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Remote sensing-based investigation of ice flow dynamics of Polar Record glacier (PRG) during December–April of 2016-2019 has been conducted in this work. Using offset tracking method on the Sentine...

Nigam, R., Tripathi, G., Priya, T., Luis, A. J., Vaz, E., Kumar, S., Shakya, A., Damásio, B., Kotha, M., & Yu, B. (Ed.) (2022). Did Covid-19 lockdown positively affect the urban environment and UN- Sustainable Development Goals? PLoS ONE, 17(9), 1-21. [e0274621]. https://doi.org/10.1371/journal.pone.0274621 This work quantifies the impact of pre-, during- and post-lockdown periods of 2020 and 2019 imposed due to COVID-19, with regards to a set of satellite-based environmental parameters (greenness using Normalized Difference Vegetation and water indices, land surface temperature, night-time light, and energy consumption) in five alpha cities (Kuala Lumpur, Mexico, greater Mumbai, Sao Paulo, Toronto). We have inferenced our results with an extensive questionnaire-based survey of expert opinions about the environment-related UN Sustainable Development Goals (SDGs). Results showed considerable variation due to the lockdown on environment-related SDGs. The growth in the urban environmental variables during lockdown phase 2020 relative to a similar period in 2019 varied from 13.92% for Toronto to 13.76% for greater Mumbai to 21.55% for Kuala Lumpur; it dropped to −10.56% for Mexico and −1.23% for Sao Paulo city. The total lockdown was more effective in revitalizing the urban environment than partial lockdown. Our results also indicated that Greater Mumbai and Toronto, which were under a total lockdown, had observed positive influence on cumulative urban environment. While in other cities (Mexico City, Sao Paulo) where partial lockdown was implemented, cumulative lockdown effects were found to be in deficit for a similar period in 2019, mainly due to partial restrictions on transportation and shopping activities. The only exception was Kuala Lumpur which observed surplus growth while having partial lockdown because the restrictions were only partial during the festival of Ramadan. Cumulatively, COVID-19 lockdown has contributed significantly towards actions to reduce degradation of natural habitat (fulfilling SDG-15, target 15.5), increment in available water content in Sao Paulo urban area(SDG-6, target 6.6), reduction in NTL resulting in reducied per capita energy consumption (SDG–13, target 13.3). publishersversion published

A review is presented on physical oceanographic features based on expendable CTD data collected in the Indian Ocean sector of the Southern Ocean. The thermohaline structure is dominated by Circumpolar Deep Water. The temperature and salinity are affected by cyclonic circulation in the Weddell Sea and Prydz Bay. High chlorophyll-a blooms (2-4 mg m-3) evolve during austral summer due to stratification which is caused by freshwater generated from the sea ice melt and the glacial outflow which traps phytoplankton in a shallow mixed layer, where they are exposed to higher irradiances of photosynthetically active radiation. Attempts have been made to relate the physical characteristics to biomass inferred from data published from previous Indian Scientific expeditions. More in-situ observations related to biophysical and chemical are recommended in the near future projects.

Surface glacier facies are superficial expressions of a glacier that are distinguishable based on differing spectral and structural characteristics according to their age and inter-mixed impurities . Increasing bodies of literature suggest that the varying properties of surface glacier facies differentially influence the melt of the glacier, thus affecting the mass balance. Incorporating these variations into distributed mass balance modelling can improve the perceived accuracy of these models. However, detecting and subsequently mapping these facies with a high degree of accuracy is a necessary precursor to such complex modelling. The variations in the reflectance spectra of various glacier facies permit multiband imagery to exploit band ratios for their effective extraction. However, coarse and medium spatial resolution multispectral imagery can delimit the efficacy of band ratioing by muddling the minor spatial and spectral variations of a glacier. Very high-resolution imagery, on the other hand, creates distortions in the conventionally obtained information extracted through pixel-based classification. Therefore, robust and adaptable methods coupled with higher resolution data products are necessary to effectively map glacier facies. This study endeavours to identify and isolate glacier facies on two unnamed glaciers in the Chandra-Bhaga basin, Himalayas, using an established object-based multi-index protocol. Exploiting the very high resolution offered by WorldView-2 and its eight spectral bands, this study implements customized spectral index ratios via an object-based environment. Pixel-based supervised classification is also performed using three popular classifiers to comparatively gauge the classification accuracies. The object-based multi-index protocol delivered the highest overall accuracy of 86.67%. The Minimum Distance classifier yielded the lowest overall accuracy of 62.50%, whereas, the Mahalanobis Distance and Maximum Likelihood classifiers yielded overall accuracies of 77.50% and 70.84% respectively. The results outline the superiority of the object-based method for extraction of glacier facies. Forthcoming studies must refine the indices and test their applicability in wide ranging scenarios.

Glaciers play a crucial role in the study of the climate change pattern of the Earth. Remote sensing with access to large archives of data has the ability to monitor glaciers frequently throughout the year. Therefore, remote sensing is the most beneficial tool for the study of glacier dynamics. Fed by many tributaries from different sides, the Amery Ice Shelf (AIS) is one of the largest ice shelves that drains ice from the Antarctic ice sheet into the Southern Ocean. This study focuses on the eastern and the western tributaries of the AIS. The primary objective of the study was to derive the velocity of the tributary glaciers and the secondary objective was to compare variations in their velocities between the summer and winter season. This study was carried on using the European Space Agency’s (ESA) Sentinel-1 satellite’s Synthetic Aperture Radar (SAR) data acquired from the Sentinel data portal. Offset tracking method was applied to the Ground Range Detected (GRD) product of the Sentinel-1 interferometric wide (IW) swath acquisition mode. The maximum velocity in summer was observed to be around 610 m/yr in the eastern tributary glacier meeting the ice shelf near the Pickering Nunatak, and around 345 m/yr in the Charybdis Glacier Basin from the western side. The maximum velocity in the winter was observed to be 553 m/yr in the eastern side near the Pickering Nunatak whereas 323 m/yr from the western side in the Charybdis Glacier Basin. The accuracy of the derived glacier velocities was computed using bias and root mean square (RMS) error. For the analysis, the publicly available velocity datasets were used. The accuracy based on RMS error was observed to be 85-90% for both seasons with bias values up to 25 m/yr and root mean square error values up to 30 m/yr.

