bluuebunny/crossref_metadata_2025 huggingface dataset:
The CAMS reanalysis of atmospheric composition
Authors: Inness Antje , Ades Melanie , Agusti-Panareda Anna , Barré Jérôme , Benedictow Anna , Blechschmidt Anne-Marlene , Dominguez Juan Jose , Engelen Richard , Eskes Henk , Flemming Johannes , Huijnen Vincent , Jones Luke , Kipling Zak , Massart Sebastien , Parrington Mark , Peuch Vincent-Henri , Razinger Miha , Remy Samuel , Schulz Michael , Suttie Martin
Published: November 2018

Abstract. The Copernicus Atmosphere Monitoring Service (CAMS) reanalysis is the latest global reanalysis data set of atmospheric composition produced by the European Centre for Medium-Range Weather Forecasts (ECMWF), consisting of 3-dimensional time-consistent atmospheric composition fields, including aerosols and chemical species. The dataset currently covers the period 2003–2016 and will be extended in the future by adding one year each year. A reanalysis for greenhouse gases is being produced separately. The CAMS reanalysis builds on the experience gained during the production of the earlier Monitoring Atmospheric Composition and Climate (MACC) reanalysis and CAMS interim reanalysis. Satellite retrievals of total column CO, tropospheric column NO2, aerosol optical depth and total column, partial column and profile ozone retrievals were assimilated for the CAMS reanalysis with ECMWF’s Integrated Forecasting System. The new reanalysis has an increased horizontal resolution of about 80 km and provides more chemical species at a better temporal resolution (3-hourly analysis fields, 3-hourly forecast fields and hourly surface forecast fields) than the previously produced CAMS interim reanalysis. The CAMS reanalysis has smaller biases compared to independent ozone, carbon monoxide, nitrogen dioxide and aerosol optical depth observations than the previous two reanalyses and is much improved and more consistent in time, especially compared to the MACC reanalysis. The CAMS reanalysis is a dataset that can be used to compute climatologies, study trends, evaluate models, benchmark other reanalyses or serve as boundary conditions for regional models for past periods.
Carbon monoxide air-pollution on sub-city scales and along arterial roads detected by the Tropospheric Monitoring Instrument
Authors: Borsdorff Tobias , aan de Brugh Joost , Pandey Sudhanshu , Hasekamp Otto , Aben Ilse , Houweling Sander , Landgraf Jochen
Published: November 2018

Abstract. The Tropospheric Monitoring Instrument (TROPOMI) on the Sentinel-5 Precursor satellite provides measurements of carbon monoxide (CO) total column concentrations based on Earthshine radiance measurements in the 2.3 μm spectral range with a spatial resolution of 7 × 7 km2 and daily global coverage. Due to the high accuracy of the observations, CO pollution can be detected over cities and industrial areas using single orbit overpasses. In this study, we analysed local CO enhancements in an area around Iran from 1 November to 20 December 2017. We employed the Weather Research and Forecasting (WRF) model v3.8.1 using the EDGAR v4.2 emission inventory and evaluated CO emissions from the cities of Tehran, Yerevan, Urmia, and Tabriz on a spatial resolution comparable to that of TROPOMI. At large scales, TROPOMI CO agrees well with the WRF simulation with a mean difference of 5.7 %. However, the emissions for the city area had to be significantly increased in order to match the observations. Moreover, significant differences at sub-city scale remain. To match the TROPOMI CO observations around the Armenian city of Yerevan, it is necessary to introduce CO emissions along a southeast arterial road of Yerevan. Overall, this hints at deficits in the EDGAR inventory in the region around Iran and indicates TROPOMI's capability to identify localised CO pollution on sub-city scales, which at the same time challenges current atmospheric modelling at high spatial and temporal resolution.
Multi-decadal climate variability in southern Iberia during the mid- to
late-Holocene
Authors: Schirrmacher Julien , Weinelt Mara , Blanz Thomas , Andersen Nils , Salgueiro Emilia , Schneider Ralph R.
Published: November 2018

Abstract. To assess the regional multi-decadal to multi-centennial climate variability at the southern Iberian Peninsula during the mid- to late- Holocene transition multi-proxy records of two marine sediment cores were established for two sites in the Alboran Sea (ODP-161-976A) and the Gulf of Cadiz (GeoB5901-2). High-resolution records of organic geochemical proxies and planktic foraminiferal assemblages are used to decipher precipitation and vegetation changes as well as the sea surface conditions with respect to Sea Surface Temperature (SST) and marine primary productivity (MPP). n-Alkane records as a proxy for precipitation changes suggest a series of six distinct drought events at 5.4 ka BP, from ca. 5.1 ka BP to 4.9 ka BP, from 4.8 to 4.7 ka BP, at 4.6  ka BP, from 4.4 to 4.3 ka BP and, from 3.8 to 3.7 ka BP. Each drought event is associated with a major vegetation change towards higher proportions of C4 vegetation. The drought events are further accompanied by annual and spring/ winter SST warming as well as decreasing MPP in the Alboran Sea. Altogether, the close correlation of the observed droughts with North Atlantic Oscillation (NAO)-like variability suggests changes in the atmospheric circulation as important driving mechanism of terrestrial and oceanic variability at southern Iberia and the Alboran Sea, respectively. Sea surface variability in the Gulf of Cadiz, instead, is intimately linked to the North Atlantic Bond Events. In particular, during Bond Events 3 and 4 a pronounced increase in seasonality is found.
The VOLNA-OP2 tsunami code (version 1.5)
Authors: Reguly Istvan Z. , Giles Daniel , Gopinathan Devaraj , Quivy Laure , Beck Joakim H. , Giles Michael B. , Guillas Serge , Dias Frederic
Published: November 2018

