This PDF file contains the front matter associated with SPIE Proceedings Volume 9877, including the Title Page, Copyright information, Table of Contents, and Conference Committee listing.

Polarimetric SAR data has proven its potential to extract scattering information for different features appearing in single resolution cell. Several decomposition modelling approaches have been developed to retrieve scattering information from PolSAR data. During scattering power decomposition based on physical scattering models it becomes very difficult to distinguish volume scattering as a result from randomly oriented vegetation from scattering nature of oblique structures which are responsible for double-bounce and volume scattering , because both are decomposed in same scattering mechanism. The polarization orientation angle (POA) of an electromagnetic wave is one of the most important character which gets changed due to scattering from geometrical structure of topographic slopes, oriented urban area and randomly oriented features like vegetation cover. The shift in POA affects the polarimetric radar signatures. So, for accurate estimation of scattering nature of feature compensation in polarization orientation shift becomes an essential procedure. The prime objective of this work was to investigate the effect of shift in POA in scattering information retrieval and to explore the effect of deorientation on regression between field-estimated aboveground biomass (AGB) and volume scattering. For this study Dudhwa National Park, U.P., India was selected as study area and fully polarimetric ALOS PALSAR data was used to retrieve scattering information from the forest area of Dudhwa National Park. Field data for DBH and tree height was collect for AGB estimation using stratified random sampling. AGB was estimated for 170 plots for different locations of the forest area. Yamaguchi four component decomposition modelling approach was utilized to retrieve surface, double-bounce, helix and volume scattering information. Shift in polarization orientation angle was estimated and deorientation of coherency matrix for compensation of POA shift was performed. Effect of deorientation on RGB color composite for the forest area can be easily seen. Overestimation of volume scattering and under estimation of double bounce scattering was recorded for PolSAR decomposition without deorientation and increase in double bounce scattering and decrease in volume scattering was noticed after deorientation. This study was mainly focused on volume scattering retrieval and its relation with field estimated AGB. Change in volume scattering after POA compensation of PolSAR data was recorded and a comparison was performed on volume scattering values for all the 170 forest plots for which field data were collected. Decrease in volume scattering after deorientation was noted for all the plots. Regression between PolSAR decomposition based volume scattering and AGB was performed. Before deorientation, coefficient determination (R²) between volume scattering and AGB was 0.225. After deorientation an improvement in coefficient of determination was found and the obtained value was 0.613. This study recommends deorientation of PolSAR data for decomposition modelling to retrieve reliable volume scattering information from forest area.

Airborne SAR data has been successfully used for forest height inversion, however there is limited applicability in space borne scenario due to high temporal decorrelation. This study investigates the potential of high resolution fully polarimetric pair of TerraSAR-X/TanDEM-X SAR data acquired over Barkot forest region of Uttarakhand state in India to analyze the backscatter and coherence and to test the height inversion algorithms. Yamaguchi decomposition was implemented onto the dataset to express total backscatter as a sum of different scattering components from a single SAR resolution cell. Coherency matrix was used to compute complex coherence for different polarization channels. Forest areas suffered from low coherence due to volume decorrelation whereas dry river bed had shown high coherence. Appropriate perpendicular baseline and hence the interferometric vertical wavenumber was selected in forest height estimation. Coherence amplitude inversion (CAI) approach overestimated the forest height and also resulted in false heights for dry river bed. This limitation was overcome by implementing three stage inversion modeling (TSI) which assumes polarization independent volume coherence and the heights in dry river bed were completely eliminated. The results were validated using ground truth data available for 49 plots, and TSI was found to be more accurate with an average accuracy of 90.15% and RMSE of 2.42 m.

Fully Polarimetric SAR (PolSAR) data is used for scattering information retrieval from single SAR resolution cell. Single SAR resolution cell may contain contribution from more than one scattering objects. Hence, single or dual polarized data does not provide all the possible scattering information. So, to overcome this problem fully Polarimetric data is used. It was observed in previous study that fully Polarimetric data of different dates provide different scattering values for same object and coefficient of determination obtained from linear regression between volume scattering and aboveground biomass (AGB) shows different values for the SAR dataset of different dates. Scattering values are important input elements for modelling of forest aboveground biomass. In this research work an approach is proposed to get reliable scattering from interferometric pair of fully Polarimetric RADARSAT-2 data. The field survey for data collection was carried out for Barkot forest during November 10th to December 5th, 2014. Stratified random sampling was used to collect field data for circumference at breast height (CBH) and tree height measurement. Field-measured AGB was compared with the volume scattering elements obtained from decomposition modelling of individual PolSAR images and PolInSAR coherency matrix. Yamaguchi 4-component decomposition was implemented to retrieve scattering elements from SAR data. PolInSAR based decomposition was the great challenge in this work and it was implemented with certain assumptions to create Hermitian coherency matrix with co-registered polarimetric interferometric pair of SAR data. Regression analysis between field-measured AGB and volume scattering element obtained from PolInSAR data showed highest (0.589) coefficient of determination. The same regression with volume scattering elements of individual SAR images showed 0.49 and 0.50 coefficients of determination for master and slave images respectively. This study recommends use of interferometric PolSAR data for reliable scattering retrieval.

Forest height plays a crucial role to investigate the bio-physical parameters of forest and the terrestrial carbon. PolInSAR based inversion modeling has been successfully implemented on airborne and space-borne SAR data. SAR tomography, which is an extension of cross-track interferometric processing is a recent approach to separate scatterers in cross range direction, thus generates its vertical profile. This study highlighted the potential of tomographic processing of fully polarimetric Radarsat-2 SAR system to retrieve backscatter power at different height levels. Teak forest in Haldwani forest division of Uttarakhand state of India was chosen as the test site. Since SAR tomography is a spectral estimation problem, Fourier transform and beamforming based spectral estimations were applied on the dataset to obtain their vertical profiles. Fourier severely suffered from high side lobes which was drastically reduced by implementing beam-forming by taking into account the multi-looking effect at the expense of radiometric accuracy. Backscattered power values were found to be different at different height levels of the forest vegetation. Vertical profile for Fourier as well as beam-forming were also retrieved.

