Colour additive viewing techniques for small-scale soil mapping in an area of Karimnagar district,Andhra Pradesh,India

Multi-spectral imagery from Landsat relating to the Karimnagar district of Andhra Pradesh, in South India was analysed using colour additive viewing techniques. The results were correlated with an existing soil association map prepared from aerial photo-interpretation with adequate ground truth. The four landscapes and their subdivisions that were identified on the soil map also could be identified on the Landsat map. An overall classification performance of 83·9, 82·4, 80·9 and 95·7 percent respectively has been achieved for rugged (R), undulating hummocky (U), very gently sloping (V) and river alluvium (A) landscapes. The best class performance was in the order of 95·7, 97·6, 81.8 and 95·7 percent and that of the lowest class performance of 50·5, 57·3, 70·1 and 95·7 percent for R, U, V and A landscapes, respectively. The overall combined performance of 85·7 percent has been achieved with respect to soilscape boundary delineations for all the four landscapes. The Student's t-test of significance revealed that for units R₄, U₄ and V₂ the differences were significant and the rest were found to be nonsignificant. This study has demonstrated the usefulness of colour additive viewing techniques in the analysis of Landsat-MSS data for small-scale soil mapping and the same could be used for the preparation of small-scale soil maps of the States and the country.     

Technical note Monitoring the Oil & Natural Gas Corporation (ONGC) oil well fire at Pasarlapudi,Andhra Pradesh,India

Methods used to infer sea surface temperatures (SSTs) from satellite have traditionally been based on regression-tuned split-window fixed-coefficient algorithms. These can give inaccurate SST results when local atmospheric conditions are significantly different from those encapsulated by the regression averaging. The new generation of SST algorithms attempts to correct for atmospheric variability. These approaches include the R₅₄ transmittance-ratio methods of other workers, and the dynamic water vapour (DWV) correction method of the authors. The relative performances of the various methods are compared by applying each to an ocean and satellite dataset obtained off the west coast of Tasmania, Australia in 1987. We also investigate the performance of the NESDIS operational multi-channel, cross-product, and nonlinear formulas for NOAA-9, -11, -12, and-14 when applied to the same dataset. We find the DWV method gives SST retrievals which have significantly smaller bias errors than those returned by the three transmittance-ratio methods. The best overall performance was returned by the NESDIS multichannel (MCSST) formula for NOAA-9, indicating that in low water vapour loading situations, the standard regression-based algorithms work well.     

Land-cover change dynamics and coastal aquaculture development: a case study in the East Godavari delta,Andhra Pradesh,India using multi-temporal satellite data

The present study focuses on the identification and quantification of land-cover changes occurring in the coastal stretches of the East Godavari delta, Andhra Pradesh, India. The analysis of series of multi-temporal satellite data provides an accurate quantification and therefore a better understanding of the process of land-cover changes during 1990–2005. Land-cover changes were quantified based on normalized difference vegetation index (NDVI) image differencing and a post-classification comparison approach. The change detection results were examined in terms of the proportion of land-cover classes and change trajectories with particular emphasis on coastal aquaculture development within the study area. The study shows that the total area under aquacultural ponds increased from 2985 ha in 1990 to 7067 ha in 2005. The major changes in the study area occurred during 1990–1994, when 2873 ha of agricultural land and 762 ha of degraded mangroves were converted into aquacultural ponds. The prediction of land-cover distribution in 2010 on the basis of a Markov chain shows a continuing upward trend of the aquaculture area (8267 ha) with less impact on the mangrove area. The analysis predicts that the agricultural land area will continue to decrease from 50 122 to 46 978 ha during 2005–2010.     

