Smart Remote Sensing of Forests

Digital Twins and Artificial Intelligence
Erscheinungsjahr 2027
ISBN: 978-0-443-51406-7
Verlag: Elsevier Science

Buch, Englisch, 340 Seiten, Format (B × H): 191 mm x 235 mm

Smart Remote Sensing of Forests: Digital Twins and Digital Twins and Artificial Intelligence present a comprehensive framework for building dynamic, data-driven representations of forests. This book addresses the urgent need for real-time, large-scale forest monitoring in the context of climate change, carbon accounting, and biodiversity conservation. It synthesizes four core components: physical foundations via radiative transfer models, high-fidelity 3D data and scene construction, AI engines for biophysical parameter retrieval, and a cohesive digital twin framework capable of monitoring and predicting forest dynamics. The book also discusses data assimilation, cloud-scale deployment, and future directions for scalable, policy-relevant forest analytics. Researchers and professionals in remote sensing, forestry, and environmental sciences will gain a rigorous, integrative guide to developing smart forest monitoring systems, from data collection to predictive decision support. The book features illustrative figures, flow diagrams, and accompanying codes and videos to facilitate hands-on adoption, with potential extensions to other ecosystems and applications.

Autoren/Hrsg.

Zhang, Huaiqing

Fachgebiete

• Geowissenschaften Geologie GIS, Geoinformatik
• Geowissenschaften Geographie | Raumplanung Geodäsie, Kartographie, GIS, Fernerkundung

Inhaltsverzeichnis

Part I: Foundations of Forest Digital Twins
1. Introduction to Smart Forest Remote Sensing
2. Principles of Remote Sensing from Optical to LiDAR and SAR
3. The Digital Twin Concept for Forestry

Part II: Building the Virtual Forest. 3D Data and Radiative Transfer
4. From Terrestrial to Satellite LiDAR. Capturing Forest Structure
5. Physics of Photon Transport. Radiative Transfer Models (RTMs)
6. Creating High-Fidelity 3D Forest Scenes

Part III: The AI Engine. From Pixels to Parameters
7. Machine Learning for Biophysical Parameter Retrieval
8. Deep Learning in Remote Sensing Imagery
9. The Synergy. Physics-Guided AI and Hybrid Modeling

Part IV: Towards Implementation. Digital Twins in Action
10. Design a Framework for Forest Remote Sensing Digital Twins
11. Case Studies. Smart Monitoring for Resources and Disturbances
12. From Monitoring to Prediction

Über Autor(innen)

Zhang, Huaiqing
Huaiqing Zhang, Chief Scientist of the Chinese Academy of Forestry, Professor, doctoral supervisor and deputy director of Institute of Forest Resource Information Techniques, Chinese Academy of Forestry, selected for the National Hundred and Ten Thousand Talents Project, National young and middle-aged expert with outstanding contribution. He also serves as the Chairman of the Forestry Computer Application Branch of the Chinese Forestry Society, and the Vice Chairman and Secretary General of the National Innovation Alliance for the Application of Forest and Grass 3D Visualization Technology. He has been engaged in research in the field of artificial intelligence and visualization for forestry and grassland, have led several national scientific research projects, published academic works, papers and authorized national invention patents, formulated standards, and obtained software copyright registrations. He has received 1 second prize of the National Science and Technology Progress Award, 3 first prizes and 1 second prize of the Provincial and Ministerial Science and Technology Progress Award, and also won awards such as the 4th "Three Centenary" Original Book Publishing Project of the State Administration of Press, Publication, Radio, Film and Television, the Youth "Innovation Award" of the Central State Organ, and the 10th China Forestry Youth Science and Technology Award, etc.