Browsing by Author "Prashant Kumar Srivastava"

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A Comprehensive Review of Empirical and Dynamic Wildfire Simulators and Machine Learning Techniques used for the Prediction of Wildfire in Australia
(Springer Science and Business Media B.V., 2025) Harikesh Singh; Kenneth Li Minn Ang; Dipak Paudyal; Mauricio A. Acuna; Prashant Kumar Srivastava; Sanjeev Kumar Srivastava
Wildfires pose significant environmental threats in Australia, impacting ecosystems, human lives, and property. This review article provides a comprehensive analysis of various empirical and dynamic wildfire simulators alongside machine learning (ML) techniques employed for wildfire prediction in Australia. The study examines the effectiveness of traditional empirical methods, dynamic physical models, and advanced ML algorithms in forecasting wildfire spread and behaviour. Key simulators discussed include PHOENIX Rapidfire, SPARK, AUSTRALIS, REDEYE, and IGNITE, each evaluated for their inputs, models, and outputs. Additionally, the application of ML methods such as artificial neural networks, logistic regression, decision trees, and support vector machines is explored, highlighting their predictive capabilities and limitations. The integration of these advanced techniques is essential for enhancing the accuracy of wildfire predictions, enabling better preparedness and response strategies. This review aims to inform future research and development in wildfire prediction and management, ultimately contributing to more effective fire mitigation efforts in Australia and beyond. © The Author(s) 2025.
A general approach to forest stand classification
(Elsevier, 2025) Megha Paul; Prashant Kumar Srivastava; Sanjeev Kumar Srivastava; Pavan Kumar
The mapping of forests, evaluation of habitat quality, research into the dynamics of forests, and development of sustainable management techniques are only a few uses for forest typologies. The forest plots vertical and horizontal structures serve as the primary categorization standards in quantitative typologies designed for forestry applications. Forest typologies in which the univariate or bivariate distribution of tree diameters or heights is combined with species composition data to calculate coefficients that assess the dissimilarity of forest stands. One of the most important steps in planning forest management is classifying forest stands, but it takes time and is subject to subjectivity. The increasing availability of LiDAR data and multispectral photos presents an opportunity to enhance stand categorization using remotely sensed data. Using OBIA, forest stands have been automatically classified using ASTER images and low-density LiDAR data. In order to segment forests, OBIA was used in conjunction with VNIR ASTER bands to extract mean height, canopy cover, and the canopy model from LiDAR data. In order to compare the segmentation results, it was necessary to evaluate the internal heterogeneity of the segments. Multispectral information combined with OBIA and low-density LiDAR data are useful tools for stand classification. When it comes to distinguishing between broad-leaved, conifer, and mixed stands, multispectral pictures offer a limited predictive relevance for species distinction. However, the performance of ASTER data could be improved with higher spatial resolution VNIR images, especially submetric VNIR orthophotos. LiDAR data, however, has a lot of possibilities for depicting forest structure. The fast developing technology of drones and the increasing demand for high-resolution datasets from government agencies are factors that contribute to this perspective. © 2026 Elsevier Inc. All rights reserved..
Advanced Geospatial and Ground Based Techniques in Forest Monitoring
(Elsevier, 2025) Pavan Kumar; Prashant Kumar Srivastava; Mohammed Latif Khan; Ayyanadar Arunachalam; Partha Sarthi Roy; Kireet Vijay Sanil Kumar
Muthu Rajkumar, Parnika Gupta, ... Mohammed Latif Khan © 2026 Elsevier Inc. All rights are reserved, including those for text and data mining, AI training, and similar technologies..
AI-driven approaches for forest growth assessment and management
(Elsevier, 2025) Srishti Gwal; Ayushi Gupta; Prachi Singh; Prashant Kumar Srivastava; Sanjeev Kumar Srivastava
The advent of digital data has markedly increased the utilization of Artificial Intelligence (AI) in forestry, significantly enhancing the precision and efficiency of forest monitoring. This chapter explores the transformative impact of AI on forest management, tracing the evolution of AI from its foundational concepts to its wide-ranging applications in diverse sectors. It highlights AI’s ability to replicate human cognitive functions, such as learning and problem-solving, emphasizing its crucial role in improving the accuracy and effectiveness of forest monitoring systems. The discussion extends to the integration of AI with cutting-edge technologies such as machine learning, deep learning, and remote sensing. A detailed description of various algorithms, including the Generalized Linear Model, Generalized Additive Model, Partial Least Squares Regression, Gradient Boost Machine, Support Vector Machines, Random Forests, and Neural Networks, is provided and their applications in forest growth assessment, change detection, and the analysis of disease and fire risks, both globally and within the Indian context, are meticulously discussed. © 2026 Elsevier Inc. All rights reserved..
