ALBERT

Description: With continued climate change, coastal areas are subjected to undesirable situations in the form of sea-level rise and its adverse outcomes like storm surge, flooding and erosion. Being a unique geographic location, low topography, relatively higher population density as well as overwhelming dependence on natural resources, Bangladesh is one of the vulnerable countries exposed to the impacts of global warming and climate change. Bhola in southern Bangladesh represents the world’s most dynamic estuary is potentially vulnerable to accelerated sea level rise and associated  calamities. The present study aims to develop a coastal vulnerability index (CVI) using eight parameters namely (a) geomorphology, (b) slope, (c) relative sea level change rate, (d) mean tide range (e) shoreline erosion and accretion, (f) population (g) bathymetry and (h) coastal flooding which were addressed as the relative risk variable for the study area using geospatial techniques i.e., Remote Sensing and GIS. The aforementioned parameters were ranked on the basis of their potential contribution to physical changes on the coast, as sea-level rises, and the final calculation was done over 263.87 km shoreline by the square root of the mean values of the ranked variables. According to the vulnerability index, about 22 % corresponding to 57.23 km of the entire coast is under very high-risk and another 29 % representing 75.26 km is under high-risk. Again, 25 % encompassing 67.69 km shoreline is at moderate risk and 24 %, that is 63.69 km shoreline is found to be at low risk. The most vulnerable coastal regions are found mainly along the western coast of Char Fasson and northern and southwestern coast of Bhola Sadar of Bhola Island.

Description: Monitoring new changes in cities adjacent to dynamic sand dunes requires precise classifier technique. Unlike traditional techniques of supervised classification which use training sites, the integration of image transformation tasseled cap and automatic feature extraction module based on spectral signatures has provided to be sensitive and realistic techniques with time and cost effective. The proposed module was applied to Al Ain district, United Arab Emirates (UAE). The module consists of four steps in terms of segmentation, thresholding and clustering and computing attributes. The obtained greenness and classified maps were then enhanced by applying a 3 × 3 Sobel filter. The new changes were detected by combining the multi-temporal greenness and classification maps. Accuracy assessment and quantitative analysis were performed using confusion matrix and ground truthing. The results showed significant increasing in urban and agricultural areas from the year from 1990 to 2000 compared with the period of time from the year 2000 to 2006. The image difference showed that the vegetation and building classes had increased 7.58 and 20.28 km 2 respectively. This study showed that image difference and fuzzy logic approach are the most sensitive techniques for detecting new changes in areas adjacent to dynamic sand dunes.

Description: Exploitation of groundwater and frequent failure of monsoons has produced rapid water table depletion in present days. Rainwater is discharged as surface runoff without replenishing the groundwater table. Identification of potential zones for groundwater recharge has become an important event to replenish the groundwater resources. The recharge potential is governed by various features of surface, subsurface and hydrometeorological parameters. Remote sensing technique is found to be very effective tool for the integration of various features of these factors. In this work, an attempt has been made to identify groundwater recharge potential zones in Namakkal district of Tamilnadu state, India. Weighted index overlay analysis (WIOA) was carried out in this study using the software ArcGIS by intergrating the thematic layers of soil, geology, geomorphology, lineament, land use/land cover, rainfall, groundwater level and slope. The groundwater potentials were delineated as Very Good, Good, Moderate to Good, Moderate and Poor zones. Good category of groundwater recharge potential zones constitutes 62.21 % in the study area. GIS based output results were validated with the water level data of Public Works Department (PWD).