Abstract. In this paper, we present the VOLNA-OP2 tsunami model and implementation; a finite-volume non-linear shallow-water equation (NSWE) solver built on the OP2 domain-specific language (DSL) for unstructured mesh computations. VOLNA-OP2 is unique among tsunami solvers in its support for several high-performance computing platforms: central processing units (CPUs), the Intel Xeon Phi, and graphics processing units (GPUs). This is achieved in a way that the scientific code is kept separate from various parallel implementations, enabling easy maintainability. It has already been used in production for several years; here we discuss how it can be integrated into various workflows, such as a statistical emulator. The scalability of the code is demonstrated on three supercomputers, built with classical Xeon CPUs, the Intel Xeon Phi, and NVIDIA P100 GPUs. VOLNA-OP2 shows an ability to deliver productivity as well as performance and portability to its users across a number of platforms.
Dealing with non-stationarity in sub-daily stochastic rainfall models
Authors: Benoit Lionel , Vrac Mathieu , Mariethoz Gregoire
Published: November 2018

Abstract. Understanding the stationarity properties of rainfall is critical
when using stochastic weather generators. Rainfall stationarity means that
the statistics being accounted for remain constant over a given period, which
is required for both inferring model parameters and simulating synthetic
rainfall. Despite its critical importance, the stationarity of precipitation
statistics is often regarded as a subjective choice whose examination is left
to the judgement of the modeller. It is therefore desirable to establish
quantitative and objective criteria for defining stationary rain periods. To
this end, we propose a methodology that automatically identifies rain types
with homogeneous statistics. It is based on an unsupervised classification of
the space–time–intensity structure of weather radar images. The
transitions between rain types are interpreted as non-stationarities. Our method is particularly suited to deal with non-stationarity in the
context of sub-daily stochastic rainfall models. Results of a synthetic case
study show that the proposed approach is able to reliably identify
synthetically generated rain types. The application of rain typing to real
data indicates that non-stationarity can be significant within meteorological
seasons, and even within a single storm. This highlights the need for a
careful examination of the temporal stationarity of precipitation statistics
when modelling rainfall at high resolution.
BUILDING FOOTPRINT EXTRACTION FROM HIGH RESOLUTION SATELLITE IMAGERY USING SEGMENTATION
Authors: Khatriker S. , Kumar M.
Published: November 2018

Abstract. Identification and mapping of urban features such as buildings and roads are an important task for cartographers and urban planners. High resolution satellite imagery supports the efficient extraction of manmade objects. For the planning and designing of Smart cities, building footprint information is an essential component, and geospatial technologies helps in creating this large mass of data inputs for designing and planning of smart cities. In this study segmentation approach is followed for building extraction. For extraction of buildings especially from the high resolution imagery, number of various semiautomatic and automatic methods have been developed till date to reduce the time and efforts required in manual building mapping. In this study, two semiautomatic image segmentation techniques are used for building extraction from high resolution imagery using algorithms- Multi-resolution segmentation and Rule based feature extraction, which are applied on Worldview 2 (2010) imagery of Dehradun area. The segmented image were further classified to extract buildings from the segmented image features. The study identify the usefulness of both the methods in building extraction and finds the optimum set of rules for extracting buildings from high resolution data sets. The True Positive Rate using Rule based feature extraction is 88.11 % compared to 85.46 % from Multi-resolution segmentation algorithm. The False Negative Rate (FNR) of Multi-resolution segmentation algorithm (16.5 %.) is very less compared to Rule based feature extraction (67.5 %). In the study the buildings were extracted with the accuracy of 88.9 %.
MODEL FOR LAND COVER ESTIMATION USING UNSUPERVISED MACHINE LEARNING ON GOOGLE MAPS COLOR IMAGES
Authors: Bhowmick R. S. , Kumar A. , Singh G. D. , Kumar S.
Published: November 2018

Abstract. Remote sensing data and satellite images are broadly used for land cover information. There are so many challenges to classify pixels on the basis of features and characteristics. Generally it is pixel classification that required the count of pixels for certain area of interest. In the proposed model, we are applying unsupervised machine learning to classify the content of the input images on the basis of pixels intensity. The study aims to compare classification accuracy of different landscape characteristics like water, forest, urban, agricultural areas, transport network and other classes adapted from CORINE (Coordination of information on the environment) nomenclature. To fulfil the aim of the model, accessing data from Google map using Google static API service which creates a map based on URL parameters sent through a standard HTTP (Hyper Text Transfer Protocol) request and returns the map as an image which can be display on any graphical user interface platform. The Google Static Maps API returns an image either in GIF, PNG or JPEG format in response to an HTTP request. To identify different land cover/use classes using k-means clustering. The model is dynamic in nature that describes the clustering as well formulate the area of the concerned class or clustered fields.
A 3D CAMPUS APPLICATION BASED ON CITY MODELS AND WEBGL
Authors: Buyukdemircioglu M. , Kocaman S.
Published: November 2018

