bluuebunny/crossref_metadata_2025 huggingface dataset:
Dissipation rate of turbulent kinetic energy in stably stratified
sheared flows
Authors: Zilitinkevich Sergej , Druzhinin Oleg , Glazunov Andrey , Kadantsev Evgeny , Mortikov Evgeny , Repina Iryna , Troitskaya Yulia
Published: November 2018

Abstract. Over the years, the problem of dissipation rate of turbulent kinetic energy (TKE) in stable stratification remained unclear because of the practical impossibility to directly measure the process of dissipation that takes place at the smallest scales of turbulent motions. Poor representation of dissipation causes intolerable uncertainties in turbulence-closure theory and, thus, in modelling stably stratified turbulent flows. We obtain theoretical solution to this problem for the whole range of stratifications from neutral to limiting stable; and validate it via (i) direct numerical simulation (DNS) immediately detecting the dissipation rate and (ii) indirect estimates of dissipation rate retrieved via the TKE-budget equation from atmospheric measurements of other components of the TKE-budget. The proposed formulation of dissipation rate will be of use in any turbulence-closure models employing the TKE budget equation and in problems requiring precise knowledge of the high-frequency part of turbulence spectra in atmospheric chemistry, aerosol science and microphysics of clouds.
An improved parameterization of leaf area index (LAI) seasonality in the Canadian Land Surface Scheme (CLASS) and Canadian Terrestrial Ecosystem Model (CTEM) modelling framework
Authors: Asaadi Ali , Arora Vivek K. , Melton Joe R. , Bartlett Paul
Published: November 2018

Abstract. Leaf area index (LAI) and its seasonal dynamics are key determinants of
vegetation productivity in nature and as represented in terrestrial biosphere
models seeking to understand land surface atmosphere flux dynamics and its
response to climate change. Non-structural carbohydrates (NSCs) and their
seasonal variability are known to play a crucial role in seasonal variation
in leaf phenology and growth and functioning of plants. The carbon stored in
NSC pools provides a buffer during times when supply and demand of carbon are
asynchronous. An example of this role is illustrated when NSCs from previous
years are used to initiate leaf onset at the arrival of favourable weather
conditions. In this study, we incorporate NSC pools and associated
parameterizations of new processes in the modelling framework of the Canadian
Land Surface Scheme-Canadian Terrestrial Ecosystem Model (CLASS–CTEM) with
an aim to improve the seasonality of simulated LAI. The performance of these
new parameterizations is evaluated by comparing simulated LAI and
atmosphere–land CO2 fluxes to their observation-based estimates,
at three sites characterized by broadleaf cold deciduous trees selected from
the FLUXNET database. Results show an improvement in leaf onset and offset
times with about 2 weeks shift towards earlier times during the year in
better agreement with observations. These improvements in simulated LAI help
to improve the simulated seasonal cycle of gross primary productivity (GPP)
and as a result simulated net ecosystem productivity (NEP) as well.
Leaf area index identified as a major source of variability in modeled CO2 fertilization
Authors: Li Qianyu , Lu Xingjie , Wang Yingping , Huang Xin , Cox Peter M. , Luo Yiqi
Published: November 2018

Abstract. The
concentration–carbon feedback (β), also called the CO2
fertilization effect, is a key unknown in climate–carbon-cycle projections.
A better understanding of model mechanisms that govern terrestrial ecosystem
responses to elevated CO2 is urgently needed to enable a more
accurate prediction of future terrestrial carbon sink. We conducted C-only,
carbon–nitrogen (C–N) and carbon–nitrogen–phosphorus (C–N–P)
simulations of the Community Atmosphere Biosphere Land Exchange model (CABLE)
from 1901 to 2100 with fixed climate to identify the most critical model
process that causes divergence in β. We calculated CO2
fertilization effects at various hierarchical levels from leaf biochemical
reaction and leaf photosynthesis to canopy gross primary production (GPP),
net primary production (NPP), and ecosystem carbon storage (cpool) for
seven C3 plant functional types (PFTs) in response to increasing
CO2 under the RCP 8.5 scenario.
Our results show that β values at biochemical and leaf photosynthesis
levels vary little across the seven PFTs, but greatly diverge at canopy and
ecosystem levels in all simulations. The low variation of the leaf-level
β is consistent with a theoretical analysis that leaf photosynthetic
sensitivity to increasing CO2 concentration is almost an invariant
function. In the CABLE model, the major jump in variation of β values
from leaf levels to canopy and ecosystem levels results from divergence in
modeled leaf area index (LAI) within and among PFTs. The correlation of
βGPP, βNPP, or βcpool
each with βLAI is very high in all simulations. Overall,
our results indicate that modeled LAI is a key factor causing the divergence
in β in the CABLE model. It is therefore urgent to constrain processes
that regulate LAI dynamics in order to better represent the response of
ecosystem productivity to increasing CO2 in Earth system models.
A MULTI THREADED FEATURE EXTRACTION TOOL FOR SAR IMAGES USING OPEN SOURCE SOFTWARE LIBRARIES
Authors: Bipin C. , Rao C. V. , Sridevi P. V. , Jayabharathi S. , Krishna B. G.
Published: November 2018

