Forest Diversity Detection by Remote Sensing Techniques

Protection and conservation of forest diversity is important for maintaining forest ecosystems’ functions and processes as well as for sustainable provisioning of ecosystem services that are important to human well-being.

Remote sensing (RS) techniques, especially following improvements over the last two decades in the spatial, spectral, temporal, and radiometric characteristics of passive earth observation sensors, can complement in situ measurements for providing consistent, spatially explicit measurements of forest ecosystems and forest species diversity. Active sensors such as the synthetic aperture radars and terrestrial and airborne laser scanners are also nowadays available for characterizing the complex three-dimensional (3-D) structure of forest vegetation, providing accurate information regarding structural forest diversity.

Τhe synergetic approach between sensors with different characteristics can provide up-to-date and cost-efficient information related to species and structural diversity of forest habitats, at multiple spatial and temporal scales. Remote sensing observations are essential to assess, scale up, and monitor forest diversity as well as the threats to it, addressing the information needs of forest managers, national and international policy makers, regional and global initiatives. Beyond improved data availability, advances in RS data-processing methods and cloud-based platforms and infrastructures can also facilitate access to analysis-ready datasets and computing power. Yet a lot of challenges and open questions still exist when it comes to the conceptualization and development of essential indicators for mapping and monitoring forest diversity as well as the threats that might lead to diversity loss, the development of transferable workflows for processing EO datasets to quantify forest structure and diversity, the evaluation of shallow and deep machine learning algorithms, the fusion of data from active and passive sensors, etc.

Dr. Giorgos Mallinis
Guest Editor

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• Earth Observation
• Machine learning
• Deep Learning
• Hyperspectral imagery
• SAR
• Multispectral imagery
• Spectral-temporal metrics
• Machine and deep learning
• Unmanned aerial vehicles
• Terrestrial Laser Scanner
• Species diversity
• Structural diversity
• Floristic composition
• Forest conservation status
• Forest ecosystem extent
• Forest structure and diversity
• Habitat mapping
• Essential biodiversity variables
• Sustainable forest management
• Threats to forests