Classification of glacial lakes using integrated approach of DFPS technique and gradient analysis using Sentinel 2A data

It is important to identify and locate glacial lakes for assessing any potential hazard. This study presents a combination of semi-automatic method Double-Window Flexible Pace Search (DFPS) and edge detection technique to identify glacial lakes using Sentinel 2A satellite data. Initially, Normalized Difference Water Index (NDWI) has been used to identify water and non-water areas, while DFPS and Edge detection technique has been used to identify an optimum threshold value to distinguish between water and shadow areas. The optimal threshold from DFPS process is 0.21, while threshold value of gradient magnitude using edge detection process is 0.318. The number of glacial lakes identified using the above algorithm is in close agreement with previously published results on glacial lakes in Gangotri glacier using different techniques. Thus, a combination of DFPS and edge detection process has successfully segregated glacial lakes from other features present in Gangotri glacier.     

Entropy-based fuzzy classification parameter optimization using uncertainty variation across spatial resolution

In the past researchers have suggested hard classification approaches for pure pixel remote sensing data and to handle mixed pixels soft classification approaches have been studied for land cover mapping. In this research work, while selecting fuzzy c-means (FCM) as a base soft classifier entropy parameter has been added. For this research work Resourcesat-1 (IRS-P6) datasets from AWIFS, LISSIII and LISS-IV sensors of same date have been used. AWIFS and LISS-III datasets have been used for classification and LISS-III and LISS-IV data were used for reference data generation, respectively. Soft classified outputs from entropy based FCM classifiers for AWIFS and LISS-III datasets have been evaluated using sub-pixel confusion uncertainty matrix (SCM). It has been observed that output from FCM classifier has higher classification accuracy with higher uncertainty but entropy-based classifier with optimum value of regularizing parameter generates classified output with minimum uncertainty.     

Sustainable Utilisation of Water Resource in Watershed Perspective - A Case Study in Alaunja Watershed, Hazaribagh, Bihar

Alaunja watershed, located on ‘Chotanagpur’ plateau of Bihar, is mainly under single cropping. The area receives more than 1000 mm of rainfall but it has not been utilised properly to increase the agricultural output. The remote sensing, geophysical, DBTM (Digital Basement Terrain Model) and GIS (Geographic information System) techniques were used for providing scientific database for sustainable utilisation of water resources in watershed perspective. The landuse, soil and surface water body maps have been prepared using remotely sensed data. DBTM has been generated based on depth of basement information derived from geophysical data to provide information regarding aquifer geometry, fracture zones and sub-surface basins. Analysis indicated that surface and groundwater resources have potential to irrigate 53 per cent of geographical area of the watershed. But at present, this available potential has been utilised only to irrigate 7.03 per cent area of the watershed. Feasibility for large scale development of groundwater through dugwell is possible only in 2 per cent area of the watershed. The available groundwater potential to irrigate 28 per cent area of watershed can not be utilised through dugwells. The surface water potential is also poorly utilised. The present study also helped in prioritising the water resource development activities.     

Multiple statistical approaches for the discrimination of mangrove species of Rhizophoraceae using transformed field and laboratory hyperspectral data

This study aims at discriminating eight mangrove species of Rhizophoraceae family of Indian east coast using field and laboratory spectra in spectral range (350–2500 nm). Parametric and non-parametric statistical analyses were applied on spectral data in four spectral modes: (i) reflectance (ii) continuum removed, (iii) additive inverse and (iv) continuum removed additive inverse. We introduced continuum removal of inverse spectra to utilize the advantage of continuum removal in reflectance region. Non-parametric test gave better separability than parametric test. Principal component analysis and stepwise discriminant analysis were applied for feature reduction and to identify optimal wavelengths for species discrimination. To quantify the separability, Jeffries–Matusita distance measure was derived. Green (550 nm), red edge (680–720 nm) and water absorption region (1470 and 1850 nm) were found to be optimal wavelengths for species discrimination. The continuum removal of additive inverse spectra gave better separability than the continuum removed spectra.     

Forest cover mapping of East district,Sikkim using IRS-1A LISS II satellite data

Remote sensing techniques have been applied to classify tour density classes within each of the forest type along with other major landuse/landcover classes in the East district, Sikkim using IRS-1A LISS II satellite data pertaining to the period of November, 1988. The shadow problem in rugged terrain and difficulty in acquiring cloud free data for different seasons pose problems to achieve considerable mapping accuracy. In the present study, the forests of the district were delineated through supervised classification techniques using maximum likelihood algorithm into five forest types as sal forests, subtropical broad-leaved forests, Himalayan wet temperate forests, Rhododendron forests and alpine forests. The alpine forests were further stratified into two categories as moist alpine scrub and dry alpine scrub. The statistical data obtained from the present study shows that 55.47 percent of the total geographical area of the East district was under forest cover. An overall accuracy of more than 85 percent in correctly delineating forest classes was achieved.     