Abstract. In this paper, we propose a software architecture for a feature extraction tool which is suitable for automatic extraction of sparse features from large remote sensing data capable of using higher order algorithms (computational complexity greater than O(n)). Many features like roads, water bodies, buildings etc in remote-sensing data are sparse in nature. Remote-sensing deals with a large volume of data usually not manageable fully in the primary memory of typical workstations. For these reason algorithms with higher computational complexity is not used for feature extraction from remote sensing images. A good number of remote sensing applications algorithms are based on formulating a representative index typically using a kernel function which is having linear or less computational complexity (less than or equal to O(n)). This approach makes it possible to complete the operation in deterministic time and memory.Feature extraction from Synthetic Aparture Radar (SAR) images requires more computationally intensive algorithm due to less spectral information and high noise. Higher Order algorithms like Fast Fourier Transform (FFT), Gray Level Co-Occurrence Matrix (GLCM), wavelet, curvelet etc based algorithms are not preferred in automatic feature extraction from remote sensing images due to their higher order of computational complexity. They are often used in small subsets or in association with a database where location and maximum extent of the features are stored beforehand. In this case, only characterization of the feature is carried out in the data.In this paper, we demonstrate a system architecture that can overcome the shortcomings of both these approaches in a multi-threaded platform. The feature extraction problem is divided into a low complexity with less accuracy followed by a computationally complex algorithm in an augmented space. The sparse nature of features gives the flexibility to evaluate features in Region Of Interest (ROI)s. Each operation is carried out in multiple threads to minimize the latency of the algorithm. The computationally intensive algorithm evaluates on a ROI provided by the low complexity operation. The system also decouples complex operations using multi-threading.The system is a customized solution developed completely in python using different open source software libraries. This approach has made it possible to carry out automatic feature extraction from Large SAR data. The architecture was tested and found giving promising results for extraction of inland water layers and dark features in ocean surface from SAR data.
APPLICATION OF VARIOUS OPEN SOURCE VISUALIZATION TOOLS FOR EFFECTIVE MINING OF INFORMATION FROM GEOSPATIAL PETROLEUM DATA
Authors: Gholba N. D. , Babu A. , Shanmugapriya S. , Singh A. , Srivastava A. , Saran S.
Published: November 2018

Abstract. This study emphasizes the use of various tools for visualizing geospatial data for facilitating information mining of the global petroleum reserves. In this paper, open-source data on global oil trade, from 1996 to 2016, published by British Petroleum was used. It was analysed using the shapefile of the countries of the world in the open-source software like StatPlanet, R and QGIS. Visualizations were created using different maps with combinations of graphics and plots, like choropleth, dot density, graduated symbols, 3D maps, Sankey diagrams, hybrid maps, animations, etc. to depict the global petroleum trade. Certain inferences could be quickly made like, Venezuela and Iran are rapidly rising as the producers of crude oil. The strong-hold is shifting from the Gulf countries since China, Sudan and Kazakhstan have shown a high rate of positive growth in crude reserves. It was seen that the global oil consumption is not driven only by population but by lifestyle also, since Saudi Arabia has a very high rate of per-capita consumption of petroleum, despite very low population. India and China have very limited oil reserves, yet have to cater to a large population. These visualizations help to understand the likely sources of crude and refined petroleum products and to judge the flux in the global oil reserves. The results show that geodata visualization increases the understanding, breaks down the complexity of data and enables the viewer to quickly digest the high volumes of data through visual association.
HICF: A MATLAB PACKAGE FOR HYPERSPECTRAL IMAGE CLASSIFICATION AND FUSION FOR EDUCATIONAL LEARNING AND RESEARCH
Authors: Maurya A. K. , Varade D. , Dikshit O.
Published: November 2018

Abstract. A significant surge has been observed with the development and research in remote sensing in recent years for hyperspectral applications in Earth observation. Subsequently, the development of software and tools have also experienced an unprecedented rise, both in research as well as in academia. Although commercial software and tools such as ENVI by ITT Visual Information Solutions, Boulder, CO, USA are available for visualizing and analyzing the hyperspectral images, such software are expensive. Some open source toolboxes such as the MATLAB-based Hyperspectral Image Analysis Toolbox (HIAT) are also available. However, mostly these toolboxes have not been packaged for dissemination and operation without the MATLAB software which is commercial. In this paper, we introduce the Hyperspectral Image Classification and Fusion (HICF) package which is being developed at the Geoinformatics laboratory, Department of Civil Engineering, Indian Institute of Technology Kanpur (IITK) in MATLAB that can be used by standalone installation with an open source supplementary MATLAB compiler. This software is intended to provide a collection of algorithms both conventional and those developed at the Geoinformatics laboratory that utilizes the numerical computing capability of MATLAB for the processing of hyperspectral and multispectral imagery. The HICF software comprises a simple design of the graphical user interface which can be efficiently used particularly for academic purposes.
LOCATION BASED ADVERTISING FOR MASS MARKETING
Authors: Vignesh Kandasamy S. , Madhu A. , Gupta P. K. , Niveditha A. , Bordoloi K.
Published: November 2018

