Browsing by Author "Aman Arora"

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Advances in remote sensing technology and the three poles
(wiley, 2023) Manish Pandey; Prem Chandra Pandey; Yogesh Ray; Aman Arora; Shridhar D. Jawak; Uma Kant Shukla
ADVANCES IN REMOTE SENSING TECHNOLOGY AND THE THREE POLES Covers recent advances in remote sensing technology applied to the "Three Poles", a concept encompassing the Arctic, Antarctica, and the Himalayas Advances in Remote Sensing Technology and the Three Poles is a multidisciplinary approach studying the lithosphere, hydrosphere (encompassing both limnosphere, and oceanosphere), atmosphere, biosphere, and anthroposphere, of the Arctic, the Antarctic and the Himalayas. The drastic effects of climate change on polar environments bring to the fore the often subtle links between climate change and processes in the hydrosphere, biosphere, and lithosphere, while unanswered questions of the polar regions will help plan and formulate future research projects. Sample topics covered in the work include: • Terrestrial net primary production of the Arctic and modeling of Arctic landform evolution • Glaciers and glacial environments, including a geological, geophysical, and geospatial survey of Himalayan glaciers • Sea ice dynamics in the Antarctic region under a changing climate, the Quaternary geology and geomorphology of Antarctica • Continuous satellite missions, data availability, and the nature of future satellite missions, including scientific data sharing policies in different countries • Software, tools, models, and remote sensing technology for investigating polar and other environments For postgraduates and researchers working in remote sensing, photogrammetry, and landscape evolution modeling, Advances in Remote Sensing Technology and the Three Poles is a crucial resource for understanding current technological capabilities in the field along with the latest scientific research that has been conducted in polar areas. © 2023 John Wiley & Sons Ltd. All rights reserved.
Climate change effect on water resources in Varanasi district, India
(John Wiley and Sons Ltd, 2020) Mărgărit-Mircea Nistor; Praveen K. Rai; Vikas Dugesar; Varun N. Mishra; Prafull Singh; Aman Arora; Virendra Kumar Kumra; Iulius-Andrei Carebia
Evapotranspiration and water availability are driven by changing climate and land cover parameters. In the present study, climatological records and land cover data were analysed simultaneously to accomplish the spatial distributions of climate change effects on water resources in Varanasi district, north India. Humidity–aridity was assessed by Lang's rain factor and De Martonne's aridity index, based on mean monthly rainfall and air temperature from seven meteorological stations. The climate change effect on water resources was evaluated using a 5 × 5 matrix that includes water availability and the aridity index by considering two time periods: 1941–1970 (1950s) and 1971–2000 (1980s). The methodology is based on seasonal crop evapotranspiration (ETc) (initial, mid-season, end season and cold season) and annual water availability calculations. The high values (≤ 1,045 mm) of ETc were identified during the mid-season stage. Water availability indicates decreases in the maximums from 718 to 636 mm during the two analysed periods, with a negative impact at the spatial scale. Lang's rain factor (< 40) indicates an arid climate in the northwest, west, east and central parts of the district and a humid climate (Lang's rain factor > 40) in the south. De Martonne's aridity index indicates rapid aridization from south to north (28.3 in the 1950s and 25.6 in the 1980s). The high and very high climate effects on water resources in Varanasi district were found mainly in the crop lands, while in the urban areas the climate effect is low. The much affected area by climate change and land cover was depicted during the recent period (1980s). This statement was proved also by the Mann and Kendall test, which indicates a negative trend for annual precipitation at all stations (for the period 1941–2000), while the mean annual temperature had a positive trend for four stations. These findings suggest that climate change had a negative effect on water resources during the last 60 years in the study area. © 2019 The Authors. Meteorological Applications published by John Wiley & Sons Ltd on behalf of the Royal Meteorological Society.
Climate Changes over the Indian Subcontinent: Scenarios and Impacts
(Springer Science and Business Media B.V., 2022) R.K. Mall; Nidhi Singh; Subhi Patel; Saumya Singh; Aman Arora; R. Bhatla; R.S. Singh; P.K. Srivastava
It has now been well established that the rise in global mercury has driven climate change phenomena that have led to extreme temperature events, sea level rise, change in the hydrological cycle, frequent droughts and floods, and cyclones and forest fires and caused a myriad of adverse impacts on vital worldwide sectors such as agriculture, water and health. The impact of climate change is anticipated to be more adverse for destitute and socioeconomically deprived populations from developing and underdeveloped nations owing to poor adaptive capacity and higher sensitivity. The present chapter focuses on the Indian context, where it presents shreds of evidence of the impact of climate change in the past, present and future such as extreme events like heat waves, diurnal temperature range, shrinking of Himalayan glaciers, shifting of rainfall patterns, increased susceptibility to floods and droughts, and its impact on some of the important sectors. The chapter shows clear evidence of a decline in crop production and productivity of some of the important crops such as wheat, rice, sugarcane, maize, potato, tomato, etc. The recent studies established an increase in morbidity and mortality associated with extreme temperature and poor air quality associated with increased particulate matter (PM), NOx, SOx, O3, black carbon and other ambient pollutants. In addition, important river basins of India, such as Gomti, Gandak, Vaigai, Mahi, Varuna and Ghaghra, have shown increased susceptibility to flooding and drought events that are more likely to be frequent and severe in the future under different climate change scenarios owing to changes in erratic rainfall patterns and increasing temperature. The chapter also discusses the potential adaptation and mitigation strategies that would help policymakers to combat climate change amid the rising susceptible population. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
Comparative evaluation of geospatial scenario-based land change simulation models using landscape metrics
(Elsevier B.V., 2021) Aman Arora; Manish Pandey; Varun Narayan Mishra; Ritesh Kumar; Praveen Kumar Rai; Romulus Costache; Milap Punia; Liping Di
Assessing the performance of land change simulation models is a critical step when predicting the future landscape scenario. The study was conducted in the district of Varanasi, Uttar Pradesh, India because the city being “the oldest living city in the world” attracts a vast population to reside here for short and long-term, leaving the city's ecosystem more exposed to fragility and less resilient. In this work, an approach based on landscape metrics is introduced for comparing the performance of the ensemble models designed to simulate the landscape changes. A set of landscape metrics were applied in this study that offered comprehensive information on the performance of scenario-based simulation models from the viewpoint of the spatial ordering of simulated results against the related reference maps. A supervised support vector machine classification technique was applied to derive the LULC maps using Landsat satellite images of the year 1988, 2001, and 2015. The LULC maps of 1988 and 2001 were used to simulate the LULC scenario for 2015 using three Markov chain-based simulation models namely, multi-layer perceptron-Markov chain (MLP_Markov), cellular automata-Markov chain (CA_Markov), and stochastic-Markov chain (ST_Markov) respectively. The mean relative error (MRE), as a measure of the success of simulation models, was calculated for metrics. The MRE values at both the class and landscape levels were accounted for 21.63 and 11.45% respectively using MLP_Markov simulation model. The MRE values at both the class and landscape levels were accounted for 39.61 and 28.31% respectively using CA_Markov simulation model. The MRE values at both the class and landscape levels were accounted for 55.36 and 45.75% respectively using ST_Markov simulation model. The MRE values considered at class and landscape levels are further evaluated qualitatively for comparing the performance of simulation models. The results indicate that the MLP_Markov performed excellently, followed by CA_Markov and ST_Markov simulation models. This work showed an ordered and multi-level spatial evaluation of the models’ performance into the decision-making process of selecting the optimum approach among them. Landscape metrics as a vital characteristic of the utilized method, employ the maximum potential of the reference and simulated layers for a performance evaluation process. It extends the insight into the main strengths and drawbacks of a specific model when simulating the spatio-temporal pattern. The quantified information of transition among landscape categories also provides land policy managers a better perception to build a sustainable city master plan. © 2021 The Author(s)
Flood Susceptibility Modeling in a Subtropical Humid Low-Relief Alluvial Plain Environment: Application of Novel Ensemble Machine Learning Approach
(Frontiers Media S.A., 2021) Manish Pandey; Aman Arora; Alireza Arabameri; Romulus Costache; Naveen Kumar; Varun Narayan Mishra; Hoang Nguyen; Jagriti Mishra; Masood Ahsan Siddiqui; Yogesh Ray; Sangeeta Soni; U.K. Shukla
