Browsing by Author "Aishwarya"

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An overview of remote sensing technology in forest management
(Elsevier, 2025) Aishwarya; Meenu V.V.N.L.Sudha Rani; Preeti Kumari; Pankaj Lavania; Garima Gupta; Prabhat Tiwari; Ram Kumar Singh; Manoj Kumar; Manmohan J.R. Dobriyal; Manish Srivastav; Pavan Kumar
Remote sensing technology has revolutionized the field of forest management, offering unparalleled capabilities for monitoring, assessing, and managing forested landscapes. This overview paper explores the diverse applications and advancements of remote sensing techniques in forest management. It delves into the various remote sensing technologies, including satellite imaging, LiDAR (Light Detection and Ranging), and drones equipped with high-resolution cameras, highlighting their roles in data collection, analysis, and interpretation. This chapter discusses the utilization of remote sensing data for forest inventory, species identification, habitat assessment, and monitoring of forest disturbances such as wildfires, pests, and diseases. Furthermore, it emphasizes the integration of remote sensing with Geographic Information Systems (GIS) and machine learning algorithms for enhanced accuracy in mapping, modeling, and decision-making processes. Challenges and limitations inherent in remote sensing applications within forest management are also addressed, including issues related to data accuracy, processing techniques, and cost-effectiveness. Additionally, the paper explores future trends and potential advancements in remote sensing technology, emphasizing the need for continued research and development to further improve its efficacy in sustainable forest management practices. This chapter aims to provide a comprehensive understanding of the role and significance of remote sensing technology in modern forest management, emphasizing its potential to contribute to informed decision-making, conservation efforts, and the sustainable utilization of forest resources. © 2026 Elsevier Inc. All rights reserved..
Cerebrospinal Fluid Leucine Rich Alpha-2 Glycoprotein in Children with Tubercular Meningitis with their Diagnostic and Prognostic Significance: A Prospective Study
(Springer, 2024) Rajniti Prasad; Hemlata Verma; Ragini Srivastava; Aishwarya; Animesh Kumar
[No abstract available]
Cerebrospinal Fluid Leucine Rich Alpha-2 Glycoprotein in Children with Tubercular Meningitis with their Diagnostic and Prognostic Significance: A Prospective Study: Authors’ Reply
(Springer, 2024) Rajniti Prasad; Hemlata Verma; Ragini Srivastava; Aishwarya; Animesh Kumar
[No abstract available]
Classification of forest stands based on tree crown morphology and dimensions
(Elsevier, 2025) Pavan Kumar; Hukum Singh; Manoj Kumar; Aishwarya; Narine Hakobayan; Manish Srivastav; Ram Kumar Singh
Accurately classifying forest stands is vital for effective forest management, ecological research, and conservation. This research explores the classification of forest stands based on tree crown morphology and dimensions, aiming to find a reliable method for categorizing forest stands through detailed analysis of tree crown characteristics. Tree crowns—the top part of trees composed of branches and leaves—serve as key indicators of forest structure and health. By examining various morphological features and dimensions of tree crowns, including their shape, size, and density, our study presents a refined classification system applicable to diverse forest ecosystems. This research not only enhances our understanding of forest stand dynamics but also provides practical tools for forest management and biodiversity preservation. © 2026 Elsevier Inc. All rights reserved..
Nitrogen dioxide as proxy indicator of air pollution from fossil fuel burning in New Delhi during lockdown phases of COVID-19 pandemic period: impact on weather as revealed by Sentinel-5 precursor (5p) spectrometer sensor
(Springer Science and Business Media B.V., 2024) Pavan Kumar; Aishwarya; Prashant Kumar Srivastava; Manish Kumar Pandey; Akash Anand; Jayanta Kumar Biswas; Martin Drews; Manmohan Dobriyal; Ram Kumar Singh; Manuel De la Sen; Sati Shankar Singh; Ajai Kumar Pandey; Manoj Kumar; Meenu Rani
There has been a long-lasting impact of the lockdown imposed due to COVID-19 on several fronts. One such front is climate which has seen several implications. The consequences of climate change owing to this lockdown need to be explored taking into consideration various climatic indicators. Further impact on a local and global level would help the policymakers in drafting effective rules for handling challenges of climate change. For in-depth understanding, a temporal study is being conducted in a phased manner in the New Delhi region taking NO2 concentration and utilizing statistical methods to elaborate the quality of air during the lockdown and compared with a pre-lockdown period. In situ mean values of the NO2 concentration were taken for four different dates, viz. 4th February, 4th March, 4th April, and 25th April 2020. These concentrations were then compared with the Sentinel (5p) data across 36 locations in New Delhi which are found to be promising. The results indicated that the air quality has been improved maximum in Eastern Delhi and the NO2 concentrations were reduced by one-fourth than the pre-lockdown period, and thus, reduced activities due to lockdown have had a significant impact. The result also indicates the preciseness of Sentinel (5p) for NO2 concentrations. © The Author(s), under exclusive licence to Springer Nature B.V. 2023.
Recapitulation of Advanced Agricultural Technologies for Climate Resilience
(Springer Science+Business Media, 2025) Aishwarya; Himanshi Singh; Pavan Kumar
Modern Technology for Climate Change Agriculture focuses on exploring innovative technological solutions to address the challenges posed by climate change in agriculture. © 2025 The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG.
Sensors-Based Irrigation for Increasing Crop and Water Productivity
(Springer Science+Business Media, 2025) Aishwarya; Arvind P. Kumar; Pavan Kumar
Sensor-based decision tools provide a quick assessment of nutritional and physiological health status of crop, thereby enhancing the crop productivity. This chapter aims to revolutionize traditional agricultural practices by implementing a state-of-the-art sensors-based irrigation system. The objective is to optimize water usage, enhance crop productivity, and contribute to sustainable farming practices. The proposed system utilizes advanced sensors and data analytics to monitor soil moisture levels, weather conditions, and crop water requirements in real time. By employing precision irrigation techniques, the project seeks to maximize water use efficiency, minimize wastage, and foster a more sustainable and resilient agricultural ecosystem. The study also includes an economic analysis to evaluate the cost-effectiveness of the sensors-based irrigation system, ensuring its viability for farmers. Through this innovative approach, the chapter aspires to address water scarcity challenges, promote resource-efficient farming, and contribute to global food security. © 2025 The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG.