Synthetic Aperture Radar (SAR) remote sensing is a state-of-the-art tool for snow monitoring and snow parameters estimation. SAR remote sensing-based techniques, such as interferometric SAR (InSAR) ...and Polarimetric SAR (PolSAR) have already proven useful in the estimation of geophysical parameters of snow. InSAR-based techniques utilize interferometric phase information from repeat-pass datasets for snow parameters retrieval. During the monitoring of snow, the large temporal gap between the repeat passes results in the temporal decorrelation in the snowpack, which leads to the loss of interferometric coherence. Hence, there is a need for a technique for snow parameters estimation, which can work with zero temporal baseline datasets. This study works on the development of a Polarimetric SAR Interferometry (PolInSAR) based modelling approach for snow-depth estimation using TerraSAR-X/TanDEM-X datasets acquired in the pursuit monostatic mode (temporal baseline = 10 seconds). The study area of this work is the Manali region of Himachal Pradesh situated in the Beas basin. Multi-temporal analysis of the snow-depth variation is executed utilizing the two pursuit-monostatic TanDEM-X interferometric quad-pol dataset pairs of the dates 21 January 2015 and 22 January 2015. In this study, the PolInSAR-based Coherence Amplitude Inversion modelling approach is used for the snow-depth retrieval. The magnitude of the complex interferometric coherence is used during the modelling implementation. The snow extinction coefficient is estimated and used as an input during PolInSAR modelling. Further, a comparison of the calculated volume coherence magnitude and the observed volume coherence magnitude is done during model implementation for the snow-depth estimation. The snow depth is estimated at a resolution of 15 m × 15 m in range and azimuth directions respectively. The estimated snow depth for both the dates shows a precise correlation with the ground datasets. The rise in model retrieved snow-depth value from 0.84 m to 1.24 m is observed during the period. The retrieved results were validated using the ground data of snow depth from the Automatic weather station (AWS) of Snow and Avalanche Study Establishment (SASE), Defence Research and Development Organization (DRDO), and Indian Institute of Remote Sensing (IIRS) installed in the Dhundi region of the study area for same dates.
Due to the penetration capacity of synthetic aperture radar (SAR) data through clouds and hazy atmospheric circumstances like fog, smog, light rain, mist etc., it has ability to continuous ...observation of flood events for producing accurate, rapid and cost effective flood mapping. A study using time series RADARSAT SAR images in flood water detection, monitoring of spatial extent and propagation of flood inundation were described and analysed in this paper. The SAR images were first calibrated, geometrically corrected and filtered. Afterward, threshold method was applied to extract the inundated areas from the SAR images. In threshold method, density slicing technique was used to separate the open water and non-water (land) areas from the images. Later, to delineate the actual flooded area, permanent water bodies (e.g. river, lake, ponds etc.) were subtracted from the open water. Flood maps were super-imposed and analysed to find out the nature of spatial extent, duration of flood and to show how flooding spread through time. This study illustrates that the SAR data along with GIS can be used effectively for flood water mapping, monitoring and analysing the propagation of flood water in a flood prone area. Therefore, the findings of this study will help to take initiative to reduce the flood hazard impact and increase the flexibility in the process of flood management.
The global demand for food and bioenergy changes associated with land use and land cover change (LULCC) has raised concerns about the environment, global warming, and climate change. There is enough ...evidence that we are passing through human-induced changes which may lead to sixth mass extinction. Information of spatial representation of land use and land cover and its dynamics of landscape pattern and habitat organisation are vital for developing land use policy, adapting, and mitigating climate change impacts and nature-based solutions, including proposing areas for conservation. Spatial data is also a leading way of providing geographically linked or located data with the help of remote sensing (including drones), geographical information systems, Global Positioning Systems, and real-time in situ measurements. This article attempts to critically review the current research on the congruence of LULCC with climate and its processes. The article discusses current research on LULCC, its environmental consequences, and its impact on regulatory systems of the terrestrial biosphere, ecosystem services, atmospheric chemistry, and climate. Finally, it emphasises the need to evolve land use policy based on scientific analysis of LULCC to achieve sustainability goals and overall socioeconomic development with a special focus on climate change. The commitments of international agreements and protocols also require land use policy which accounts for concerns of climate change.