Description: To maximize the non-gaussianity in sphered satellite data, many authors have proposed different independent component analysis (ICA) based approaches to classify images by reducing the mixing effect in classes. In multispectral data, few heterogeneous classes have little variation in spectral resolution. Even though, a classified image should exhibit high spectral variance among different classes, while it should be less within a particular class. To improve the classification accuracy in the presence of mixed classes i.e., having similar spectral characteristics, a novel method improved fixed point independent component analysis (IFPICA) is proposed. This method segregates the objects from mixed classes on maximizing the approximation of negentropy, which reduces the effect of quite similar spectral characteristics among different classes. It can easily estimate the independent component of this non-gaussian distribution of data with the help of nonlinearity. Therefore, this nonlinearity helps to optimize the performance of this approach, which minimizes the variance among similar classes. Due to the presence of neural algorithms, it is quite robust, computationally simple and has very fast convergence, in respect to the spectral distributions of satellite images. Hence, this proposed IFPICA approach plays a major role in the classification of satellite images such as road, vegetation, buildings and grassland area. The images used in the study doesn’t have any initial or additive noise, which would obstruct the process of IFPICA algorithm used in the work, therefore preprocessing is not required for noise suppression in this work. The post-processing, e.g., deflation, denoising, filtering, etc. are also not required due to similar reason.

Description: There is no doubt that every classification technique involves advantages and drawbacks like any people thoughts, and as gathering different thoughts aids people to ensure and support for a beneficial decision, the combining of different classification techniques helps to construct strong classification system supplying better results. Ensemble classification techniques are applied today in most image classification fields, and remote sensing image classification is one of those areas giving a growing number of trust with this kind of classification system. In this paper, we used and combined three popular classification techniques using majority vote method. First will be the unsupervised segmentation technique k-means that we follow by SVM (support vector machine) regularization. The last technique will be the supervised neuronal technique LVQ (linear vector quantization). The main contribution of this work is to combine three different kinds of classification methods; the unsupervised k-means technique to get an initial view of the image site that will helps us to choose the best training area for SVM classification system which is the second kind. The last kind is the neuronal supervised LVQ technique. Finally, we used in this specific work a LANDSAT image of a well known south Algerian region which is the Ouargla oasis acquired on December, 20 in 2000.

Description: A cross-calibration model is developed for calibrating the reflectance of HJ-1A/CCD1 sensor to MODIS/Terra sensor in the post-launch era. The vicarious cross-calibration models are initialized using the image pairs of HJ-1A/CCD1 and MODIS/Terra sensors collected from two testing sites, Nam Co Lake and Qinghai Lake, China. Our results show the cross-calibration model produced good performance in calibrating reflectance of HJ-1A/CCD1 sensor to MODIS/Terra sensor, whose mean relative error do not exceed 8.5 % for three visible channels of HJ-1A/CCD1 sensor. Moreover, the theoretical difference between the MODIS/Terra and HJ-1A/CCD1 reflectance are simulated using the 6S code. The results indicate that it would cause a pronounced difference. Therefore, it necessary to reinitialize the coefficients of the water color remote sensing models, when those models with HJ-1A/CCD1 band were applied to the vicarious cross-calibration data.

Description: In the present study, attempt has been made to understand the structural controls on preservation and dynamics of coal fires by establishing spatial relations of these fires with the faults, in the Jharia coal field. This is carried out, using recent distribution of the coal fires mapped from Landsat 8 TIRS data and historical coal fire distribution data derived from earlier studies (2003, 2006 and 2012). The major mapped fires are restricted to five different locales in the coalfield, namely Kujama, Kusunda, Katras-Mudidih, Shatabdi and Nadhkarkee-Jayramdih. Spatial analysis of different coal fire locales with respect to the geological map indicates that the fires may be associated with faults. Areal extension of fires coincide with the trend of faults (NW-SE around Kusunda and E-W around Katras). Further, the temporal preservation of the fires are also controlled by these faults. Fires associated with faults sustain for longer time than the fires that are not in proximity to the faults. This indicates that, these faults may have acted as conduits in supplying oxygen for preserving the coal fires over a period of time. Further, these faults permit the propagation and spread of the fire, through the arealy exposed fractures developed along their trends.