Abstract. In parallel with the technological developments, the conventional ways of mapping and the presentation of the geospatial data have changed significantly. 3D city models including the digital terrain models (DTMs) have become important for many application fields, such as simulation and visualization tasks for navigation, urban planning, environmental monitoring, disaster management, etc. Although currently most 3D city models are employed for visualization purposes, their application areas are increasing continuously. The presentation of these models on the web is also becoming more common than before while overcoming the performance issues with newer data types and functionalities. The biggest advantage of using web browsers is that they can be accessed everywhere without any additional software requirements. Therefore, the tools for web-based implementations of virtual globes, which allow users to navigate their data in 3D, have been available with greater numbers of functionality they offer. Online virtual web globes provide a good base for the 3D Geographical Information System (GIS) applications as well. 3D city models have also become virtual environments where different spatial queries and analysis can be performed. As a part of a 3D WebGIS, a city model enriched with semantic information provides a virtual platform for decision makers and allows realistic simulations for planning. The main aims of this study are to develop a prototype of a 3D GIS environment for Hacettepe University Beytepe campus, including 3D building geometries enriched with semantic information and a high resolution DTM; and to design a web interface using an open source virtual globe.
ANALYSIS OF PERFORMANCE OF FLAT EARTH PHASE REMOVAL METHODS
Authors: Desai K. , Joshi P. , Chirakkal S. , Putrevu D. , Ghosh R.
Published: November 2018

Abstract. Interferometric synthetic aperture radar (InSAR) has been widely used in remote sensing field, which can reflect actual topographic trend or possible surface deformation. Due to the orbit attitude influence, the flat-earth phase usually causes the interferogram dense and difficult to be used in further procedures. Before phase unwrapping, interferogram must be flattened to derive accurate topographic or deformation information. In this paper, analysis of performance of two methods of flat-earth removal is done. First method uses imaging geometry and second method uses precise orbital information. Further, 3-degree, 5-degree and 7-degree polynomials are fitted in the method using precise orbital information. Validation is done both visually and empirically using entropy as the evaluation index.
ASSESSING CROP MONITORING POTENTIAL OF SENTINEL-2 IN A SPATIO-TEMPORAL SCALE
Authors: Ghosh P. , Mandal D. , Bhattacharya A. , Nanda M. K. , Bera S.
Published: November 2018

Abstract. Spatio-temporal variability of crop growth descriptors is of prime importance for crop risk assessment and yield gap analysis. The incorporation of three bands (viz., B5, B6, B7) in ‘red-edge’ position (i.e., 705 nm, 740 nm, 783 nm) in Sentinel-2 with 10–20 m spatial resolution images with five days revisit period have unfolded opportunity for meticulous crop monitoring. In the present study, the potential of Sentinel-2 have been appraised for monitoring phenological stages of potato over Bardhaman district in the state of West Bengal, India. Due to the competency of Vegetation indices (VI) to evaluate the status of crop growth; we have used the Normalized Difference Vegetation Index (NDVI), the Green Normalized Difference Vegetation Index (GNDVI), and the Normalized Difference Index45 (NDI45) for crop monitoring. Time series analysis of the VIs exhibited increasing trend as the crop started approaching maturity and attained a maximum value during the tuber development stage and started decreasing as the crop advances to senescence. Inter-field variability of VIs highlighted the need of crop monitoring at high spatial resolution. Among the three vegetation indices, the GNDVI (r = 0.636), NDVI (r = 0.620) had the highest correlation with biomass and Plant Area Index (PAI), respectively. NDI45 had comparatively a lower correlation (r = 0.572 and 0.585 for PAI and biomass, respectively) with both parameters as compared to other two indices. It is interesting to note that the use of Sentinel-2 Green band (B3) instead of the Red band (B4) in GNDVI resulted in 2.5% increase of correlation with biomass. However, the improvement in correlations between NDI45 with crop biophysical parameters is not apparent in this particular study with the inclusion of the Vegetation Red Edge band (B5) in VI. Nevertheless, the strong correlation of VIs with biomass and PAI asserted proficiency of Sentinel-2 for crop monitoring and potential for crop biophysical parameter retrieval with optimum accuracy.
EVALUATING CROSTA TECHNIQUE FOR ALTERATION MINERAL MAPPING IN MALANJKHAND COPPER MINES, INDIA
Authors: Guha S. , Govil H. , Tripathi M. , Besoya M.
Published: November 2018

Abstract. Landsat-8 Operational Land Imager (OLI) data has been successfully employed in the field of mineral exploration to detect important minerals. In this study, Crosta technique was applied to identify the diagnostic features of hydroxyl minerals, carbonate minerals and iron oxides in Malanjkhand copper mines, India. The Crosta technique was applied to six [blue, green, red, near-infrared (NIR), shortwave infrared1 (SWIR1), shortwave infrared2 (SWIR2) bands and two sets of four (blue, red, NIR, SWIR1; and blue, near-infrared, SWIR1, SWIR2) bands of OLI data. Results show that the areas with alteration zones are enhanced much better by using six bands of OLI data. The alteration differences are examined with the Crosta technique using four band combinations. Crosta technique is very useful in generating the images of hydroxyl minerals, carbonate minerals, and iron oxides.
ASSESSING THE IMPACT OF CANAL NETWORK ON SURFACE WATERLOGGING USING REMOTE SENSING DATASETS IN ROHTAK DISTRICT, HARYANA
Authors: Kaushik S. , Dhote P. R. , Thakur P. K. , Aggarwal S. P.
Published: November 2018