Rock glaciers, regarded as cryospheric ice/water resource in the terrestrial-glacial systems based on their tongue/lobate-shaped flow characteristic and subsurface investigation using ground-penetrating radar. We examined the subsurface, geomorphology, climate-sensitivity and thermophysical properties of a Lobate Flow Feature (LFF) on Mars (30°-60° N and S hemispheres) to compare/assess the potentials of rock glaciers as an analog in suggesting LFFs to be a source of subsurface ice/water. LFFs are generally observed at the foot of impact craters’ wall. HiRISE/CTX imageries from MRO spacecraft were used for geomorphological investigation of LFF using ArcMap-10.0 and subsurface investigation was carried out using data from MRO-SHARAD (shallow radar) after integrating with SiesWare-8.0. ENVI-5.0 was used to retrieve thermophysical properties of LFF from nighttime datasets (12.57 μm) acquired by THEMIS instrument-onboard the Mars Odyssey spacecraft and derive LFFs morphometry from MOLA altimeter point tracks onboard MGS spacecraft. Integrating crater chronology tool (Craterstats) with Arc Map, we have derived the formation age of LFF. Our investigation and comparison of LFF to rock glaciers revealed: (1) LFFs have preserved ice at depth ~50m as revealed from SHARAD radargram and top-layer composed of rocky-debris material with thermal inertia (~300-350 Jm^-2 K^-1s-^1/2). (2) LFF formation age (~10-100 Ma) corresponds to moderate scale debris covered glaciation of a shorter-span suggesting high sensitivity to obliquity-driven climatic shifts. (3) Presence of polygon cracks and high linear-arcuate furrow-and-ridges on the surface indicates presence of buried ice. This work is a significant step towards suggesting LFF to be a potential source of present-day stored ice/water on Mars.

The water status of cultivated plants can have a significant impact not only on food production, but also on the appropriate usage of increasingly scarce freshwater supplies. Accordingly, the cost-effective detection and monitoring of changes in their water content are longstanding remote sensing goals. Existing procedures employed to achieve these goals are largely based on the spectral responses of plant leaves in the infrared domain where the light absorption within the foliar tissues is dominated by water. Recently, it has been suggested that such procedures could be implemented using spectral responses, more specifically spectral subsurface reflectance to transmittance ratios, obtained in the visible domain. The basis for this proposition resides on the premise that a reduced water content (RWC) can result in histological changes whose effects on the foliar optical properties may not be limited to the infrared domain. However, the experiments leading to this proposition were performed on detached leaves, which were not influenced by the whole plant’s adaptation mechanisms to water stress. In this work, we investigate whether the spectral responses of living plant leaves in the visible domain can lead to reliable RWC estimations. We employ measured biophysical data and predictive light transport simulations in order to extend qualitatively and quantitatively the scope of previous studies in this area. Our findings indicate that the living specimens’ physiological responses to water stress should be taken into account in the design of new procedures for the cost-effective RWC estimation using visible subsurface reflectance to transmittance ratios.

Remotely sensed imagery data are widely used for monitoring crop growth and prescribing management practices. However, acquiring multiple images throughout the growing season for several years could be cost prohibitive. Moderate spatial (30m) resolution Landsat data could be a potential source for accomplishing these objectives. While delineating within-field management zones in large fields using Landsat data is well documented, fewer attempts have been made in smaller fields because of the restrictions imposed by the spatial resolution. On the other hand, Landsat data are acquired once every 16 days which increases the possibility of obtaining several images in a growing season. Landsat spectral bands are rigorously calibrated enabling multi-year comparison. This paper reports on the utility of multi-year Landsat images for monitoring crop growth and delineating management zones in small fields in Wyoming (USA). Spectral reflectance values derived from Landsat images acquired in each growing season were converted to vegetation indices. Based on these values the pixels within the field were grouped into low, medium and high growth classes. Using multi-year growth patterns, crop management zones were delineated for each field. Results from this study could provide valuable insights for farmers to identify problem areas within their fields and better manage them.

Groundwater is an important requirement for the massive population of India. Generally the groundwater level is monitored by using monitoring wells. In this study, Gravity Recovery and Climate Experiment (GRACE) Terrestrial Water Storage (TWS), Land surface state variable GLDAS and Soil Moisture (SM) data were tested for estimating ground water information and based on these groundwater assessments were carried out over the years 2003 to 2012 for Jharkhand State. Additionally, Tropical Rainfall Measuring Mission (TRMM) accumulated rainfall data was also used for the year’s 2008 to 2012.From the study over 120 months span of various districts the maximum depletion in storage of groundwater averaged over the six districts is ±5cm/yr in the year 2010 and maximum storage year (in term of Equivalent water thickness) groundwater average over the six districts is ±4.4cm in the year 2003. The study also utilized ground based Seasonal changes in the groundwater resource over 287 monitoring wells and estimated groundwater data using map analysis over Jharkhand. This study analyzed seasonal water level variations based on groundwater anomaly. Remote sensing generated result compared with well data shows R² = 0.6211 and RMSE = 39.46 cm at average seasonal cycle. Also information of different time periods of rainfall (i.e., pre-monsoon and post-monsoon) was analyzed. The trend analysis of rainfall and estimated groundwater gives the basic knowledge that groundwater storage loss and gain showed similarities with increase and decrease in rainfall.

Satellite remote sensing of soil related content for hydrological purposes have been considerably studied and developed over past three decades. This soil estimation by means of remote sensing depends upon the measurements of electromagnetic energy that has either been reflected or emitted from the soil surface and are accessible to remote sensing through measurements at the thermal infrared and microwave wavelengths. Recent advances in remote sensing, in the last few years, have shown that microwave techniques have the ability to measure soil moisture/wetness monitoring under a variety of topographic and vegetation cover conditions quantitatively. This is due to the all-weather and all-time capability of these data, as well as to their high sensitivity to water content in the soil.

This study utilize the approach to investigate the soil wetness variation over the Jammu and Kashmir(J&K), which experienced one of the worst floods in the past 60 years, during first week of September 2014, due to unprecedented and intense rains. The Soil Wetness Estimation (SWE) has been computed from the data acquired by real time direct broadcast (DB) receiving system installed at three places of India Meteorological Department (IMD) using microwave radiometer AMSU (Advanced Microwave Sounding Unit), flying aboard NOAA (National Oceanic and Atmospheric Administration) polar satellites. A multi-temporal analysis of AMSU channel 15 (at 89 GHz) and channel 1 (at 23 GHz) have been used to find the variation of SWE. In the present analysis, the proposed SWE indicator has been very well brought out the soil wetness changes specifically for the flood event which could give some indication of early 'signals' of an anomalous value of soil water content. In order to improve the forecast capabilities over the tropics, SWE approach is found to be promising for operational use.