Glacier movement is a crucial factor for assessing cryospheric climate change. Traditional methods of field surveys for studying glacier movement and velocity are often not possible owing to inaccessibility and harsh terrains. Furthermore, as it is not feasible to physically monitor and survey many glaciers around the globe, these traditional methods are limited in their global coverage. Remote sensing is an ideal tool to study such phenomena on a global scale. Optical remote sensing employs techniques such as feature tracking and pixel tracking, whereas, microwave remote sensing uses intensity tracking, speckle tracking, Interferometric SAR and Differential InSAR (DInSAR). This study focuses on estimation of glacier velocity and its seasonal variations using the image-matching technique for optical images for the Fisher glacier, a tributary glacier of the Amery ice shelf in Antarctica. The tool used in this study is the COSI-Corr module embedded in ENVI which provides the velocity in both azimuth and range resolution. The principle of estimating velocity using this tool is pixel tracking wherein similar pixels on two images are tracked where one is the master image and the other is a slave. This technique correlates the master and slave images over a time period and generates three outputs: displacements in the East-West and North-South directions and signal-to-noise ratio (SNR) image. Landsat 8 image pairs were used for cross correlation over a time span of four years spanning 2013–2017. With a resolution of 15 m, the panchromatic band (Band 8) was the ideal choice for pixel tracking as the resolution of other bands is coarser. The initial window size for correlation was 64 while the final window size was 16. The resolution of the displacement images produced is dependent on the value assigned for the step size, which was set to 8. The resultant velocity was derived using the result of the two displacement images. The SNR image was used to remove all the pixels from the velocity output having the value of SNR less than 0.9, in order to reduce the effect of noise. The annual velocity of the Fisher glacier was estimated to be around 600 to 650 myr−1 near the tongue where it merges with the Amery Ice Shelf, which was reduced to 150 myr−1 as it recedes. The resultant velocity images have a resolution of 120 m. The seasonal variation in velocity for the year 2013–2014 is 1.8 myr−1, while in the succeeding year 2014-2015 it subdued to 1.7 myr−1. The seasonal variation for the year 2015–2016 was estimated to be 7.9 myr−1. The seasonal variation for 2016–2017 was 17.4 myr−1.

On January 30, 2020, India recorded its first COVID-19 positive case in Kerala, which was followed by a nationwide lockdown extended in four different phases from 25th March to 31st May, 2020, and an unlock period thereafter. The lockdown has led to colossal economic loss to India; however, it has come as a respite to the environment. Utilizing the air quality index (AQI) data recorded during this adverse time, the present study is undertaken to assess the impact of lockdown on the air quality of Ankleshwar and Vapi, Gujarat, India. The AQI data obtained from the Central Pollution Control Board was assessed for four lockdown phases. We compared air quality data for the unlock phase with a coinciding period in 2019 to determine the changes in pollutant concentrations during the lockdown, analyzing daily AQI data for six pollutants (PM10, PM2.5, CO, NO2, O3, and SO2). A meta-analysis of continuous data was performed to determine the mean and standard deviation of each lockdown phase, and their differences were computed in percentage in comparison to 2019; along with the linear correlation analysis and linear regression analysis to determine the relationship among the air pollutants and their trend for the lockdown days. The results revealed different patterns of gradual to a rapid reduction in most of the pollutant concentrations (PM10, PM2.5, CO, SO2), and an increment in ozone concentration was observed due to a drastic reduction in NO2 by 80.18%. Later, increases in other pollutants were also observed as the restrictions were eased during phase-4 and unlock 1. The comparison between the two cities found that factors like distance from the Arabian coast and different industrial setups played a vital role in different emission trends.

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.

The study aimed to understand beach litter status at some of the world-famous beaches of Goa, West India, to comprehend the impact of the Covid-19 lockdown. We characterize litter in six categories (Nylon+Rubber, Plastics, Footwear, Glass, Metal, and Thermocol) for eight sampled beaches in the north and south Goa. All beaches show increased glass and decreased plastics (significant litter) during the lockdown period compared to the unlock period that marked the high tourist inflow. Beaches were classified and graded with colour codes using litter density exhibit light blue-green colour coding during the lockdown or unlock period, suggests clean maintenance. The Miramar beach located in the heart of the capital city showed relatively more litter density (yellow code) due to the combination of local people and tourist inflow. Morjim, Palolem, Velsao were littered the least during both periods.

This study discusses the calving event took place in Prydz Bay of East Antarctica during the epoch of 2013–2015 using high resolution multispectral data from Indian Linear Imaging Self Scanning Sensor (LISS-IV) aboard IRS-P6 satellite. The present study has been conducted on Larsemann Hills, Prydz Bay, East Antarctica. The two LISS-IV images (5.8 m spatial resolution) acquired specifically 384 days apart (December 31, 2013 and January 19, 2015) were utilized to study the significant changes that have occurred in icebergs during this short epoch. A total of 369 common icebergs present in both images were identified for analysing the changes in their dimensions because of surface melting. All of these icebergs were found to have lost mass because of surface melting and ocean forced base melting; therefore, they have reduced in dimension depicted by 12.51% lapse in terms of surface area. In addition, the coastline was visually observed to have retracted, instigated by calving events from the polar ice sheet and generation of new icebergs in Prydz Bay. The average drift distance of these newly formed icebergs from the coastline was found to be 51.59 m. Our analysis estimates that the total number of icebergs decreased by 70, suggesting either the complete disintegration or significant drifting of these icebergs away from the coast during 2013–2015 period.

This study addresses the potential of geospatial information extraction by using light detection and ranging (LiDAR) data and aerial optical images in an urban landscape. We have adopted an advanced geographic object-based image analysis (GEOBIA) technique consisting of rule-based procedures relying upon the integration of spectral, textural, and spatial characteristics of aerial imagery and roughness of point cloud of LiDAR to fuse aerial imagery and airborne LiDAR for effective urban geospatial information extraction. This study is focused on the extraction of four tangible geospatial features, e.g., buildings, trees, marine vessels, and cars. LiDAR-derived normalized digital surface model (nDSM) was insufficient in delineating the polygon features because of the sparse point cloud density at the edges of features, which greatly affected the accuracy of extracting the polygon features. Therefore aerial imagery was supplemented in order to enhance the quality of extraction. The final feature extraction accuracy was assessed against manual digitization by visual interpretation, statistical analysis, and confusion matrix. The accuracy of feature extraction was found to be ranging from 90 to 95%. The accuracy of buildings class was improved using intensity image generated from LiDAR data and Hough image along with morphological operations. In a nutshell, this study highlights robust improvements in the geospatial extraction of urban features by merging more than one dataset synergistically.

Using the Expendable CTDs (XCTDs) collected along two transects between Africa and Antarctica during 1–11 January (Cape Town−Prydz Bay, track-2) and 12-21 April 2018 (Prydz Bay−Cape Town, track-1), frontal locations are delineated and compared with the satellite-based maps of absolute dynamic topography (MADT). The MADT maps revealed that the intensity of the Agulhas Current (AC) was 1.3 ms−1 during both periods; the southernmost limit of the Agulhas Retroflection (AR) was traced to 40°S, 20.5°E/42°S, 15°E on track-1/track-2. The vertical sections of temperature and salinity revealed the Agulhas anticyclonic eddy (AAE) whose core was detected at ~38.4°S/~37.6°S on track-1/track-2. The cumulative transport across track-2 was 50% higher than across track-1 which is attributed to deep-sea upwelling and its subsequent transport across the track-2 to the south of 50°S. On both transects, the winter water was detected up to 50°S in the upper 100 m. The highest AC transport in the upper 1000 m was detected at 37.75°S/38.28°S on track-1/track-2, which was related to the flow characteristics of the AC. The eddies that detached from the AC system transported nutrient-rich water southward which mixed with the ambient water resulting in peak chlorophyll-a concentration (Chl-a) exceeding 0.5 mg/m3 to the north of 42°S on both tracks. The shallow topographic features such as the Ob-Lena rise (54°S) and the southwest Indian Ridge (44°−46°S) also enhanced Chl-a by 39% on track-2.

