Table of Contents

1. Introduction

• Contribution and Positioning of the Book
• Overview of the Importance of Conservation in the Modern World
• The Role of Technology in Advancing Conservation Efforts
• Purpose and Scope of the Book
• From Data-Rich Conservation to Actionable Conservation Evidence
• Conclusion

2. Overview of Remote Sensing Techniques

• Learning Objectives
• 2.1 Introduction
• 2.2 History and Development of Remote Sensing in Environmental Science
• 2.3 Types of Remote Sensing
• 2.4 Remote Sensing Technologies: LiDAR, Radar, Multispectral, and Hyperspectral Imaging
• 2.5 Sensor Platforms
• 2.6 Data Acquisition and Preprocessing Methods
• 2.7 Spatial, Spectral, Temporal, and Radiometric Resolution
• 2.8 Data Quality, Accuracy Assessment, and Uncertainty
• 2.9 Challenges and Limitations of Remote Sensing in Conservation
• 2.10 Linking Remote Sensing with Field Data
• 2.11 Conclusion
• Key Takeaways
• Practical Exercise

3. Overview of Machine Learning and Deep Learning Techniques

• Learning Objectives
• 3.1 Introduction
• 3.2 Basics of Machine Learning and Its Relevance to Geospatial Data
• 3.3 Geospatial Data Characteristics and Feature Design
• 3.4 Supervised Learning: Classification and Regression Techniques
• 3.5 Unsupervised Learning: Clustering and Anomaly Detection
• 3.6 Deep Learning: Convolutional Neural Networks and Their Applications
• 3.7 Deep Learning: Recurrent Neural Networks and Temporal Models
• 3.8 Deep Learning: Autoencoders and Their Applications
• 3.9 Deep Learning: Generative Adversarial Networks and Their Applications
• 3.10 Deep Learning: Vision Transformers and Their Applications
• 3.11 Reinforcement Learning
• 3.12 Challenges in Applying Machine Learning and Deep Learning to Conservation Data
• 3.13 Selecting Appropriate Models for Conservation Workflows
• 3.14 Conclusion
• Key Takeaways
• Practical Exercise

4. Integrating Remote Sensing and Machine Learning for Conservation

• Learning Objectives
• 4.1 Introduction
• 4.2 Synergies Between Remote Sensing and Machine Learning
• 4.3 Feature Extraction and Environmental Predictor Design
• 4.4 Data Fusion and Multi-Source Integration
• 4.5 Training Data Design and Reference Information
• 4.6 Model Selection and Workflow Design
• 4.7 Validation, Accuracy Assessment, and Area Estimation
• 4.8 Interpretability, Uncertainty, and Explainable Geospatial AI
• 4.9 Case-Study Structures for Integrated Conservation Workflows
• 4.10 Technical Challenges and Data Limitations
• 4.11 Ethical, Governance, and Practical Considerations
• 4.12 Conclusion
• Key Takeaways
• Practical Exercise

5. Applications of Remote Sensing and Machine Learning in Conservation

• Learning Objectives
• 5.1 Introduction
• 5.2 Habitat Mapping and Ecosystem Classification
• 5.3 Species Distribution Modeling and Biodiversity Assessment
• 5.4 Environmental Variables and Habitat Suitability Modeling
• 5.5 Monitoring Ecosystem Change, Disturbance, and Restoration
• 5.6 Water, Wetlands, and Coastal Conservation
• 5.7 Sensor Networks, Camera Traps, Acoustic Monitoring, and UAV-Based Conservation
• 5.8 Climate Adaptation, Risk Mapping, and Early Warning
• 5.9 Practical Limitations and Responsible Use in Conservation Applications
• 5.10 Conclusion
• Key Takeaways
• Practical Exercise

6. Wildlife Monitoring and Conservation with Remote Sensing, Sensor Networks, and Machine Learning

• Learning Objectives
• 6.1 Introduction
• 6.2 Camera Traps and Computer Vision for Wildlife Monitoring
• 6.3 Acoustic Monitoring and Sound Classification
• 6.4 GPS Telemetry, Movement Ecology, and Habitat Use
• 6.5 UAV and Satellite-Based Wildlife Detection
• 6.6 Human–Wildlife Interactions, Poaching Risk, and Disturbance Monitoring
• 6.7 Validation, Uncertainty, and Ethical Considerations in Wildlife Monitoring
• 6.8 From Wildlife Monitoring Outputs to Conservation Decisions
• 6.9 Conclusion
• Key Takeaways
• Practical Exercise

7. Conservation Challenges and Technology-Based Solutions

• Learning Objectives
• 7.1 Introduction
• 7.2 Climate Change Monitoring and Future Environmental Scenarios
• 7.3 Poaching, Illegal Activities, and Protected-Area Threat Monitoring
• 7.4 Urban Expansion, Habitat Fragmentation, and Biodiversity Loss
• 7.5 Marine and Coastal Conservation Challenges
• 7.6 Invasive Species Detection and Management
• 7.7 Data Accuracy, Modeling Gaps, and Uncertainty in Conservation Solutions
• 7.8 Conclusion
• Key Takeaways
• Practical Exercise

8. Practical Case Studies and Project Design in Conservation GeoAI

• Learning Objectives
• 8.1 Introduction: from methods to applied conservation projects
• 8.2 Designing a conservation GeoAI project
• 8.3 From one-time map to operational monitoring system
• 8.4 Validation, uncertainty, and cross-case comparison
• 8.5 Communication and decision products
• 8.6 Conclusion
• Key Takeaways
• Practical Exercise

9. Cloud-Based Platforms, Open Data, and Reproducible Conservation Workflows

• Learning Objectives
• 9.1 Introduction
• 9.2 Open Satellite Data and Environmental Data Infrastructures
• 9.3 Cloud-Based Processing Platforms
• 9.4 Preprocessing, Harmonization, and Analysis-Ready Data
• 9.5 Reproducible Workflow Design and Documentation
• 9.6 Validation, Quality Control, and Operational Monitoring
• 9.7 Communication, Dashboards, and Decision-Ready Outputs
• 9.8 Capacity Building, Collaboration, and Sustainable Implementation
• 9.9 Conclusion
• Key Takeaways
• Practical Exercise

10. Policy, Governance, Ethics, and Responsible Use of Conservation Technologies

• Learning Objectives
• 10.1 Introduction
• 10.2 Data Governance, Ownership, and Stewardship
• 10.3 Privacy, Surveillance, and Sensitive Conservation Data
• 10.4 Fairness, Bias, and Accountability in AI-Supported Conservation
• 10.5 Indigenous Knowledge, Local Participation, and Community-Based Governance
• 10.6 Policy Integration and Institutional Decision-Making
• 10.7 Practical Principles for Responsible Conservation Technology
• 10.8 Conclusion
• Key Takeaways
• Practical Exercise

11. Future Directions in Remote Sensing, Machine Learning, and Conservation Intelligence

• Learning Objectives
• 11.1 Introduction
• 11.2 Foundation Models, Transfer Learning, and Large-Scale Earth Observation AI
• 11.3 Multimodal GeoAI and Cross-Scale Conservation Monitoring
• 11.4 Ecosystem Digital Twins and Scenario-Based Conservation Planning
• 11.5 Real-Time Monitoring, Edge AI, and Autonomous Conservation Sensing
• 11.6 Participatory Platforms, Citizen Science, and Human-in-the-Loop Conservation Intelligence
• 11.7 Future Research Agenda for Responsible Conservation AI
• 11.8 Conclusion
• Key Takeaways
• Practical Exercise