Accurate measurement of surface soil moisture of bare and vegetation covered soil over agricultural field and monitoring the changes in surface soil moisture is vital for estimation for managing and mitigating risk to agricultural crop, which requires information and knowledge to assess risk potential and implement risk reduction strategies and deliver essential responses. The empirical and semi-empirical model-based soil moisture inversion approach developed in the past are either sensor or region specific, vegetation type specific or have limited validity range, and have limited scope to explain physical scattering processes. Hence, there is need for more robust, physical polarimetric radar backscatter model-based retrieval methods, which are sensor and location independent and have wide range of validity over soil properties. In the present study, Integral Equation Model (IEM) and Vector Radiative Transfer (VRT) model were used to simulate averaged backscatter coefficients in various soil moisture (dry, moist and wet soil), soil roughness (smooth to very rough) and crop conditions (low to high vegetation water contents) over selected regions of Gujarat state of India and the results were compared with multi-temporal Radar Imaging Satellite-1 (RISAT-1) C-band Synthetic Aperture Radar (SAR) data in σ°_HH and σ°_HV polarizations, in sync with on field measured soil and crop conditions. High correlations were observed between RISAT-1 HH and HV with model simulated σ°_HH & σ°_HV based on field measured soil with the coefficient of determination R² varying from 0.84 to 0.77 and RMSE varying from 0.94 dB to 2.1 dB for bare soil. Whereas in case of winter wheat crop, coefficient of determination R² varying from 0.84 to 0.79 and RMSE varying from 0.87 dB to 1.34 dB, corresponding to with vegetation water content values up to 3.4 kg/m². Artificial Neural Network (ANN) methods were adopted for model-based soil moisture inversion. The training datasets for the NNs were obtained from theoretical forward-scattering models with controlled parameters, thus allowing the control of wide range of soil and crop parameters with which the network was trained. A preliminary performance analysis showed good results with estimation of soil moisture with RMSE better than 6%.

This paper discusses about an estimation of soil moisture in agricultural region using SAR data with the use of HH and HV polarization. In this study the semi empirical approach derived by Dubois et al (¹) was modified to work using (σdeg;HH) and σ°VV) so that soil moisture can be obtained for the larger area extent. The optical remote sensing is helps to monitor changes in vegetation biomass and canopy cover surface reflectance by using NDVI and LAI from which the site suitability from different land use/land cover are identified. The second use involves retrieve the backscattering coefficient valuesσ°) derived from SAR for soil moisture studies. In SAR techniques, the relative surface roughness can be directly estimate using surface roughness derivation empirical algorithms. The mid incidence angle is used to overcome the incidence angle effect and it worked successfully to this study. The modified Dubois Model (MDM) in combination with The Topp’s et al (²) model is used to retrieve soil moisture. These two models have equations (HH, VV) and two independent variables i.e. root mean square height (s) and dielectric constant (epsilon). The linear regression analysis is performed and the surface roughness derived from SAR is well correlated with ground surface roughness having the value of (r² = 0.69). By using the dielectric constant (epsilon) the modified Dubois model in combination with Topp’s model are performed and the soil moisture is derived from SAR having value of (r² = 0.60). Thus, the derived model is having good scope for soil moisture monitoring with present availability of SAR datasets.

Advanced Land Observing Satellite-2 (ALOS-2, "DAICHI-2") performed various emergency observation with its Phased Array type L-band Synthetic Aperture Radar-2 (PALSAR-2) in Asia in 2015. Especially for corresponding to the emergency request from Sentinel Asia related to the Mw 7.8 Gorkha Nepal Earthquake 2015, PALSAR-2 successfully detected not only the crustal deformation but also the avalanches and local displacements. In this presentation, we describe these performances, analysis and the other emergency observations.

The relationship between surface temperature and Soil Adjusted Vegetation Index (SAVI) associated with changing land‐use pattern due to intensive mining and mine fires as discussed in Jharia coalfield, India using data collected by the Thematic Mapper (TM), Enhanced Thematic Mapper Plus (ETM+) and Optical land imager (OLI) and thermal infrared sensor (TIRS) from 1991 to 2013. Jharia coalfield is under fire since the last century due to unsustainable mining activities. On visual interpretation of the surface temperature and SAVI images, it was observed that the spatial distribution of SAVI is opposite to that of LST for the whole coalfield. A subset of typical mining area known to have mines under fire was taken for further analysis. Profiles were taken along north-south and east-west directions in the subset in order to disclose variance based on the pixel values of surface temperature and SAVI images. The profiles show that peak SAVI values are in areas having dense vegetation and peak surface temperature values correspond to areas under fire. These two show an obvious negative correlation. Areas with water bodies show low temperature as well as low vegetation index values. Thus, it could be concluded that moderate resolution remote sensing data provides a convenient way to evaluate the impact of mine fires on vegetation over a period of time.

Accurate, timely and spatially detailed information on the snow cover distribution and on the snow pack properties is needed in various research and practical applications including numerical weather prediction, climate modeling, river runoff estimates and flood forecasts. Owing to the wide area coverage, high spatial resolution and short repeat cycle of observations satellites present one of the key components of the global snow and ice cover monitoring system. The Global Multisensor Automated Snow and Ice Mapping System (GMASI) has been developed at the request of NOAA National Weather Service (NWS) and NOAA National Ice Center (NIC) to facilitate NOAA operational monitoring of snow and ice cover and to provide information on snow and ice for use in NWP models. Since 2006 the system has been routinely generating daily snow and ice cover maps using combined observations in the visible/infrared and in the microwave from operational meteorological satellites. The output product provides continuous (gap free) characterization of the global snow and ice cover distribution at 4 km spatial resolution. The paper presents a basic description of the snow and ice mapping algorithms incorporated in the system as well as of the product generated with GMASI. It explains the approach used to validate the derived snow and ice maps and provides the results of their accuracy assessment.

Retreat of glaciers is an important phenomenon to be monitored as they have a direct bearing on flow of water in rivers and rise of water level in sea. Use of Synthetic Aperture Radar (SAR) images in glacier dynamics studies has been gaining interest in the recent years. The present study discusses the use of SAR coherence images for demarking the snout position on a glacier and thus measure the retreat. Being sensitive for even the slightest changes over the terrain, SAR coherence images seems to be very useful in glacier retreat measurement.

Gangotri is one of the major Himalayan glaciers which has been subjected to dominant retreat since 1850 AD.¹⁶ The retreat of Gangotri glacier has a huge impact on the flow of Ganges, the largest perennial river in India. Coherence images were generated over Gangotri glacier from SAR images with different repeat periods from 1996 (ERS-1 & 2), 2004 (Envisat) & 2012 (TerraSAR-X) and are resampled to 50x50 m grid using SRTM DEM. Profiles near the snout position were precisely marked in a GIS environment and the distance between the profiles (1996, 2004, 2012) is reported as retreat. It has been observed that the Gangotri glacier has been retreating at the rate of 24+/- 1 m per year which is in good agreement with several other studies.