Mapping of surface glacier facies has been a part of several glaciological applications. The study of glacier facies in the Himalayas has gained momentum in the last decade owing to the implications imposed by these facies on the melt characteristics of the glaciers. Some of the most commonly reported surface facies in the Himalayas are snow, ice, ice mixed debris, and debris. The precision of the techniques used to extract glacier facies is of high importance, as the result of many cryospheric studies and economic reforms rely on it. An assessment of a customized semi-automated protocol against conventional and advanced mapping algorithms for mapping glacier surface facies is presented in this study. Customized spectral index ratios (SIRs) are developed for effective extraction of surface facies using thresholding in an object-based environment. This method was then tested on conventional and advanced classification algorithms for an evaluation of the mapping accuracy for five glaciers located in the Himalayas, using very high-resolution WorldView-2 imagery. The results indicate that the object-based image analysis (OBIA) based semi-automated SIR approach achieved a higher average overall accuracy of 87.33% (κ = 0.85) than the pixel-based image analysis (PBIA) approach. Among the conventional methods, the Maximum Likelihood performed the best, with an overall accuracy of 78.71% (κ = 0.75). The Constrained Energy Minimization, with an overall accuracy of 68.76% (κ = 0.63), was the best performer of the advanced algorithms. The advanced methods greatly underperformed in this study. The proposed SIRs show a promise in the mapping of minor features such as crevasses and in the discrimination between ice-mixed debris and debris. We have efficiently mapped surface glacier facies independently of short-wave infrared bands (SWIR). There is a scope for the transferability of the proposed SIRs and their performance in varying scenarios.

Iceberg calving is the detachment of ice from ice shelves or glaciers. Although calving is a natural phenomenon, an abnormal rate of calving can be a threat to ice shelves. Some of the events were so large, that an iceberg of approximately 150 × 50 km area was calved in a single event. The most recent reported iceberg calving event was Larsen C and it took place in July 2017. In addition to the large and widely reported calving events, there are several small calving events, which are also of great significance and contribute to the overall mass loss from Antarctica. This study focuses on small calving events in Antarctica along various coasts. Three calving events are studied here, all of them have occurred in the past. This study was performed using Google Earth and Landsat satellite imageries. The first event is identified to have occurred at the Knox coast in 2016. Even after the icebergs were calved, they remained intact with the ice shelf due to ice fronts. The second event took place at the Queen Mary Coast in the year 2014. This event was studied from 2009 to 2016 using Landsat satellite images and many rifts were observed. The third event took place at the Princess Astrid Coast in the year 2016. This event was monitored from 2014 and three icebergs were calved between the years 2014 to 2016. This study emphasizes the exploitation of optical satellite data for studying calving events in Antarctica. Various crevasses and rifts are observed on Landsat imageries, which can be the first sign of a calving process.

This study addresses the hydrodynamics inferred from the expendable conductivity-temperature-depth (XCTD) observations carried out in the southwestern Indian Ocean sector of the Southern Ocean along two transects: the Lazarev Sea to Cape Town (track-1) and Cape Town to Prydz bay (track-2) during the austral summer of 2019. The vertical temperature and salinity structures revealed an eddy extending up to 44°S (45°S) on track-1 (track-2). South of the eddy, we encountered frontal zones extending to 53°S (59°S) on track-1(track-2). The frontal locations identified from XCTD and satellite-based sea surface temperature and absolute dynamic topography coincided, with the latter placed within the latitudinal limits identified from the XCTD data. Meandering of the Polar front (PF), the southern Antarctic Circumpolar Current (ACC) Front, and the Southern Boundary of the ACC was observed from 90 to 550 km southward. The Winter Water which was confined to the south of 50°S was detected at deeper depth (~350 m) on track-1, compared to a depth of 100 m on track-2, and its thickness varied from zero to 1.2 m on track-1 and from 0.5 to 2.5 m on track-2. The vertical thermohaline structure revealed the northward subduction of a mixture of Antarctic Surface Water and Subantarctic Surface Water up to 45.5°S and down to 500 (320) m on track-1 (track-2). The volume transport (relative to 1000 m) accounted for 87% of that estimated in the literature (90 ± 2.4 Sv) and 34.7 Sv across track-1 and -2, respectively. It was found that 70% of the volume transport was confined to the ACC frontal region and 26% of the total transport occurred in the 100–500 m slab. The cumulative heat and salt content was 2% and 1.2% higher between 39° and 66°S, compared to that estimated from 2008 data along track-1. We used a satellite-based absolute dynamic topography field, to trace out Agulhas current (AC) and its retroflection current, and eddies detached from the meanders. A higher dynamic topography gradient across the polar front facilitates enhanced transport. Sea surface temperature fields revealed that the meanders in the AC propagate southwest with an offshore extent of 30–300 km.

The study explores modes of variability in the Southern Polar Cell and their relationship with known global climate modes and Antarctic sea ice. It is found that Polar Cell is barotropic in nature and 500 hPa geo-potential height (Z500) field can satisfactorily represent variability in the Polar Cell. First, three leading Empirical Orthogonal Function (EOF) modes of Z500 account for nearly 80% of observed variability in the Polar Cell. Dominant mode (PC1500) comprises of high pressure divergence zone over Antarctica. Second leading mode (PC2500) is low pressure zone covering Amundsen-Bellingshausen Sea (ABS) similar to ABS low feature. A new climate mode called Polar Coastal Index (PCI) is defined, which describes more than 15% and close to 30% variability of circumpolar trough and ABS low, respectively. Out of four modes defined in this study, only PCI and PC2500 show linear trends and clear seasonality. Interestingly, both modes are affected by modulation of ABS low due to tropical ENSO forcing. SAM signature is present in Polar Cell as PC1500 shares large variance with it. The largest impact on sea ice comes from PC2500 followed by PC1500 in the Antarctic Dipole regions. However, this study suggests contemporary sea ice trends cannot be sustained, and can reverse given that trends in PCI and PC2500 favour a reversal. These results indicate that ENSO-driven Polar Cell variability plays a crucial role influencing Antarctic sea ice as it interacts with other climate modes and leads the combined impact at the interannual time scale.