Pedestrian detection and tracking in infrared imagery using shape and appearance

In this paper, we present an approach toward pedestrian detection and tracking from infrared imagery using joint shape and appearance cues. A layered representation is first introduced and a generalized expectation-maximization (EM) algorithm is developed to separate infrared images into background (still) and foreground (moving) layers regardless of camera panning. In the two-pass scheme of detecting pedestrians from the foreground layer: shape cue is first used to eliminate non-pedestrian moving objects and then appearance cue helps to locate the exact position of pedestrians. Templates with varying sizes are sequentially applied to detect pedestrians at multiple scales to accommodate different camera distances. To facilitate the task of pedestrian tracking, we formulate the problem of shot segmentation and present a graph matching-based tracking algorithm that jointly exploits the shape, appearance and distance information. Experimental results with both OSU Infrared Image Database and WVU Infrared Video Database are reported to demonstrate the accuracy and robustness of our algorithm.     

Wheat production forecasting for a predominantly unirrigated region in Madhya Pradesh )India)

The area under wheat was estimated and a forecast of production made in a predominantly un-irrigated region (36 per cent irrigated wheal crop, geographical area 5-61 Mha) of Madhya Pradesh (India) using digital data from LISS-I (Linear Imaging Self Scanner) onboard Indian Remote Sensing Satellite (IRS-IB), for the crop season 1991-92. A stratified sampling approach based on 5 km by 5 km sample segments, 10 per cent sampling fraction in conjunction with supervised maximum likelihood (MXL) classification was used for wheat acreage estimation. Yield forecasts were based on an optimal combination of forecasts from two different methodologies, viz., wheat yield-spectral relationship and time series analysis using ARIMA (Auloregressive Integrated Moving Average) approach. In the former, a two-year (1989-90, 1990-91) pooled regression relating LISS-I derived Near Infrared/Red (NIR/R) radiance ratio to district wheat yields was developed and used to forecast wheat yields for the year 1991-92 based on classified wheat pixels. In the latter case, historical district-wise wheat yield data of 35 years was used to develop appropriate ARIMA models and used to forecast 1991-92 yields. The relative deviation of remotely-sensed-based forecasted production, acreage and yield from the post-harvest estimates released later by the State Department of Agriculture were — 15.8, — 1002 and — 601 per cent, respectively. The acreage and yield meet the accuracy of 85 per cent at 90 and 95 per cent confidence levels, respectively.     

Eyed drainages observed in IRS imagery in Tamil Nadu,India and their geological significance

It has been established beyond doubt that the drainage architecture and the fluvial histories of river systems give excellent indications not only on the surficial lithology but also on the ongoing morphotectonic processes of the planet Earth. Among the various drainage patterns, the 'eyed drainage' pattern is considered to be one of the most significant anomalies and such 'mega eyed' drainages interpreted from IRS-1A imagery in some of the major river systems in Tamil Nadu, Indian Peninsula, are found to signify ongoing tectonic movements in an otherwise seismically inert shield area.     

Satellite derived crop calendar for canal operation schedule in Bhadra project command area,India

Spatial information on crop calendars in the command areas of irrigation systems is useful to irrigation engineers in order to achieve chronological synchronization between water delivery pattern and crop life cycle. Such synchronization is vital for efficient use of water. The capability of satellite remote sensing technology to generate spatial crop calendar information in an irrigated command area and its usefulness in the evaluation of water delivery patterns are demonstrated in this paper. The study pertains to the major crop paddy during the rabi season (December to June) of 1992-93, in the Bhadra project command area of Karnataka state, India. Analyses of multidate Normalized Difference Vegetation Index (NDVI) profiles of paddy crop generated from Indian Remote Sensing (IRS) satellite data for each distributary command reveal three distinct growth patterns in the study area with each pattern characterized by a particular crop calendar. The spatial variability in crop calendar over the total command area has thus been derived. The ground truth data obtained in crop cutting experiments (CCEs) validate the satellite derived crop calendar. Distributary wise, water delivery data have been studied in conjunction with the satellite derived crop calendar to determine whether the existing pattern of water delivery covers the required length of crop life cycle in the command area. It was found that the water supply was stopped about 30 days before harvesting in some distributaries and in some about 20-30 days before harvesting. A list of distributaries with greater lags between cessation of water supply and crop harvest was provided to irrigation system engineers to aid their plans for providing a reliable and predictable irrigation service. This is possible either through reorganization of canal operation schedule or through educating farmers about the need for adjusting their agricultural activities to match water supply patterns.     