Satellite borne SAR imagery has become an invaluable tool in the field of sea ice monitoring. Previously, single polarimetric imagery were employed in supervised and unsupervised classification schemes for sea ice investigation, which was preceded by image processing techniques such as segmentation and textural features. Recently, through the advent of polarimetric SAR sensors, investigation of polarimetric features in sea ice has attracted increased attention. While dual-polarimetric data has already been investigated in a number of works, full-polarimetric data has so far not been a major scientific focus. To explore the possibilities of full-polarimetric data and compare the differences in C- and X-bands, we endeavor to analyze in detail an array of datasets, simultaneously acquired, in C-band (RADARSAT-2) and X-band (TerraSAR-X) over ice infested areas. First, we propose an array of polarimetric features (Pauli and lexicographic based). Ancillary data from national ice services, SMOS data and expert judgement were utilized to identify the governing ice regimes. Based on these observations, we then extracted mentioned features. The subsequent supervised classification approach was based on an Artificial Neural Network (ANN). To gain quantitative insight into the quality of the features themselves (and reduce a possible impact of the Hughes phenomenon), we employed mutual information to unearth the relevance and redundancy of features. The results of this information theoretic analysis guided a pruning process regarding the optimal subset of features. In the last step we compared the classified results of all sensors and images, stated respective accuracies and discussed output discrepancies in the cases of simultaneous acquisitions.

Antarctic research station’s existence largely depend on the supply of fuel, food and other commodities through Antarctic Scientific Expedition using ship voyage. Safer Ship Navigation demands high resolution satellite monitoring of the ice conditions which varies from 30 km to 200 km from the Antarctic coast of Research stations. During the last couple of years Indian Satellites play a major role in safer ship navigation in sea ice regions of the Arctic and the Antarctic. Specifically Indian Scientific Expedition to the Antarctica (ISEA) through National Centre for Antarctic and Oceanic Research (NCAOR) is one of the beneficiaries for safer ship navigation using information derived from Indian Satellite data. Space Applications Centre, Indian Space Research Organisation (SAC-ISRO) is providing Sea Ice Advisories for the safer optimum entry and exit for the expedition ship at two of the Research stations Bharati and Maitri. Two of the Indian Satellites namely Radar Imaging Satellite-1 (RISAT-1) and ResourceSAT-2 (RS-2) are the two major workhorses of ISRO for monitoring and mapping of the Antarctic terrain. The present study demonstrate the utilisation potential of these satellite images for various Polar Science Applications. Mosaic of the Antarctic Terrain was generated from RISAT-1 CRS data. The preliminary results of the mosaic from CRS- circular polarisation data is presented. Demonstration of the study is extended for other applications such as change detection studies, safer ship navigation and extreme events of Antarctica. The use of multi resolution multi sensor data is also shown in the study.

The main objective of this study was to explore the potential of the multi-temporal PolSAR data in LULC mapping and to evaluate the accuracy of classification using single date and multi-temporal data. Multi-temporal data acquired on three different dates were used. Advanced classification techniques Support Vector Machine and Rule Based Hierarchical approaches were performed on multitemporal ALOS PALSAR data to classify features at different temporal combinations. In this study, SVM classification was applied on the derived output of Yamaguchi decomposition model, for which kernel approach of second order polynomial was used. In Rule Based Hierarchical approach, Backscattering coefficients, Yamaguchi and H/A/Alpha decomposition statistics are computed and analyzed to estimate the decision boundaries of the features to separate feature at different hierarchical levels. SVM classified the PolSAR data efficiently of single data, highest overall accuracy and kappa statistics achieved was 67.65% and 0.61 from the individual image. Rule based classified map of single date, highest overall accuracy and kappa statistics achieved was 68% and 0.67. Based on the accuracy assessment, SVM and Rule Based classification both are approximately of same accuracy but comparatively Rule Based classification was accurate temporally. Rule Based classification was further considered for multi-temporal classification and achieved high overall accuracy and kappa statistics of 80% and 0.76. This proves that multi-temporal PolSAR data helps to increase the accuracy of classification in LULC mapping.

Ice sheet features, glacier velocity estimation and glacier zones or facies classification are important research activities highlighting the dynamics of ice sheets and glaciers in Polar Regions and in inland glaciers. The Cband inSAR data is of ERS 1/2 tandem pairs with one day interval for spring of 1996 and L-band PolinSAR data of ALOS-PALSAR-2 for spring of 2015 is used in glacier velocity estimation. Glacier classification is done using multi-temporal C-and L-band SAR data and also with single date full polarization and hybrid polarization data. In first part, a mean displacement of 9 cm day-1 was recorded using SAR interferometric technique using ERS 1/2 tandem data of 25-26 March 1996. Previous studies using optical data based methods has shown that Gangotri glacier moves with an average displacement of 4 cm and 6 cm day-1. As present results using ERS 1/2 data were obtained for one day interval, i.e., 25th March 05:00pm to 26th March 05:00 pm, 1996, variation in displacement may be due to presence of snow or wet snow melting over the glacier, since during this time snow melt season is in progress in Gangotri glacier area. Similarly the results of glacier velocity derived using ALOSPALSAR- 2 during 22 March – 19 April 2015 shows the mean velocity of 5.4 to 7.4 cm day-1 during 28 day time interval for full glacier and main trunk glacier respectively. This L-band data is already corrected for Faraday’s rotation effects by JAXA, and tropospheric correction are also being applied to refine the results. These results are significant as it is after gap of 20 years that DInSAR methods has given glacier velocity for fast moving Himalayan glacier. RISAT-1 FRS-1 hybrid data is used to create Raney’s decompositions parameters, which are further used for glacier zones classification using support vector machine based classification method. The Radarsat-2 and ALOS-PALSAR-2 fully polarized data of year 2010 and 2015 are also used for glacier classification. The identified and classified glaciers zones in Gangotri area are debris covered ice, clean ice, percolation zone, wet snow zone, ice wall, supra-glacier lakes and moraines, similarly ice sheet features and glacier landforms such as such as nunataks, wind scoop, glacier flow paths, moraine, horn, sastrugi, and crevasses were identified in Antarctic. RISAT-1 FRS-1 data was also successful in mapping the Crevasses hidden under wind-blown ice in Antarctic’s study area.