This study has developed a new ensemble model and tested another ensemble model for flood susceptibility mapping in the Middle Ganga Plain (MGP). The results of these two models have been quantitatively compared for performance analysis in zoning flood susceptible areas of low altitudinal range, humid subtropical fluvial floodplain environment of the Middle Ganga Plain (MGP). This part of the MGP, which is in the central Ganga River Basin (GRB), is experiencing worse floods in the changing climatic scenario causing an increased level of loss of life and property. The MGP experiencing monsoonal subtropical humid climate, active tectonics induced ground subsidence, increasing population, and shifting landuse/landcover trends and pattern, is the best natural laboratory to test all the susceptibility prediction genre of models to achieve the choice of best performing model with the constant number of input parameters for this type of topoclimatic environmental setting. This will help in achieving the goal of model universality, i.e., finding out the best performing susceptibility prediction model for this type of topoclimatic setting with the similar number and type of input variables. Based on the highly accurate flood inventory and using 12 flood predictors (FPs) (selected using field experience of the study area and literature survey), two machine learning (ML) ensemble models developed by bagging frequency ratio (FR) and evidential belief function (EBF) with classification and regression tree (CART), CART-FR and CART-EBF, were applied for flood susceptibility zonation mapping. Flood and non-flood points randomly generated using flood inventory have been apportioned in 70:30 ratio for training and validation of the ensembles. Based on the evaluation performance using threshold-independent evaluation statistic, area under receiver operating characteristic (AUROC) curve, 14 threshold-dependent evaluation metrices, and seed cell area index (SCAI) meant for assessing different aspects of ensembles, the study suggests that CART-EBF (AUCSR = 0.843; AUCPR = 0.819) was a better performant than CART-FR (AUCSR = 0.828; AUCPR = 0.802). The variability in performances of these novel-advanced ensembles and their comparison with results of other published models espouse the need of testing these as well as other genres of susceptibility models in other topoclimatic environments also. Results of this study are important for natural hazard managers and can be used to compute the damages through risk analysis. Copyright © 2021 Pandey, Arora, Arabameri, Costache, Kumar, Mishra, Nguyen, Mishra, Siddiqui, Ray, Soni and Shukla.
Optimization of state-of-the-art fuzzy-metaheuristic ANFIS-based machine learning models for flood susceptibility prediction mapping in the Middle Ganga Plain, India
(Elsevier B.V., 2021) Aman Arora; Alireza Arabameri; Manish Pandey; Masood A. Siddiqui; U.K. Shukla; Dieu Tien Bui; Varun Narayan Mishra; Anshuman Bhardwaj
This study is an attempt to quantitatively test and compare novel advanced-machine learning algorithms in terms of their performance in achieving the goal of predicting flood susceptible areas in a low altitudinal range, sub-tropical floodplain environmental setting, like that prevailing in the Middle Ganga Plain (MGP), India. This part of the Ganga floodplain region, which under the influence of undergoing active tectonic regime related subsidence, is the hotbed of annual flood disaster. This makes the region one of the best natural laboratories to test the flood susceptibility models for establishing a universalization of such models in low relief highly flood prone areas. Based on highly sophisticated flood inventory archived for this region, and 12 flood conditioning factors viz. annual rainfall, soil type, stream density, distance from stream, distance from road, Topographic Wetness Index (TWI), altitude, slope aspect, slope, curvature, land use/land cover, and geomorphology, an advanced novel hybrid model Adaptive Neuro Fuzzy Inference System (ANFIS), and three metaheuristic models-based ensembles with ANFIS namely ANFIS-GA (Genetic Algorithm), ANFIS-DE (Differential Evolution), and ANFIS-PSO (Particle Swarm Optimization), have been applied for zonation of the flood susceptible areas. The flood inventory dataset, prepared by collected flood samples, were apportioned into 70:30 classes to prepare training and validation datasets. One independent validation method, the Area-Under Receiver Operating Characteristic (AUROC) Curve, and other 11 cut-off-dependent model evaluation metrices have helped to conclude that the ANIFS-GA has outperformed other three models with highest success rate AUC = 0.922 and prediction rate AUC = 0.924. The accuracy was also found to be highest for ANFIS-GA during training (0.886) & validation (0.883). Better performance of ANIFS-GA than the individual models as well as some ensemble models suggests and warrants further study in this topoclimatic environment using other classes of susceptibility models. This will further help establishing a benchmark model with capability of highest accuracy and sensitivity performance in the similar topographic and climatic setting taking assumption of the quality of input parameters as constant. © 2020 Elsevier B.V.
