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The satellite-based nighttime lights (NTL) data from the Defense Meteorological Satellite Program’s Operational Linescan System (DMSP-OLS), available in the public domain from 1992 to 2013, are ext...

The night-time light (NTL) data from Visible Infrared Imaging Radiometer Suite’s Day/Night Band (VIIRS-DNB) onboard Suomi National Polar-Orbiting Partnership (S-NPP) spacecraft available si...

The LULC change vis-à-vis climate change inherently encompassing human dimensions consequently impact hydrological processes. A slight change in it may affect the water yield, as both are explicitly linked through various hydrological processes. The future availability of water resources largely depends upon planning and management of land use in this changing environment. However, the continuous human interactions keep on modifying the land use land cover (LULC) to fulfil the enhanced demand especially due to significant increase in population and development towards better facilities. These changes consequently impact each and every hydrological process vis-à-vis water availability. It has now become a major concern for water resources planners and managers. Moreover, these changes in LULC pattern along with changing climate put forth a huge challenge in front of them. The present study investigates the capabilities of Variable Infiltration Capacity (VIC) model, which has been developed as soil-vegetation-atmosphere transfer schemes, to assess the runoff potential. Initially, the required model inputs were derived and simulated at the national scale. It was found that the model has assessed the runoff potential of major river basins with high accuracy. The successful implication of the model at the national scale offered an opportunity to carry out a detailed hydrological simulation to study the impact of LULC change on hydrological regime of a basin. For the analysis, the Pennar River Basin, located in Southern part of India, was selected. The LULC change detection analysis showed that there is human induced alteration to LULC pattern, as the area under urban landscape is increasing (0.14%) and natural forest is declining (0.7%). These changes resulted in increase of runoff potential by around 45%. The study on Indian River basin clearly indicates that the LULC change influence and alter the hydrological regime of the river basin. Keywords: Land use land cover change, Runoff potential, Hydrological modeling, Variable infiltration capacity model, Impact assessment

In 2020, the world faced an unexpected health crisis in form of the COVID-19 pandemic. Globally, lockdowns were imposed to control its spread. These lockdowns disrupted normal life and are estimated to cause large economic losses in India. Literature is replete with studies linking satellite-based nighttime light (NTL) observations with electrification, socioeconomic, and demographic growth. This chapter attempts to explore several such indicators for Delhi, India from March to May 2020. Human mobility, electricity power consumption (EPC), and NTL observations showed a significant decline in the lockdown months. However, results indicate that during the lockdown period, a weak correlation exists between NTL and EPC. The hypothesis of this study is, thus, built on the fact that, during the lockdown, the effect of EPC on NTL was impacted by other factors including COVID-19 cases and reduced mobility in the region. Further analysis was done by spatially, temporally, and quantitatively harmonizing all the datasets to a pre-COVID baseline period. The study finds positive correlation between mobility and EPC; and positive correlation between NTL and mobility in parks. A symbolical regression model is also generated to express EPC as function of NTL and mobility. The study therefore shows that NTL and EPC have been impacted by factors prevailing during COVID-19 lockdown. The study can be further expanded to other parts of the world with different socioeconomic settings and by including granular datasets having sectoral EPC values.

Synthetic aperture radar (SAR) observations of real world flood extents are often the only source of information in data scarce catchments and the only reliable resource for channels with s...

Traditional geoprocessing techniques often rely on the use of multiple softwares for data handling and management which consumes almost 80% of the time and requires the user to be well versed with all the intricacies of pre-processing. Therefore, there is a need to reverse the trend on analysis and data management, so as to enable scientists and researchers to focus on the science rather than data handling and pre-processing. The concept of a Data Cube which is a massive multi-dimensional array of raster or gridded data, ‘stacks’ satellite images and addresses the problems faced by traditional remote sensing practices and provides an interactive environment where datasets can be analysed with relative ease as compared to its traditional counterparts. This framework allows multi-format and multi-projection datasets spanning decades to be used in various geoprocessing techniques from simple GIS tasks such as data conversion, time series generation, and to do more complex tasks such as change detection, NDVI generation, unsupervised classification and modelling. LISS III data for the state of Uttarakhand, India was used on an interactive interface called the Jupyter Notebook where scripts written in Python allowed data to be ingested, analysed and visualised. The Data Cube framework hence proved to be a flexible and extensive development environment which can be extended to meet more complex modelling requirements.