Abstract. GIS and machine learning (ML) are powerful ICT tools in retail industry which helps the sellers understand their markets. For the consumers, however, there always lies an ambiguity with respect to the quality and quantity of the product to be purchased, vis-à-vis the price paid for it. Most retail businesses today adopt “Discount Pricing Strategies” or “Offers” to make new customers and increase sales. Owing to several establishments selling the same product and offering a variety of offers, the process of identifying the shops where the consumer can get the best value for his money, requires a lot of manual effort. A prototype has been developed in this study to allow the consumers to locate such prospective shops based on advertisements in newspapers. This solution has a two-pronged approach. First, all the offers advertised in the newspaper are pre-processed and text extraction is performed using a ML algorithm named Tesseract OCR. Second the location of shops is collected and stored in a geodatabase. Finally, the advertisement is matched to the respective geo-located shop based on its name and location. Further based on the location of the consumer and his purchase choice, shops offering discounts are shown on a web based map. This prototype provides the consumer, a platform for geo-discovery of establishments of interest through the clutter of unrelated endorsements, by the use of Open Source GIS, Python programming and ML techniques.
DEVELOPMENT OF CROP SPECTRA FOR VARIATIONS IN NITROGEN AND WATER APPLIED IN UNREGULATED FARMER’S FIELD PLOTS BASED ON SPECTRAL SIMILARITY WITH SPECTRA COLLECTED FROM REGULATED TEST FIELD PLOTS
Authors: Chauhan H.
Published: November 2018

Abstract. To develop crop spectra for variation in application of nitrogen (as fertilizer) and water at farmer’s field plots is very difficult as there are large variations in nitrogen and water applied also time of applications were different. During field visit it was found that various crops growth was not uniform in the study area even though local conditions (seed quality, soil and weather) were same this is possible due to variation in application of nitrogen and water. Development of crop spectra for nitrogen and water variations for unregulated farmer’s field plots based on spectral similarity analysis with regulated test field plots (where required amount of nitrogen and water applied at appropriate time) provides a fresh opportunity to develop and evaluate the crop spectra for nitrogen and water variations for real time applications. Spectral Information Divergence (SID) based spectral similarity analysis was carried out among spectra collected from unregulated farmer’s field plots and regulated test field plots for chickpea, sorghum and wheat crops. An average SID values equivalent to coefficient of correlation computed for four groups of nitrogen and water variations for chickpea, sorghum and wheat were 0.998, 0.996 and 0.994 respectively.
IDENTIFICATION OF IRON OXIDES MINERALS IN WESTERN JAHAJPUR REGION, INDIA USING AVIRIS-NG HYPERSPECTRAL REMOTE SENSING
Authors: Govil H. , Tripathi M. K. , Diwan P. , Guha S. ,   Monika
Published: November 2018

Abstract. Hyperspectral remote sensing is being considered as an advanced technique for mineral identification of surficial deposits. In this research different iron oxides minerals such as limonite, goethite has been identified using AVIRIS-NG airborne hyperspectral remote sensing covering the Omkarpura, Itwa, and Chhabadiya mines area in Jahajpur Bhilwara, Rajasthan, India. AVIRIS-NG has shown robust performance in iron oxide identification in the study area. Mineral spectral signatures of the AVIRIS-NG data were compared with spectra of USGS spectral library, and field investigated mineral spectra of iron oxides and found very promising. The results allow us to conclude that due the high signal to noise ratios of the AVIRIS-NG, it is capable to identify the different iron bearing minerals in the visible and infrared portion of the electromagnetic spectrum.
IDENTIFICATION OF ROCKS AND THEIR QUARTZ CONTENT IN AMARKANTAK,
INDIA USING ASTER TIR DATA
Authors: Guha S. , Govil H. , Tripathi M. , Besoya M.
Published: November 2018

Abstract. Quartz (SiO2) abundance in rock is an important indicator of mineralization in many metal deposits and quartz detection has a great role in mineral exploration. The present study identified the quartz contained rocks in Amarkantak region, India applying thermal infrared bands (bands 10–14) of the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) image. After atmospheric correction, principal component analysis technique was applied on the TIR bands and the resulting principal component images were analyzed. The three optimal principal components were selected based on the spectral interaction strength and the eigenvalues of each band of the ASTER data. The result presented that extrusive igneous rock and carbonate sedimentary rocks are quartz-poor while sedimentary rocks made up of organic particles and sandstone is quartz-rich.
3-D STRUCTURE OF INDIAN FORESTS – PERSPECTIVES FROM EXPERIMENTS ON THE FIRST FULLY-POLARIMETRIC TANDEM-X TOMOGRAMS
Authors: Khati U. , Singh G. , Lavalle M.
Published: November 2018

Abstract. This paper provides a first hand view of the 3-D structure of the forests when viewed by X-band SAR data. Tomograms are generated using multi-polarimetric space-borne TerraSAR-X/TanDEM-X acquisitions and analysed over a multi-species forest range. The paper analysed these generated tomograms and puts forth-interesting observations of these unique forest species. The high- and low-canopy density plantations provide unique tomograms and vertical structure profiles where the effect of varying extinction is observed in X-band. Further, the scattering powers are shown relative to their backscatter powers. In-depth analysis in currently underway and would be reported in future.
ASSESSING IMPACTS OF CLIMATE CHANGE ON TEAK AND SAL LANDSCAPE USING MODIS TIME SERIES DATA
Authors: Patasaraiya M. K. , Sinha B. , Bisaria J. , Saran S. , Jaiswal R. K.
Published: November 2018