Analyzing multi-year rice-fallow dynamics in Odisha using multi-temporal Landsat-8 OLI and Sentinel-1 Data

ABSTRACT

Sustainable intensification of existing cropland is one of the most viable options for meeting the escalating food demands of the ever-increasing population in the world. Accurate geospatial data about the potential single-crop (rice-fallows) areas is vital for policymakers to target the agro-technologies for enhancing crop productivity and intensification. Therefore, the study aimed to evaluate and understand the dynamics of rice-fallows in the Odisha state of India, using SAR (Sentinel-1) and Optical (Landsat OLI) datasets. This study utilized a decision-tree approach and Principal component analysis (PCA) for the segmentation and separation of different vegetation classes. The estimated overall accuracy of extracted rice-fallow maps was in the range of 84 to 85 percent. The study identified about 2.2, 2.0 and 2.1mha of Rice-Fallows in the years 2015–16, 2016–17, and 2017–18, respectively. The combined analysis (all three years) of rice-fallow maps identified about 1.34mha of permanent rice-fallows, whereas the remaining 0.6–0.8mha area was under the current-fallow category. About 50% of the total permanent rice-fallows were detected in the rainfed areas of Mayurbhanj, Bhadrak, Bolangir, Sundargarh, Keonjhar, Baleswar, Nawarangpur and Bargarh districts. The study also illustrated the time-series profiles of SMAP (soil moisture) datasets for the ten agroclimatic zones of the Odisha, which can be utilized (along with rice-fallow maps) for the selection of crop and cultivars (e.g. short or medium duration pulses or oilseeds) to target the rice fallows. The approach utilized in the current study can be scaled up in similar areas of South and South-east Asia and Africa to identify single-crop areas for targeting improved technologies for enhanced crop productivity and intensification.     

Evaluation of significant sources influencing the variation of physico-chemical parameters in Port Blair Bay,South Andaman,India by using multivariate statistics

Port Blair is the capital city of Andaman & Nicobar Islands, the union territory of India. More than 50% of the population of these islands lives around Port Blair Bay. Therefore the anthropogenic effects in the bay water were studied for monitoring purpose from seven stations. Physico-chemical parameters of seawater were analyzed in samples collected once in every 3 months for 2 years from seven sampling stations located in Port Blair Bay, South Andaman Island to evaluate the spatial and tidal variation. Cluster analysis and factor analysis were applied to the experimental data in an attempt to understand the sources of variation of physico-chemical parameters. In cluster analysis, the stations Junglighat Bay and Phoenix Bay having high anthropogenic influence formed a separate group. The factors obtained from factor analysis indicated that the parameters responsible for physico-chemical variations are mainly related to land run-off, sewage outfall and tidal flow.     

Spherical variable-energy blast wave in self-gravitating gas

Modified similarity method has been used to study the propagation of spherical-variable energy blast waves through a self-gravitating gas. For an energy inputE =E ₀t^4/3, whereE is the energy released up to timet andE ₀ is a functional constant, the similarity solutions correct up to third approximation have been obtained. It is found that the effects of self-gravitational forces are of third order. An increase in the parameterA ² (characterising the gravitational field) increases the shock velocity.     

Hydrogeochemistry of the Koyna River basin,India

Hydrogeochemistry of the Koyna River basin, famous for the Koyna earthquake (magnitude 7) of 1967, has been studied. Basalt is the primary aquifer; laterites, alluvium, and talus deposits form aquifers of secondary importance. Groundwater generally occurs under water table conditions in shallow aquifers. Deeper aquifers are associated only with basalts. One hundred and 87 water samples were collected from various sources, such as dugwells, borewells, springs, and surface water, including 40 samples for analysis of iron. Only major constituents were analyzed. Analyses show that the concentrations of Ca²⁺ exceed that of Mg²⁺ in almost all water samples; the concentrations of Na⁺ are generally next to Ca²⁺ and are always higher than that of K⁺; and CO₃ ^2– and SO₄ ^2– are very low and are often negligible. Groundwater in borewells tapping deeper aquifers has higher mineralization compared to that in dugwells representing shallow aquifers. Majority of the water samples are dominated by alkaline earths (Ca²⁺, Mg²⁺) and weak acids (HCO₃ ⁻, CO₃ ^2–). Groundwater from shallow aquifers is generally calcium-bicarbonate type (53%) and calcium-magnesium-bicarbonate type (27%). In case of deeper aquifer, it is mostly calcium-magnesium-bicarbonate type (29%), sodium-bicarbonate type (24%), calcium-bicarbonate type (19%), calcium-magnesium-sodium-bicarbonate type (19%) and sodium-calcium-bicarbonate type (9%). Groundwater water is generally fit for drinking and irrigation purposes, except in the lower reaches of the Koyna River basin, which is affected by near water logging conditions.