Special Issue "LiDAR Metrics for Habitat Condition Indicators"

Dear Colleagues,

In the last two decades, LiDAR systems have rapidly advanced and sensors are gathering information from satellite platforms as well as terrestrial, airplane, drone, and hand-held systems. LiDAR has found use in a variety of scientific domains, especially in forestry and geomorphology, but also in ecology and biodiversity science. Applications of LiDAR data have been substantially assisted by the use of cutting-edge computational techniques. The use of LiDAR for ecological applications is thus rapidly increasing. LiDAR plays a powerful role in quantifying the three-dimensional structure of vegetation, and is becoming an important tool for remotely estimating vegetation characteristics, for monitoring changes in ecosystem structure, and for managing animal habitats.

The special issue “LiDAR Metrics for Habitat Condition Indicators” is seeking innovative and original papers that show how LiDAR can be used to derive indicators about the condition and quality of habitats, incl. abiotic factors, biotic resources, or ecosystem structure and vegetation dynamics. We particularly welcome contributions that address how ecosystem and ecological remote sensing research with LiDAR can be used to (1) quantify the role of habitat structure and niche space for the distribution, abundance, and diversity of species, (2) indicate and monitor the dynamics and condition of animal habitats with LiDAR-derived metrics, and (3) challenge or inform habitat management paradigms and practices, e.g. in the context of novel interventions such as re-wilding or ecosystem restoration. LiDAR metrics for habitat condition indicators may include features that capture vegetation structure, habitat heterogeneity, or topographic descriptors related to hydrology and terrain. Examples are metrics which can quantify bare ground, sward height, woody cover, vegetation density, vertical variability and distribution of biomass, structural diversity, patch size, small-scale water bodies, openness, topographic wetness, or linear features such as hedgerows, tree lines, trails, stonewalls, streams and ditches.

Invited subjects consist of:

• Different types of LiDAR data, such as discrete-return, full-waveform, photon counting, multi/hyper-spectral LiDAR, and how they can be used for habitat condition applications;
• New methods and algorithms, including machine and deep learning approaches, for processing LiDAR data for use in habitat condition indicators;
• New high resolution, open-access datasets with LiDAR metrics for habitat condition indicators;
• Synergistic approaches for the use of LiDAR with optical and radar data for habitat condition assessments;
• Advances in and linkages between LiDAR remote sensing, animal habitat monitoring, and management strategies;
• Terrain analysis, vegetation mapping, biomass estimation and monitoring of vegetation structure and habitat heterogeneity with multi-temporal LiDAR;
• Surveying, mapping and modelling of biodiversity or wildlife and insect distributions in relation to habitat condition derived from LiDAR;
• Other applications of LiDAR to animal ecology studies

Dr. W. Daniel Kissling
Dr. Yifang Shi
Dr. Jinhu Wang
Guest Editors

Manuscript Submission Information

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• LiDAR metrics
• modelling
• data products
• laser scanning
• earth science
• ecosystem structure
• habitat management
• restoration
• rewilding
• machine learning
• deep learning
• algorithms
• vegetation mapping
• ecological remote sensing
• biodiversity indicators