Appraisal of historical trends in maximum and minimum temperature using multiple non-parametric techniques over the agriculture-dominated Narmada Basin, India
(Springer Science and Business Media Deutschland GmbH, 2022) Sabyasachi Swain; Surendra Kumar Mishra; Ashish Pandey; Deen Dayal; Prashant Kumar Srivastava
In this study, the long-term trends in climatological parameters, viz., maximum temperature (TMAX) and minimum temperature (TMIN), are determined over 68 years (i.e., June 1951 to May 2019) using the gridded observation datasets (1° × 1° spatial resolution) of India Meteorological Department over the Narmada river basin, India. Multiple non-parametric techniques, viz., modified Mann-Kendall (MMK), Sen’s slope (SS), and Spearman’s rho (SR) tests, are used to determine monthly, seasonal, and annual trends over individual grids. The trends are also analyzed for the climatic variables spatially averaged over the entire basin to draw general conclusions on historical climate change. The results reveal a significant spatiotemporal variation in trends of TMAX and TMIN over the basin. In general, both the parameters are found to be increasing. Furthermore, the hottest months (April and May) have become hotter, and the coldest month (January) has become colder, implying a higher probability of increasing temperature extremes. Furthermore, the entire duration of 68 years is divided into two epochs of 34 years, i.e., 1951–1984 and 1985–2018, and the trend analysis of TMAX and TMIN is also carried out epoch-wise to better understand/assess the signals of climate change in recent years. In general, a relatively higher warming trend was observed in the latter epoch. As a majority of the basin area is dominated by agricultural lands, the implications of the temperature trends and their impacts on agriculture are succinctly discussed. The information reported in this study will be helpful for proper planning and management of water resources over the basin under the changing climatic conditions. © 2022, The Author(s), under exclusive licence to Springer Nature Switzerland AG.
Assessing climate-driven phenological responses of tomato crops under future climate change trajectories: A Central India perspective
(Elsevier B.V., 2025) Pashupati Nath Singh; Prashant Kumar Srivastava; Bhawana Sharma; R. K. Mall
Climate change poses a serious challenge to global agriculture, particularly by altering crop phenology and yield dynamics. This study investigates the phenological responses of tomato crops to anticipated climate scenarios by employing a Crop Simulation model, Decision Support System for Agrotechnology Transfer (DSSAT). Simulations were conducted for Central India under two Shared Socioeconomic Pathways (SSP 4.5 and SSP 8.5) across three temporal windows: near-century (2010–2039), mid-century (2040–2069), and far-century (2070–2099). Historical climate data and calibrated genetic coefficients were used to project shifts in flowering and fruiting stages under varying climate conditions. The study assessed the impacts of projected changes in temperature (T), solar radiation (Srad), and precipitation (PPT) patterns on phenological development. Climate input datasets were sourced from IMD, IPCC, and six CMIP6- Global Climate Models. Results revealed a distinct phenological advancement, characterised by a reduction in days to flowering and fruiting, along with a concurrent decline in tomato yield (Ton/ha) across all future timeframes. Increased growing season temperatures and marginal reductions in Srad were observed to accelerate crop development, while altered rainfall patterns influenced spatial variability in production. Notably, enhanced evapotranspiration demand driven by warming trends may be partially moderated by decreased radiation levels. Spatial rainfall analysis indicated intensified PPT in central zones, whereas western and northwestern regions may experience monsoonal weakening and prolonged dry spells. Model performance showed robust agreement with observed yields (R = 0.78), with validation metrics—MAE = 5.9, RMSE = 6.93, and Bias = -1.43—demonstrating consistent predictive accuracy with slight underestimation. The Nash–Sutcliffe Efficiency (NSE = 0.59) further affirms the model's applicability under future climate conditions. This research underscores the utility of process-based models in decoding climate–phenology–yield relationships and provides critical insights to inform climate-resilient agricultural strategies for sustainable tomato production in vulnerable agro-ecological regions. © 2025
Assessing soil organic carbon and its relation with biophysical and ecological parameters in tropical forest ecosystem India
(Taylor and Francis Ltd., 2025) Haroon Sajjad; Pavan Kumar; Prashant Kumar Srivastava; Shakti Om Pathak; Meraj Ahmed; Vikas Kumar; Manmohan J.R. Dobriyal; Preeti Kumari; Prem C. Pandey
Organic matter in soil is an essential parameter for assessing the agrodynamic productivity of soils. Forest productivity and health largely depend on soil organic carbon (SOC). This study aims to assess SOC levels and analyze their relationship with biophysical parameters in tropical forests. SOC was predicted using normalized difference vegetation index (NDVI) values derived from Sentinel-2A imagery. A total of 30 samples were collected through stratified random sampling based on NDVI values to estimate SOC. Regression analysis was performed between the estimated and predicted SOC, showing a strong correlation. The results indicated that SOC decreased with increasing soil depth in the Sariska Tiger Reserve, ranging from 8.27 - 26.54 t/ha at 5 cm depth and 1.9- 12.4 t/ha at 10 cm depth. NDVI was positively correlated with SOC, while the Bare Soil Index (BSI) showed a negative correlation. Additionally, soil pH and SOC were positively correlated, indicating high SOC levels. © 2024 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.