Abstract. Climate change poses a severe threat to the forest ecosystems by impacting its productivity, species composition and forest biodiversity at global and regional level. The scientific community all over the world is using remote sensing techniques to monitor and assess the impact of climate change on forest ecosystems. The consistent time series data provided by MODIS is immensely used for developing a different type of Vegetation indices like NDVI (Normalized difference vegetation indices) products at different spatial and temporal resolution. These vegetation indices have significant potential to detect forest growth and health, vegetation seasonality and different phenological events like budding and flowering. The current study aims to understand the impact of climate change on Teak and Sal forest of STR (Satpura tiger reserve) in central India by using Landsat and MODIS time series data. The rationale for taking STR as study site was to attribute the changes exclusively to climate change as there is no anthropogenic disturbance in STR. A change detection analysis was carried out to detect changes between the period 2017 and 1990 using Landsat data of October month. To understand the inter-annual and seasonal variation of Teak and Sal forests, freely available MOD13Q1 product (250 m, 16 days’ interval) was used to extract NDVI values for each month and four seasons (DJF, JJAS, ON, MAM) for the period 2000 to 2015. The climatic data (rainfall and temperature) was sourced from IMD (India Meteorological Department) at different resolutions (1, 0.5 and 0.25 degree) for the given period of the study. A correlation analysis was done to establish a causal relationship between climate variable (temperature and rainfall) and vegetation health (NDVI) on a different temporal scale of annual, seasonal and month. The study found an increasing trend in annual mean temperature and no consistent trend in total annual rainfall over the period 2000 to 2015. The maximum percentage change was observed in minimum temperature over the period 2000 to 2015. The average annual NDVI of Teak and Sal forests showed an increasing trend however, no trend was observed in seasonal and monthly NDVI over the same period. The maximum and minimum NDVI was found in the post-monsoon months (ON) and summer months (MAM) respectively. As STR is a Teak and Sal dominated landscape, the findings of the current study can also be applied in developing silvicultural and adaptation strategies for other Teak and Sal dominated landscapes of central India.
IDENTIFICATION OF POTENTIAL SITES FOR WATER HARVESTING STRUCTURES USING GEOSPATIAL TECHNIQUES AND MULTI-CRITERIA DECISION ANALYSIS
Authors: Saha A. , Patil M. , Karwariya S. , Pingale S. M. , Azmi S. , Goyal V. C. , Rathore D. S.
Published: November 2018

Abstract. Changing hydrological phenomenon and increase the water demand in all sectors create a huge problem of water scarcity. Precipitation is one and only major source to mitigate this problem. In Central India, the average annual precipitation is sufficient in most of the part but not capable to harvest and utilize during the summer season. Identification of suitable site for water harvesting structures plays a key role to enhance the water level for watershed management. In the present study, suitable sites have been demarcated to build up a water resource development plan in Mandri river watershed of Kanker district, Chhattisgarh using Remote Sensing and GIS techniques. Basic guidelines of Integrated Mission for Sustainable Development (IMSD) have been used for identifying the suitable sites for water harvesting structures. Remote Sensing and GIS are used for site suitability analysis of water harvesting structure to extract the information of various features such as lineament, land use/land cover, soil, slope, lithology, geomorphology, drainage order, rainfall etc. In order of importance, we have assigned weights to each feature. Slope and design discharge are key parameters in site suitability analysis, so that we assigned maximum weight to this parameters. A possible site suitability map for water harvesting/conservation structures was derived following an Analytical Hierarchy Process (AHP) and Multi-Criteria Decision Analysis (MCDA). This work attempts to identify the probable zone for water harvesting structures such as boulder check/gabion, gully plug/bori bandhan, check dams and stop dams. Multi-criteria analysis is applied in GIS to assist the decision-makers in determining appropriate zones for water harvesting structures in view of external characteristics of the watershed. Further, using weighted overlay method, the catchment was grouped into categories of suitable or highly suitable.
INDIAN INLAND WATER AND PARTS OF ANTARCTIC ICE SHEET ELEVATION AND ICE SHEET VELOCITY MONITORING USING ALTIMETRY AND SAR BASED DATASETS
Authors: Thakur P. K. , Chouksey A. , Kalura P. , Ghosh S. , Dhote P. , Swain A. , Kalia M. , Garg V. , Nikam B. R. , Kumar P. , Aggarwal S. P. , Chauhan P. , Kumar A. S.
Published: November 2018

Abstract. The monitoring of inland water and continental ice sheets is very important from water management and global climate related studies. The current study utilizes the SGDR data from Saral-Altika during 2013–2017 to estimate and monitor water level in 24 major reservoirs of India. The R2 value for majority of reservoirs was more than 0.99 and RMSE error value also was less than 0.40 m. In addition, wide rivers of India such as Mahanadi River, was also monitored using Altika data covering part of Mahanadi River from Khairmal to Naraj gauging sites during 2013–2016 time period. One dimensional hydro-dynamic (1D-HD) model was setup for this part of river to generate river Discharge at virtual gauge. The part of Antarctic ice sheet South of Indian research station Maitri, East Antarctica, was studied for ice sheet elevation changes using ground based stake network as well as space based altimeter/LIDAR datasets during 2003–2017 time period. 2003–2009 time was used for getting elevation changes using Icesat-1 level 2 altimetry product, and Geophysical Data Record (GDR) data from Altika was used with slope correction from 2013–2016 time period. An extensive network of ground based stake networks were used for validating the derived elevation changes. The ice sheet and glacier line of site velocity was estimated using Sentinel-1 based InSAR data with 6 to 12 day time interval data sets for year 2016 and 2017. The derived glacier velocity was comparable with optical image (Landsat-8) based glacier velocity for same year and also with historical Radarsat-1 based glacier velocity results.
SNOW COVER AND GLACIER DYNAMICS STUDY USING C-AND L-BAND SAR DATASETS IN PARTS OF NORTH WEST HIMALAYA
Authors: Thakur P. K. , Garg V. , Nikam B. R. , Singh S. ,   Jasmine, Chouksey A. , Dhote P. R. , Aggarwal S. P. , Chauhan P. , Kumar A. S.
Published: November 2018