Retrieval of snow density using hybrid polarimetric RISAT-1 synthetic aperture radar (SAR) data is implemented in this study. The C-band datasets were acquired in the fine resolution stripmap mode-1 ...(FRS-1) in which microwaves EM are transmitted in the right circular polarization from the sensor and are received in linear (horizontal or vertical) polarizations resulting in hybrid polarizations-RH and RV. The datasets were acquired for the date of February 23, 2014 covering the Dhundi region in Manali district of Himachal Pradesh. In this season, the area experiences regular snowfall, hence the whole area was covered with the dry snow layer. Hybrid decomposition technique along with the integral equation model (IEM) is utilized for snow density extraction of the snowpack in the study area. The modeled and theoretical approaches are used to retrieve the dielectric constant of the snowpack. Fresnel coefficients, utilized in the IEM modeling approach, are the function of snowpack dielectric constant and the local incidence angle of the incident wave from the sensor. The retrieved snow density map is generated for the area. The validation of the retrieved results is done using ground data collected during the period.
Urban flooding and waterlogging are causing menace in many cities around the world from the perspective of day-to-day functioning, health and hygiene, communication, and the consequent damages they ...cause to urban environment. The present study is an attempt to understand the urban flood risks in parts of Bhubaneswar City, India, based on its hydrodynamic set-up and level of urbanisation. The Storm Water Management Model is used for peak flow analysis, and the flooding extent has been assessed while taking into consideration the elevation, slope, land use/land cover (LULC) and design Storm Water Drain (SWD) infrastructure of the city. The micro-watersheds for each SWD are delineated using digital surface model derived from airborne Light Detection and Ranging (LiDAR) data (1 m), and the LULC information is obtained from high-resolution optical remote sensing data. After the model simulation, it is estimated that peak runoff is relatively higher, i.e. 0.1–0.5 cumecs for a large number of micro-watersheds, even rising to more than 1.5 cumecs for some, indicating the severity of urban floods in the city. After integrating the simulated flooding pattern with the vulnerability associated with socio-economic characteristics of urban dwellers, the flood risk has been assessed. The study suggests that capacity of design SWD systems needs augmentation according to present and predicted flooding conditions for the city.
Rapid urban growth processes give rise to impervious surfaces and are regarded as the primary cause of urban flooding or waterlogging in urban areas. The high rate of urbanization has caused ...waterlogging and urban flooding in many parts of Dhaka city. Therefore, the study is undertaken to quantify the changes in land use/land cover (LULC) and urban runoff extent based on the Natural Resources Conservation Service (NRCS) Curve Number (CN) during 1978–2018. The five-decadal LULC has been analyzed using three-generation Landsat time-series data considering six different classes, namely agriculture, built-up, wetland, open land, green spaces, and water bodies for the years 1978, 1988, 1998, 2007, and 2018. Significant changes in LULC for the study area from 1978–2018 are observed as 13.1%, 4.8%, and 7.8% reduction in agricultural land, green spaces, and water bodies, respectively, and a 22.1% increase in the built-up area is estimated. Within Dhaka city, 14.6%, 16.0%, and 12.3% reduction in agricultural land, green spaces, and water bodies, respectively, and a radical increase of 41.9% in built-up area are reckoned. The decadal runoff assessment has been carried out using the NRCS-CN method, considering an extreme rainfall event of 341 mm/day (13 September 2004). The catchment area under very high runoff category is observed as 159.5 km2 (1978) and 318.3 km2 (2018), whereas, for Dhaka city, the setting is dynamic as the area under the very high runoff category has increased from 74.24 km2 (24.44%) to 174.23 km2 (57.36%) in years 1978 and 2018, respectively, and, mostly, the very high runoff potential areas correspond to the dense built-up surfaces.
