Remote Sensing and Machine Learning in Conservation
Applications and Techniques
- 1st Edition - March 1, 2027
- Latest edition
- Author: Alireza Sharifi
- Language: English
Remote Sensing and Machine Learning in Conservation: Applications and Techniques presents a structured, decision-oriented guide to the responsible use of Earth observation, geospa… Read more
Description
Description
Remote Sensing and Machine Learning in Conservation: Applications and Techniques presents a structured, decision-oriented guide to the responsible use of Earth observation, geospatial analysis, and artificial intelligence in biodiversity conservation and ecosystem management. It connects conservation questions with appropriate evidence, data preparation, model development and validation, uncertainty communication, ecological interpretation, and decision-ready outputs.
Beginning with the foundations of remote sensing, it explains major machine-learning and deep-learning approaches, from supervised classification to Vision Transformers and reinforcement learning. Chapters then show how remote sensing and GeoAI support habitat mapping, species distribution modeling, biodiversity assessment, ecosystem-change detection, water and coastal conservation, wildlife monitoring, climate adaptation, invasive-species detection, restoration planning, and threat monitoring.
Dedicated chapters translate theory into practice through project design, cloud-based platforms, open data, reproducible workflows, validation, dashboards, and operational monitoring, and address data governance, privacy, bias, accountability, Indigenous and local knowledge, participation, and responsible use. The closing chapter examines foundation models, multimodal GeoAI, ecosystem digital twins, real-time and edge AI, participatory monitoring, and future research priorities, helping readers move from tool-driven analysis toward scientifically grounded and actionable conservation evidence.
Beginning with the foundations of remote sensing, it explains major machine-learning and deep-learning approaches, from supervised classification to Vision Transformers and reinforcement learning. Chapters then show how remote sensing and GeoAI support habitat mapping, species distribution modeling, biodiversity assessment, ecosystem-change detection, water and coastal conservation, wildlife monitoring, climate adaptation, invasive-species detection, restoration planning, and threat monitoring.
Dedicated chapters translate theory into practice through project design, cloud-based platforms, open data, reproducible workflows, validation, dashboards, and operational monitoring, and address data governance, privacy, bias, accountability, Indigenous and local knowledge, participation, and responsible use. The closing chapter examines foundation models, multimodal GeoAI, ecosystem digital twins, real-time and edge AI, participatory monitoring, and future research priorities, helping readers move from tool-driven analysis toward scientifically grounded and actionable conservation evidence.
Key features
Key features
- Connects remote sensing and machine learning within a complete, decision-oriented conservation workflow
- Covers optical, thermal, radar, LiDAR, hyperspectral, UAV, satellite, camera-trap, acoustic, and telemetry data
- Explains classical machine learning, deep learning, Vision Transformers, generative models, reinforcement learning, and multimodal GeoAI
- Provides applied guidance for habitat, biodiversity, wildlife, water, coastal, climate-risk, invasive-species, restoration, and threat-monitoring applications
- Emphasizes validation, uncertainty, explainability, reproducibility, quality control, governance, ethics, and decision-ready communication
- Includes practical project-design guidance, case-study structures, learning objectives, key takeaways, exercises, and references in every chapter
- Examines emerging directions including foundation models, ecosystem digital twins, real-time sensing, edge AI, citizen science, and human-in-the-loop conservation intelligence
Readership
Readership
Conservationists, resource managers, environmental scientists, and academics studying novel technologies in conservation
Table of contents
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
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
Product details
Product details
- Edition: 1
- Latest edition
- Published: March 1, 2027
- Language: English
About the author
About the author
AS
Alireza Sharifi
Dr. Alireza Sharifi is an Associate Professor of Remote Sensing at Shahid Beheshti University. His work focuses on remote sensing, geospatial analysis, and machine learning for environmental monitoring, ecosystem assessment, and sustainable resource management. He received his Ph.D. in Remote Sensing Engineering from the University of Tehran, where his doctoral research examined forest biomass modeling using multi-polarized SAR imagery. His earlier graduate research addressed hyperspectral image classification through spectral-signature analysis.
His research interests include GeoAI, deep learning, hyperspectral and multispectral image analysis, SAR applications, anomaly detection, land-cover mapping, crop monitoring, vegetation-health assessment, flood and drought monitoring, environmental-pollution mapping, forest-biomass estimation, and precision agriculture. His experience with optical, radar, and hyperspectral data provides the methodological foundation for the book's practical and scientifically grounded treatment of conservation, ecosystem monitoring, and sustainable environmental management.
Affiliations and expertise
Associate Professor of Remote Sensing, Department of Surveying Engineering, Shahid Beheshti University, Tehran, Iran