Accurate monitoring of satellites plays a pivotal role in analysing critical mission specific parameters for estimating orbital position uncertainties. An appropriate database management system (DBMS) at the software end, could prove its potential as a convenient solution over the existing file based two line element (TLE) data structure. The current web-based satellite tracking systems, such as n2yo, satview, and satflare, are unable to provide location-based satellite monitoring. Moreover, the users need to zoom into the displayed world map for obtaining information of the satellites that are currently over the respective area. Also, satellite searching is a cumbersome task in these web-based systems. In this research work, a systematic approach has been utilised to develop a generic open-source Web-GIS based tool for addressing the aforementioned issues. This tool incorporates a PostgreSQL database for storing the parsed TLE data which are freely available on the CelesTrak (NORAD) repository. Our choice of selecting PostgreSQL as a backend DB is primarily due to its open source and scalable properties compared to other resource intensive databases. Using suitable python libraries (e.g. Skyfield and Orbit-Predictor), the position and velocity at any point of time can be accurately estimated. For this purpose, the tool has been tested on several cities for displaying location-based satellite tracking that includes different types of space-objects.

Urban climate studies crucially depend on various urban morphological parameters. The availability of these parameters assists in understanding wind flow inside urban areas, Urban Heat Island (UHI) phenomenon and also aids in estimation of anthropogenic heat in Numerical Weather Prediction models for improved forecast. Of the two approaches used, micrometeorological approach is un-suitable for urban applications due to various operational complexities. Morphometric approaches are more feasible to use in urban areas but their estimation requires automation. The paper presents a software suite which uses novel and existing algorithms to compute variety of urban morphometric parameters including frontal area, height-to-width ratio and sky view factor, which further can be used for the computation of aerodynamic parameters. The application is developed using open source technologies and each component is tested over a sample dataset. The estimated values were validated against manually calculated values and error percentage was found to be within 3% and then demonstrated on a real city database. The developed application can be effectively utilized for urban climate modeling studies, as it enables users to compute urban morphology parameters.

Coal fire is a serious threat in major coal producing countries across the globe and poses significant constraints in mining operations, often leading to environmental degradation. The applications of thermal and shortwave infrared remote sensing play a substantial role in systematically detecting and monitoring the coal fire. Over the last few decades, researchers have extensively examined the importance of spectral radiance for retrieving reliable pixel-integrated temperature threshold to delineate coal fire from its background. However, such an assumption does not necessarily consider the local information, thereby leading to difficulty in isolating the actual coal fire affected pixels. Therefore, we propose to utilise the channel specific reflectance to retrieve the thermally anomalous pixels in coal fire related applications using Landsat 8 OLI data. This paper explores the practicability of incorporating the active fire detection technique using channel specific reflectances based on both fixed and contextual thresholds in the Jharia coalfield, India.

Cloud cover creates obstruction in Earth Observation studies. The obstruction is harder to distinguish from features having similar reflectance on the ground, such as snow. To distinguish clouds from snow in a VNIR image, we use an additional SWIR band. The images were fed into a deep Fully Convolutional Network that can fuse the multiresolution SWIR and VNIR bands together, in order to produce pixelwise classification. The accuracy obtained by the model on the test image was 93.35%. We compare the performance of this model with a more commonly used technique, Random Forests. To analyze the effect of SWIR, we use another deep learning model, trained only on the VNIR image, and compare the accuracies obtained.