Changes in the dynamics of glaciers must be assessed, as they are important for sea level changes. Glacier velocity is the most important parameter used in glacier dynamics studies. Various image matching techniques, which are implemented in different domains, have been utilized to estimate the surface velocity of glaciers, since the first use of remote sensing technology. In this study, we derived the precise velocity of the Polar Record Glacier (PRG), east Antarctica, in recent years, using optical remote sensing. The secondary objective of the study was to comparatively test the accurate geospatial tools for velocity estimation. The study was first conducted on a single image pair, and four different tools were used for the estimation of the glacier velocity, which are the COSI-Corr (Co-registration of Optically Sensed Images and Correlation) tool in ENVI (Exelis Visual Information Solutions), the IMGRAFT (Image GeoRectification and Feature Tracking) in MATLAB, the IMCORR (Image correlation) feature tracking tool in SAGA-GIS, and the image correlation software CIAS. After evaluation of the four feature tracking tools, COSI-Corr yielded a pixel-level velocity with both magnitude and directions, while IMGRAFT provided the glacier speed without the directions. On the other hand, IMCORR yielded good results with respect to magnitude and directions of the glacier velocity, but the pixel-wise magnitude was not produced. CIAS also provided closely bundled velocity products without pixel-wise velocity. COSI-Corr and IMGRAFT were found to be the best of the four tools, and COSI-Corr is recommended for further studies to estimate the velocity of the PRG.

The ice flow velocity is a critical variable in understanding the glacier dynamics. The Synthetic Aperture Radar Interferometry (InSAR) is a robust technique to monitor Earth’s surface mainly to measure its topography and deformation. The phase information from two or more interferogram further helps to extract information about the height and displacement of the surface. We used this technique to derive glacier velocity for Polar Record Glacier (PRG), East Antarctica, using Sentinel-1 Single Look Complex images that were captured in Interferometric Wide mode. For velocity estimation, Persistent Scatterer interferometry (PS-InSAR) method was applied, which uses the time coherent of permanent pixel of master images and correlates to the same pixel of the slave image to get displacement by tracking the intensity of those pixels. C-band sensor of European Space Agency, Sentinel-1A, and 1B data were used in this study. Estimated average velocity of the PRG is found to be approximately ≈400 ma−1, which varied from ≈100 to ≈700 ma−1. We also found that PRG moves at ≈700 and 200 ma−1 in the lower part and the upper inland area, respectively.

Glaciers exhibit a wide range of surface facies that can be analyzed as proxies for mass balance studies. Along with hydrological implications, these are in turn quintessential indicators of climate change. Moderate-to-high-resolution (MHR) data for mapping glacier facies have been used previously; however, the use of very high-resolution (VHR) data for this purpose has not yet been fully exploited. This study uses WorldView-2 (WV-2) VHR data to classify available glacier surface facies on the Samudra Tapu glacier, located in the Himalayas. Traditional methods of facies classification using conventional multispectral data involve band rationing and/or supervised classification. This study explores glacier surface facies classification by using the unique bands available in the multispectral range of WV-2 to develop customized spectral index ratios (SIRs) within an object-oriented domain. The results of this object-oriented classification (OOC) are then compared with five popular supervised classification algorithms using error matrices to determine the classification accuracies. The overall accuracy achieved by the object-based image analysis (OBIA) approach is 97.14% (κ = 0.96), and the highest overall accuracy among the pixel-based classification methods is 74.28% (κ = 0.70). The present results show that the object-based approach is far more accurate than the pixel-based classification techniques. Further studies should test the robustness of the object-oriented domain for the classification of glacier surface facies using customized sensor-specific as well as transferable indices, and the resultant accuracies.

Bathymetry is a technique of measuring depths to determine the topography of water bodies. The derivation of bathymetry from satellite imagery is one of the basic researches in remote sensing of the aquatic environment, which has several practical implications to the coastal environment and it's monitoring. The spectral band information provided by the multispectral (eight bands) Worldview-2 (WV-2) satellite improves the quality of coastal environmental products including bathymetry. This study provides a method for mapping of shallow lake bathymetry (depth) in Larsemann hills, eastern Antarctica, using digital WV-2 imagery. Out of various bathymetry determination models available for multispectral images, two most popular models- linear band and linear ratio model were used to estimate water depth of the shallow water bodies. These models were executed using various band combinations to study their specific role in bathymetry derivation. The derived depths were validated against the in-situ measurements and root mean square errors (RMSE) were computed. We quantify the error between in-situ measured and satellite-estimated lake depth values for lakes selected in this study. Our results indicate the high correlation (0.7∼0.9) between estimated and in situ depth measurements, with RMSE ranging from 0.2 to 1 m. This study suggests that the coastal band in the WV-2 imagery could retrieve accurate bathymetry information compared to other bands.

The cryosphere is the frozen part of the Earth’s system. Snow and ice are the main constituents of the cryosphere and may be found in different states, such as snow, freshwater ice, sea ice, perma-frost, and continental ice masses in the form of glaciers and ice sheets. The present review mainly deals with state-of-the-art applications of synthetic aperture radar (SAR) with a special emphasize on cryospheric information extraction. SAR is the most important active microwave remote sensing (RS) instrument for ice monitoring, which provides high-resolution images of the Earth’s surface. SAR is an ideal sensor in RS technology, which works in all-weather and day and night conditions to provide useful unprecedented information, especially in the cryospheric regions which are almost inaccessible areas on Earth. This paper addresses the technological evolution of SAR and its applications in studying the various components of the cryosphere. The arrival of SAR radically changed the capabilities of information extraction related to ice type, new ice formation, and ice thickness. SAR applications can be divided into two broad classes-polarimetric applications and interferometric applications. Polarimetric SAR has been effectively used for mapping calving fronts, crevasses, surface structures, sea ice, detection of icebergs, etc. The paper also summarizes both the operational and climate change research by using SAR for sea ice parameter detection. Digital elevation model (DEM) generation and glacier velocity mapping are the two most important applications used in cryosphere using SAR interferometry or interferometric SAR (InSAR). Space-borne InSAR techniques for measuring ice flow velocity and topography have developed rapidly over the last decade. InSAR is capable of measuring ice motion that has radically changed the science of glaciers and ice sheets. Measurement of temperate glacier velocities and surface characteristics by using airborne and space-borne interferometric satellite images have been the significant application in glaciology and cryospheric studies. Space-borne InSAR has contributed to major evolution in many research areas of glaciological study by measuring ice-stream flow velocity, improving understanding of ice-shelf processes, yielding velocity for flux-gate based mass-balance assessment, and mapping flow of mountain glaciers. The present review summarizes the salient development of SAR applications in cryosphere and glaciology.