Abstract. Remote Sensing is a very reliable and expeditious technique for assessment and mapping of surface waterlogged areas. In this study band ratio based NDWI index (Normalized Difference Water Index) was used for extracting water pixels from optical imageries. To overcome the limitation of false positives and cloud penetration associated with optical imageries waterlogged areas was also extracted using SAR (Synthetic Aperture Radar) images. Thresholding of NDWI for optical image and Sigma0 for SAR images was done using their respective histograms to distinguish water and terrestrial features. The total surface waterlogged areas in the district was calculated by integrating the results from both optical and SAR images. It was found that surface waterlogged areas varies temporally from pre-monsoon to post-monsoon period in Rohtak district, Haryana. The surface waterlogged area for pre monsoon period is around 9.7 km2 and for post monsoon period is 17.86 km2. The canal and surface drain network in the district was digitized using the high resolution Sentinel 2 MSS images. Since most of the canals in the area are unlined a buffer of 500 m either side of the canals and surface drains was considered to assess the impact of seepage and leakage from canals and surface drains. It was found that more than 50% of the total surface waterlogged areas fall within this buffer, clearly indicating the contribution of leakages from canals and surface drains on surface waterlogging.
COMBINED ANALYSIS OF RADARSAT-2 SAR AND SENTINEL-2 OPTICAL DATA FOR IMPROVED MONITORING OF TUBER INITIATION STAGE OF POTATO
Authors: Mandal D. , Kumar V. , Rao Y. S. , Bhattacharya A. , Bera S. , Nanda M. K.
Published: November 2018

Abstract. Tuber initiation and tuber bulking stages are critical part of various phenological phases for potato production. Tuber initiation covers the period from the formation of spherical rhizome ends, the flowering and the start of tuber bulking. In general, the tuberization spans from 3 to 5 weeks after emergence and ends with the row closer i.e. canopies in adjacent rows touch each other across the furrow. Hence, this rapid growth seeks critical agronomic management practices such as irrigation and fertilization. It majorly influences the growth of stems, foliar area, dry weight and number of tubers particularly at the phase of tuber initiation. During these phenological stages, potato crops are susceptible to the infestation of late blight diseases caused by Phytophthora infestans and largely affects the potato production. Thus identifying the crop risk using remote sensing approaches can provide an efficient potato growth monitoring framework. In the context of monitoring crop dynamics, quad-pol Synthetic Aperture Radar (SAR) data has proven to be effective due to its sensitivity towards dielectric and geometric properties. In addition to SAR data, optical remote sensing data derived vegetation information can provide an improved insight into crop growth when combined with SAR data. In this research, quad-pol RADARSAT-2 and Sentinel-2 optical data are analyzed to monitor potato tuberization phase over Bardhaman district in the state ofWest Bengal, which is one of the major potato growing regions in India. The proposed approach uses polarimetric parameters such as backscatter intensities, ratio (HH/VV, VH/VV, linear depolarization ratio), and co-pol correlation (ρHH–VV) along with the vegetation indices derived from the Sentinel-2 data for understanding the spatio-temporal dynamics. The initial results show a promising accuracy in monitoring the dynamics of potato tuberization. Integration of such earth observation (EO) data, in conjunction with in-situ field measurements, might significantly enhance the current capabilities for crop monitoring.
A NOVEL METHOD FOR WATER AND WATER CANAL EXTRACTION FROM LANDSAT-8 OLI IMAGERY
Authors: Reddy S. L. K. , Rao C. V. , Kumar P. R. , Anjaneyulu R. V. G. , Krishna B. G.
Published: November 2018

Abstract. Constituents of hydrologic network, River and water canals play a key role in Agriculture for cultivation, Industrial activities and urban planning. Remote sensing images can be effectively used for water canal extraction, which significantly improves the accuracy and reduces the cost involved in mapping using conventional means. Using remote sensing data, the water Index (WI), Normalized Difference Water Index (NDWI) and Modified NDWI (MNDWI) are used in extracting the water bodies. These techniques are aimed at water body detection and need to be complemented with additional information for the extraction of complete water canal networks. The proposed index MNDWI-2 is able to find the water bodies and water canals as well from the Landsat-8 OLI imagery and is based on the SWIR2 band. In this paper, we use Level-1 precision terrain corrected OLI imagery at 30 meter spatial resolution. The proposed MNDWI-2 index is derived using SWIR2 (B7) band and Green (B3) band. The usage of SWIR2 band over SWIR1 results in very low reflectance values for water features, detection of shallow water and delineation of water features with rest of the features in the image. The computed MNDWI-2 index values are threshold by making the values greater than zero as 1 and less than zero as zero. The binarised values of 1 represent the water bodies and 0 represent the non-water body. This normalized index detects the water bodies and canals as well as vegetation which appears in the form of noise. The vegetation from the MNDWI-2 image is removed by using the NDVI index, which is calculated using the Top of Atmosphere (TOA) corrected images. The paper presents the results of water canal extraction in comparison with the major available indexes. The proposed index can be used for water and water canal extraction from L8 OLI imagery, and can be extended for other high resolution sensors.
CLASSIFICATION OF ORCHARD CROP USING SENTINEL-1A SYNTHETIC APERTURE RADAR DATA
Authors: Sahu H. , Haldar D. , Danodia A. , Kumar S.
Published: November 2018