Abstract. The seasonal snow cover and permanent ice in form of Himalayan glaciers provide fresh water to many perineal rivers of Himalayas. The melt water from seasonal snow and glaciers, especially during of 15 March to 15 June acts as important source of water for drinking, hydropower and irrigation requirements of many areas in North India. This work has highlights the use of C-band Synthetic Aperture Radar (SAR) data from RISAT-1, Sentinel-1A and 1B satellites and ALOS-PALSAR-2 PolInSAR data for snow cover and glacier dynamics study for parts of North West Himalaya. Glacier velocity was derived using InSAR based method using 6 day temporal interval images from Sentinel-1 satellites and 14 day interval for PALSAR-2 satellite. High coherence was obtained for main glacier in both the data sets, which resulted accurate line of site (LOS) glacier velocity estimates for test glaciers. These InSAR data glacier velocity results are obtained after a gap of 21 years. Glacier facies was estimated using multi-temporal SAR image composition based classification. All these maps were verified by extensive ground surveys done at these sites during 2014–2017. The time series data of C-band SAR in VV/VH polarisation was also used to map snow cover in test basins of Bhagirathi and Beas River. The VV/VH data clearly shows difference between dry and wet snow, thus helping in improved snow cover mapping using SAR data. This study will help in refining algorithms to be used for such studies using upcoming NASA-ISRO SAR (NISAR) mission.
STUDY OF GPM-IMERG RAINFALL DATA PRODUCT FOR GANGOTRI GLACIER
Authors: Verma P. , Ghosh S. K.
Published: November 2018

Abstract. This study presents a comparison of new generation weather observatory satellites Global Precipitation Measurement (GPM) Integrated Multi-satellite Retrievals for GPM (IMERG) rainfall products with field data collected for Gangotri glacier in India. The meteorological analysis of rainfall estimates has been performed on GPM IMERG Final, Late and Early precipitation products available at daily scale with a spatial resolution of 0.1° × 0.1° for melting season from May to September for the year 2014 and 2015 respectively. The comparison of satellite products with field data was done using correlation coefficient and standard anomaly. The Late run curve showed a high degree of similarity with final run curve while early run showed variation from them. The satellite meteorological data correctly identified non-rainy days with an average of ∼86.7%, ∼67.5% and ∼95% for pre-monsoon, monsoon and post-monsoon season respectively. The rmse for final run data product for 2014 and 2015 are 4.5, 1.23, 1.55, 1.24, 0.8 and 1.14, 7.1, 1.82, 1.15, 1.52 from May to September respectively. Overall, it has been observed that for medium to heavy rainfall final run estimates are close to field data and for light to medium rainfall late run estimates are close. Similar results have been obtained from both datasets for non-rainy days in the study area.
A COMPARATIVE ANALYSIS OF DROUGHT INDICES ON VEGETATION THROUGH REMOTE SENSING FOR LATUR REGION OF INDIA
Authors: Dhawale R. , Paul S. K.
Published: November 2018

Abstract. Drought intensifies stress on the water resource which is already in a critical condition due to rapid urbanization and population growth thus, affecting people, economy, and environment. The drought conditions are worsening in many parts of India due to deficit rainfall, change in land and surface temperature, and vegetation pattern coupled with mismanagement of water resources and poor governance. The present study conducted for Latur, Marathwada is an agricultural rich land which is severely affected due to prolonged drought conditions. A comparative study is presented using the three drought indices VCI, VHI, TCI to analyze the vegetation condition for drought years. The results through TCI detects the drought only during the dry period or in the months where the temperature is high. The VCI detects drought conditions as more sensitive in wet seasons. The VHI combines both the indicators to give comprehensive results about drought conditions. Further, Land Surface Temperature study is conducted to substantiate the analyzed drought conditions. Our study illustrates that the comparative analysis of various indices represents a better interpretation and monitoring of drought for the areas which are majorly affected due to vegetative drought.
LANDSCAPE VULNERABILITY ASSESSMENT USING REMOTE SENSING AND GIS TOOLS IN THE INDIAN PART OF KAILASH SACRED LANDSCAPE
Authors: Hussain A. , Singh G. , Rawat G. S.
Published: November 2018

Abstract. The Indian part of Kailash Sacred Landscape (KSL) is prone to flash floods, landslides and forest fires leading to various environmental and socio-economic problems. This study aims to identify areas vulnerable to these disasters by preparing hazard maps to curtail their impact on the overall landscape. The Indian part of KSL covering seven forest ranges in Pithoragarh district which is spread over an area of 7,212 km2. This paper integrated the Geographic Information System and Remote Sensing and the multi criteria analysis through AHP to determine the disaster vulnerable areas in the landscape. All the thematic layers and final maps are prepared in ArcGIS 10.2. A total of ten variables for a landslide, six variables for flood and seven variables (topographic, climatic, and anthropogenic) were used to carry out the pairwise comparison for relatively weighting the variables through AHP. Consistency ratio (CR = 0.01) for landslide and forest fire and for flood (CR = 0.06) which shows the matrix was consistent. We identified 174 km2 of the area which is highly fired prone to forest fire, 76 km2 of area vulnerable to landslide and 24 km2 of the area comes under hotspot of the flood. The sites vulnerable to key drivers identified and mapped through this study will form the basis for further conservation and development planning at landscape level by policy makers.
IMPACT OF OZONE CONCENTRATION ON OCEAN COLOUR RETRIEVALS FOR OCM-2
Authors: Bhavani I. V. G. , Rao T. D. V. P , Das A. , Choudhury S. B. , Nagamani P. V. , Prasad K. V. S. R. , Sasmal S. K.
Published: November 2018