Antarctic coastal oases are essential sources of spatially distributed fresh water bodies. Mapping water bodies from remote places, such as polar regions, using traditional surveying method is a laborious and logistically expensive task. A rapid method for extracting and monitoring water bodies in Antarctic coastal oases has a tremendous application in remote sensing. This study discusses the design of a rapid and novel method to extract water body features in Antarctic coastal oasis environment from remotely-sensed images. We devised semiautomatic approach for extracting water body features based on a novel set of normalized difference water index (NDWI) by incorporating high-resolution WorldView-2 (WV-2) panchromatic and multispectral image data. This study highlights and compares the viability of state-of-the-art spectral processing water body extraction approaches with the newly designed NDWI approach. An extensive quantitative evaluation was carried out to test the newly designed NDWI approach for extracting water bodies on Larsemann Hills, eastern Antarctica. The results suggest that the modified NDWI approach render intermediate performance with bias error varying from ∼1 to ∼34 m2 (least amount of misclassified pixels). We also analyzed the distinctive 8-band capability of WV-2 data coupled with semiautomatic extraction methods to compare their reliability in extracting the water body area. The results indicate that the use of the modified NDWI approach on 8-band WV-2 data can significantly improve the semiautomatic extraction of water body features, which can ultimately contribute to an enhanced perceptive of the Antarctic coastal oasis in the context of climate change.

Water on the Earth’s surface is an essential part of the hydrological cycle. Water resources include surface waters, groundwater, lakes, inland waters, rivers, coastal waters, and aquifers. Monitoring lake dynamics is critical to favor sustainable management of water resources on Earth. In cryosphere, lake ice cover is a robust indicator of local climate variability and change. Therefore, it is necessary to review recent methods, technologies, and satellite sensors employed for the extraction of lakes from satellite imagery. The present review focuses on the comprehensive evaluation of existing methods for extraction of lake or water body features from remotely sensed optical data. We summarize pixel-based, object-based, hybrid, spectral index based, target and spectral matching methods employed in extracting lake features in urban and cryospheric environments. To our knowledge, almost all of the published research studies on the extraction of surface lakes in cryospheric environments have essentially used satellite remote sensing data and geospatial methods. Satellite sensors of varying spatial, temporal and spectral resolutions have been used to extract and analyze the information regarding surface water. Multispectral remote sensing has been widely utilized in cryospheric studies and has employed a variety of electro-optical satellite sensor systems for characterization and extraction of various cryospheric features, such as glaciers, sea ice, lakes and rivers, the extent of snow and ice, and icebergs. It is apparent that the most common methods for extracting water bodies use single band-based threshold methods, spectral index ratio (SIR)-based multiband methods, image segmentation methods, spectral-matching methods, and target detection methods (unsupervised, supervised and hybrid). A Synergetic fusion of various remote sensing methods is also proposed to improve water information extraction accuracies. The methods developed so far are not generic rather they are specific to either the location or satellite imagery or to the type of the feature to be extracted. Lots of factors are responsible for leading to inaccurate results of lake-feature extraction in cryospheric regions, e.g. the mountain shadow which also appears as a dark pixel is often misclassified as an open lake. The methods which are working well in the cryospheric environment for feature extraction or landcover classification does not really guarantee that they will be working in the same manner for the urban environment. Thus, in coming years, it is expected that much of the work will be done on object-based approach or hybrid approach involving both pixel as well as object-based technology. A more accurate, versatile and robust method is necessary to be developed that would work independent of geographical location (for both urban and cryosphere) and type of optical sensor.

Image classification is one of the most basic operations of digital image processing. The present review focuses on the strengths and weaknesses of traditional pixel-based classification (PBC) and the advances of object-oriented classification (OOC) algorithms employed for the extraction of information from remotely sensed satellite imageries. The state-of-the-art classifiers are reviewed for their potential usage in urban remote sensing (RS), with a special focus on cryospheric applications. Generally, classifiers for information extraction can be divided into three catalogues: 1) based on the type of learning (supervised and unsupervised), 2) based on assumptions on data distribution (parametric and non-parametric) and, 3) based on the number of outputs for each spatial unit (hard and soft). The classification methods are broadly based on the PBC or the OOC approaches. Both methods have their own advantages and disadvantages depending upon their area of application and most importantly the RS datasets that are used for information extraction. Classification algorithms are variedly explored in the cryosphere for extracting geospatial information for various logistic and scientific applications, such as to understand temporal changes in geographical phenomena. Information extraction in cryospheric regions is challenging, accounting to the very similar and conflicting spectral responses of the features present in the region. The spectral responses of snow and ice, water, and blue ice, rock and shadow are a big challenge for the pixel-based classifiers. Thus, in such cases, OOC approach is superior for extracting information from the cryospheric regions. Also, ensemble classifiers and customized spectral index ratios (CSIR) proved extremely good approaches for information extraction from cryospheric regions. The present review would be beneficial for developing new classifiers in the cryospheric environment for better understanding of spatial-temporal changes over long time scales.

This paper discusses the bathymetric mapping technologies by means of satellite remote sensing (RS) with special emphasis on bathymetry derivation models, methods, accuracies, advantages, limitations, and comparisons. Traditionally, bathymetry can be mapped using echo sounding sounders. However, this method is constrained by its inefficiency in shallow waters and very high operating logistic costs. In comparison, RS technologies present efficient and cost-effective means of mapping bathymetry over remote and broad areas. RS of bathymetry can be categorised into two broad classes: active RS and passive RS. Active RS methods are based on active satellite sensors, which emit artificial radiation to study the earth surface or atmospheric features, e.g. light detection and ranging (LIDAR), polarimetric synthetic aperture radar (SAR), altimeters, etc. Passive RS methods are based on passive satellite sensors, which detect sunlight (natural source of light) radiation reflected from the earth and thermal radiation in the visible and infrared portion of the electromagnetic spectrum, e.g. multispectral or optical satellite sensors. Bathymetric methods can also be categorised as imaging methods and non-imaging methods. The non-imaging method is elucidated by laser scanners or LIDAR, which measures the distance between the sensor and the water surface or the ocean floor using a single wave pulse or double waves. On the other hand, imaging methods approximate the water depth based on the pixel values or digital numbers (DN) (representing reflectance or backscatter) of an image. Imaging methods make use of the visible and/or near infrared (NIR) and microwave radiation. Imaging methods are implemented with either analytical modelling or empirical modelling, or by a blend of both. This paper presents the development of bathymetric mapping technology by using RS, and discusses the state-of-the-art bathymetry derivation methods/algorithms and their implications in practical applications.

We introduce a new semi-automated approach by coupling spectral index ratios (SIR) and object-based image analysis (OBIA) to classify very high resolution WorldView 2 (WV 2) satellite image to extract land cover features using eCognition© software. This study aims to develop rule sets for object-based classification of WV-2 image to accurately delineate the boundaries of land cover features in the Larsemann Hills, eastern Antarctica. Multi-level segmentation process was applied to WV-2 image to generate different sizes of image objects representing different land cover features with respect to scale parameter. Several new SIRs were investigated and applied to objects at different segmentation levels to accurately classify land cover classes of landmass, man-made features, snow/ice, and water bodies. A specific attention was given to water body class to identify water areas in image level considering their different appearance on landmass and ice. The results illustrate that synergetic usage of newly developed SIRs and image segmentation in the OBIA can provide highly accurate identification of land cover classes with an overall classification accuracy of ≈98%. Furthermore, our results suggest that OBIA can contribute to powerful automatic and semiautomatic analysis for most remote sensing applications in coastal Antarctica, and synergetic use of pixel-based SIRs explores the rich geo-information of coastal areas.