Forest fire has been regarded as one of the major causes of degradation of Himalayan forests in Uttarakhand. Forest fires occur annually in more than 50% of forests in Uttarakhand state, mostly due to anthropogenic activities and spreads due to moisture conditions and type of forest fuels. Empirical drought indices such as Keetch–Byram drought index, the Nesterov index, Modified Nesterov index, the Zhdanko index which belongs to the cumulative type and the Angstrom Index which belongs to the daily type have been used throughout the world to assess the potential fire danger. In this study, the forest fire danger index has been developed from slightly modified Nesterov index, fuel and anthropogenic activities. Datasets such as MODIS TERRA Land Surface Temperature and emissivity (MOD11A1), MODIS AQUA Atmospheric profile product (MYD07) have been used to determine the dew point temperature and land surface temperature. Precipitation coefficient has been computed from Tropical Rainfall measuring Mission (TRMM) product (3B42RT). Nesterov index has been slightly modified according to the Indian context and computed using land surface temperature, dew point temperature and precipitation coefficient. Fuel type danger index has been derived from forest type map of ISRO based on historical fire location information and disturbance danger index has been derived from disturbance map of ISRO. Finally, forest fire danger index has been developed from the above mentioned indices and MODIS Thermal anomaly product (MOD14) has been used for validating the forest fire danger index.

Forest height is an important indicator of the health of the forest ecosystem and can be utilized for accurate estimation of important parameters such as forest above-ground biomass. PolInSAR techniques have been utilized for forest height estimation using airborne and space-borne platforms. However, temporal decorrelation severely limits the ability of space-borne PolInSAR observations for meaningful height inversion. With the launch of the TerraSAR-X/TanDEM-X platforms, acquisition of Polarimetric SAR data in bistatic mode, without the undesired effects of temporal decorrelation, is possible. Full-PolInSAR bistatic data is acquired over Indian tropical forests and the height inversion results are presented in this research article. The inverted height shows a good correlation with field measured height, with r = 0.8. The inversion shows over-estimation over low height forests, while providing an accurate estimation for tall forested areas.

In the present study, trend of satellite based annual evapotranspiration (ET) and natural forcing factors responsible for this were analyzed. Thirty years (1981-2010) of ET data at 0.08° grid resolution, generated over Indian region from opticalthermal observations from NOAA PAL and MODIS AQUA satellites, were used. Long-term data on gridded (0.5° x 0.5°) annual rainfall (RF), annual mean surface soil moisture (SSM) ERS scatterometer at 25 km resolution and annual mean incoming shortwave radiation from MERRA-2D reanalysis were also analyzed. Mann-Kendall tests were performed with time series data for trend analysis. Mean annual ET loss from Indian ago-ecosystem was found to be almost double (1100 Cubic Km) than Indian forest ecosystem (550 Cubic Km). Rainfed vegetation systems such as forest, rainfed cropland, grassland showed declining ET trend @ - 4.8, -0.6 &-0.4 Cubic Kmyr^-1, respectively during 30 years. Irrigated cropland initially showed ET decline upto 1995 @ -0.8 cubic Kmyr^-1 which could possibly be due to solar dimming followed by increasing ET @ 0.9 cubic Kmyr-1 after 1995. A cross-over point was detected between forest ET decline and ET increase in irrigated cropland during 2008. During 2001-2010, the four agriculturally important Indian states eastern, central, western and southern showed significantly increasing ET trend with S-score of 15-25 and Z-score of 1.09-2.9. Increasing ET in western and southern states was found to be coupled with increase in annual rainfall and SSM. But in eastern and central states no significant trend in rainfall was observed though significant increase in ET was noticed. The study recommended to investigate the influence of anthropogenic factors such as increase in area under irrigation, increased use of water for irrigation through ground water pumping, change in cropping pattern and cultivars on increasing ET.

Glacier surface ice velocity is one of the important parameters which determine the glacier dynamics. If the surface ice velocity is high in upper zone (accumulation zone) of the glacier, more ice is brought to the lower zone (ablation zone) of the glacier where it melts more rapidly. The surface ice velocity depends on multiple factors like geomorphology of a glacier and glacier valley, ice load, orientation of the glacier, slope and debris cover. In this study, we have used latest multi-temporal Landsat-8 satellite images to calculate the surface ice velocity of different glaciers from the Himalayan region and a relationship of velocity and geomorphology and geo-morphometry of the glacier has been studied. The standard procedure has been implied to estimate the glacial velocity using image to image correlation technique. The geo-morphometric parameters of the glacier surface have been derived using SRTM 90 m global DEM. It has been observed that the slope of the glacier is one of the main factors on which the velocity is dependent i.e. higher the slope higher is the velocity and more ice is brought by the glacier to the ablation zone. The debris cover over the glacier and at the terminus also affects the velocity of the glacier by restricting ice flow. Thus, observations suggest that the geomorphology and geo-morphometry of the glacier has a considerable control on the surface ice velocity of the glacier.

The paper outlines comparisons between different methods for the mapping of debris covered glaciers. The supervised classification method like Maximum Likelihood Classifier (MLC) has been tested using different data set for deriving the glacier area. Along with MLC the semi-automated method like Hierarchical Knowledge Based Classifier (HKBC) has also been used here. All the results were tested for accuracy, processing time and complexities. The results were also tested against the manually digitized boundary. The results suggests that the MLC when used with other ancillary data like geo-morphometric parameters and temperature image takes slightly more time than HKBC due to some to higher amount of post processing time but the output is satisfactory (89 % overall accuracy). Results show that the time taken in different classifications is significantly different which ranges from 1-2 hours in MLC to 5-10 hours in manual digitization. Depending on the classification method, some to large amount of post processing is always required to achieve the crisp glacial boundary. Classical classifier like maximum likelihood classification is less time consuming but the time taken in post-processing is higher than HKBC. Another factor which is important for a better accuracy is the prior knowledge of glacier terrain. In knowledge based classification method, it is required initially to establish crisp rules which are later used during classification, without this per-classification exercise the accuracy may significantly decrease. This is a time consuming procedure (2-3 hours in this case) but a minimal amount of post-processing is required. Thermal and geo-morphometric data when used synergistically, classified glacier boundaries are more crisp and accurate.

Light absorbing impurities such as black carbon and dust reduce the reflectance of snow/ice surface. The impurities absorb the incoming solar radiation thereby accelerating snow aging and melting. This further accelerates the processes of snow albedo reduction and melting. A recently-conducted ice core study in Mera Peak shows that annual dust mass fluxes (10.4+/-2.8 g m^-2 yr^-1) are a few orders of magnitude higher than black carbon (7.9+/-2.8 g m^-2 yr^-1). A similar study conducted in the Tibetan Plateau showed a decrease in the amount of mineral dust deposition since 1940s indicating that the increased glacier melt can be attributed to increased black carbon emission than dust. The concentrations of black carbon and dust peak during the pre-monsoon season. Spectral reflectance curves derived from satellite imagery for the Himalayan Tibetan Plateau showed domination of dust-induced solar absorption during the pre-monsoon season. Spatial distribution of reflectance also depends on the transport pathway of impurities, with the south western Hindu Kush and Himalaya experiencing greater dust influx, deposition and snow albedo reduction than northern regions of Karakoram. In this study, we characterize the light absorbing impurities deposited in Himalayan regions using multi spectral data from MODIS and LANDSAT. On comparing the spectral reflectance curves derived from MODIS rand LANDSAT for the overlapping periods and areas and by observing the VIS-NIR gradient of spectral reflectance, determination of the type of light absorbing impurity, mainly mineral dust, and its relation to snow properties are derived.