Coupled correction and classification of hyperspectral imagery for mapping coral reefs of Agatti and Flat Islands,India

Among the various remote-sensing options available today to map ecomorphological classes of corals, hyperspectral remote sensing is one of the best options by virtue of its spectral capabilities, while high spatial resolution is a necessary condition to resolve finer morphological features spatially. Given high-spatial resolution data of equal to or better than 30 m, the discrimination capability of end-members of multi-/hyperspectral satellite data is dependent on the efficacy of the correction for atmospheric effects and the intervening water column. In this study, a coupled approach to account for oceanic and atmospheric radiative contributions, called the Coupled Ocean Atmosphere Radiative Transfer (COART), was applied to Earth Observing 1 (EO-1) mission Hyperion image data acquired over the coral reefs of Agatti Island in the Lakshadweep Islands, Arabian Sea and Flat Island in the Andaman Islands, Bay of Bengal, India. The paper presents an open-source approach to correct and perform unsupervised classification of Hyperion imagery using a custom-built software toolkit called HyperCorals. The study finds that Hyperion has sufficient capabilities for discrimination of a few ecomorphological classes and can be improved further by using coupled radiative transfer models. Correcting for the intervening water column helps in classifying submerged features. The k-means classification offers a simpler classification method to classify an image of a subset with 42 selected spectral channels of Hyperion in the visible and near infrared (VNIR) region than the traditional Iterative Self-Organizing Data Analysis Technique (ISODATA). The classification results using the cosine distance metric over 42 selected spectral channels of Hyperion in the VNIR region offer the potential to differentiate between various ecomorphological zones. The study also presents results from sensitivity analysis experiments and discusses the relative importance of three parameters: water column depth, bottom albedo, and chlorophyll concentration on the overall correction and classification of the imagery.     

Temporal and multisensor approach in forest/vegetation mapping and corridor identification for effective management of Rajaji National Park,Uttar Pradesh,India

Abstract

The usefulness of satellite data in forest mapping has been proved time and again all over the world. The potential use of these data in the management of National Parks in India has also been successfully demonstrated in the past. In the present study, temporal images of Landsal Thematic Mapper and IRS-1A LlSS-ll have been used to prepare a detailed forest/vegetation map for planning and management of the Rajaji National Park, Uttar Pradesh, India. IRS-1A LISS-II imagery and temporal data have provided additional information by increasing the accuracy and level of classification. Fifteen vegetation classes and four other land-use classes could be identified and mapped using large scale false colour composites (FCCs). Species composition of each of the forest/ vegetation class has also been described.

The corridor identification for the movement of elephants has been attempted and the most suitable site for corridor development was suggested. The classification has been found to be accurate and very useful for planning and management of the national park by the user department.     

Remote sensing estimates of glacier mass balances in the Himachal Pradesh (Western Himalaya, India)