This work uses the canopy height model (CHM) based workflow for individual tree crown delineation and 3D feature extraction approach (Overwatch Geospatial's proprietary algorithm) for building feature delineation from high-density light detection and ranging (LiDAR) point cloud data in an urban environment and evaluates its accuracy by using very high-resolution panchromatic (PAN) (spatial) and 8-band (multispectral) WorldView-2 (WV-2) imagery. LiDAR point cloud data over San Francisco, California, USA, recorded in June 2010, was used to detect tree and building features by classifying point elevation values. The workflow employed includes resampling of LiDAR point cloud to generate a raster surface or digital terrain model (DTM), generation of a hill-shade image and an intensity image, extraction of digital surface model, generation of bare earth digital elevation model (DEM) and extraction of tree and building features. First, the optical WV-2 data and the LiDAR intensity image were co-registered using ground control points (GCPs). The WV-2 rational polynomial coefficients model (RPC) was executed in ERDAS Leica Photogrammetry Suite (LPS) using supplementary *.RPB file. In the second stage, ortho-rectification was carried out using ERDAS LPS by incorporating well-distributed GCPs. The root mean square error (RMSE) for the WV-2 was estimated to be 0.25 m by using more than 10 well-distributed GCPs. In the second stage, we generated the bare earth DEM from LiDAR point cloud data. In most of the cases, bare earth DEM does not represent true ground elevation. Hence, the model was edited to get the most accurate DEM/ DTM possible and normalized the LiDAR point cloud data based on DTM in order to reduce the effect of undulating terrain. We normalized the vegetation point cloud values by subtracting the ground points (DEM) from the LiDAR point cloud. A normalized digital surface model (nDSM) or CHM was calculated from the LiDAR data by subtracting the DEM from the DSM. The CHM or the normalized DSM represents the absolute height of all aboveground urban features relative to the ground. After normalization, the elevation value of a point indicates the height from the ground to the point. The above-ground points were used for tree feature and building footprint extraction. In individual tree extraction, first and last return point clouds were used along with the bare earth and building footprint models discussed above. In this study, scene dependent extraction criteria were employed to improve the 3D feature extraction process. LiDAR-based refining/ filtering techniques used for bare earth layer extraction were crucial for improving the subsequent 3D features (tree and building) feature extraction. The PAN-sharpened WV-2 image (with 0.5 m spatial resolution) was used to assess the accuracy of LiDAR-based 3D feature extraction. Our analysis provided an accuracy of 98 % for tree feature extraction and 96 % for building feature extraction from LiDAR data. This study could extract total of 15143 tree features using CHM method, out of which total of 14841 were visually interpreted on PAN-sharpened WV-2 image data. The extracted tree features included both shadowed (total 13830) and non-shadowed (total 1011). We note that CHM method could overestimate total of 302 tree features, which were not observed on the WV-2 image. One of the potential sources for tree feature overestimation was observed in case of those tree features which were adjacent to buildings. In case of building feature extraction, the algorithm could extract total of 6117 building features which were interpreted on WV-2 image, even capturing buildings under the trees (total 605) and buildings under shadow (total 112). Overestimation of tree and building features was observed to be limiting factor in 3D feature extraction process. This is due to the incorrect filtering of point cloud in these areas. One of the potential sources of overestimation was the man-made structures, including skyscrapers and bridges, which were confounded and extracted as buildings. This can be attributed to low point density at building edges and on flat roofs or occlusions due to which LiDAR cannot give as much precise planimetric accuracy as photogrammetric techniques (in segmentation) and lack of optimum use of textural information as well as contextual information (especially at walls which are away from roof) in automatic extraction algorithm. In addition, there were no separate classes for bridges or the features lying inside the water and multiple water height levels were also not considered. Based on these inferences, we conclude that the LiDAR-based 3D feature extraction supplemented by high resolution satellite data is a potential application which can be used for understanding and characterization of urban setup.

Available digital elevation models (DEMs) of Antarctic region generated by using radar altimetry and the Antarctic digital database (ADD) indicate elevation variations of up to hundreds of meters, which necessitates the generation of local DEM and its validation by using ground reference. An enhanced digital elevation model (eDEM) of the Schirmacher oasis region, east Antarctica, is generated synergistically by using Cartosat-1 stereo pair-derived photogrammetric DEM (CartoDEM)-based point elevation dataset and multitemporal radarsat Antarctic mapping project version 2 (RAMPv2) DEM-based point elevation dataset. In this study, we analyzed suite of interpolation techniques for constructing a DEM from RAMPv2 and CartoDEM-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 improves the elevation accuracy of DEM from synergistically fused RAMPv2 and CartoDEM point elevation datasets. RAMPv2 points and CartoDEM points were used as primary data for various interpolation techniques such as ordinary kriging (OK), simple kriging (SK), universal kriging (UK), disjunctive kriging (DK) techniques, inverse distance weighted (IDW), global polynomial (GP) with power 1 and 2, local polynomial (LP) and radial basis functions (RBF). Cokriging of 2 variables with second dataset was used for ordinary cokriging (OCoK), simple cokriging (SCoK), universal cokriging (UCoK) and disjunctive cokriging (DCoK). The IDW, GP, LP, RBF, and kriging methods were applied to one variable, while Cokriging experiments were employed on two variables. The experiment of dataset and its combination produced two types of point elevation map categorized as (1) one variable (RAMPv2 Point maps and CartoDEM Point maps) and (2) two variables (RAMPv2 Point maps + CartoDEM Point maps). Interpolated surfaces were evaluated with the help of differential global positioning system (DGPS) points collected from study area during the Indian Scientific Expedition to Antarctic (ISEA). Accuracy assessment of the RAMPv2 DEM, CartoDEM, and combined eDEM (RAMPv2 + CartoDEM) by using DGPS as ground reference data shows that eDEM achieves much better accuracy (average elevation error 8.44 m) than that of existing DEM constructed by using only CartoDEM (13.57 m) or RAMPv2 (41.44 m) alone. The newly constructed eDEM achieves a vertical accuracy of about 7 times better than RAMPv2 DEM and 1.5 times better than CartoDEM. After using accurate DGPS data for accuracy assessment, the approximation to the actual surface of the eDEM extracted here is much more accurate with least mean root mean square error (RMSE) of 9.22 m than that constructed by using only CartoDEM (RMSE = 14.15 m) point elevation data and RAMPv2 (RMSE = 69.48 m) point elevation data. Our results indicate that, the overall trend of accuracy for the interpolation methods for generating continuous elevation surface from CartoDEM + RAMPv2 point elevation data, based on RMSE, is as follows: GP1 > IDW > GP2 > OK > LP2 > DK > LP1 > RBF > SK > UK. In case of cokriging interpolation methods, OCoK yields more accurate eDEM with the least RMSE of 8.16 m, which can be utilized to generate a highly accurate DEM of the research area.. Based on this work, it is inferred that GP2 and OCok interpolation methods and synergistic use of RAMPv2 and CartoDEM-based point elevation datasets lead to a highly accurate DEM of the study region. This research experiment demonstrates the stability (w.r.t multi-temporal datasets), performance (w.r.t best interpolation technique) and consistency (w.r.t all the experimented interpolation techniques) of synergistically fused eDEM. On the basis of average elevation difference and RMSE mentioned in present research, the newly constructed eDEM may serve as a benchmark for future elevation models such as from the ICESAT-II mission to spatially monitor ice sheet elevation.