Rapid urbanisation in Dhaka (Bangladesh) with high dependency on the groundwater have recently raised concern over the sustainability of the aquifers. The present study focusses on quantifying the dynamic groundwater resource vis-a-vis the status of groundwater development and understanding the different recharge processes in the Dhaka City, using the water balance based lumped and distributed approaches. In the lumped approach, the groundwater assessment is carried out using groundwater balance method following the methodology and norms recommended by the Groundwater Estimation Committee-2015 (referred hereafter as GEC). In the distributed approach, a physics-based hydrological model (Variable Infiltration Capacity, VIC) is utilised to model the baseflow (i.e., potential recharge). Then, an attempt is made to establish the relation between VIC-based baseflow and actual (GEC-based) groundwater recharge from rainfall. Remote sensing data derived spatio-temporal information along with field-based observations have been used in the process. Dhaka City is divided into 10 zones and the groundwater assessment is made individually in these zones as well as for the entire city during 2001–2015 period. The total annual recharge is estimated as 635.35 million m3 (or ~2065 mm/year per unit area). Lateral and unaccounted recharge is found to be the dominant (51.8%) process in the study area, followed by recharge from urban return flow (35.5%), rainfall (7.1%), and surface water bodies/wetlands (5.6%). The study underscores that the lateral and unaccounted recharge must not be ignored in the urban landscape underlain by alluvial aquifers and with non-hydrologic unit boundaries. The stage of groundwater development is found to vary from 117 to 320%, thus indicating overexploitation of aquifers in all the zones. The higher groundwater abstraction as compared to recharge in all the zones has led to consistent decline in groundwater level by 2–3.2 m/year across the study area. The VIC model, on the other hand, depicted that nearly 17.2% of the annual rainfall contributes towards baseflow. Further, it is observed that ~31% of baseflow contributes to groundwater recharge due to rainfall. Researchers can use this relationship for quick assessment of the groundwater recharge from rainfall in Dhaka, based on hydrological models with varying meteorological and land cover scenarios, especially in the light of climate change and rapid urbanisation.

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.

The present study aims to investigate the impact of climate change on water availability and hydrological extremes in the Krishna River basin, the second largest eastward draining river of Peninsular India. The hydrological response of the basin for the past observed climatic data (1985–2005) and future climatic scenarios (RCP 4.5 and RCP 8.5, respectively, for 2006–2099) is simulated using the Variable Infiltration Capacity (VIC) model. The soil, vegetation, topographic, and meteorological inputs for the model are derived from remote sensing and field-observed data. The model calibration is performed using observed discharge data at 4 gauging stations for the time period of 21 years (1985–2005); the coefficients of determination (R2) are in the range of 0.81–0.95. The model validation is carried out at the Vijayawada station (R2 = 0.82), near the basin outlet. The meteorological forcing consisting of future climatic inputs for the entire century (2006–2099) for RCP 4.5 and RCP 8.5 scenarios extracted from IITMRegCM4-4 predictions are used to simulate the future hydrological regime of the basin. The hydrological response analysis shows increase in annual discharge by 13.8 and 27.8 cumec under RCP 4.5 and RCP 8.5, respectively. The hydrological extreme events are also found to increase under RCP 4.5 and RCP 8.5 scenarios as compared with the past records of 1985–2005. The peak discharge is observed to increase in the range of 57.7–76.8 and 68.5–77.5% under RCP 4.5 and RCP 8.5, respectively. The study highlights that the annual water potential and hydrological extremes in the Krishna basin will increase under the future climatic scenarios.

Geoinformatics is a highly specialized discipline that deals with Remote Sensing, Geographical Information System (GIS), Global Positioning System (GPS) and field surveys for assessing, quantification, development and management of resources, planning and infrastructure development, utility services etc. Indian Institute of Remote Sensing (IIRS), a premier institute and one of its kinds has played a key role for capacity Building in this specialized area since its inception in 1966. Realizing the large demand, IIRS has started outreach program in basics of Remote Sensing, GIS and GPS for universities and institutions. EDUSAT (Educational Satellite) is the communication satellite built and launched by ISRO in 2004 exclusively for serving the educational sector to meet the demand for an interactive satellite based distance education system for the country. IIRS has used EDUSAT (shifted to INSAT 4 CR recently due to termination of services from EDUSAT) for its distance learning program to impart basic training in Remote Sensing, GIS and GPS, catering to the universities spread across India. The EDUSAT based training is following similar to e-learning method but has advantage of live interaction sessions between teacher and the students when the lecture is delivered using EDUSAT satellite communication. Because of its good quality reception the interactions are not constrained due to bandwidth problems of Internet. National Natural Resource Management System, Department of Space, Government of India, under Standing Committee in Training and Technology funded this unique program to conduct the basic training in Geoinformatics. IIRS conducts 6 weeks basic training course on "Remote Sensing, GIS and GPS" regularly since the year 2007. The course duration is spread over the period of 3 months beginning with the start of the academic year (1st semester) i.e., July to December every year, for university students. IIRS has utilized EDUSAT satellite for conducting 4 six weeks training course during 2007 till 2009 and INSAT 4CR for conducting the next 2 programs. Till March 2011, fifty four universities with the participation of over 4000 students have benefited from the program (Table 7 and Figure 8). IIRS also organized workshops on "EDUSAT based distance learning: experiences & future learning" in 2007, 09 and 2011. Feedbacks have been taken to address the issues on course structure, duration etc. and plan for improvement in future programs and wider participation. Majority of the participants expressed satisfaction and provided positive feedback and willing to participate in the future programs.