Abstract. A study was conducted in Saharanpur District of Uttar Pradesh to asses the potential of Sentinel-1A SAR Data in orchard crop classification. The objective of the study was to evaluate three different classifiers that are maximum likelihood classifier, decision tree algorithm and random forest algorithm in Sentinel-1A SAR Data. An attempt is made to study Sentinel-1A SAR Data to classify orchard crop using this approach. Here the rule-based classifiers such as decision tree algorithm and random forest algorithm are compared with conventional maximum likelihood classifier. Statistical analysis of the classification show that the distribution of the crop, forest orchard, settlement and waterbody was 17.47 %, 0.47 %, 28.3 %, 28.3 % and 25.5 % respectively in all the classification algorithm but root mean square error for maximum likelihood classifier (1.278) is more than decision tree algorithm (1.196) and random forest algorithm (1.193). Out of three, a percentage correct prediction is highest in case of decision tree algorithm (73.4) than random forest algorithm (72.5) and least for maximum likelihood classifier (66.8) in December 2017. The accuracy for orchard class is 0.81 for maximum likelihood classifier, 0.80 for decision tree algorithm and 0.78 for random forest algorithm. Thus Sentinel-1A SAR Data was effectively utilized for the classification of orchard crops.
A COMPARATIVE STUDY OF LAND SURFACE TEMPERATURE WITH DIFFERENT INDICES ON HETEROGENEOUS LAND COVER USING LANDSAT 8 DATA
Authors: Bala R. , Prasad R. , Yadav V. P. , Sharma J.
Published: November 2018

Abstract. The temperature rise in urban areas has become a major environmental concern. Hence, the study of Land surface temperature (LST) in urban areas is important to understand the behaviour of different land covers on temperature. Relation of LST with different indices is required to study LST in urban areas using satellite data. The present study focuses on the relation of LST with the selected indices based on different land cover using Landsat 8 OLI (Operational Land Imager) and TIRS (Thermal Infrared Sensor) data in Varanasi, India. A regression analysis was done between LST and Normalized Difference Vegetation index (NDVI), Normalized Difference Soil Index (NDSI), Normalized Difference Built-up Index (NDBI) and Normalized Difference Water Index (NDWI). The non-linear relations of LST with NDVI and NDWI were observed, whereas NDBI and NDSI were found to show positive linear relation with LST. The correlation of LST with NDSI was found better than NDBI. Further analysis was done by choosing 25 pure pixels from each land cover of water, vegetation, bare soil and urban areas to determine the behaviour of indices on LST for each land cover. The investigation shows that NDSI and NDBI can be effectively used for study of LST in urban areas. However, NDBI can explain urban LST in the better way for the regions without water body.
1D-2D MODELING OF URBAN FLOODS AND RISK MAP GENERATION FOR THE PART OF HYDERABAD CITY
Authors: Rangari V. A. , Gonugunta R. , Umamahesh N. V. , Patel A. K. , Bhatt C. M.
Published: November 2018

Abstract. Space for water is now becoming guiding principle of urban planning because urban flooding is the major problem facing by most of the cities in India. Urban development in developing countries like India usually occurs with high population concentrating in small areas, with poor drainage conditions. People occupy floodplain areas in low flood years and when larger flood occurs it causes high damage. The origin for urban floods is floodplains encroachment and unplanned drainage systems. Complexities in the urban environment and drainage infrastructure have an inherent influence on surface runoff. This runoff generates urban flooding which poses challenges to modeling urban flood hazard and risk. As like in river flooding satellite images are not available for unban flooding scenario. So better modelling provides minimizing loss of life and property. The present study focuses on recognizing the highly effected areas which are liable to flooding when extreme rainfall occurs for part of Hyderabad city (Zone XIII). The entire Hyderabad city is divided into 16 zones and each zone having details of existing drain network. A coupled 1D-2D flood modelling approach is used to identify flood prone areas and develop flood inundation and flood risk maps. 1D model for pilot area is developed using storm water management model (SWMM) and coupled with 2D PCSWMM. A web based GIS platform INPPINS is used to geo reference the existing network details and exported to 1D SWMM model. The model is simulated for extreme flood event occurred in past. The simulation run results identifies overflowing drainage nodes and flood inundation maps and risk maps prepared. The flood risk maps identify the low lying areas which need immediate attention in case of emergency. The overflowing nodes suggest the need of improvement of drainage in the area to safely dispose of the storm water and minimize the flooding.
ASSESSMENT OF SNOW AVALANCHE SUSCEPTIBILITY OF ROAD NETWORK - A CASE STUDY OF ALAKNANDA BASIN
Authors: Singh V. , Thakur P. K. , Garg V. , Aggarwal S. P.
Published: November 2018