Description: Observations from Aerosol Robotic Network (AERONET) radiometers during 2003–2012 in Beijing are assessed and used to characterize a series of aerosol properties and their variations. Some distinctive features are found. (1) Aerosol optical depth contributed by fine particles at 440 nm (AOD440-F) in spring showed a decreasing trend in the decade; compared with 2003 it decreased about 54 % in the spring of 2012. The annual averaged AOD440-F increased from 2003 to 2007 and decreased after Beijing Olympics (2008). (2) The spectrum of single-scattering albedo (SSA) followed a rapid increasing pattern from 440 to 675 nm and a slow decreasing or neutral pattern from 675 to 1020 nm, but in summer it was nearly wavelength independent. After Beijing Olympics, in addition to summer in which the SSA remained almost unchanged, in the other seasons the SSA increased, this suggests the decline of absorbing aerosols loading. (3) The imaginary part of aerosol refractive indices k(λ) showed a wintertime maximum and summertime minimum associated with the variation of black carbon content. After Beijing Olympics k(λ) significantly decreased. A good correlation between k(λ) and SSA was observed. However, different from the electromagnetic theory predicted, high (low) real part n(λ) sometimes corresponded to low (high) SSA. (4) The volume median radius of fine mode (R vf ) and aerosol optical depth (AOD) showed a good correlation, but the volume median radius of coarse mode (R vc ) and AOD exhibited a minor and negative correlation which different from previous studies.

Description: Snow physical properties, snow cover and glacier facies are important parameters which are used to quantify snowpack characteristics, glacier mass balance and seasonal snow and glacier melt. This study has been done using C-band synthetic aperture radar (SAR) data of Indian radar imaging satellite, radar imaging satellite-1 (RISAT)-1, to estimate the seasonal snow cover and retrieve snow physical properties (snow wetness and snow density), and glacier radar zones or facies classification in parts of North West Himalaya (NWH), India. Additional SAR data used are of Radarsat-2 (RS-2) satellite, which was used for glacier facies classification of Smudra Tapu glacier in Himachal Pradesh. RISAT-1 based snow cover area (SCA) mapping, snow wetness and snow density retrieval and glacier facies classification have been done for the first time in NWH region. SAR-based inversion models were used for finding out wet and dry snow dielectric constant, dry and wet SCA, snow wetness and snow density. RISAT-1 medium resolution scan-SAR mode (MRS) in HV polarization was used for first time in NWH for deriving time series of SCA maps in Beas and Bhagirathi river basins for years 2013–2014. The SAR-based inversion models were implemented separately for RISAT-1 quad pol. FRS2, for wet snow and dry snow permittivity retrieval. Masks for layover and shadow were considered in estimating final snow parameters. The overall accuracy in terms of R 2 value comes out to be 0.74 for snow wetness and 0.72 for snow density based on the limited ground truth data for subset area of Manali sub-basin of Beas River up to Manali for winter of 2014. Accuracy for SCA was estimated to be 95 % when compared with optical remote sensing based SCA maps with error of ±10 %. The time series data of RISAT-1 MRS and hybrid data in RH/RV mode based decompositions were also used for glacier radar zones classification for Gangotri and Samudra Tapu glaciers. The various glaciers radar zones or facies such as debris covered glacier ice, clean or bare glacier ice radar zone, percolation/refreeze radar zone and wet snow, ice wall etc., were identified. The accuracy of classified maps was estimated using ground truth data collected during 2013 and 2014 glacier field work to Samudra Tapu and Gangotri glaciers and overall accuracy was found to be in range of 82–90 %. This information of various glacier radar zones can be utilized in marking firn line of glaciers, which can be helpful for glacier mass balance studies.

Description: In this paper, we propose an adaptive filtering technique for Synthetic Aperture Radar (SAR) images. A new windowing technique is introduced where the total window is divided into five equal sized overlapping sub-windows. The pixel to be filtered is a part of each of these sub-windows. A weighted mean of all sub-windows is computed for the pixel under consideration. The weights are accounted from a measure of heterogeneity calculated for each sub-windows. The filter is able to adapt automatically and adjust the speckle suppression strength based on local statistics. This allows the filter to preserve edges while strongly suppressing speckle over homogeneous areas. The proposed filter was compared with some well known SAR filtering techniques in terms of speckle suppression and edge preservation ability. Several experiments were performed on datasets acquired from both air-borne and space-borne SAR platforms. Some well known indices were used for quantitative comparison with other filters. Among the filters compared, the proposed filter shows good speckle suppression ability while still exhibiting reasonable edge preservation ability.