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Lieferstatus:	i.d.R. innert 5-10 Tagen versandfertig
Veröffentlichung:	Mai 2025
Genre:	EDV / Informatik
	Artificial Intelligence (AI) / Big Data Analytics in Forestry / biodiversity monitoring / Climate-Driven Forest Stress / data fusion techniques / Deep Learning / Environmental Monitoring / Explainable AI (XAI) / Forest Disease Mapping / forest health monitoring / Forest Pest Detection / Geographic Information Systems (GIS) / Hyper spectral Imaging / Internet of Things (IoT) in Forestry / LiDAR / Machine Learning (ML) / NDVI) / Policy and Institutional Frameworks / predictive modeling / Remote sensing / satellite imagery / spatiotemporal analysis / sustainable forest management / Unmanned Aerial Vehicles (UAVs) / Vegetation Indices (e.g. / Wildfire Prediction mehr...
ISBN:	9783389142219
EAN-Code:	9783389142219
Verlag:	Grin Verlag
Einband:	Kartoniert
Sprache:	English
Dimensionen:	H 210 mm / B 148 mm / D 6 mm
Gewicht:	118 gr
Seiten:	72
Bewertung:	Titel bewerten / Meinung schreiben

Inhalt:

Scientific Study from the year 2025 in the subject Computer Sciences - Artificial Intelligence, , language: English, abstract: Forest ecosystems play a pivotal role in global ecological stability, biodiversity conservation, and climate regulation. Monitoring forest health is critical to combating deforestation, disease outbreaks, and climate-induced stressors. This book presents the integration of Artificial Intelligence (AI) and Remote Sensing (RS) technologies as transformative tools for forest health monitoring. The book explores AI-based approaches, data fusion techniques, satellite and UAV applications, and real-world case studies, highlighting the potential for predictive, scalable, and real-time ecosystem management. Forests are indispensable components of Earth's ecological and climatic systems, serving as critical reservoirs of biodiversity, carbon sinks, and providers of ecosystem services. However, they are increasingly threatened by deforestation, climate-induced stressors, pest outbreaks, and anthropogenic disturbances. Traditional forest health monitoring methods-such as manual ground surveys and visual inspections-are labor-intensive, limited in spatial and temporal scope, and often insufficient for large-scale, dynamic assessments. Recent advancements in Artificial Intelligence (AI) and Remote Sensing (RS) technologies have enabled transformative approaches to monitoring forest health with improved scalability, accuracy, and temporal frequency. This book investigates the synergistic integration of AI and RS for comprehensive forest health monitoring. Key themes include the use of satellite and Unmanned Aerial Vehicle (UAV) platforms, spectral and thermal indices, machine learning and deep learning algorithms, and real-world applications in detecting deforestation, disease outbreaks, and drought stress. By leveraging multisource data fusion and AI-driven analytics, forest monitoring systems can achieve predictive, automated, and near real-time capabilities. The book also discusses technological challenges, data limitations, and future directions, underscoring the potential of AI-RS integration in enhancing ecosystem resilience and supporting sustainable forest management in the Anthropocene era.