Abstract. Snow avalanche occurring in a micro-climatic condition causing hydro-geo (Hydrological and geological) hazard to the deployed armed forces and nearby inhabitant to the North Western Himalaya about 3000 MSL. In recent years, frequencies of snow avalanche have increase and consequently the death toll have also surged to many folds. These unavoidable occurrences not only cause road blocks which disrupts transportation connectivity in the rugged terrain of Himalaya as well as loss of infrastructure and life. Here, in this study an attempt has been made to assess the susceptibility of road network of Alaknanda Basin from snow avalanche. Potential avalanche formation zones have been generated using Analytical Hierarchical Process (AHP) of Multi-Criteria Decision Making (MCDM. Advance Thermal Emission Reflection Radiometer (ASTER) Global Digital Elevation (GDEM) 30 meter has been used to generate static parameters like slope, aspect, curvature etc. using GIS platform. ISRO-Geosphere Biosphere Program Land Use Land Cover (LULC) used as another static parameter. Weights are generated using comparison matrix and ratings to different static parameter layers assigned on the basis of field visit and literature review while the road network are digitized from Google earth. A methodology has been prepared to categorize the road stretches on the basis of potential snow avalanche formation zone including hydrological processing. Buffer zone are assigned with weights according to potential snow avalanche formation zones. Later roads are intersected with sub basin with assigned values that resulted very high avalanche potential zonation, considered as most susceptible to snow avalanche hazard.
MAPPING OF FOREST FIRE BURNED SEVERITY USING THE SENTINEL DATASETS
Authors: Suresh Babu K. V. , Roy A. , Aggarwal R.
Published: November 2018

Abstract. Forest fires are frequent phenomena in Uttarakhand Himalayas especially in the months of April to May, causing major loss of valuable forest products and impact on humans through the emissions and therefore effects the climate change. The major forest fire was started on May 19, 2018 and spread in 10 districts out of 13 districts of Uttarakhand state till the fire was suppressed after May 30, 2018. The burned area mapping is essential for the forest officials to plan for mitigation measures and restoration activities after the fire season. In this study, sentinel 2A & 2B satellite datasets were used to map burned severity over Uttarakhand districts. Differenced Normalized Burn Ratio (dNBR) and Relativized Burn Ratio (RBR) were calculated and compared with the active fire points. Results shows that both the dNBR and RBR are in good agreement with the actual occurence of forest fires.
COMPARISON OF PIXEL AND OBJECT-BASED CLASSIFICATION TECHNIQUES FOR GLACIER FACIES EXTRACTION
Authors: Jawak S. D. , Wankhede S. F. , Luis A. J.
Published: November 2018

Abstract. Glacier facies are zones of snow on a glacier that have certain specific spectral characteristics that enable their characterization. The accuracy of their extraction will determine the end accuracy of the distributed mass balance model calibrated by this information. Therefore, coarse to medium resolution satellites are not preferable for this particular function as the data derived from such sensors will potentially blur out the minute spatial variations on the surface of a glacier. Very high resolution (VHR) sensors (such as, WorldView (WV)-1, 2, 3) are thus much more suited for this particular task. Hence, this study aims to extract the available glacier facies on the Sutri Dhaka glacier, Himalayas, using very high-resolution WorldView-2 (WV-2) imagery. Extensive pre-processing of the imagery was performed to prepare the data for this purpose. The steps incorporated for this purpose consist of 1) Data Calibration, 2) Mosaicking, 3) Pan Sharpening, 4) Generation of 3D surface, and 5) Digitization. Using image classification as the primary method of information extraction, this study tests the ever-popular pixel-based classification technique against the uprising object-based classification technique. In doing so, this study aims to determine the most accurate technique of information extraction for the WV-2 imagery in the given scenario. The presence of unique bands (Coastal (0.40–0.45 μm), Red Edge (0.705–0.745 μm), NIR-1 (0.770–0.895 μm) and NIR-2 (0.86–1.04 μm) in the multispectral range of WV-2, allows this study to perform facies classification through the development of customized spectral index ratios (SIRs) in the object-based domain. Establishment of thresholds was hence necessitated for information extraction through the developed SIRs. Three supervised classifiers, namely, a) Mahalanobis distance, b) Maximum likelihood, and c) Minimum distance to mean, were then used to perform classification, thereby allowing a comparative analysis between the classification schemes. Accuracy assessment for each classification scheme was performed using error matrices. The object-based approach achieved an overall accuracy of 90% (κ = 0.88) and the highest overall accuracy among the pixel-based classification methods is 78.57% (κ = 0.75). The results clearly portray that the object-based method delivered much higher accuracy than the pixel-based methods. The carry home message is that future studies must examine the transferability and accuracy of the customized SIRs in varying scenarios, as different scenarios will require varying threshold adjustments. Forthcoming studies can also develop sensor specific and unique indices for other sensors that are suitable for such applications.
EXTRACTION OF BLUE ICE AREA USING ALBEDO VALUE DERIVED FROM LANDSAT-8 SATELLITE DATA
Authors: Luis A. L. , Pandit P. H. , Jawak S. D.
Published: November 2018