Agricultural land use conversion is inevitable to meet the needs of growing population, together with climate change issue which has become global concern. This research aims at investigating the impact of climate change on agriculture by identifying land use conversion in part of Central Java, Indonesia, namely Tegal District. Research was carried out in August 2014 until March 2015.This is a survey research with explorative descriptive method, data processing using ENVI 4.5. and ArcGIS 10.1. The satellite image of Landsat was analyzed by determining and comparing the land use changes of the last 20 years, then the interview data with farmers was analyzed using logistic regression. The results showed that many lands converted into settlement, with increasing rate in 2003-2014 was almost twice than 1994-2003, while the reduce of irrigation rice field lands are lower in the period of 2003-2014 than 1994-2003. It is presumed that the factors encourage irrigation rice field land conversion are erratic rainfall, floods in the 1990s, and water lack in the 2000s. This paper discusses briefly about agricultural land use conversion as the impact of the past and current climate variability on farm land.

In this work, we present urban area mapping from full-polarimetric synthetic aperture radar (SAR) data using fuzzy inference system (FIS). In particular, our aim is to utilize the profound knowledge available about scattering mechanism from urban targets to delineate urban environment. In this approach, we have utilized the recently developed polarimetric SAR scattering power decomposition technique (SD-Y4O) given in Bhattacharya et. al. The improved powers along with some other polarimetric parameters were used in this study. A suitable normalization procedure was adapted to handle the skewness in the estimated parameters. The fuzzy if-then rules were constructed from the in-depth knowledge of scattering mechanisms from an urban environment. Suitable methods were introduced to define the fuzzy inference system. The defuzzified membership values were thresholded using an unsupervised clustering method (k-means). The pixels lying in the range [μmax−σ, μmax+σ] corresponds to urban areas where µmax is the largest cluster center and σ is the standard deviation of the cluster corresponding to µmax. The extracted urban area is in visually good agreement with the high resolution optical image. ALOS PALSAR full-polarimetric L-band SAR data has been used in this study.

Polarization orientation angle (POA) shift in the backscattered SAR wave induced, due to irregularity of the target surface. Polarimetric signatures of the backscatter SAR wave gets affected by the POA shift, causes error in the decomposition modelling as shift in POA makes coherency matrix asymmetric. POA shift compensation is very necessary to avoid misinterpretation of decomposition modelling results. POA shift effect has been observed using coherency matrix and decomposition model results. This study is conducted over Dudhwa National Park in the state of Uttar Pradesh, using high resolution, TDM SAR COSSC Product of TerraSAR-X and TanDEM-X in Bistatic mode. Present study mainly focused on the comparative analysis of resultant scattering component of decomposition model before and after POA shift compensation. Shift in POA is investigated using circular polarization technique. Yamaguchi four component decomposition model is used to express total backscatter information in terms of volume, double bounce, surface and helix scattering. Volume scattering is overestimated however double bounce and surface scattering is under estimated in decomposition model due to POA shift present in the backscatter SAR wave. Different scattering mechanisms resulted after POA compensation were analyzed using 100 random points taken from forest structure. The results obtained by TerraSAR-X and TanDEM-X shows an overall increase in double bounce scattering and decrease in volume scattering component after POA shift compensation. It is observed that there is negligible effect of POA shift on surface scattering. POA shift compensation necessarily required to improve the accuracy of decomposition models used in the forest parameter retrieval applications.

Heat Waves can have notable impacts on human mortality, ecosystem, economics and energy supply. The effect of heat wave is much more intense during summer than the other seasons. During the period of April to June, spells of very hot weather occur over certain regions of India and global warming scenario may result in further increases of such temperature anomalies and corresponding heat waves conditions. In this paper, satellite observations have been used to detect the heat wave conditions prevailing over India for the period of May-June 2015. The Kalpana-1 VHRR derived land surface temperature (LST) products have been used in the analysis to detect the heat wave affected regions over India. Results from the analysis shows the detection of heat wave affected pixels over Indian land mass. It can be seen that during the study period the parts of the west India, Indo-gangetic plane, Telangana and part of Vidarbh was under severe heat wave conditions which is also confirmed with Automatic Weather Station (AWS) air temperature observations.

Forests act as sink of carbon and as a result maintains carbon cycle in atmosphere. Deforestation leads to imbalance in global carbon cycle and changes in climate. Hence estimation of forest biophysical parameter like biomass becomes a necessity. PolSAR has the ability to discriminate the share of scattering element like surface, double bounce and volume scattering in a single SAR resolution cell. Studies have shown that volume scattering is a significant parameter for forest biophysical characterization which mainly occurred from vegetation due to randomly oriented structures. This random orientation of forest structure causes shift in orientation angle of polarization ellipse which ultimately disturbs the radar signature and shows overestimation of volume scattering and underestimation of double bounce scattering after decomposition of fully PolSAR data. Hybrid polarimetry has the advantage of zero POA shift due to rotational symmetry followed by the circular transmission of electromagnetic waves. The prime objective of this study was to extract the potential of Hybrid PolSAR and fully PolSAR data for AGB estimation using Extended Water Cloud model. Validation was performed using field biomass. The study site chosen was Barkot Forest, Uttarakhand, India. To obtain the decomposition components, m-alpha and Yamaguchi decomposition modelling for Hybrid and fully PolSAR data were implied respectively. The RGB composite image for both the decomposition techniques has generated. The contribution of all scattering from each plot for m-alpha and Yamaguchi decomposition modelling were extracted. The R2 value for modelled AGB and field biomass from Hybrid PolSAR and fully PolSAR data were found 0.5127 and 0.4625 respectively. The RMSE for Hybrid and fully PolSAR between modelled AGB and field biomass were 63.156 (t ha-1) and 73.424 (t ha-1) respectively. On the basis of RMSE and R2 value, this study suggests Hybrid PolSAR decomposition modelling to retrieve scattering element for AGB estimation from forest.

Snow cover plays an important role in many applications like runoff estimation, hydro-power generation, irrigation and disaster management. In Indian Himalaya, snow cover monitoring on daily basis is a difficult task due to presence of cloud cover and lack of high spatial resolution datasets. Therefore, for long term monitoring, the maximum snow cover products are much reliable and preferred for the studies on snow-melt runoff, hydropower etc. In this study, the maximum snow cover products (MOD 10A2) from MODIS Terra satellite were used to study the spatial and temporal variations in five river basins namely Chandra, Bhaga, Baspa, Beas and Parvati. In addition, MODIS LST (MOD 11A1) and Tropical Rainfall Measuring Mission (3B42) products were used to study the temperature and precipitation trends at basin level. Further, the obtained snow cover information can be used as inputs for the models like runoff and hydropower generation.

