Browsing by Author "Manish Srivastav"

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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..
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..