Assessment of tropical cyclone amphan affected inundation areas using sentinel-1 satellite data
(Springer, 2022) Mukunda Dev Behera; Jaya Prakash; Somnath Paramanik; Sujoy Mudi; Jadunandan Dash; Roma Varghese; Partha Sarathi Roy; P.C. Abhilash; Anil Kumar Gupta; Prashant Kumar Srivastava
Tropical cyclones as natural disturbances, influence ecosystem structure, function and dynamics at the global scale. This study assesses the inundation due to the super cyclone Amphan in coastal districts of eastern India by leveraging the computational power of Google Earth Engine (GEE) and the availability of high resolution Sentinel-1 Synthetic Aperture Radar (SAR) data. A cloud-based image processing framework was developed and implemented in GEE for classification using Random Forest algorithm. The inundation areas due to storm surge owing to cyclone Amphan, were mapped and further categorised to different land use and land cover classes based on an existing land cover map. Sentinel-1 images were useful in post-cyclone studies for the change detection analysis due to its higher temporal resolution and cloud penetration ability. The study found that the majority of agricultural and agricultural fallow lands were inundated in the coastal districts. The availability of open-source cloud-based data processing platforms provides cost effective way to rapidly gather accurate geospatial information. Such information could be useful for emergency response planning and post-event disaster management including relief, rescue and rehabilitation measures; and crop yield loss assessment. Cyclone and Land Use and Land Cover (LULC) change can have significant impacts on the human population and if both coexist, the consequences for people and the surrounding environment may be severe. © 2021, International Society for Tropical Ecology.
Characterization and assessment of hydrological droughts using GloFAS streamflow data for the Narmada River Basin, India
(Springer, 2024) Sabyasachi Swain; Surendra Kumar Mishra; Ashish Pandey; Prashant Kumar Srivastava; Saswata Nandi
Hydrological droughts severely affect the demand of water for domestic water supply, irrigation, hydropower generation, and several other purposes. The pervasiveness and consequences of hydrological droughts necessitate a thorough investigation of their characteristics, which is hindered due to unavailability of continuous streamflow records at desirable resolutions. This study aims to assess the hydrological drought characteristics and their spatial distribution using high-resolution Global Flood Awareness System (GloFAS) v3.1 streamflow data for the period 1980 to 2020. Streamflow Drought Index (SDI) was used to characterize droughts at 3-, 6-, 9-, and 12-monthly timescales starting from June, i.e., the start of water year in India. GloFAS is found to capture the spatial distribution of streamflow and its seasonal characteristics. The number of hydrological drought years over the basin varied from 5 to 11 during the study duration, implying that the basin is prone to frequent abnormal water deficits. Interestingly, the hydrological droughts are more frequent in the eastern portion of the basin, i.e., the Upper Narmada Basin. The trend analysis of multi-scalar SDI series using non-parametric Spearman’s Rho test exhibited increasing drying trends in the easternmost portions. The results were not similar for the middle and western portions of the basin, which may be due to presence of a large number of reservoirs in these regions and their systematic operations. This study highlights the importance of open-access global products that can be used for monitoring hydrological droughts, especially over ungauged catchments. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023.