The sustainability of water resources mainly depends on planning and management of land use; a small change in it may affect water yield largely, as both are linked through relevant hydrological processes, explicitly. However, human activities, especially a significant increase in population, in-migration and accelerated socio-economic activities, are constantly modifying the land use and land cover (LULC) pattern. The impact of such changes in LULC on the hydrological regime of a basin is of widespread concern and a great challenge to the water resource engineers. While studying these impacts, the issue that prevails is the selection of a hydrological model that may be able to accommodate spatial and temporal dynamics of the basin with higher accuracy. Therefore, in the present study, the capabilities of variable infiltration capacity hydrological model to hydrologically simulate the basin under varying LULC scenarios have been investigated. For the present analysis, the Pennar River Basin, Andhra Pradesh, which falls under a water scarce region in India, has been chosen. The water balance components such as runoff potential, evapotranspiration (ET) and baseflow of Pennar Basin have been simulated under different LULC scenarios to study the impact of change on hydrological regime of a basin. Majorly, increase in built-up (13.94% approx.) and decrease in deciduous forest cover (2.44%) are the significant changes observed in the basin during the last three decades. It was found that the impact of LULC change on hydrology is balancing out at basin scale (considering the entire basin, while routing the runoff at the basin outlet). Therefore, an analysis on spatial variation in each of the water balance components considered in the study was done at grid scale. It was observed that the impact of LULC is considerable spatially at grid level, and the maximum increase of 265 mm (1985–2005) and the decrease of 48 mm (1985–1995) in runoff generation at grid were estimated. On the contrary, ET component showed the maximum increase of 400 and decrease of 570 mm under different LULC change scenario. Similarly, in the base flow parameter, an increase of 70 mm and the decrease of 100 mm were observed. It was noticed that the upper basin is showing an increasing trend in almost all hydrological components as compared to the lower basin. Based on this basin scale study, it was concluded that change in the land cover alters the hydrology; however, it needs to be studied at finer spatial scale rather than the entire basin as a whole. The information like the spatial variation in hydrological components may be very useful for local authority and decision-makers to plan mitigation strategies accordingly.

Background: Early screening plays a pivotal role in management of breast cancer. Given the socio-economic situation in India, there is a strong felt need for a screening tool which reaches the masses rather than waiting for the masses to reach tertiary centers to be screened. Digital infra-red thermal imaging (DITI) or breast thermography as a screening test offers this possibility and needs to be carefully assessed in Indian scenario.Methods: The study involved 1008 female patients of age 20–60 years that had not been diagnosed of cancer of breast earlier. All the subjects in this population were screened for both the breasts using DITI. Based on the measured temperature gradients (ΔT) in thermograms, the subjects were classified in one of the three groups, normal (ΔT ≤ 2.5), abnormal (ΔT > 2.5, 2.5) underwent triple assessment that consisted of clinical examination, radiological and histopathological examination. Those with normal thermograms were subjected to only clinical examination. Results: Forty nine female breasts had thermograms with temperature gradients exceeding 2.5 and were subjected to triple assessment. Forty one of these which had ΔT ≥ 3 were proven to be having cancer of breast and were offered suitable treatment. Eight thermograms had temperature gradients exceeding 2.5 but less than 3. Most of these were lactating mothers or had fibrocystic breast diseases. As a screening modality, DITI showed sensitivity of 97.6%, specificity of 99.17%, positive predictive value 83.67% and negative predictive value 99.89%. Conclusion: Based on the results of this study involving 1008 subjects for screening of breast cancer, thermography turns out to be a very useful tool for screening. Because it is non-contact, pain-free, radiation free and comparatively portable it can be used in as a proactive technique for detection of breast carcinoma.