Strategic ground-based sampling of soil moisture across multiple scales is necessary to validate remotely sensed quantities such as NASA’s Soil Moisture Active Passive (SMAP) product. In the present study, in-situ soil moisture data were collected at two nested scale extents (0.5 km and 3 km) to understand the trend of soil moisture variability across these scales. This ground-based soil moisture sampling was conducted in the 500 km2 Rana watershed situated in eastern India. The study area is characterized as sub-humid, sub-tropical climate with average annual rainfall of about 1456 mm. Three 3x3 km square grids were sampled intensively once a day at 49 locations each, at a spacing of 0.5 km. These intensive sampling locations were selected on the basis of different topography, soil properties and vegetation characteristics. In addition, measurements were also made at 9 locations around each intensive sampling grid at 3 km spacing to cover a 9x9 km square grid. Intensive fine scale soil moisture sampling as well as coarser scale samplings were made using both impedance probes and gravimetric analyses in the study watershed. The ground-based soil moisture samplings were conducted during the day, concurrent with the SMAP descending overpass. Analysis of soil moisture spatial variability in terms of areal mean soil moisture and the statistics of higher-order moments, i.e., the standard deviation, and the coefficient of variation are presented. Results showed that the standard deviation and coefficient of variation of measured soil moisture decreased with extent scale by increasing mean soil moisture.

Thermal Infrared (TIR) remote sensing measures emitted radiation of Earth in the thermal region of electromagnetic spectrum. This information can be useful in studying sub-surface features such as buried palaeochannels, which are ancient river systems that have dried up over time and are now buried under soil cover or overlying sediments in the present landscape. Therefore they have little or no expression on the surface topography. Study of these paleo channels has wide applications in the fields of uranium exploration and ground water hydrology. Identifying paleo channels using remote sensing technique is a cost-effective means of narrowing down search areas and thereby aids in ground exploration. The difference in thermal properties between the paleo channel-fill sediments and the surrounding bed-rock is the key to demarcate these channels. This study uses five TIR bands of day-time Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) L1A data for delineation of paleo-systems in the DeGrussa area of the Capricorn Orogen in Western Australia. The temperature-emissivity separation algorithm is applied to obtain kinetic temperature and emissivity images. Sharp contrasts in kinetic temperature and emissivity values are used to demarcate the channel boundaries. Profiles of topographic elevation, temperature and emissivity values are plotted for different sections of the interpreted channels and compared to distinguish the surface channels from sub-surface channels, and also to interpret the thickness and nature of the paleo channel-fill sediments. The results are validated using core-drilling litho logs and field exploration data.

Digital elevation model (DEM) is indispensable for analysis such as topographic feature extraction, ice sheet melting, slope stability analysis, landscape analysis and so on. Such analysis requires a highly accurate DEM. Available DEMs of Antarctic region compiled by using radar altimetry and the Antarctic digital database indicate elevation variations of up to hundreds of meters, which necessitates the generation of local improved DEM. An improved DEM of the Schirmacher Oasis, East Antarctica has been generated by synergistically fusing satellite-derived laser altimetry data from Geoscience Laser Altimetry System (GLAS), Radarsat Antarctic Mapping Project (RAMP) elevation data and Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) global elevation data (GDEM). This is a characteristic attempt to generate a DEM of any part of Antarctica by fusing multiple elevation datasets, which is essential to model the ice elevation change and address the ice mass balance. We analyzed a suite of interpolation techniques for constructing a DEM from GLAS, RAMP and ASTER DEM-based point elevation datasets, in order to determine the level of confidence with which the interpolation techniques can generate a better interpolated continuous surface, and eventually improve the elevation accuracy of DEM from synergistically fused RAMP, GLAS and ASTER point elevation datasets. The DEM presented in this work has a vertical accuracy (≈ 23 m) better than RAMP DEM (≈ 57 m) and ASTER DEM (≈ 64 m) individually. The RAMP DEM and ASTER DEM elevations were corrected using differential GPS elevations as ground reference data, and the accuracy obtained after fusing multitemporal datasets is found to be improved than that of existing DEMs constructed by using RAMP or ASTER alone. This is our second attempt of fusing multitemporal, multisensory and multisource elevation data to generate a DEM of Antarctica, in order to address the ice elevation change and address the ice mass balance. Our approach focuses on the strengths of each elevation data source to produce an accurate elevation model.

Emergence of extensively large computational facilities have enabled the scientific world to use earth system models for understating the prevailing dynamics of the earth's atmosphere, ocean and cryosphere and their inter relations. The sea ice in the arctic and the Antarctic has been identified as one of the main proxies to study the climate changes. The rapid sea-ice melting in the Arctic and disappearance of multi-year sea ice has become a matter of concern. The earth system models couple the ocean, atmosphere and sea-ice in order to bring out the possible inter connections between these three very important components and their role in the changing climate. The Indian monsoon is seen to be subjected to nonlinear changes in the recent years. The rapid ice melt in the Arctic sea ice is apparently linked to the changes in the weather and climate of the Indian subcontinent. The recent findings reveal the relation between the high events occurs in the Indian subcontinent and the Arctic sea ice melt episodes. The coupled models are being used in order to study the depth of these relations. However, the models have to be validated extensively by using measured parameters. The satellite measurements of sea-ice starts from way back in 1979. There have been many data sets available since then. Here in this study, an evaluation of the existing data sets is conducted. There are some uncertainties in these data sets. It could be associated with the absence of a single sensor for a long period of time and also the absence of accurate in-situ measurements in order to validate the satellite measurements.

The advent of satellite remote sensing and its use in hydrology has facilitated a huge leap in the understanding of the various water resources, its interaction with ecological systems and anthropogenic creations. Recently, NASA and German Aerospace Research Agency-DLR launched the Gravity Recovery and Climate Experiment (GRACE) satellite mission consisting of two satellites. They measure the time varying gravity which gives changes in the distribution of mass on the surface of the earth which after removing atmospheric and oceanic effects is majorly caused by changes in Terrestrial Water Storage (TWS) changes. GRACE data is generally available as spherical harmonic coefficients, which is difficult for hydrologists to understand and interpret. JPL’s TELLUS website is now providing gridded global data set in the form of mass anomaly derived from the Level-2 data sets of spherical harmonic coefficients of 3 sources, viz. CSR, GFZ and JPL. Before using these data sets for solving hydrological problems, it is important to understand the differences and similarities between these data sets as direct calibration of GRACE data is not possible. In this study we do an inter-comparison of the Level-3 Release 05 data sets over India. We compare the data sets using Pearson, Spearman and Kendall correlation. CSR and GFZ data sets appear to be closest to each other whereas JPL and GFZ data sets are most different from each other.