Abstract. Blue Ice Areas (BIAs) or bare ice areas are zones of glacier where surface mass balance is negative, sublimation forms the major ablation process and surface albedo is relatively small. Exceptionally dry and windy meteorological conditions over Antarctica favor the formation of large areas of net ablation on the ice sheet leading to formation of BIRs (Schytt, 1961). BIAs are major source of drinking water to research stations and serve as runways for airplanes in Antarctica. This study has been conducted on the Polar Record Glacier (PRG), Princess Elizabeth Land, East Antarctica, where more than 30% of area is covered by BIAs. The BIAs are extracted and estimated using the value of albedo which is the fraction of solar energy reflected from the surface back to space. A surface having a higher (lower) reflectivity occupies higher (lower) albedo. With an average value of blue ice albedo (also known as bare ice) of 0.55, it ranges from 0.52 to 0.66, due to its geographical area, katabatic wind and wind patterns, the direction of ice flow, rate of sublimation and ablation, surface temperature, etc. The extent of BIAs also depends upon climate and seasonal changes. Albedo is calculated using the Level-1 product of Landsat, this data product (images) is processed according to standard parameters such as Geo-referencing, re-sampling, re-projection and north-up image re-orientation. These data (Digital Numbers) were further calibrated to standard pixel value using multiplicative and additive rescaling factors from metadata provided with the Level-1 product and scaled for absolute reflectance. A further algorithm was applied to get albedo from Landsat-8 dataset. After processing the data, we detected some error in a few pixels, (∼20) which was normalized by using band math. Our result indicates that the range of albedo for the BIAs is decreasing (more surface absorption of solar radiation), which subsequently could promote warming of surface due to increase in the surface temperature. The decreasing rate of albedo suggest the possibility of less reflection of radiation to the atmosphere, more melting which leads to depletion in the BIAs. The carry home message is that the variation in different parameters like albedo of the glacier causes significant variation in the surface area and spatial extent of BIAs.
ON VERTICAL DISTRIBUTION OF MAJOR AEROSOL TYPES OVER DIFFERENT PARTS OF ASIA
Authors: Mehta M. , Singh N. ,   Anshumali, Mittal M. , Gumber M.
Published: November 2018

Abstract. Asia, being the largest continent, both in its size and population is affected by different types of atmospheric aerosols. Also, owing to different local emissions/sources along with the long range transport mechanisms, there is a large variability in the distribution of different types of aerosols across the continent. Not only the effects are pertinent to the regional and global climate system, but the loadings also affect human health. Though there have been studies in the past focussing on columnar distribution of aerosol types over Asian region at continental and regional scales, studies focussed on regional vertical distribution of major aerosol types over different regions of Asia need attention. This paper presents the decadal (2007–16) vertical distribution of major aerosol components, i.e., dust, polluted dust and smoke over different parts of Asia as seen from the space-borne lidar, i.e., Cloud Aerosol Lidar with Orthogonal Polarisation (CALIOP) onboard Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO). Such a study can be useful in capacity building focussed on aerosol variability through remote sensing techniques.
ESTIMATION OF REVISED CAPACITY IN GOBIND SAGAR RESERVOIR USING GOOGLE EARTH ENGINE AND GIS
Authors: Singh S. , Dhasmana M. K. , Shrivastava V. , Sharma V. , Pokhriyal N. , Thakur P. K. , Aggarwal S. P. , Nikam B. R. , Garg V. , Chouksey A. , Dhote P. R.
Published: November 2018

Abstract. Capacity studies of reservoirs are important to evaluate sedimentation and optimize reservoir operation schedule based on realistic assessment of available storage. Capacity study at regular interval provide information about rate and pattern of sedimentation between various levels, loss of capacity due to sedimentation, remaining time span of reservoir, etc. In the present study, evaluation of revised capacity of Gobind Sagar reservoir in Bilaspur district, Himachal Pradesh, India has been done using Google Earth Engine. Landsat 8 (OLI) data for September 2015 and for the period September 2017 to May 2018 covering full extent of Gobind Sagar reservoir is taken to compute the water spread area of this reservoir at different dates. Subsequently, the reservoir water level and volume of water stored on the corresponding dates is acquired from India-WRIS. By using trapezoidal formula capacity between two elevations is determined using water spread area obtained from Google Earth Engine analysis and elevation data obtained from India-WRIS. The comparison of water spread areas of different water levels, as obtained from remotely sensed data from September 2017 to May, 2018 with those from survey carried out during 1996/97, indicates a reduction in the capacity by 10.71% and sedimentation rate was estimated to be 14.24 Mm3/year.
RANDOM FOREST REGRESSION FOR THE ESTIMATION OF LEAF AREA INDEX OF OKRA CROP USING GROUND BASED BISTATIC SCATTEROMETER
Authors: Yadav S. A. , Prasad R. , Vishwakarma A. K. , Yadav V. P.
Published: November 2018

Abstract. The specular bistatic scattering mechanism of Okra's crop was analyzed using dual polarized ground based bistatic scatterometer system at X, C, and L bands in the specular direction with the azimuthal angle(θ = 0°). An outdoor Okra crop bed of area 10 × 10 m2 was specially prepared for the estimation of leaf area index (LAI) at HH and VV polarizations over the angular range of incidence angle 20° to 60° at steps of 10°. The regression analysis was done between bistatic specular scattering coefficients and crop biophysical parameter at X, C, and L bands for HH and VV polarization at different angle of incidence to determine the optimum parameters of bistatic scatterometer system. The linear regression analysis showed the high correlation at 40° angle of incidence for all bands and polarizations for the Okra crop. The computed scattering coefficients and measured LAI of Okra crop for the seven growth stages at 40° angle of incidence were interpolated into 61 data sets. The data sets were divided into input, validation and testing for the training and testing of the developed random forest regression (RF) model for the estimation of LAI for Okra crop. The estimated values of LAI of Okra crop, by the developed RF regression model, were found more closer to the observed values at X band for VV polarization with coefficient of determination (R2 = 0.928) and low root mean square error (RMSE = 0.260 m2/m2) in comparison to C and L bands.
EXTRACTION OF BUILT-UP AREA BY COMBINING TEXTURAL FEATURES AND SPECTRAL INDICES FROM LANDSAT-8 MULTISPECTRAL IMAGE
Authors: Bramhe V. S. , Ghosh S. K. , Garg P. K.
Published: November 2018