Although they correspond to an important fraction of the total area of mountain glaciers (33,000 km² out of 546,000 km²), Himalayan glaciers and their mass balance are poorly sampled. For example, between 1977 and 1999, the average area surveyed each year on the field was 6.8 km² only. No direct mass balance measurement is available after 1999. To contribute to fill this gap, we use remote sensing data to monitor glacier elevation changes and mass balances in the Spiti/Lahaul region (32.2°N, 77.6°E, Himachal Pradesh, Western Himalaya, India). Our measurements are obtained by comparing a 2004 digital elevation model (DEM) to the 2000 SRTM (Shuttle Radar Topographic Mission) topography.The 2004 DEM is derived from two SPOT5 satellite optical images without any ground control points. This is achieved thanks to the good on-board geolocation of SPOT5 scenes and using SRTM elevations as a reference on the ice free zones. Before comparison on glaciers, the two DEMs are analyzed on the stable areas surrounding the glaciers where no elevation change is expected. Two different biases are detected. A long wavelength bias affects the SPOT5 DEM and is correlated to an anomaly in the roll of the SPOT5 satellite. A bias is also observed as a function of altitude and is attributed to the SRTM dataset. Both biases are modeled and removed to permit unbiased comparison of the two DEM on the 915 km² ice-covered area digitized from an ASTER image.On most glaciers, a clear thinning is measured at low elevations, even on debris-covered tongues. Between 1999 and 2004, we obtain an overall specific mass balance of − 0.7 to − 0.85 m/a (water equivalent) depending on the density we use for the lost (or gained) material in the accumulation zone. This rate of ice loss is twice higher than the long-term (1977 to 1999) mass balance record for Himalaya indicating an increase in the pace of glacier wastage. To assess whether these ice losses are size-dependant, all glaciers were classified into three samples according to their areal extent. All three samples show ice loss, the loss being higher for glaciers larger than 30 km². In the case of the benchmark Chhota Shigri glacier, a good agreement is found between our satellite observations and the mass balances measured on the field during hydrological years 2002-2003 and 2003-2004. Future studies using a similar methodology could determine whether similar ice losses have occurred in other parts of the Himalaya and may allow evaluation of the contribution of this mountain range to ongoing sea level rise.     

Comparative analysis between VIIRS-DNB and DMSP-OLS night-time light data to estimate electric power consumption in Uttar Pradesh,India

ABSTRACT

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 since April, 2012 have successfully addressed some of the limitations of Defense Meteorological Satellite Program’s Operational Linescan System (DMSP-OLS), owing to improved radiometric and spatial resolutions and onboard calibration. Earlier, DMSP-OLS NTL images majorly contributed for understanding the spatio-temporal dynamics of socio-economic factors such as Gross Domestic Product (GDP) and Electric Power Consumption (EPC), but the problem of saturated pixels and blooming effect affected the correlation with sum of lights (SOL). In the present study, we evaluate different methods to generate the annual VIIRS-DNB NTL composites from available monthly products for estimating EPC at different scales, and compare their performance with DMSP-OLS annual composites. The VIIRS-DNB annual NTL composites are prepared using various statistical measures, seasonal decomposition and principal component analysis (PCA). The SOL derived from these composites were analysed with respect to EPC at Zonal and Electricity Distribution Company (DISCOM) levels in the Uttar Pradesh State of India to identify the most appropriate annual representation of NTL from VIIRS-DNB. It is found that the annual composites prepared using PCA method provide the best results at both Zonal and DISCOM levels. Further, VIIRS-DNB annual NTL composites perform better than the DMSP-OLS composites. At the Zonal level, some of the highly populated and fully electrified Zones exhibit a distinct SOL-EPC relationship, attributable to enhanced power consumption by indoor equipment and industrial activities, which is not captured by satellite. Finally, empirical EPC prediction models are developed based on time-series VIIRS-DNB data from 2013 to 2017, which will help understanding the EPC patterns at regional level in the State of Uttar Pradesh.     

Search and tracking algorithms for swarms of robots: A survey

Target search and tracking is a classical but difficult problem in many research domains, including computer vision, wireless sensor networks and robotics. We review the seminal works that addressed this problem in the area of swarm robotics, which is the application of swarm intelligence principles to the control of multi-robot systems. Robustness, scalability and flexibility, as well as distributed sensing, make swarm robotic systems well suited for the problem of target search and tracking in real-world applications. We classify the works we review according to the variations and aspects of the search and tracking problems they addressed. As this is a particularly application-driven research area, the adopted taxonomy makes this review serve as a quick reference guide to our readers in identifying related works and approaches according to their problem at hand. By no means is this an exhaustive review, but an overview for researchers who are new to the swarm robotics field, to help them easily start off their research.     