In view of an exponential increase in the negative impacts of flash-floods globally, the present work aims at the identification of flash-floods-prone river reaches in the Beas river basin, Himachal ...Pradesh, India using a multi-criteria indexing technique. The flood hazard index (FHI) was computed by implementing analytical hierarchy process (AHP) model on 6 hydrologic parameters influencing flood hazard, namely rainfall intensity, curve number (CN) grid, time of travel, slope, Manning's roughness coefficient and drainage density. The CN grid (empirical parameter to estimate direct surface runoff) was used as one of the parameters which depend upon the land use, hydrologic soil group and hydrologic conditions. It is imperative to mention that remote sensing and geographical information system (GIS) techniques played a crucial role in the preparation of these 6 parameter layers. The AHP model calculates the normalized weights for each parameter using pair-wise comparison matrices. The rainfall intensity and curve number were the factors having the highest normalized weight of 34.52 each. Subsequently, the estimated weights of the parameters and hazard level-wise rating scores were used in a GIS environment to generate FHI. The generated FHI raster was masked using floodplain layer within geomorphology map and river buffer to identify flash-floods-affected river reaches. The generated flash-floods map was validated by historical flash-floods ground points, field observations and remote sensing data. The results depicted that the river reaches in the north and east of the Beas basin are susceptible to flash-floods which are mainly governed by heavy rainfall intensity and high runoff characteristics. The river stretches namely Bahang–Manali (Beas), Kullu–Bhuntar (Beas) and Manikaran–Kheer-Ganga (Parvati) have been categorized into very high and high flash-floods zones. Decreasing trend of normalized differential vegetation index (NDVI) was observed for river reaches falling within the very high and high zones indicating the vegetation loss post successive flash-floods events. The river order 2 lies in the very high and high flash-floods zones, indicating the fact that the contribution of tributaries is significant to flood events. Flash-floods map will serve as catastrophic product, which will help policymakers to take suitable measures to reduce the risk of flash-floods.
AbstractSynthetic aperture radar (SAR) observations of real world flood extents are often the only source of information in data scarce catchments and the only reliable resource for channels with ...subkilometer widths. Accordingly, this study aimed at evaluating the reliability of SAR-based flood maps for flood model performance assessment for an extremely data poor region in India. SAR images were converted to probabilistic flood maps by combining inundation extents obtained using visual interpretation, histogram thresholding, and texture-based classification. Flood extents simulated by a hydrodynamic model were compared with SAR-derived inundation extents using spatial objective functions to calibrate the lumped channel and floodplain friction parameters. The agreement between the modeled and observed flood extents showed an R2 value of 0.938 and a root mean squared error of 0.278 pixels for the validation. The results indicate that the proposed method has the potential to support flood model calibration and evaluation in ungauged basins.
ISRO’s Imaging InfraRed Spectrometer (IIRS) onboard Chandrayaan-2 is relatively the most advanced spectrometer in the lunar orbit till date. IIRS operates from 0.8 to 5µm spectral range and has been ...sampling the lunar surface in 250 spectral channels with 20 nm spectral and 80 m spatial resolution. The data are transmitted in strips of hyper-spectral cubes. These datasets do not have spatial information embedded in them. Spatial information is provided as a separate geometry file containing line/sample wise lat/long information. In order to impart spatial information to cubes, multiple steps are involved requiring manual intervention. This approach is time consuming as well as prone to error. Since most tools available are proprietary, it is not possible to acquire and understand the source code and supplement it to run on multiple datasets automatically. In order to overcome these challenges SelenoRef, a python-based tool has been developed to automate the process of Seleno-referencing of Ch-2 IIRS hyper-spectral cubes. SelenoRef requires three inputs viz. input folder containing downloaded zipped files, output folder for processed data and desired projection system. SelenoRef autonomously unzips all datasets from the input folder and navigates through the directory structure to locate hyper-spectral cubes, geometry information file and metadata file to retrieve the dataset, lat/long information and projection, respectively. Cubes are seleno-referenced using Ground Control Points (GCPs), band statistics are calculated and output is generated in the output directory. SelenoRef is open source and has a graphical user interface.
Groundwater contamination assessment is a challenging task due to inherent complex dynamisms associated with the groundwater. DRASTIC is a very widely used rapid regional tool for the assessment of ...vulnerability of groundwater to contamination. DRASTIC has many lacunas in the form of subjectivities associated with weights and ratings of its hydro-geological parameters, and, therefore, the accuracy of the DRASTIC-based vulnerability map is questioned. The present study demonstrates the optimisation of the DRASTIC parameters along with a scientific consideration to the anthropogenic factors causing groundwater contamination. The resulting scientific consistent weights and ratings to DRASTIC parameters assist in the development of a very precise groundwater vulnerability map highlighting different zones of different gravity of contamination. One of the most important aspects of this study is that we have considered the impact of vadose zone in a very comprehensive manner by considering every sub-surface layer from the earth surface to the occurrence of groundwater. The study area for our experiment is Fatehgarh Sahib district of Punjab which is facing several groundwater issues.
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