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Special Issue "LiDAR Metrics for Habitat Condition Indicators"

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Ecological Remote Sensing".

Deadline for manuscript submissions: 31 December 2023 | Viewed by 321

Special Issue Editors

Dr. W. Daniel Kissling
E-Mail Website
Guest Editor
Institute for Biodiversity and Ecosystem Dynamics (IBED), University of Amsterdam, P.O. Box 94240, 1090 GE Amsterdam, The Netherlands
Interests: biodiversity; ecology; animal habitats; airborne LiDAR
Dr. Yifang Shi
E-Mail Website
Guest Editor
Institute for Biodiversity and Ecosystem Dynamics (IBED), University of Amsterdam, P.O. Box 94240, 1090 GE Amsterdam, The Netherlands
Interests: remote sensing; artificial intelligence; LiDAR; data fusion; ecosystem structure
Dr. Jinhu Wang
E-Mail Website
Guest Editor
Institute for Biodiversity and Ecosystem Dynamics (IBED), University of Amsterdam, P.O. Box 94240, 1090 GE Amsterdam, The Netherlands
Interests: laser scanning; HD mapping; object recognition; semantic segmentation

Special Issue Information

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

Manuscripts should be submitted online at by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2500 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.


  • 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

Published Papers

This special issue is now open for submission.
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