Abstract. Ozone being an absorbing gas have a significant impact on OCM-2 bands 510 nm, 555 nm and 620 nm which are using for retrieval of major geophysical parameters like Chlorophyll and suspended sediment concentrations. A study has been carried out to analyse the impact of Ozone concentration by ingesting near real time from OMI (Ozone Monitoring Instrument) on Aura instead of Climatology V2013 (2004–13) with a resolution of 1 × 1 deg for ocean colour retrievals from OCM-2 sensor using SeaWiFS Data Analysis System (SeaDAS). The spectral behaviour of Ozone transmittance has been studied for various bands of OCM-2 by using both Climatology and near real time inputs. We could observe maximum relative error percentage about 12 % from Climatology to NRT in Ozone concentration and 0.28 %, 0.66 % and 0.72 % of maximum mean relative error in ozone transmittance at 512 nm, 557 nm and 620 nm. We calculated error budgets induced by ozone in remote sensing reflectance(/sr) where in we could observe mean relative error percentage of 0.52 % in 491 nm, 1.12 % in 512 nm and 4.28 % 557 nm a bands respectively.
OBSERVATION OF DUST LOADINGS FROM SPACE-BORNE MEASUREMENTS (2005–2016)
Authors: Gumber M. , Mehta M. , Mittal M.
Published: November 2018

Abstract. Dust particles of different size and origins, form major part of air pollution in the atmosphere. The qualitative and quantitative estimates of dust from ground-based measurements provide accurate information at regional scales. However, data from space-borne instruments provide continuous spatial information at a variety of different spatial scales. In this paper, we attempt to study signals of dust as observed from CALIOP (Cloud Aerosol Lidar with Orthogonal Polarization) sensor on-board CALIPSO satellite. Utilization of these freely available datasets can effectively contribute to the capacity building in the domain of aerosol science. This analysis is supplemented by absorbing aerosol levels as seen by OMI (Ozone Monitoring Instrument) sensor on-board AURA satellite. This study was conducted over the Middle-East and North Africa during JJA (June, July and August) season. For this study, we have used Level-3 AOD (Aerosol Optical Depth) due to dust from CALIOP at spatial resolution of 2° × 5° and Level-3 Absorbing Aerosol Optical Depth (AAOD) data from OMI at spatial resolution of 1° × 1°.The time frame for the study was 2005 to 2016.
MULTI-SATELLITE OBSERVATION OF MEGH CYCLONE
Authors:   Jagdish, John J. , Shukla B. P. , Kumar R.
Published: November 2018

Abstract. Cyclone Megh, a category-3 (Saffir-Simpson scale) cyclonic storm is regarded as the worst tropical cyclone to ever strike Yemen’s island of Socotra. In this paper, we aim to investigate the wind structure of cyclone Megh using Synthetic Aperture Radar (RISAT-1 SAR) observations. An algorithm for the cyclone wind retrieval has been applied for SAR data of Nov 8, 2015 at 0238:09 UTC in the Arabian Sea. The intensity of cyclone is 30 m/s with the 16.65 km radius of maximum wind speed from the centre of the cyclone. The high resolution SAR data analysis bring to focus the possible presence of eyewall mesovortex in case of Megh. Recent work has shown that vorticity mixing in the tropical cyclone (TC) inner core can promote mesovortex (MV) formation and impact storm intensity. This has further been corroborated using INSAT-3D and MODIS optical band observations of clouds. Analysis of these satellite derived cloud microphysical properties shows the presence of larger hydrometeors surrounding the eye due to possible embedding of stratus and stratocumulus cloud decks. Thus, this kind of study helps in understanding the microphysical processes within a TC as well estimating their impacts on cyclone intensity and lifetime.
SEASONAL COMPARISON OF ERA-INTERIM PRECIPITATION DATASET FOR ENTIRE INDIAN REGION
Authors: Sharma V. , Pandey K. , Thakur P. K. , Aggarwal S. P. , Nikam B. R. , Dhasmana M. K. , Shrivastava V. , Singh S.
Published: November 2018