IIRS has initiated its interactive distance education based capacity building under IIRS outreach programme in year 2007 where more than 15000+ students were trained in the field of geospatial technology using Satellite based interactive terminals and internet based learning using A-View software. During last decade the utilization of Internet technology by different user groups in the society is emerged as a technological revaluation which has directly affect the life of human being. The Internet is used extensively in India for various purposes right from entrainment to critical decision making in government machinery. The role of internet technology is very important for capacity building in any discipline which can satisfy the needs of maximum users in minimum time. Further to enhance the outreach of geospatial science and technology, IIRS has initiated e-learning based certificate courses of different durations. The contents for e-learning based capacity building programme are developed for various target user groups including mid-career professionals, researchers, academia, fresh graduates, and user department professionals from different States and Central Government ministries. The official website of IIRS e-learning is hosted at http://elearning.iirs.gov.in. The contents of IIRS e-learning programme are flexible for anytime, anywhere learning keeping in mind the demands of geographically dispersed audience and their requirements. The program is comprehensive with variety of online delivery modes with interactive, easy to learn and having a proper blend of concepts and practical to elicit students' full potential. The course content of this programme includes Image Statistics, Basics of Remote Sensing, Photogrammetry and Cartography, Digital Image Processing, Geographical Information System, Global Positioning System, Customization of Geospatial tools and Applications of Geospatial Technologies. The syllabus of the courses is as per latest developments and trends in geo-spatial science and technologies with specific focus on Indian case studies for geo-spatial applications. The learning is made available through interactive 2D and 3D animations, audio, video for practical demonstrations, software operations with free data applications. The learning methods are implemented to make it more interactive and learner centric application with practical examples of real world problems.

Digital elevation model (DEM) provides elevation information in raster format for an area which help in analysis as these phenomena are gravity depended. Hydrological study requires creation of drainage network map. DEM is the primary input for this process. Generally 6 or more processes are required to be completed to get the drainage network. These are available in form of hydrology tools in the ArcGIS software. These processes are manual and time consuming which increases the chances of human error. An automated process is constructed in this paper which aims to create a tool that can handle multiple files in an intelligent manner. The input DEM plays a significant role in hydrological studies. This study attempts to do a comparative study to analyse the variation in the drainage network and the intermediate products with the change in the input DEM. A tool is created using ArcPy site package in Python programming language to integrate all required hydrology tools. The script is then used to create a tool in ArcGIS 10 which takes location as an input parameter and perform the process on all the DEM files inside the directory. The tool creates separate directory for every DEM file and thus reduces the chances of file mismanagement. The proposed tool is tested on two different datasets namely ASTER GDEM and Cartosat DEM. The tool runs efficiently on both the datasets and thus provides results to compare the drainage pattern produced by these different data sources.

Automated generalization, rooted from conventional cartography, has become an increasing concern in both geographic information system (GIS) and mapping fields. All geographic phenomenon and the processes are bound to the scale, as it is impossible for human being to observe the Earth and the processes in it without decreasing its scale. To get optimal results, cartographers and map-making agencies develop set of rules and constraints, however these rules are under consideration and topic for many researches up until recent days. Reducing map generating time and giving objectivity is possible by developing automated map generalization algorithms (McMaster and Shea, 1988). Modification of the scale traditionally is a manual process, which requires knowledge of the expert cartographer, and it depends on the experience of the user, which makes the process very subjective as every user may generate different map with same requirements. However, automating generalization based on the cartographic rules and constrains can give consistent result. Also, developing automated system for map generation is the demand of this rapid changing world. The research that we have conveyed considers only generalization of the roads, as it is one of the indispensable parts of a map. Dehradun city, Uttarakhand state of India was selected as a study area. The study carried out comparative study of the generalization software sets, operations and algorithms available currently, also considers advantages and drawbacks of the existing software used worldwide. Research concludes with the development of road network generalization tool and with the final generalized road map of the study area, which explores the use of open source python programming language and attempts to compare different road network generalization algorithms. Thus, the paper discusses the alternative solutions for automated generalization of linear objects using GIS-technologies. Research made on automated of road network generalization, which is summarized in this paper, was conducted in IIRS, Dehradun, India.