This study presents an operational case of advancements in urban land cover classification and change detection by using very high resolution spatial and multispectral information from 4-band QuickBird (QB) and 8-band WorldView-2 (WV-2) image sequence. Our study accentuates quantitative, pixel based, image difference approach for operational change detection using very high resolution pansharpened QB and WV-2 images captured over San Francisco city, California, USA (37° 44" 30N', 122° 31" 30' W and 37° 41" 30° N ,122° 20" 30' W). In addition to standard QB image, we compiled three multiband images from eight pansharpened WV-2 bands: (1) multiband image from four traditional spectral bands, i.e., Blue, Green, Red and near-infrared 1 (NIR1) (henceforth referred as "QB equivalent WV-2"), (2) multiband image from four new spectral bands, i.e., Coastal, Yellow, Red Edge and NIR2 (henceforth referred as "new band WV-2"), and (3) multiband image consisting of four traditional and four new bands (henceforth referred as "standard WV-2"). All the four multiband images were classified using support vector machine (SVM) classifier into four most abundant land cover classes, viz, hard surface, vegetation, water and shadow. The assessment of classification accuracy was performed using random selection of 356 test points. Land cover classifications on "standard QB" image (kappa coeffiecient, κ = 0.93), "QB equivalent WV-2" image (κ = 0.97), and "new band WV-2" image (κ = 0.97) yielded overall accuracies of 96.31 %, 98.03 % and 98.31 %, respectively, while "standard WV-2" image (κ = 0.99) yielded an improved overall accuracy of 99.18 %. It is concluded that the addition of four new spectral bands to the existing four traditional bands improved the discrimination of land cover targets, due to increase in the spectral characteristics of WV-2 satellite. Consequently, to test the validity of improvement in classification process for implementation in operational change detection application, comparative assessment of transition of various landcover classes in three WV-2 images with respect to "standard QB" image was carried out using image difference method. As far as waterbody class is concerned, there was no significant transition observed in all the three WorldView-2 Images, whereas, hard surface class showed lowest transition in "standard WV-2" image and highest in case of "new band WV-2". The most significant transition was occurred in vegetation class in all of the three images, showing positive change (increase) in standard WV-2 image (0.31 Sq. Km) and negative change (decrease) in other two images (−0.12 Sq. Km for "QB equivalent WV-2" image and −31.15 Sq. Km in "new band WV-2" image) with considerable amount. Similar case was observed with the shadow class, but the difference is, transition from shadow to other classes was negative in all the three WV-2 images which can be attributed to the fact that, "standard QB" image had more shadow area (based on acquisition time and sun position) than WV-2, that means all the band combinations of WV-2 succeeded in extracting the features hidden below the shadow in "standard QB" image. These trends indicate that the overall bandwise transition in landcover classes in case of "standard WV-2" is more precise than other two images. We note that "QB equivalent WV-2" image had narrower band widths than those of "standard QB" image but the observed vegetation change is not prominent as in case of other two images, but at the same time, transition in hard surface and waterbody was discerned more efficiently than "new band WV-2" image. The addition of new bands in WV-2 enabled more effective vegetation analysis, so the vegetation transition results shown by "new band WV-2" image were at par with the "standard WV-2" image, showing the importance of these newly added bands in the WV-2 imagery, with comparatively lower transitions in other classes. In a nutshell, it can be claimed that incorporation of new bands along with even narrower Red, Green, Blue and Near Infrared-1 bands in WV-2 image holds remarkable importance which leads to enhancement in the potential of WV-2 imagery in change detection and other feature extraction studies.

We devised a semiautomatic approach for extracting lake features based on a customized set of normalized difference water index (NDWI) information which was obtained by incorporating high resolution, 8-band WorldView-2 data. An extensive accuracy assessment was carried out for three semiautomatic feature extraction approaches for extracting 36 lake features on Larsemann Hills, Antarctica. The method was tested on five existing PAN-sharpening algorithms, which suggest that the customized NDWI approach renders intermediate performance (root mean square error varies from ~227 to ~235 m 2 ) and highest stability when compared with existing feature extraction techniques. In general, the customized NDWI rendered a least misclassification ( ≈11 percent), followed by target detection (≈16 percent) and spectral processing (≈17 percent) methods for extraction of 36 lakes. We also found that customized NDWI caused consistently least misclassification ( ≈21 percent) than the target detection (≈23 percent) and spectral processing (≈30 percent) methods for extraction of partially snow or ice-covered 11 lakes. Our results indicate that the use of the customized NDWI approach and appropriate PANsharpening algorithm can greatly improve the semiautomatic extraction of lake features in cryospheric environment.

Using satellite-based wind and sea surface temperature (SST) observations, linear trend and inter-annual variability of wind stress, turbulent heat flux (Q) and wind stress curl are addressed for the Indian Ocean sector of the Southern Ocean (ISO, 0°E - 155°E) for the period 2000-2009. The analysis reveals that spatial mean of Q varies between 70 and 73 Wm-2 in the austral summer and winter, respectively, while the mean wind stress is nearly same at 0.22 Nm-2 for both seasons. The anticyclonic curl dominates the ISO, which increases from 0.15 × 10-7 to 0.35 × 10-7 Nm-3 during the austral summer. The detrended box-mean time series of Q, wind stress, and wind stress curl exhibits a decreasing trend of –6.3 ± 1.6 Wm-2·decade-1, -0.012 ± 0.004 Nm-2·decade-1 and -0.48 ± 0.6 × 10-8 Nm-3·decade-1, respectively. The Empirical Orthogonal Function (EOF) analysis was carried out to study inter-annual variability. EOF-1 of Q captures 25% of the total variance, which mimics the austral summer pattern; its time coefficient is highly and negatively correlated with a 2-month lagged Nino3.4 SST index (r =-0.8 at 95% confidence). EOF-1 of wind stress accounts for 35% of the total variance and its time coefficient is strongly correlated with the Antarctic Oscillation (r= 0.86 at 95% confidence). EOF-1 of wind stress curl captures 15% of the total variance; its time coefficient is correlated to the Nino3.4 SST index (r= 0.65 at 95% confidence) with the former lagging the latter by two years. The repercussions of the weakening trends of the climatic parameters on the air-sea interaction and ocean circulation are highlighted.