Performance evaluation of textural features in improving land use/land cover classification accuracy of heterogeneous landscape using multi-sensor remote sensing data
(Springer Verlag, 2019) Varun Narayan Mishra; Rajendra Prasad; Praveen Kumar Rai; Ajeet Kumar Vishwakarma; Aman Arora
Texture analysis of remote sensing images has been received a substantial amount of attention as it plays a vital role in improving the classification accuracy of heterogeneous landscape. However, it is inadequately studied that how the images from different sensors with varying spatial resolutions influence the choice of textural features. This study endeavors to examine the textural features from the Landsat 8-OLI, RISAT-1, Resourcesat 2-LISS III, Sentinel-1A and Resourcesat 2-LISS IV satellite images with spatial resolution of 30, 25, 23.5, 5×20 and 5.8 m respectively, for improving land use/land cover (LULC) classification accuracy. The textural features were extracted from the aforesaid sensor data with the assistance of gray-level co-occurrence matrix (GLCM) with different moving window sizes. The best combination of textural features was recognized using standard deviations and correlation coefficients following separability analysis of LULC categories based on training samples. A supervised support vector machine (SVM) classifier was employed to perform LULC classification and the results were evaluated using ground truth information. This work demonstrates the significance of textural features in improving the classification accuracy of heterogeneous landscape and it becomes more significant as the spatial resolution improved. It is also revealed that textures are vital especially in the case of SAR data. © 2018, Springer-Verlag GmbH Germany, part of Springer Nature.
Preface
(wiley, 2023) Manish Pandey; Prem Chandra Pandey; Yogesh Ray; Aman Arora; Shridhar D. Jawak; Uma Kant Shukla
[No abstract available]
Structural control on the landscape evolution of son alluvial fan system in ganga foreland basin
(wiley, 2021) Manish Pandey; Yogesh Ray; Aman Arora; U.K. Shukla; Shyam Ranjan
We have applied transform index in the bedrock-alluvium mixed and alluvial sub-basins of thick alluvial cover in the marginal plain of the Ganga River Basin, located in the Himalayan foredeep. This index has been tested to delineate zones of drainage (dis)equilibrium wherein divide reorganization is in progress. The Himalayan foreland basin (HFB) is one of the largest and most dynamic terrestrial basins on the Earth's surface. It is interspersed with a number of mega- and micro-sized alluvial fans deposited by progenitors of the three of the world's largest rivers and their tributary systems. The thick alluvial deposits record sensitivity of the controlling factors continuously engaged in the Himalayan orogenesis. The disequilibrium of the stream channels and their corresponding basins also record the interplay of control factors responsible for shaping the channel and watershed ridge geometry. Landscape (in)stability and stream network reorganization of the bedrock channel stream profiles quantified through the transform have been proved to be a promising tool to work out the behavioral changes in the stream basin and network geometry. transform maps predict the direction of movement of (dis)equilibrium in the landscape through assessment across divide anomalies. This metric has never been applied in purely alluvial basins. Here, we have applied it as an experiment for streams flowing over the Son Alluvial Fan System (SAFS) to examine whether the effects of surface, subsurface, lithologic, and climatic controls are discernible in the transform map. Geomorphological and other field photo evidences have also been used to corroborate the findings. We found that zones of some of the reported surface and subsurface faults, e.g. East Patna fault (EPF) and West Patna fault (WPF), Munger-Saharsa Ridge fault (MSRF), and many such reported tectonic features, are apparently highlighted in the map of the first order basins in the map of the SAFS and surroundings. We have also attempted to attribute the probable dominant factors which might have contributed to the differences in the values along the first-order streams in the alluvial fan setting and its surroundings at the southern margin of the HFB leading to landscape evolution of the present day SAFS. The problem of delineating the extent of SAFS has also been revisited. © 2021 John Wiley & Sons Ltd. All rights reserved.
The three poles: Advances in remote sensing in relation to spheres of the planet earth
(wiley, 2023) Manish Pandey; Prem Chandra Pandey; Yogesh Ray; Aman Arora; Shridhar D. Jawak; Uma Kant Shukla
[No abstract available]