RS studies in Svalbard to train the next generation of polar scientists, (2) infrastructure development (like the current infrastructure investment programme SIOS-InfraNor) that can attract Cal/Val activities to Svalbard (3) SIOS’s airborne remote sensing activities, and (4) SIOS remote sensing service tools for field scientists. Ongoing and future activities include (1) the development of unified platform for satellite data availability for Svalbard, (2) establishing an EO and RS researcher’s forum on SIOS website, (3) community-based observations e.g. developing a citizen science project model for supporting satellite cal/val activities in Svalbard, (4) ongoing surveys on user requirements, product inventory and citizen science project, and (5) the ‘Satellite image of the week campaign’ on social media for outreach. The sustained and coordinated efforts by SIOS to develop a long-term monitoring system are expected to contribute to integrated monitoring, modelling and supporting decision making in Svalbard in the coming decades.</p>
This work presents various normalized difference water indices (NDWI) to delineate lakes from Sch... more This work presents various normalized difference water indices (NDWI) to delineate lakes from Schirmacher Oasis, East Antarctica, by using a very high resolution WorldView-2 (WV-2) satellite imagery. Schirmacher oasis region hosts a number of fresh as well as saline water lakes, such as epishelf lakes, ice-free or landlocked lakes, which are completely frozen or semi-frozen and in a ice-free state. Hence, detecting all these types of lakes distinctly on satellite imagery was the major challenge, as the spectral characteristics of various types of lakes were identical to the other land cover targets. Multiband spectral index pixel-based approach is most experimented and recently growing technique because of its unbeatable advantages such as its simplicity and comparatively lesser amount of processing-time. In present study, semiautomatic extraction of lakes in cryospheric region was carried out by designing specific spectral indices. The study utilized number of existing spectral indices to extract lakes but none could deliver satisfactory results and hence we modified NDWI. The potentials of newly added bands in WV-2 satellite imagery was explored by developing spectral indices comprising of Yellow (585 – 625 nm) band, in combination with Blue (450 – 510 nm), Coastal (400 – 450 nm) and Green (510 – 580 nm) bands. For extraction of frozen lakes, use of Yellow (585 – 625 nm) and near-infrared 2 (NIR2) band pair, and Yellow and Green band pair worked well, whereas for ice-free lakes extraction, a combination of Blue and Coastal band yielded appreciable results, when compared with manually digitized data. The results suggest that the modified NDWI approach rendered bias error varying from ~1 to ~34 m2.
High resolution calibrated PAN-sharpened images from WorldView-2 (WV-2) were used for extracting ... more High resolution calibrated PAN-sharpened images from WorldView-2 (WV-2) were used for extracting blue ice areas in Schirmacher Oasis, east Antarctica. The Schirmacher oasis extends from 70°45′ S to 70° 75′ S and 11°38′ E to 11° 38′ E. Blue ice areas represents long-term ablation. The amplitude of blue ice is lower than that of snow, because the ice surface is smoother than the latter. But the difference is not so obvious when applying automatic extraction techniques. To achieve desirable results and support comparative analysis, multiband image combinations were generated from atmospherically-corrected WV-2 data. For feature extraction process, regions of interest (ROI) were considered in which blue ice was used as target and white snow/ice appearing on the blue ice was considered as non-target. Various semiautomatic feature extraction methods, such as, target detection, mapping methods, etc, and many trials were used for extracting blue ice areas. Surface patterns of alternating snow and blue ice bands were found in east Antarctica which becomes obstacle to clearly extract blue ice feature. From the high resolution WV-2 data, reference data (digitized data) were prepared for blue ice area. By comparing reference data and extracted data, bias and root mean square (RMS) error values were calculated. Accuracy assessment was done considering the entire necessary prior results of the blue ice area. Our results indicate that the pixel-based supervised classification methods yielded an overall accuracy ranging from 82%-89% for extraction of blue ice areas.
High-resolution pansharpened images from WorldView-2 were used for bathymetric mapping around Lar... more High-resolution pansharpened images from WorldView-2 were used for bathymetric mapping around Larsemann Hills and Schirmacher oasis, east Antarctica. We digitized the lake features in which all the lakes from both the study areas were manually extracted. In order to extract the bathymetry values from multispectral imagery we used two different models: (a) Stumpf model and (b) Lyzenga model. Multiband image combinations were used to improve the results of bathymetric information extraction. The derived depths were validated against the in-situ measurements and root mean square error (RMSE) was computed. We also quantified the error between in-situ and satellite-estimated lake depth values. Our results indicated a high correlation (R = 0.60~0.80) between estimated depth and in-situ depth measurements, with RMSE ranging from 0.10 to 1.30 m. This study suggests that the coastal blue band in the WV-2 imagery could retrieve accurate bathymetry information compared to other bands. To test the effect of size and dimension of lake on bathymetry retrieval, we distributed all the lakes on the basis of size and depth (reference data), as some of the lakes were open, some were semi frozen and others were completely frozen. Several tests were performed on open lakes on the basis of size and depth. Based on depth, very shallow lakes provided better correlation (≈ 0.89) compared to shallow (≈ 0.67) and deep lakes (≈ 0.48). Based on size, large lakes yielded better correlation in comparison to medium and small lakes.
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Conference Presentations by Shridhar Jawak