Cloud computing platforms-based remote sensing big data applications
(Elsevier, 2025) Swati Suman; Swati Maurya; Varsha K. Pandey; Prashant Kumar Srivastava; Dileep Kumar Gupta
Google Earth Engine (GEE) stands as the leading cloud-based geospatial remote sensing data processing platform. GEE repositories contain a range of satellite imageries, which can be used for various environmental applications, thanks to its easy and user-friendly application programming interface (API). One of the most compelling features of GEE includes enabling its users to explore, analyze, and visualize big geospatial data easily, all without requiring access to supercomputers or specialized coding expertise. Remarkably, even a decade after GEE's launch, its impact on remote sensing and geospatial science remains largely unnoticed. In this review, we provide a state-of-the-art report on the usage of cloud computing platforms such as GEE for processing various remote sensing data sources. We further explore the application of GEE for assessing vegetation health, agricultural monitoring, disaster management, image processing, and numerous other environmental applications using GEE. © 2025 Elsevier Ltd. All rights reserved.
Crop phenology and soil moisture applications of SCATSAT-1
(Indian Academy of Sciences, 2019) Nilima R. Chaube; Sasmita Chaurasia; Rojalin Tripathy; Dharmendra Kumar Pandey; Arundhati Misra; B.K. Bhattacharya; Prakash Chauhan; Kiran Yarakulla; G.D. Bairagi; Prashant Kumar Srivastava; Preeti Teheliani; S.S. Ray
SCATSAT-1 measures the backscattering coefficient over land surfaces, which is a function of vegetation structure, surface roughness, soil moisture content, incidence angle and dielectric properties of vegetation. Scatterometer image reconstruction techniques provide fine resolution data to explore the emerging applications over land by using ambiguous backscatter from land. In this paper, 2 km resolution products of ISRO's scatterometer SCATSAT-1 are exploited for land target detection, rice crop phenology stages detection for kharif and rabi seasons and estimation of relative soil moisture over parts of India. Temporal and spatial backscatter changes are due to seasonal and changes in Land Use Land Cover. Crop phenology stages such as transplanting, maximum tillering, panicle emergence and physiological maturity stages are identified by analysing SCATSAT-1 time series along with NDVI and findings are supported by appropriate ground truth observations and crop cutting experiment (CCE) data. The relative soil moisture change detection is validated with in situ ground truth measurements using Hydraprobe, carried for SCATSAT-1 ascending and descending passes. © 2019 Current Science Association, Bengaluru.
Crop variables estimation by adaptive neuro-fuzzy inference system using bistatic scatterometer data
(Institute of Electrical and Electronics Engineers Inc., 2016) D.K. Gupta; R. Prasad; P. Kumar; V.N. Mishra; P.K.S. Dikshit; S.B. Dwivedi; A. Ohri; R.S. Singh; V. Srivastav; Prashant Kumar Srivastava
The aim of present study is to estimate the crop variables by means of high performing technique like adaptive neuro-fuzzy inference system (ANFIS) using the bistatic scatterometer data. An outdoor 4×4 m2 crop bed of rice crop was prepared for performing all the experiments. The bistatic measurements were carried out over the entire growing stages of the rice crop from transplanting to ripening stage at the angular range of 200 to 700 with the steps 50 at both HH- and VV-polarizations in X-band. The ANFIS algorithm was used for the estimation of rice crop variables. The observed bistatic scattering coefficients and crop variables (biomass, leaf area index, plant height and chlorophyll content) were interpolated with the phenological stages of the rice crop. The 80% data sets were used for training while the remaining 20% were kept separately for the testing purposes. The bistatic scattering coefficients were used as the input data sets and the rice crop variables as the target data sets of fuzzy inference system for both the polarizations. The estimated values were found closer to the observed values of rice crop variables that indicate a satisfactory performance of ANFIS algorithm for estimating rice crop variables. © 2015 IEEE.