Effective monitoring of changes in the geographic distribution of cryospheric vegetation requires high-resolution and accurate baseline maps. The rationale of the present study is to compare multiple feature extraction approaches to remotely mapping vegetation in Antarctica, assessing which give the greatest accuracy and reproducibility relative to those currently available. This study provides precise, high-resolution, and refined baseline information on vegetation distribution as is required to enable future spatiotemporal change analyses of the vegetation in Antarctica. We designed and implemented a semiautomated customized normalized difference vegetation index (NDVI) approach for extracting cryospheric vegetation by incorporating very high resolution (VHR) 8-band WorldView-2 (WV-2) satellite data. The viability of state-of-the-art target detection, spectral processing/matching, and pixel-wise supervised classification feature extraction techniques are compared with the customized NDVI approach devised in this study. An extensive quantitative and comparative assessment was made by evaluating four semiautomatic feature extraction approaches consisting of 16 feature extraction standalone methods (four customized NDVI plus 12 existing methods) for mapping vegetation on Fisher Island and Stornes Peninsula in the Larsemann Hills, situated on continental east Antarctica. The results indicated that the customized NDVI approach achieved superior performance (average bias error ranged from ~6.44 ± 1.34% to ~11.55 ± 1.34%) and highest statistical stability in terms of performance when compared with existing feature extraction approaches. Overall, the accuracy analysis of the vegetation mapping relative to manually digitized reference data (supplemented by validation with ground truthing) indicated that the 16 semi-automatic mapping methods representing four general feature extraction approaches extracted vegetated area from Fisher Island and Stornes Peninsula totalling between 2.38 and 3.72 km2 (2.85 ± 0.10 km2 on average) with bias values ranging from 3.49 to 31.39% (average 12.81 ± 1.88%) and average root mean square error (RMSE) of 0.41 km2 (14.73 ± 1.88%). Further, the robustness of the analyses and results were endorsed by a cross-validation experiment conducted to map vegetation from the Schirmacher Oasis, East Antarctica. Based on the robust comparative analysis of these 16 methods, vegetation maps of the Larsemann Hills and Schirmacher Oasis were derived by ensemble merging of the five top-performing methods (Mixture Tuned Matched Filtering, Matched Filtering, Matched Filtering/Spectral Angle Mapper Ratio, NDVI-2, and NDVI-4). This study is the first of its kind to detect and map sparse and isolated vegetated patches (with smallest area of 0.25 m2) in East Antarctica using VHR data and to use ensemble merging of feature extraction methods, and provides access to an important indicator for environmental change.

This work uses the canopy height model (CHM) based workflow for individual tree crown delineation from LiDAR point cloud data in an urban environment and evaluates its accuracy by using very high-resolution PAN (spatial) and 8-band WorldView-2 imagery. LiDAR point cloud data were used to detect tree features by classifying point elevation values. The workflow includes resampling of LiDAR point cloud to generate a raster surface or digital terrain model, generation of hill-shade image and intensity image, extraction of digital surface model, generation of bare earth digital elevation model and extraction of tree features. Scene dependent extraction criteria were employed to improve the tree feature extraction. LiDAR-based refining/filtering techniques used for bare earth layer extraction were crucial for improving the subsequent tree feature extraction. The PAN-sharpened WV-2 image (with 0.5 m spatial resolution) used to assess the accuracy of LiDAR-based tree features provided an accuracy of 98%. Based on these inferences, we conclude that the LiDAR-based tree feature extraction is a potential application which can be used for understanding vegetation characterization in urban setup.

Evolution of remote sensing sensors technologies is presented, with emphasis on its suitability in observing the polar regions. The extent of influence of polar regions on the global climate and vice versa is the spearhead of climate change research. The extensive cover of sea ice has major impacts on the atmosphere, oceans, and terrestrial and marine ecosystems of the polar regions in particular and teleconnection on other processes elsewhere. Sea ice covers vast areas of the polar oceans, ranging from ~18 × 106 km2 to ~23 × 106 km2, combined for the Northern and Southern Hemispheres. However, both polar regions are witnessing contrasting rather contradicting effects of climate change. The Arctic sea ice extent is declining at a rate of 0.53 × 106 km2·decade–1, whereasAntarcticaexhibits a positive trend at the rate of 0.167 × 106 km2·decade–1. This work reviews literature published in the field of sea ice remote sensing, to evaluate and access success and failures of different sensors to observe physical features of sea ice. The chronological development series of different sensors on different satellite systems, sensor specifications and datasets are examined and how they have evolved to meet the growing needs of users is outlined. Different remote sensing technology and observational methods and their suitability to observe specific sea ice property are also discussed. A pattern has emerged, which shows that microwave sensors are inherently superior to visible and infrared in monitoring seasonal and annual changes in sea ice. Degree of successes achieved through remote sensing techniques by various investigators has been compared. Some technologies appear to work better under certain conditions than others, and it is now well accepted that there is no algorithm that is ideal globally. Contribution of Indian remote sensing satellites is also reviewed in the context of polar research. This review suggests different primary datasets for further research on sea ice features (sea ice extent, ice type, sea ice thickness, etc.). This work also examines past achievements and how far these capabilities have evolved and tap into current state of art/direction of sensor technologies. Effective monitoring and syntheses of past few decades of research pinpoint useful datasets for sea ice monitoring, thereby avoiding wastage of resources to find practical datasets to monitor these physically inaccessible regions.

A hydrographic section between Cape Town,South AfricaandIndiaBay,Antarcticawas sampled by deploying expendable CTD probes during the austral summer of 2008 and 2009. In 2009, the Agulhas Retroflection (AR) Front was displaced southward by 1.5° latitude, while the northern and southern Polar Front meandered southward by 1° and 1.4° latitude, respectively, relative to their positions in 2008. Geostrophic transport, relative to1000 m, indicates that Antarctic Circumpolar Current (ACC) flow decreased by 2.5 Sv in 2009 compared to that in 2008. The anticyclones which are detached from the AR facilitates a transport to the southeast Atlantic Ocean ranging between 8 and 12 Sv. Nearly 50% of the ACC transport is confined to the 100 - 500 m layer. A comparison of water mass distribution for 2008 and 2009 suggests that Mode Water distribution was restricted to 42.5°S and 41.9°S, and the Antarctic Surface Water ex°tended to 57.6°S and 46.6°S, respectivel°. In 2009, the along track Heat Content (HC) and Salt Content (SC) for the upper750 mof the water column decreased each by 1% compared to those in 2008. In the ACC domain, the HC and SC dip by 36% and 40% in 2009, respectively. The HC and SC associated with Agulhas Retroflection Front increase in 2009 by 1% and 2%, respectively, due to an enhanced Agulhas transport of warm and saline water from the tropics by 2%.