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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.

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.

Observations of atmospheric mercury over the Southern Ocean and Antarctica are still limited. We performed a ship cruise from Cape Point, South Africa, to the Indian Bharati station, Prydz Bay, Antarctica (42.72°S 24.52°E to 69.41°S 76.19°E, 2015.01.18–27) and subsequently stationed the ship near the Bharati till 2015.02.19. We report relatively constant values of gaseous elemental mercury (GEM) concentration during the cruise (1.08 ± 0.08 ng/m3, n = 541) and highly variable values at the coast (0.96 ± 0.36 ng/m3, n = 1050). Meteorological parameters: relative humidity, temperature and solar radiation had almost negligible effect on GEM concentration (adjusted R2 = 0.09 in multiple linear regression model) during the ship cruise and a limited effect on GEM (adjusted R2 = 0.34) during the ship stay at Bharati. Analysis of NOAA-HYSPLIT 24-h back trajectories showed that all trajectories were extremely shallow during the ship cruise and therefore results likely were affected by the marine boundary layer rather than long-range transport. On the other hand, highly variable values of GEM near Bharati station were explained as a result of interplay of shallow and descending air masses coming from the Antarctic coast and from the terrain. These factors together with temperature dependency indicated that the coast of Antarctica acted as a temperature-driven source of GEM during the austral summer. Measurements were performed with a portable atomic absorption (CVAAS) mercury analyzer model Gardis-5. In our knowledge, this was the first time the CVAAS method was successfully applied for the background Antarctic observations.

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.

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.

The merging of a panchromatic (PAN) image with a multispectral satellite image (MSI) to increase the spatial resolution of the MSI, while simultaneously preserving its spectral information is classically referred as PAN-sharpening. We employed a recent dataset derived from very high resolution of WorldView-2 satellite (PAN and MSI) for two test sites (one over an urban area and the other over Antarctica), to comprehensively evaluate the performance of six existing PAN-sharpening algorithms. The algorithms under consideration were the Gram-Schmidt (GS), Ehlers fusion (EF), modified hue-intensity-saturation (Mod-HIS), high pass filtering (HPF), the Brovey transform (BT), and wavelet-based principal component analysis (W-PC). Quality assessment of the sharpened images was carried out by using 20 quality indices. We also analyzed the performance of nearest neighbour (NN), bilinear interpolation (BI), and cubic convolution (CC) resampling methods to test their practicability in the PAN-sharpening process. Our results indicate that the comprehensive performance of PAN-sharpening methods decreased in the following order: GS > W-PC > EF > HPF > Mod-HIS > BT, while resampling methods followed the order: NN > BI > CC.

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 m 2 .

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.

A robust monitoring of the changes in the distribution and density of cryospheric plant species requires accurate and high-resolution baseline maps of vegetation. Mapping such change at the landscape scale is often problematic, particularly in remote areas, such as Antarctica. Vegetation mapping of plant communities at fine spatial scales is increasingly supported by remote sensing technology in cryospheric regions. Less frequent imaging with high spatial resolution satellite sensors enable more detailed analyses of vegetation change frequently. This study is the first to use high-resolution WorldView-2 (WV-2) imagery to classify vegetation communities on Antarctic oases and to provide semi-automated means to map vegetation, as an imperative indicator for environmental change. Multispectral imagery (MSI) and panchromatic imagery (PAN) from very high resolution WV-2 have been used for mapping of vegetation in different forms in Antarctic environment. A range of supervised classification methods have been executed using pan-sharpened WV-2 data. This study comparatively and statistically evaluates vegetation mapping results using supervised and unsupervised classification methods to extract vegetation in Larsemann Hills and Schirmacher oasis, east Antarctica. We also discuss on the use of supervised pixel-based classifiers and textural measures, in addition to standard multispectral information, to improve the classification of Antarctic vegetation communities. Classification results were validated with independent reference datasets. This work indicates that the overall accuracy of mapping vegetation using WV-2 imagery and semi-automated target extraction methods ranged from 90% to 94%.

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.

Supraglacial debris was mapped in the Schirmacher Oasis, east Antarctica, by using WorldView-2 (WV-2) high resolution optical remote sensing data consisting of 8-band calibrated Gram Schmidt (GS)-sharpened and atmospherically corrected WV-2 imagery. This study is a preliminary attempt to develop an object-oriented rule set to extract supraglacial debris for Antarctic region using 8-spectral band imagery. Supraglacial debris was manually digitized from the satellite imagery to generate the ground reference data. Several trials were performed using few existing traditional pixel-based classification techniques and color-texture based object-oriented classification methods to extract supraglacial debris over a small domain of the study area. Multi-level segmentation and attributes such as scale, shape, size, compactness along with spectral information from the data were used for developing the rule set. The quantitative analysis of error was carried out against the manually digitized reference data to test the practicability of our approach over the traditional pixel-based methods. Our results indicate that OBIA-based approach (overall accuracy: 93%) for extracting supraglacial debris performed better than all the traditional pixel-based methods (overall accuracy: 80−85%). The present attempt provides a comprehensive improved method for semiautomatic feature extraction in supraglacial environment and a new direction in the cryospheric research.