Future projections of crop water and irrigation water requirements using a bias-corrected regional climate model coupled with CROPWAT
(IWA Publishing, 2023) Abhishek Agrawal; Prashant Kumar Srivastava; Vinod Kumar Tripathi; D.J. Shrinivasa; Swati Maurya; Reema Sharma
The study is conducted to examine the climate change impact on rice Crop Water Requirement (CWR) and Net Irrigation Requirement (NIR) using the NASA Earth Exchange Global Daily Downscaled Projection (NEX-GDDP) coupled with the CROPWAT 8.0 model. The maximum temperature (Tmax ), minimum temperature (Tmin), and rainfall projections for the baseline (years 1981–2015) and future (years 2030 and 2040) under Representative Concentration Pathway (RCP) 4.5 were derived from NEX-GDDP. To reduce the bias, linear scaling (LS) and the modified difference approach (MDA) were employed. Results show that LS performed better than the MDA along with improved statistical measures such as mean (μ), standard deviation (σ), and percent bias (Pbias), in the case of Tmax and Tmin (μ ¼ 31.14 and 19.63 °C, σ ¼ 5.75 and 6.78 °C, Pbias ¼ 1.43 and 0.33%), followed by rainfall (μ ¼ 2.67 mm, σ ¼ 4.94 mm, and Pbias ¼ 2.4%). The future climatic projections showed an increasing trend in both Tmax and Tmin, which are expected to increase by 1.7 °C by 2040. This would cause an increased range of 1.2 and 2% in 2030 and 2040, respectively. Due to a wide variation in effective rainfall (Peff ), NIR could increase by 4 and 9% in 2030 and 2040, respectively. The above results may help formulate adaptation measures to alleviate the impacts of climate change on rice production. © 2023 The Authors.
Globally validated non-unique inversion framework to estimate optically active water quality indicators using in situ and space-borne hyperspectral data sets
(Higher Education Press Limited Company, 2025) Shishir Gaur; Rajarshi Bhattacharjee; Shard Chander; Anurag Ohri; Prashant Kumar Srivastava
As water quality is a combination of multiple optically active parameters, there is a growing interest in probabilistic models to predict water quality. This study aims to add to the water quality prediction studies by introducing ensemble learning with deep learning-based mixture density networks with multiple probabilistic Gaussian distributions. We named the approach as Ensembled Gaussian Mixture Density Network (GMDN). Many existing water quality algorithms rely on localized data sets, which limits their applicability. This research addresses this by developing and evaluating the proposed model using the global in situ water quality data set GLORIA (Global Reflectance community data set for Imaging and optical sensing of Aquatic environments). We focused on estimating two key biogeochemical components (BPs): Total Suspended Solids (TSS) and Chlorophyll-a(Chla), along with one inherent optical property (IoP), the absorption coefficient of colored dissolved organic matter (αCDOM). The proposed approach performs quite reliably when evaluated on the data samples of individual countries. The GMDN algorithm has been fine-tuned on the satellite-matchup for the river Ganga near Varanasi city. The fine-tuning was implemented using the remote sensing reflectance (Rrs) of the spaceborne hyperspectral data set PRISMA (PRecursore IperSpettrale della Missione Applicativa). The contribution of the riverbed floor to the Rrs of PRISMA has been computed using physics-based simulations in the Water Color Simulator (WASI). Overall, the simultaneous use of multiple probabilistic distributions and ensembled architectures improves the predictive accuracy of WQ parameters compared to the existing operational algorithms. © Higher Education Press 2025.
Harnessing Spectral Libraries From AVIRIS-NG Data for Precise PFT Classification: A Deep Learning Approach
(John Wiley and Sons Inc, 2025) Agradeep Mohanta; Garge Sandhya Kiran; Ramandeep Kaur M. Malhi; Pankajkumar C. Prajapati; Kavi K. Oza; Shrishti Rajput; Sanjay S. Shitole; Prashant Kumar Srivastava
The generation of spectral libraries using hyperspectral data allows for the capture of detailed spectral signatures, uncovering subtle variations in plant physiology, biochemistry, and growth stages, marking a significant advancement over traditional land cover classification methods. These spectral libraries enable improved forest classification accuracy and more precise differentiation of plant species and plant functional types (PFTs), thereby establishing hyperspectral sensing as a critical tool for PFT classification. This study aims to advance the classification and monitoring of PFTs in Shoolpaneshwar wildlife sanctuary, Gujarat, India using Airborne Visible/Infrared Imaging Spectrometer-Next Generation (AVIRIS-NG) and machine learning techniques. A comprehensive spectral library was developed, encompassing data from 130 plant species, with a focus on their spectral features to support precise PFT classification. The spectral data were collected using AVIRIS-NG hyperspectral imaging and ASD Handheld Spectroradiometer, capturing a wide range of wavelengths (400–1600 nm) to encompass the key physiological and biochemical traits of the plants. Plant species were grouped into five distinct PFTs using Fuzzy C-means clustering. Key spectral features, including band reflectance, vegetation indices, and derivative/continuum properties, were identified through a combination of ISODATA clustering and Jeffries-Matusita (JM) distance analysis, enabling effective feature selection for classification. To assess the utility of the spectral library, three advanced machine learning classifiers—Parzen Window (PW), Gradient Boosted Machine (GBM), and Stochastic Gradient Descent (SGD)—were rigorously evaluated. The GBM classifier achieved the highest accuracy, with an overall accuracy (OAA) of 0.94 and a Kappa coefficient of 0.93 across five PFTs. © 2025 John Wiley & Sons Ltd.