Satellite remote sensing is recognized as one of the effective tools for detecting and monitoring affected areas due to natural disasters. Since SAR sensors can capture images not only at daytime but also at nighttime and under cloud-cover conditions, they are especially useful at an emergency response period. In this study, multi-temporal high-resolution TerraSAR-X images were used for damage inspection of the Kathmandu area, which was severely affected by the April 25, 2015 Gorkha Earthquake. The SAR images obtained before and after the earthquake were utilized for calculating the difference and correlation coefficient of backscatter. The affected areas were identified by high values of the absolute difference and low values of the correlation coefficient. The post-event high-resolution optical satellite images were employed as ground truth data to verify our results. Although it was difficult to estimate the damage levels for individual buildings, the high resolution SAR images could illustrate their capability in detecting collapsed buildings at emergency response times.

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

High resolution calibrated PAN-sharpened images from WorldView-2 (WV-2) were used for extracting blue ice areas in Schirmacher Oasis, east Antarctica. The Schirmacher oasis extends from 70°45′ S to 70° 75′ S and 11°38′ E to 11° 38′ E. Blue ice areas represents long-term ablation. The amplitude of blue ice is lower than that of snow, because the ice surface is smoother than the latter. But the difference is not so obvious when applying automatic extraction techniques. To achieve desirable results and support comparative analysis, multiband image combinations were generated from atmospherically-corrected WV-2 data. For feature extraction process, regions of interest (ROI) were considered in which blue ice was used as target and white snow/ice appearing on the blue ice was considered as non-target. Various semiautomatic feature extraction methods, such as, target detection, mapping methods, etc, and many trials were used for extracting blue ice areas. Surface patterns of alternating snow and blue ice bands were found in east Antarctica which becomes obstacle to clearly extract blue ice feature. From the high resolution WV-2 data, reference data (digitized data) were prepared for blue ice area. By comparing reference data and extracted data, bias and root mean square (RMS) error values were calculated. Accuracy assessment was done considering the entire necessary prior results of the blue ice area. Our results indicate that the pixel-based supervised classification methods yielded an overall accuracy ranging from 82%-89% for extraction of blue ice areas.

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

The impact of rapid urbanization in cities on their microclimate is at present a great cause of global concern. One of the major consequences is the unexpected rise in temperatures in the cities compared to their surrounding areas, termed as the Urban Heat Island (UHI) effect. Over the past many years, several Indian cities are under severe stress owing to such extreme anomalous changes in their micro-meteorological conditions making them unfriendly for habitation. Presented here is a case study on Bhubaneswar - one such city on the east coast of India undergoing rapid urbanization in recent times. In this study, Land Surface Temperatures (LST) from MODIS Terra and Aqua instruments at 1 km² spatial resolution along with the Land Use and Land Cover (LULC) change data from Landsat was used over a 25 km radius about the city for a 15 years' period from 2000 to 2014. Preliminary analyses indicate spatio-temporal changes in LULC to be one of the primary and significant factors responsible for changes in the UHI effect over the city. Investigations on the spatio-temporal variations in LST across the city and its relationship with vegetation cover indicate that overexploitation of various resources demanded by a fast growing population has led to significant changes in LULC patterns in the last few years. Analysis of the changes in the urban energy balance and resulting UHI effect across the city under various urban growth scenarios and different proportions of green urban area are in progress.

The study involves the climate change prediction and its effects over a local sub grid scale of smaller area in Cauvery basin. The consequences of global warming due to anthropogenic activities are reflected in global as well as regional climate in terms of changes in key climatic variables such as temperature, precipitation, humidity and wind speed. The key objectives of the study are to define statistical relationships between different climate parameters, to estimate the sensitivities of climate variables to future climate scenarios by integrating with GIS and to predict the land use/ land cover change under the climate change scenarios. The historical data set was analyzed to predict the climate change and its impact on land use/land cover (LULC) is observed by correlating the Land Surface Temperature (LST) and Normalized Difference Vegetation Index (NDVI) values for two different times for the same area. It is so evident that due to the rise in temperature there is a considerable change in the land use affecting the vegetation index; increased temperature results in very low NDVI values or vegetation abundance.

The ionosphere, which extends from 50-450 kms in earth’s atmosphere, is a particularly important region with regards electromagnetic wave propagation and radio communications in the L-band and lower frequencies. These ions interact with the traversing electromagnetic wave and cause rotation of polarization of the radar signal. In this paper, a potentially computable method for quantifying Faraday rotation (FR), is discussed with the knowledge of full polarimetric ALOS/PALSAR data and ALOS-2/PALSAR-2 data. For a well calibrated monostatic, full-pol ALOS-2/PALSAR-2 data, the reciprocal symmetry of the received scattering matrix is violated due to FR. Apart from FR, other system parameters like residual system noise, channel amplitude, phase imbalance and cross-talk, also account for the non-symmetry. To correct for the FR effect, firstly the noise correction was performed. PALSAR/PALSAR-2 data was converted into 4×4 covariance matrix to calculate the coherence between cross-polarized elements. Covariance matrix was modified by the coherence factor. For FR corrections, the covariance matrix was converted into 4×4 coherency matrix. The elements of coherency matrix were used to estimate FR angle and correct for FR. Higher mean FR values during ALOS-PALSAR measurements can be seen in regions nearer to the equator and the values gradually decrease with increase in latitude. Moreover, temporal variations in FR can also be noticed over different years (2006-2010), with varying sunspot activities for the Niigata, Japan test site. With increasing sunspot activities expected during ALOS-2/PALSAR-2 observations, more striping effects were observed over Mumbai, India. This data has also been FR corrected, with mean FR values of about 8°, using the above mentioned technique.

Earthquakes are the most vulnerable disasters in nature and are inevitable. Electromagnetic detection of earthquakes using Global Positioning System (GPS) has lead to better understanding of our planet earth and related atmospheric systems. In the present work an attempt is made to analyze seismo-ionospheric perturbations for an earthquake which has occurred in Indonesia on 15th January 2014 with a magnitude of 4.5 on Richter scale. Modified covariance algorithm is applied on the GPS vertical total electron content data taken from the International Global Navigation Satellite System Service station named BAKO It is clearly observed that there is an enhancement of energy in the ionosphere for every 15 minutes from the starting of perturbations.