Contained nomadic information environments: Technology,organization, and environment influences on adoption of hospital RFID patient tracking

This paper reports on a case study of adoption of a contained nomadic information environment, exemplified by the use of Radio Frequency Identification (RFID) to keep track of hospital patients. The technology, organization, and environment (TOE) framework is applied to gain insights concerning contextual influences on the adoption of patient tracking RFID, including some RFID-specific issues. The results of this study provide insights to health care organizations embarking on RFID. The resultant model is a step towards developing a theory of adoption of contained nomadic information environments, of which RFID is one instance.     

Natural disasters detection in social media and satellite imagery: a survey

Multimedia Tools and Applications - The analysis of natural disaster-related multimedia content got great attention in recent years. Being one of the most important sources of information, social...     

Capability of TM data for evaluation of soil and land resources in the Malwa Plateau (Chambal and Mahi catchment) in Madhya Pradesh,India

Abstract

Owing 10 the advent of the Thematic Mapper with higher resolution, it has now become much easier to map various natural resources efficiently and accurately in a shorter period of time. In the work described in this paper an attempt has been made to prepare various soil and land resources maps by visual interpretation of a false colour composite from the Thematic Mapper at the average scale of 1:250 000 in conjunction with field checks covering an area of 340000 ha in the Malwa Plateau. The soil map was prepared in association with subgroup level and a total of nine soil mapping untits were established. Four physiographic units, seven land-use units and three waste-land mapping units were found to occur in the area during preparation of the respective map.     

Frontal changes in the Manimahesh and Tal Glaciers in the Ravi basin,Himachal Pradesh,northwestern Himalaya (India), between 1971 and 2013

The Manimahesh and Tal Glaciers are located in the Budhil fifth-order sub-basin of the Ravi, Himachal Himalaya, Northwestern Himalaya (India). These glaciers were analysed using high- (Corona KH-4A) to medium- (Landsat TM/ETM+/OLI, ASTER) spatial resolution satellite data between 1971 and 2013, along with extensive field measurements (2011–2014) of frontal changes. The results show that the Manimahesh and Tal Glaciers retreated by 157 ± 34 m (4 ± 1 m year^–1) and 45 ± 34 m (1 ± 1 m year^–1), respectively, whereas, the total area lost is estimated at 0.21 ± 0.01 km² (0.005 km² year^–1) and 0.010 ± 0.003 km² (0.0002 km² year^–1), respectively, between 1971 and 2013. The rate of retreat is significantly lower than that previously reported. Our field measurements (2011–2014) also suggest a retreating trend and validate the measured glacier changes using remotely sensed temporal data.     

A study of sediment transport in a shallow estuary using MODIS imagery and particle tracking simulation

The study utilized the Moderate Resolution Imaging Spectroradiometer (MODIS) red-channel reflectance with a spatial resolution of 250 m to estimate suspended sediment concentration (SSC) in the Mobile Bay estuary, Alabama. Based on monthly in situ sampling, a new algorithm was developed using an exponential regression model. The concentration of inorganic suspended sediments (ISS) in Mobile Bay and Mississippi Sound was concerned and mapped by applying the new algorithm. The ISS maps during a cold front passage have revealed how the resuspension and transport of sediments respond to the variable wind forcing in this micro-tidal system. Particle tracking based on a three-dimensional hydrodynamic model was utilized to explain what was observed from the satellite imagery. It has been found that the rapid disappearance of the surface ISS after a cold front passage was mainly caused by settling of sediments rather than flushing out of the estuary. The study demonstrates that a combination of ISS mapped from the MODIS band-1 reflectance and three-dimensional numerical modelling is an effective tool to analyse sediment dynamics in the Mobile Bay estuary and other similar estuaries.     