Highlighting the compound risk of COVID-19 and environmental pollutants using geospatial technology
(Nature Research, 2021) Ram Kumar Singh; Martin Drews; Manuel De la Sen; Prashant Kumar Srivastava; Bambang H. Trisasongko; Manoj Kumar; Manish Kumar Pandey; Akash Anand; S.S. Singh; A.K. Pandey; Manmohan Dobriyal; Meenu Rani; Pavan Kumar
The new COVID-19 coronavirus disease has emerged as a global threat and not just to human health but also the global economy. Due to the pandemic, most countries affected have therefore imposed periods of full or partial lockdowns to restrict community transmission. This has had the welcome but unexpected side effect that existing levels of atmospheric pollutants, particularly in cities, have temporarily declined. As found by several authors, air quality can inherently exacerbate the risks linked to respiratory diseases, including COVID-19. In this study, we explore patterns of air pollution for ten of the most affected countries in the world, in the context of the 2020 development of the COVID-19 pandemic. We find that the concentrations of some of the principal atmospheric pollutants were temporarily reduced during the extensive lockdowns in the spring. Secondly, we show that the seasonality of the atmospheric pollutants is not significantly affected by these temporary changes, indicating that observed variations in COVID-19 conditions are likely to be linked to air quality. On this background, we confirm that air pollution may be a good predictor for the local and national severity of COVID-19 infections. © 2021, The Author(s).
Hydrological modelling for post-monsoon agricultural drought assessment and implications for the agro-economy
(Taylor and Francis Ltd., 2024) Varsha Pandey; Prashant Kumar Srivastava; Pulakesh Das; Mukunda Dev Behera; Eswar Rajasekaran
Reliable drought monitoring is a prerequisite for minimizing potential agricultural losses. Soil moisture is a key variable for monitoring agricultural drought assessment. This study conducted in the Bundelkhand region of Uttar Pradesh uses a macroscale variable infiltration capacity (VIC) hydrological model to simulate soil moisture and calculate soil moisture deficit index (SMDI) for agricultural drought assessment for the Rabi crop growing season, 1998–2016. Crop yield was linked with SMDI and other covariates using a random forest machine learning-based regression technique. The results show that the VIC model effectively simulated root zone soil moisture when compared with the reference data. Major droughts were identified in the years 2000–01, 2007–08, and 2015–16 in the study region. The RF-based crop yield prediction accuracy improved when irrigational factors were added. The study provides a noteworthy reference for drought assessment and prevention, water resource management, and ensuring food security. © 2024 IAHS.
