AI Review: “Remote Sensing and GIS Applications for Monitoring and Managing Urban Forests” (Datta & Dash, 2024)

1. Scope and Relevance

The chapter explores the role of remote sensing (RS) and geographic information systems (GIS) in monitoring, mapping, and managing urban forests. Given the rapid pace of urbanization and its associated environmental challenges, urban forests serve as critical green infrastructure, providing ecosystem services such as carbon sequestration, cooling, biodiversity support, and improved air quality. The study is highly relevant in the context of sustainable urban planning and climate change mitigation, as it underscores how geospatial tools can guide evidence-based management of urban green spaces.

2. Methodological Approach

The chapter primarily reviews existing methods and applications rather than presenting a case study or primary dataset. Key methodological components discussed include:

• Use of multispectral and hyperspectral satellite data (e.g., Landsat, Sentinel-2) for vegetation mapping.

• Application of LiDAR and UAVs for fine-scale canopy structure analysis.

• GIS-based spatial analysis for urban forest inventory, fragmentation studies, and land use/land cover (LULC) change detection.

• Incorporation of machine learning and AI-based classification techniques to improve accuracy in vegetation mapping and health monitoring.

The chapter highlights how these methods collectively contribute to quantifying forest cover, health, biomass, and ecosystem services in urban settings.

3. Key Findings

• Remote sensing provides reliable spatial and temporal data to monitor the distribution and dynamics of urban forests.

• GIS acts as a powerful platform for integrating multisource datasets, enabling spatial analysis of urban forest functions and vulnerabilities.

• Machine learning algorithms (e.g., Random Forest, SVM, CNNs) improve the accuracy of vegetation classification and stress detection compared to traditional methods.

• The integration of RS and GIS facilitates sustainable urban planning, including identification of potential afforestation sites, assessment of green infrastructure connectivity, and monitoring of policy interventions.

  
  

4. Contributions and Strengths

• Provides a comprehensive synthesis of how geospatial technologies support urban forest management.

• Highlights the importance of multi-scale and multi-sensor approaches (satellite, UAV, LiDAR).

• Strengthens the case for GIS as a decision-support tool for urban planners and policymakers.

• Positions urban forests as nature-based solutions for mitigating climate change, enhancing resilience, and improving urban quality of life.

  
  

5. Limitations

• The chapter is largely review-based, with limited original empirical data or case studies for demonstration.

• While several technologies are discussed, challenges such as data accessibility, costs of high-resolution imagery, and capacity gaps in local governance are not deeply addressed.

• The integration of citizen science, IoT-based monitoring, or ground-based ecological surveys with RS-GIS frameworks receives minimal attention.

• Lacks an explicit quantitative comparison of techniques, which would have strengthened the argument for specific methods in different urban contexts.

  
  

6. Future Research Directions

• Development of low-cost, high-resolution monitoring frameworks combining satellite, UAV, and IoT-based sensors.

• Greater focus on urban forest ecosystem services modeling (carbon storage, heat island mitigation, biodiversity).

• Application of deep learning and AI-driven models for species-level classification and stress detection.

• Integration of citizen science and participatory GIS to complement satellite-based monitoring.

• Long-term monitoring to assess the effectiveness of urban greening policies and their impact on climate adaptation.

  
  

7. Overall Assessment

The chapter offers a valuable review of geospatial approaches for urban forest monitoring and management. Its strength lies in synthesizing diverse technologies and positioning urban forests as critical components of sustainable cities. However, its reliance on secondary sources and absence of case-specific analysis limits practical applicability. Despite these gaps, it successfully highlights the potential of RS-GIS integration in urban forestry research and planning, making it an important contribution for both academics and practitioners.

Rating (for scientific contribution): ★★★★☆ (4.3/5)