Abstract. Remote sensing techniques provide efficient and cost-effective approach to monitor the expansion of built-up area, in comparison to other traditional approaches. For extracting built-up class, one of the common approaches is to use spectral and spatial features such as, Normalized Difference Built- up index (NDBI), GLCM texture, Gabor filters etc. However, it is observed that classes such as river soil and fallow land usually mix up with built-up class due to their close spectral similarity. Intermixing of classes have been observed in the classified image when using spectral channels. In this paper, an approach has been proposed which uses urban based spectral indices and textural features to extract built-up areas. Three well known spectral indices i.e. NDBI, Built-up Area Extraction Index (BAEI) and Normalized Difference Bareness Index (NDBai) have been used in this work. Along with spectral indices, local spatial dependency of neighborhood regions is captured using eight GLCM based textural feature, such as, Contrast, Correlation, Energy and Homogeneity etc. for each image band. All textural and spectral indices bands are combined and used for extracting built-up areas using Support Vector Machine (SVM) classifier. Results suggest 4.91% increase in overall accuracy when using texture and spectral indices in comparison with 84.38% overall accuracy achieved when using spectral data only. It is observed that built-up class are more separable in the projected spectral-spatial feature space in comparison to spectral channels. Incorporation of textural features with spectral features reduces the misclassification error and provides results with less salt and pepper noise.
IMPACT ANALYSIS OF URBANIZATION ON LAND USE LAND COVER CHANGE FOR KHULNA CITY, BANGLADESH USING TEMPORAL LANDSAT IMAGERY
Authors: Moniruzzam M. , Roy A. , Bhatt C. M. , Gupta A. , An N. T. T. , Hassan M. R.
Published: November 2018

Abstract. Urbanization has given a massive pace in Land Use Land Cover (LULC) changes in rapidly growing cities like Khulna, i.e. the third largest city of Bangladesh. Such impacting changes have taken place in over-decadal scale. It is important because detailed analysis with regularly monitoring will be fruitful to drag the attention of decision maker and urban planner for sustainable development and to overcome the problem of urban sprawl. In this present study, changes in LULC as an impact of urbanization, have been investigated for years 1997, 2002, 2007, 2012 and 2017; using three generation of Landsat data in geographic information system (GIS) domain which has the height competence in recent time. Initially, LULC have categorised into Built-up, Vegetation, Vacant Land, and Waterbody with the help of supervised classification technique. Field work had been carried out for acquiring training dataset and validation. The accuracy has been achieved more than 85% for the changes assessed. Analysis has an outlet with increase in built-up area by 27.92% in year 1997 to 2017 and continued respectively in each successive interval of half a decade at the given years. On the other side waterbody and vacant land decreased correspondingly. Bound to mention, instead to having largest temporal durability, the moderate spatial resolution of Landsat data has a limitation for such urban studies. These changes are responsible by both of natural or anthropogenic factors. Such study will provide a better way out of optimization of land-use to prepare detail area plan (DAP) of Khulna City Corporation (KCC) and Khulna development authority (KDA).
CLASSIFICATION AND ASSESSMENT OF THE LAND USE – LAND COVER CHANGES IN JODHPUR CITY USING REMOTE SENSING TECHNOLOGIES
Authors: Saharan M. A. , Vyas N. , Borana S. L. , Yadav S. K.
Published: November 2018

Abstract. Land Use – Land Cover (LULC) classification mapping is an important tool for management of natural resources of an area. The remote sensing technology in recent times has been used in monitoring the changing patterns of land use-land cover. The aim of the study is to monitor the LULC changes in Jodhpur city over the period 1990–2018. Satellite imagery of Landsat 8 OLI (June, 2018) & Landsat TM (Oct, 1990) were used for classification analysis. Supervised classification-maximum likelihood algorithm is used in ENVI software to detect land use land cover changes. Five LULC categories were used, namely- urban area, mining area, vegetation, water bodies and other area (Rock outcrops and barren land). The LULC classified maps of two different periods i.e. 2018 and 1990 were generated on 1 : 50,000 scale. The accuracy assessment method was used to measure the accuracy of classified maps. This study shall be of good assistance to the town planners of Jodhpur city for the purpose of the sustainable development as per the master plan 2031.
INTEROPERABLE MODEL FOR BIORESOURCE DISTRIBUTED DATABASES
Authors: Saran S. , Singh P. , Ganeshaiah K. N. , Padalia H. , Oberoi K.
Published: November 2018

Abstract. Recently, numerous frameworks and tools are being developed for enhancing access to data and services with a standardized view to communicate the advances in open information sharing. Another emerging field of data exploration is encountered in the coordination, examination and perception of bioresource data and are prompting corresponding new innovations. The bioresource information team aims to develop standards for nationwide data exchange by the establishment of a catalog service to locate and access biological data and information from across the country and information tool for decision makers. With the growth of open data sharing initiatives, the sharing of data among different and myriad sources has increased significantly, but major challenge lies in addressing the issues of interoperability during exchange and use since the data sources are heterogeneous and the data being organization specific is prepared with different (organization) specific data standards and platforms. This paper presents the model based on the study of different metadata standards and to develop a recommended standard for biodiversity information to support interoperability among heterogeneous databases under the umbrella of Indian Bioresource Information Network (IBIN) portal. The paper presents the mapping of different data standards into the IBIN standard for sharing species data in the form of distributed and interoperable web services to set the stage for interoperability.