The night-time lights (NTL) captured by the Defense Meteorological Satellite Program’s Operational Linescan System (DMSP/OLS) are widely used as proxy for studying human activities. Lack of on-board calibration necessitates inter-calibration of time-series images acquired by different satellites before using them in long-term studies. Pseudo-invariant features (PIFs) i.e., stable night-light features over time, are often used as references for calibrating the temporal images. This study presents an algorithm for inter-calibration of DMSP/OLS time-series images using a semi-automatic PIFs identification procedure through the combined use of Getis statistic (Gi*) and coefficient of variation (CV). Annual stable NTL images over India from 1992 to 2006 are used in this process. While Gi* helps finding lit clusters of spatial homogeneity, CV is useful in finding areas of low spatial variability. The combined use of both these statistic across the time-series images thus helps identifying the PIFs. The identified PIFs are then used to develop calibration models for all the uncalibrated images taking one year image as a reference. Among the three parameter estimation methods evaluated in this study, least trimmed squares is most suitable for inter-calibration. The quality of calibrated images is evaluated using three metrics, sum of lights, gross domestic product and urban population. The calibration outputs are also compared with that of Elvidge et al. (Energies, 2009. https://doi.org/10.3390/en20300595 ). We find that the algorithm developed in this study for inter-calibration of time-series NTL images provides good results. The PIFs identification procedure proposed here is primarily data dependent and thus minimizes the human errors introducible by manual selection method.

Climate change, whether as a natural cycle variability and/or due to anthropogenic reasons, is affecting and likely to further affect the water resources, which is a vital necessity for existence of life form. The predicted intensification of hydrological cycle would change all of its constituents both in time and space domain. This is a long term phenomenon and the necessity is to understand the intensity of the effects on various aspects of water resources by way of scientific studies backed by the available field data. Therefore, in the present study, the impact of climate and land use land cover change on entire India under different assumed plausible hypothetical scenarios has been studied. These scenarios were developed by increasing; temperature by 1, 2 and 30C; rainfall by 5, 10 and 15%; and then the combination of both. To carry out this analysis, variable infiltration capacity (VIC) semi-distributed macroscale hydrological model has been investigated. It was found that slight change in climate may pose huge difference on hydrological cycle and its component.

Snow cover is one of the most important land surface parameters in global water and energy cycle. Large area of North West Himalaya (NWH) receives precipitation mostly in the form of snow. The major share of discharge in rivers of NWH comes from snow and glacier melt. The hydrological models, used to quantify this runoff contribution, use snow-covered area (SCA) along with hydro-meteorological data as essential inputs. In this context, information about SCA is essential for water resource management in NWH region. Regular mapping and monitoring of snow cover by traditional means is difficult due to scarce snow gauges and inaccessible terrain. Remote sensing has proven its capability of mapping and monitoring snow cover and glacier extents in these area, with high spatial and temporal resolution. In this study, 8-day snow cover products from MODIS, and 15-daily snow cover fraction product from AWiFS were used to generate long-term SCA maps (2000–2017) for entire NWH region. Further, the long term variability of 8-daily SCA and its current status has been analysed. The SCA mapped has been validated using AWiFS derived SCA. The analysis of current status (2016–17) of SCA has indicated that the maximum extent of snow cover in NWH region in last 17 years. In 2nd week of February 2017, around 67% of NWH region was snow covered. The comparison of SCA during the 1st week of March and April in 2016–17 against 2015–16 indicates 7.3% and 6.5%, increased SCA in current year. The difference in SCA during 1st week of March 2017 and 1st week of April 2017 was observed to be 14%, which indicates that the 14% SCA has contributed to the snow melt during this period. The change in snow water equivalent retrieved using SCATSAT-1 data also validates this change in snow volume.

Most endmember extraction algorithms are based on the spectral properties of the dataset only to discriminate between the pixels. Endmembers with distinct spectral profiles or high spectral contrast are easier to detect, whereas the endmembers having low spectral contrast with respect to the whole image are difficult to determine. The spatial-spectral integration approach searches for endmembers by analyzing the image in subsets such that it increases the local spectral contrast of the low contrast endmembers and increases their odds of selection. Spatial spectral integration process utilizes Hyperspectral subspace identification by minimum error (HySime) to determine a set of locally defined eigenvectors explaining the maximum variability of the subsets of the image. The image data is then projected onto these locally defined eigenvectors which produces a set of candidate endmember pixels. The candidate endmember pixels, that are spectrally similar and having similar spatial coordinates, are averaged together and grouped into different endmember classes. The method is applied to spaceborne hyperspectral dataset to illustrate the effects of using spatial measures in the process of endmember extraction. The spatial-spectral integration results show that the endmember pixels obtained by imposing spatial constraints are cleaner and more representative of the land use land cover classes.