Abstract. Era-Interim (ECMWF Re- analysis) is a global reanalysis atmospheric product which is being continuously updated in real time since 1979. It is also termed as third generation reanalysis product. The Era–Interim gives meteorological products like precipitation, temperature, etc. In the present work, 3-hourly Era–Interim product for the entire India is compared with gridded data provided by IMD for period 1979–2013 and APHRODITE data for period 1979–2007, respectively. The comparison is done on seasonal basis and the seasons are taken based on the pattern of rainfall, hence, the four seasons selected are DJF (December, January & February), MAM (March, April & May), JJAS (June, July, August & September) and ON (October & November). In the methodology the Era-Interim 3-hourly products are converted into the daily products and then it is used to form seasonal images for each year. All the images are then taken to form four images as outcome for the entire study period which represents the average rainfall (mm/day) for the entire region. This is being done for the IMD and for the APHRODITE Data. All the four images are then taken for the comparison with the reference images of the IMD 0.5º × 0.5º gridded rainfall data and with the APHRODITE 0.5º × 0.5º gridded rainfall data. The correlation coefficient and the RMSE for each season is calculated. The mean value is compared with the mean of IMD and APHRODITE rainfall products, respectively and a bias in mean is also calculated along with the scatter plots of Era-Interim with the reference datasets. The Era – Interim data came out with suitable comparative parameters with high correlation coefficient and low RMSE value in certain regions and in specific seasons. Scatter plots have also given good correlation in all the seasons. Bias maps have also shown very less bias in specific seasons for certain regions. The suitability maps prepared for the study region also shows that most of the region lies in most suitable range and very less in unsuitable range.
CONSIDERATIONS ON THE USE OF SENTINEL-1 DATA IN FLOOD MAPPING IN URBAN AREAS: ANKARA (TURKEY) 2018 FLOODS
Authors: Tavus B. , Kocaman S. , Gokceoglu C. , Nefeslioglu H. A.
Published: November 2018

Abstract. Flood events frequently occur due to -most probably- climate change on our planet in the recent years. Rapid urbanization also causes imperfections in city planning, such as insufficient considerations of the environmental factors and the lack of proper infrastructure development. Mapping of inundation level following a flood event is thus important in evaluation of flood models and flood hazard and risk analyzes. This task can be harder in urban areas, where the effect of the disaster can be more severe and even cause loss of lives.With the increased temporal and spatial availability of SAR (Synthetic Aperture Radar) data, several flood detection applications appear in the literature although their use in urban areas so far relatively limited. In this study, one flood event occurred in Ankara, Turkey, in May 2018 has been mapped using Sentinel-1 SAR data. The preprocessing of Sentinel-1 data and the mapping procedure have been described in detail and the results have been evaluated and discussed accordingly. The results of this study show that SAR sensors provide fast and accurate data during the flooding using appropriate methods, and due to the nature of the flood events, i.e. heavy cloud coverage, it is currently irreplaceable by optical remote sensing techniques.
FUSION OF HYPERSPECTRAL AND MULTISPECTRAL IMAGERY WITH REGRESSION KRIGING AND THE LULU OPERATORS; A COMPARISON
Authors: Jeevanand N. , Verma P. A. , Saran S.
Published: November 2018

Abstract. In this digital world, there is a large requirement of high resolution satellite image. Images at a low resolution may contain relevant information that has to be integrated with the high resolution image to obtain the required information. This is being fulfilled by image fusion. Image fusion is merging of different resolution images into a single image. The output image contains more information, as the information is integrated from both the images Image fusion was conducted with two different algorithms: regression kriging and the LULU operators. First, regression Kriging estimates the value of a dependent variable at unsampled location with the help of auxiliary variables. Here we used regression Kriging with the Hyperion image band as the response variables and the LISS III image bands are the explanatory variables. The fused image thus has the spectral variables from Hyperion image and the spatial variables from the LISS III image. Second, the LULU operator is an image processing methods that can be used as well in image fusion technique. Here we explored to fuse the Hyperion and LISS III image. The LULU operators work in three stages of the process, viz the decomposition stage, the fusion and the reconstruction stage. Quality aspects of the fused image for both techniques have been compared for spectral quality (correlation) and spatial quality (entropy). The study concludes that the quality of the fused image obtained with regression kriging is better than that obtained with the LULU operator.
TREE CANOPY HEIGHT ESTIMATION USING MULTI BASELINE RVOG INVERSION TECHNIQUE
Authors: Babu A. , Kumar S.
Published: November 2018

Abstract. Polarimetric Interferometric Synthetic Aperture Radar (PolInSAR) technique utilizes the characteristics of both SAR polarimetry and Interferometry. PolInSAR technique is proved to be very useful for vegetation parameters retrieval. Estimation of the tree canopy height parameter is very important for the estimation of the Above Ground Biomass (AGB). The baseline separation between different PolInSAR datasets has a very important role in the tree canopy height estimation due to the sensitivity of the baseline to the tree height and the forest structure. So for accurately estimating the tree canopy height of a forest with varying tree heights and species several pairs of PolInSAR datasets with different baselines separations are required. Multi-baseline Random Volume over Ground (RVoG) inversion technique is the most successful method for tree height inversion. UAVSAR, the Quad-Pol L-band airborne SAR of JPL/NASA acquired PolInSAR datasets over the Gabon forest as a part of the AfriSAR campaign. Nine PolInSAR SLC datasets of this campaign acquired over the Mondah Forest site of Gabon forest is used for this study. Tree canopy height map produced from this datasets shows that the tree height is varying at this site and has a maximum height of 50 m. The results obtained are validated using the field data collected by JPL/NASA during March 2016. The comparison of the results with the field data showed that both are in good agreement with an average deviation of 3.75 m.
SPECTRAL-SPATIAL CLASSIFICATION OF HYPERSPECTRAL REMOTE SENSING IMAGES USING VARIATIONAL AUTOENCODER AND CONVOLUTION NEURAL NETWORK
Authors: Belwalkar A. , Nath A. , Dikshit O.
Published: November 2018