Assessing the efficacy of Landsat-8 OLI imagery derived models for remotely estimating chlorophyll-a concentration in the Upper Ganga River,India

ABSTRACT

Chlorophyll-a (chl-a) serves as an indicator of productivity in surface water. Estimating chl-a concentration is pivotal for monitoring and subsequent conservation of surface water quality. Artificial neural network (ANN) based models were validated and tested for their efficacy against various regression models to determine the chl-a concentration in the Upper Ganga river. Landsat-8 Operational Land Imager (OLI) surface reflectance (SR) imagery for May and October along with in-situ data over a period of 2 years (2016–2017) was used to develop and validated models. Regression model performance was acceptable with a coefficient of determination (R²) of 0.57, 0.63, 0.66 and 0.68 for linear, exponential, logarithmic and power model, respectively. However, there was a significant improvement in the efficacy of chl-a determination using ANN model performance having a root mean square error (RMSE) of 1.52 µg l^–1 and R² = 0.97 in comparison to the best-performing regression model (power) with RMSE = 9.86 µg l^–1 and R² = 0.68. ANN exhibited comparatively more precise spatial and seasonal variability with mean absolute error (MAE) of 1.26 µg l^–1 as compared to the best regression model (power) MAE = 7.98 µg l^–1 suggesting the applicability of ANN for large-scale spatial and temporal monitoring river stretches using Landsat-8 OLI SR images.     

Project Indravati: Phase I. An appraisal of the natural resources of the Indravati basin,Orissa, Madhya Pradesh and Maharashtra,India

Project Indravati has been taken up to demonstrate the capabilities of remote-sensing data for the exploration of natural resources and also the structural linkages necessary to evolve a National Natural Resources Management System (NNRMS). The Geological Survey of India, the National Atlas and Thematic Mapping Organization and the Forest Survey of India are the collaborating agencies under this project. The National Bureau of Soil Survey and Land-use Planning has supplied data on soils of the area. This densely forested, undercultivated region with a relatively virgin status in mineral exploration offers ample scope for using remote-sensing techniques in evaluating its natural resource potential.

Data on geology, structure and lineaments, geomorphology, drainage, soil and vegetation were compiled and collated, based on visual interpretation of LANDSAT imagery. The area has a complex physiographic and geological history and varied soil types. Vegetation is mostly mixed deciduous types.

Two test areas, which are relatively remote and difficult to approach, have been selected for detailed aerial and multispectral scanner survey for the assessment of natural resources.     

A survey of sensory data boundary estimation,covering and tracking techniques using collaborating sensors

Boundary estimation and tracking have important applications in the areas of environmental monitoring and disaster management. A boundary separates two regions of interest in a phenomenon. It can be visualized as an edge if there is a sharp change in the field value between the two regions or alternatively, as a contour with a field value $f=\tau$ separating two regions with field values $f>\tau$ and $f<\tau$. Examples include contours/boundaries of hazardous concentration in a pollutant spill, frontal boundary of a forest fire, isotherms, isohalines etc. Recent advances in the area of embedded sensor devices and robotics have led to deployments of networks of sensors capable of sensing, computing, communication and mobility. They are used to estimate the boundaries of interest in physical phenomena, monitor or track them over time and also in some cases, mitigate the spatial spread of the phenomena. Since these sensors work autonomously in the environment, minimizing the energy consumed while maximizing the accuracy of estimation or tracking is the main challenge for algorithms for boundary estimation and tracking. Several algorithms with these objectives have been proposed in the literature. In this work, we focus on the algorithms that estimate and cover boundaries found in the sensory data in a field and not the topological boundary of the sensor network per se, which is beyond the scope of this paper.Here, our objective is to provide a comprehensive survey of the algorithms for boundary estimation and tracking by providing a taxonomy based on two broad categories — (i) Boundary estimation and tracking, where the sensors estimate the boundary without physically covering the boundary and (ii) Boundary covering — where the sensors not only predict the location and estimate the entire boundary but also physically cover the boundary by surrounding and bounding it. We further classify the techniques based on (a) sensing capabilities —in situ, range or remote sensing (b) movement capabilities — static or mobile sensors and (c) boundary type — static or dynamic and (d) type of estimation — field estimation where the entire field is sampled to search for contours and localized estimation where sampling is done near the boundary and (e) different types of mobility models in the case of mobile sensors. We believe that such a survey has not been performed before. By capturing and classifying the current state-of-the-art and identifying open research problems, we hope to ignite interest and stimulate efforts towards promising solutions for real-world boundary estimation and tracking problems.