Hyperspectral endmember extraction using convexity based purity index
(Elsevier Ltd, 2025) Dharambhai Shah; Y. N. Trivedi; Bimal Kumar Bhattacharya; Priyank B. Thakkar; Prashant Kumar Srivastava
The endmember extraction is a challenging problem in spectral unmixing (SU) of a mixed pixel in hyperspectral imagery. There are plenty of attempts to solve the endmember extraction problem. Still, the pure pixel assumption-based algorithms have probably been used most in solving the endmember extraction of SU due to the light computational burden. These pure pixel assumption-based algorithms usually follow one of the criteria: (1) Maximum simplex volume or (2) Extreme projection on a subspace. We propose a novel integrated framework that uses both the criteria mentioned above and the proposed one is referred to as the Convexity-based Pure Index (CPI) algorithm. The CPI generates a fixed number of convex sets based on the number of available bands in the hyperspectral image. The algorithm defines the purity score based on the availability of pixels in the convex sets for the two-band data. The CPI has been compared with contemporary algorithms such as Automatic Target Generation Process (ATGP), Vertex Component Analysis (VCA), Pixel Purity Index (PPI), Successive Volume MAXimization (SVMAX), Alternating Volume MAXimization (AVMAX), TRIple-P: P-norm based Pure Pixel identification (TRIP), Successive Decoupled Volume Max–Min (SDVMM), Negative ABundance-Oriented (NABO), and Entropy-based Convex Set Optimization (ECSO). The metrics, Spectral Angle Distance (SAD) and Spectral Information Divergence (SID) used in the comparison were improved up to 5.9% and 9%, respectively. The CPI outperforms prevailing algorithms on real benchmark data and new AVIRIS-NG data. The robustness of the CPI is also tested for various noisy synthetic data. The efficacy of the proposed algorithm is also tested by using qualitative analysis by visualizing the spectra comparison, and abundance maps for all real data. © 2024 COSPAR
Identification of malachite and alteration minerals using airborne AVIRIS-NG hyperspectral data
(Elsevier Ltd, 2021) Gaurav Mishra; Himanshu Govil; Prashant Kumar Srivastava
Hyperspectral remote sensing technique is a robust technique for the delineation and mapping of hydrothermally altered and weathered mineral deposits. The purpose of present study is to identify, delineate and map the altered zones by AVIRIS-NG Airborne hyperspectral data and validate the output by field survey and analysis of lab spectra. Major lithologies of the study area includes the presence of dolomite, banded iron formation, orthoquartzite, grey phyllite, granites and carbonaceous phyllite. Hydrothermal altered zones are delineated in the study area based on Airborne Visible Infrared Imaging Spectrometer-Next Generation (AVIRIS-NG) hyperspectral data. FLAASH atmospheric correction was applied to hyperspectral data followed by minimum noise fraction, pixel purity index, n-dimensional visualisation and spectral angle mapper (SAM). SAM classified image was used for identification of altered minerals such as kaolinite, talc, kaosmec, sensitive to the shortwave infrared, whereas iron oxides such as goethite and limonite were distinguished in Visible and Near Infrared region. With the help of alteration minerals, Malachite mineral stains in the study area were identified. Representative rock samples are collected from field and lab spectra generated between wavelength region of 350–2500 nm. ASD spectra and image spectra compared with the USGS spectral library minerals which shows good spectral match with the collected samples. Therefore, it can be concluded that AVIRIS-NG hyperspectral data is useful in identification, delineation and mapping of altered and weathered minerals. © 2021
Impact of extreme weather events on cropland inundation over Indian subcontinent
(Springer Science and Business Media Deutschland GmbH, 2023) A Jaya Prakash; Shubham Kumar; Mukunda Dev Behera; Pulakesh Das; Amit Kumar; Prashant Kumar Srivastava
Cyclonic storms and extreme precipitation lead to loss of lives and significant damage to land and property, crop productivity, etc. The “Gulab” cyclonic storm formed on the 24th of September 2021 in the Bay of Bengal (BoB), hit the eastern Indian coasts on the 26th of September and caused massive damage and water inundation. This study used Integrated Multi-satellite Retrievals for GPM (IMERG) satellite precipitation data for daily to monthly scale assessments focusing on the “Gulab” cyclonic event. The Otsu’s thresholding approach was applied to Sentinel-1 data to map water inundation. Standardized Precipitation Index (SPI) was employed to analyze the precipitation deviation compared to the 20 years mean climatology across India from June to November 2021 on a monthly scale. The water-inundated areas were overlaid on a recent publicly available high-resolution land use land cover (LULC) map to demarcate crop area damage in four eastern Indian states such as Andhra Pradesh, Chhattisgarh, Odisha, and Telangana. The maximum water inundation and crop area damages were observed in Andhra Pradesh (~2700 km2), followed by Telangana (~2040 km2) and Odisha (~1132 km2), and the least in Chhattisgarh (~93.75 km2). This study has potential implications for an emergency response to extreme weather events, such as cyclones, extreme precipitation, and flood. The spatio-temporal data layers and rapid assessment methodology can be helpful to various users such as disaster management authorities, mitigation and response teams, and crop insurance scheme development. The relevant satellite data, products, and cloud-computing facility could operationalize systematic disaster monitoring under the rising threats of extreme weather events in the coming years. © 2022, The Author(s), under exclusive licence to Springer Nature Switzerland AG.