One of the prime activities of Indian Space Research Organisation's (ISRO) Space Program is providing satellite communication services, viz., television broadcasting, mobile communication, cyclone disaster warning and rescue operations etc. so as to improve their economic conditions, disseminate technical / scientific knowledge to improve the agriculture production and education for rural people of India. ISRO, along with National Aeronautical and Space Administration (NASA) conducted experimental satellite communication project i.e. Satellite Instructional Television Experiment (SITE) using NASA’s Advanced Telecommunication Satellite (i.e. ATS 6) with an objective to educate poor people of India via satellite broadcasting in 1975 and 1976, covering more than 2600 villages in six states of India and territories. Over the years India built communication satellites indigenously to meet the communication requirements of India. This has further lead to launch of an exclusive satellite from ISRO for educational purposes i.e. EDUSAT in 2004 through which rich audio-video content is transmitted / received, recreating virtual classes through interactivity. Indian Institute of Remote Sensing (IIRS) established in 1966, a premier institute in south East Asia in disseminating Remote Sensing (RS) and Geographical Information System (GIS), mainly focusing on contact based programs. But expanded the scope with satellite based Distance Learning Programs for Universities, utilizing the dedicated communication satellite i.e. EDUSAT in 2007. IIRS conducted successfully eight Distance Learning Programs in the last five years and training more than 6000 students mainly at postgraduate level from more than 60 universities /Institutions spread across India. IIRS obtained feedback and improved the programs on the continuous basis. Expanded the scope of IIRS outreach program to train user departments tailor made in any of the applications of Remote Sensing and Geoinformation, capacity building for ISRO’s operational projects / new satellite missions, developing e-learning contents and launching e-learning courses under twelfth five year (i.e. 2012–17) plan period of Government of India, in addition to continuing of existing distance learning programs for universities.

Tree counting can be a challenging and time consuming task, especially if done manually. This study proposes and compares three different approaches for automatic detection and counting of trees in different vegetative regions. First approach is to mark extended minima’s, extended maxima’s along with morphological reconstruction operations on an image for delineation and tree crown segmentation. To separate two touching crowns, a marker controlled watershed algorithm is used. For second approach, the color segmentation method for tree identification is used. Starting with the conversion of an RGB image to HSV color space then filtering, enhancing and thresholding to isolate trees from non-trees elements followed by watershed algorithm to separate touching tree crowns. Third approach involves deep learning method for classification of tree and non-tree, using approximately 2268 positive and 1172 negative samples each. Each segment of an image is then classified and sliding window algorithm is used to locate each tree crown. Experimentation shows that the first approach is well suited for classification of trees is dense vegetation, whereas the second approach is more suitable for detecting trees in sparse vegetation. Deep learning classification accuracy lies in between these two approaches and gave an accuracy of 92% on validation data. The study shows that deep learning can be used as a quick and effective tool to ascertain the count of trees from airborne optical imagery.

Open source software (OSS) coding has tremendous advantages over proprietary software. These are primarily fuelled by high level programming languages (JAVA, C++, Python etc...) and open source geospatial libraries (GDAL/OGR, GEOS, GeoTools etc.). Quantum GIS (QGIS) is a popular open source GIS package, which is licensed under GNU GPL and is written in C++. It allows users to perform specialised tasks by creating plugins in C++ and Python. This research article emphasises on exploiting this capability of QGIS to build and implement plugins across multiple platforms using the easy to learn – Python programming language. In the present study, a tool has been developed to assimilate large spatio-temporal datasets such as national level gridded rainfall, temperature, topographic (digital elevation model, slope, aspect), landuse/landcover and multi-layer soil data for input into hydrological models. At present this tool has been developed for Indian sub-continent. An attempt is also made to use popular scientific and numerical libraries to create custom applications for digital inclusion. In the hydrological modelling calibration and validation are important steps which are repetitively carried out for the same study region. As such the developed tool will be user friendly and used efficiently for these repetitive processes by reducing the time required for data management and handling. Moreover, it was found that the developed tool can easily assimilate large dataset in an organised manner.