Cryospheric surface feature classification is one of the widely used applications in the field of polar remote sensing. Precise surface feature maps derived from remotely sensed imageries are the major requirement for many geoscientific applications in polar regions. The present study explores the capabilities of C-band dual polarimetric (HH & HV) SAR imagery from Indian Radar Imaging Satellite (RISAT-1) for land cryospheric surface feature mapping. The study areas selected for the present task were Larsemann Hills and Schirmacher Oasis, East Antarctica. RISAT-1 Fine Resolution STRIPMAP (FRS-1) mode data with 3-m spatial resolution was used in the present research attempt. In order to provide additional context to the amount of information in dual polarized RISAT-1 SAR data, a band HH+HV was introduced to make use of the original two polarizations. In addition to the data calibration, transformed divergence (TD) procedure was performed for class separability analysis to evaluate the quality of the statistics before image classification. For most of the class pairs the TD values were comparable, which indicated that the classes have good separability. Fuzzy and Artificial Neural Network classifiers were implemented and accuracy was checked. Nonparametric classifier Support Vector Machine (SVM) was also used to classify RISAT-1 data with an optimized polarization combination into three land-cover classes consisting of sea ice/snow/ice, rocks/landmass, and lakes/waterbodies. This study demonstrates that C-band FRS1 image mode data from the RISAT-1 mission can be exploited to identify, map and monitor land cover features in the polar regions, even during dark winter period. For better landcover classification and analysis, hybrid polarimetric data (cFRS-1 mode) from RISAT-1, which incorporates phase information, unlike the dual-pol linear (HH, HV) can be used for obtaining better polarization signatures.

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.

High resolution satellite data provide high spatial, spectral and contextual information. Spatial and contextual information of image objects are in demand to extract the information from high resolution satellite data. The supraglacial environment includes several features that are present on the surface of the glacier. The extraction of features from supraglacial environment is quite challenging using pixel-based image analysis. To overcome this, objectoriented approach is implemented. This paper aims at the extraction of geo-information from the supraglacial environment from high resolution satellite image by object-oriented image analysis using the fuzzy logic approach. The object-oriented image analysis involves the multiresolution segmentation for the creation of objects followed by the classification of objects using the fuzzy logic approach. The multiresolution segmentation is executed on the pixel level initially which merges pixels for the creation of objects thus minimizing their heterogeneity. This is followed by the development of rule sets for the classification of various features such as blue ice, debris, snow from the supraglacial environment in WorldView-2 data. The area of extracted feature is compared with the reference data and misclassified area of each feature using various bands is determined. The present object oriented classification achieved an overall accuracy of ≈ 92% for classifying supraglacial features. Finally, it is suggested that Red band is quite effective in the extraction of blue ice and snow, while NIR1 band is effective in debris extraction.

An accurate spatial mapping and characterization of land cover features in cryospheric regions is an essential procedure for many geoscientific studies. A novel semi-automated method was devised by coupling spectral index ratios (SIRs) and geographic object-based image analysis (OBIA) to extract cryospheric geospatial information from very high resolution WorldView 2 (WV-2) satellite imagery. The present study addresses development of multiple rule sets for OBIA-based classification of WV-2 imagery to accurately extract land cover features in the Larsemann Hills, east Antarctica. Multilevel segmentation process was applied to WV-2 image to generate different sizes of geographic image objects corresponding to various land cover features with respect to scale parameter. Several SIRs were applied to geographic objects at different segmentation levels to classify land mass, man-made features, snow/ice, and water bodies. We focus on water body class to identify water areas at the image level, considering their uneven appearance on landmass and ice. The results illustrated that synergetic usage of SIRs and OBIA can provide accurate means to identify land cover classes with an overall classification accuracy of ≈97%. In conclusion, our results suggest that OBIA is a powerful tool for carrying out automatic and semiautomatic analysis for most cryospheric remote-sensing applications, and the synergetic coupling with pixel-based SIRs is found to be a superior method for mining geospatial information.

An enhanced digital elevation model (DEM) of the Larsemann Hills region, east Antarctica, is constructed synergistically by using highly accurate ground-based GPS measurements, satellite-derived laser altimetry (GLAS/ICESat) and Radarsat Antarctic Mapping Project (RAMPv2) DEM-based point elevation dataset. Our DEM has a vertical accuracy of about 1.5 times better than RAMPv2 DEM and seven times better than GLAS/ICESAT-based DEM. The accuracy is improved by validating the RAMPv2 DEM elevation by supplementing with GLAS/ICESat and DGPS survey data, when compared to that of DEM constructed by using GLAS/ICESat or RAMPv2 alone. With the use of accurate GPS data as ground control points reference elevations, the DEM extracted is much more accurate with least mean RMSE of 34.5 m than that constructed by using a combination of GLAS/ICESat and RAMPv2 as true reference. The newly constructed DEM 7 achieves highest accuracy with the least average elevation difference of 0.27 m calculated using 46 ground reference points. Available DEMs of Antarctic region generated by using radar altimetry and the Antarctic Digital Database indicate elevation variations in the range of 50–100 m, which necessitates the generation of local DEM and its validation by using ground truth. This is our first attempt of fusing multi-temporal, multi-sensor and multi-source elevation data to generate a DEM of any part of Antarctica, in order to address the ice elevation change to infer the ice mass balance. Our approach focuses on the strengths of each elevation data source to produce an accurate DEM.