Abstract. In this paper, we propose a spectral-spatial feature extraction framework based on deep learning (DL) for hyperspectral image (HSI) classification. In this framework, the variational autoencoder (VAE) is used for extraction of spectral features from two widely used hyperspectral datasets- Kennedy Space Centre, Florida and University of Pavia, Italy. Additionally, a convolutional neural network (CNN) is utilized to obtain spatial features. The spatial and spectral feature vectors are then stacked together to form a joint feature vector. Finally, the joint feature vector is trained using multinomial logistic regression (softmax regression) for prediction of class labels. The classification performance analysis is done through generation of the confusion matrix. The confusion matrix is then used to calculate Cohen’s Kappa (Κ) to get a quantitative measure of classification performance. The results show that the K value is higher than 0.99 for both HSI datasets.
FEATURES OF GEODETIC MAINTENANCE FOR WATER SUPPLY PROJECTS IN AZERBAIJAN
Authors: Gojamanov M. H. , Gurbanov C. Z.
Published: November 2018

Abstract. Drinking water supply and sewerage system designing and reconstruction projects are being extensively conducted in Azerbaijan Republic. During implementation of such projects, collecting large amount of information about the area and detailed investigations are crucial. Joint use of the aerospace monitoring and GIS play an essential role for the studies of the impact of environmental factors, development of the analytical information systems and others, while achieving the reliable performance of the existing and designed major water supply pipelines, as well as construction and exploitation of the technical installations. With our participation the GIS has been created in “Azersu” OJSC that includes systematic database of the drinking water supply and sewerage system, and rain water networks to carry out necessary geo information analysis. The system software was developed based on “Microstation” platform, which identifies the coordinate system and cartographic projection along with the geo-referencing through direct connection to Google Earth platform. Should be mentioned that, in the country, specifically in large cities (i.e. Baku, Ganja, Sumqait, etc.,) drinking water supply pipelines cross regions with different physico-geographical conditions, geo-morphological compositions and seismotectonics. Therefore, constant control of the plan-height positioning, geodetic measurements for the detailed examination of the dynamics, repetition of the geodetic measurements for certain time intervals, or in other words regular monitoring is very important. During geodetic monitoring using the GIS has special significance. Given that, collecting geodetic monitoring measurements of the main pipelines on the same coordinate system and processing these data on a single GIS system allows the implementation of overall assessment of plan-height state of major pipes.
AN ALGORITHM FOR GENERATING NATURAL COLOR IMAGES FROM FALSE COLOR USING SPECTRAL TRANSFORMATION TECHNIQUE WITH HIGHER POLYNOMIAL ORDER
Authors: Kala S. , Kumar A. , Joshi A. K. , Bothale V. M. , Krishna B. G.
Published: November 2018

Abstract. Satellite imageries in True color composite or Natural Color composite (NCC) serves the best combination for visual interpretation. Red, Green and Infrared channels form false color composite which might not be as useful as NCC to a non-remote sensing professional. As blue band is affected by large atmospheric scattering, satellites like IRS-LISS IV, SPOT do not have blue band. To generate NCC from such satellite data blue band must be simulated. Existing algorithms of spectral transformation do not provide robust coefficients leading to wrong NCC colors especially in water bodies. To achieve more robust coefficients, we have proposed new algorithm to generate NCC for IRS-LISS IV data using second order polynomial regression technique. Second order polynomial transformation functions consider even minor variability present in the image as compared to 1st order so that the derived coefficients are adjustable to accommodate spatial and temporal variability while generating NCC. In this study, Sentinel-2 image was used for deriving coefficients with blue band as dependent and green, red and infrared as independent variables. Simulated Sentinel band showed high accuracy with correlation of 0.93 and 0.97 for two test sites. Using the same coefficients, blue band was simulated for LISS-IV which also showed good correlation of 0.90 with sentinel original blue band. On comparing LISS-IV simulated NCC with simulated NCC from other algorithms, it was observed that higher order polynomial transformation was able to achieve higher accuracy especially for water bodies where expected color is green. Thus, proposed algorithms can be used for transforming false color image to natural color images.
IDENTIFICATION OF FOOD INSECURE ZONES USING REMOTE SENSING AND ARTIFICIAL INTELLIGENCE TECHNIQUES
Authors: Nivedita Priyadarshini K. , Kumar M. , Kumaraswamy K.
Published: November 2018

Abstract. The basic human need is to ensure adequate access to food without any combat, loss of productivity and cognitive impairment in the supply chain. When an individual is limited to proper procurement of food through various determinants there stems sustained hunger which is termed as ‘food insecurity’. The study portrays to identify the food insecure zones using indicators which are implemented methodically through remote sensing and artificial intelligence techniques. Madhya Pradesh being a semi arid region faces reduction in the agro ecosystem due to the climatic changes and rainfall impacts which are the key trends for demand of food and production thus resulting in risk of malnutrition and hunger. Tackling food shortage requires addressing both environmental and socio-demographic factors in order to minimize food insecurity. The spatial variation of rainfall over years along with significant land degradation affects the common cultivation pattern among the households. In this study, a neural network approach is employed to identify the zones that ensure less access to food using indicators which mainly focuses on child population below five years, hunger index measuring parameters like child stunted, child wasted, children undernourished, child mortality below five years along with supporting environmental factors such as land use/land cover, NDVI and rainfall prevailing in the study area. The result shows a bleak statistics of villages representing the hunger index score that are categorized into low, serious, alarming and extremely alarming estimating a count of 70, 73, 23 and 7 villages respectively in the entire study area.