Special Issue "LiDAR Remote Sensing of Forest Resources and Wildland Fires"

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

Deadline for manuscript submissions: 31 July 2021.

Special Issue Editors

Dr. Adrian Cardil
E-Mail Website
Guest Editor
Department of Vegetal Production and Forestry Science, Universitat de Lleida, Spain
Interests: Wildfire; satellite remote sensing; extreme weather events; fire management; fire ecology; global change; burn severity.
Special Issues and Collections in MDPI journals
Dr. Carlos Alberto Silva
E-Mail Website
Guest Editor
1. Department of Geographical Sciences, University of Maryland, College Park, MD, USA; 2. School of Forest Resources and Conservation, University of Florida, Gainesville, FL, USA
Interests: LiDAR and hyperspectral remote sensing; tropical forest structure and ecology; industrial forest plantations; algorithms and tools development; data integration and change detection
Special Issues and Collections in MDPI journals
Prof. Dr. Carine Klauberg
E-Mail Website
Guest Editor
Federal University of São João Del Rei – UFSJ, Sete Lagoas, MG, -35701-970, Brazil
Interests: forests and nontimber forest products; tropical forest ecology; remote sensing; LiDAR, forest inventory; wildfire, forest inventory; data integration; change detection; fire ecology and fire behavior modeling
Special Issues and Collections in MDPI journals
Dr. Veraldo Liesenberg
E-Mail Website
Guest Editor
Santa Catarina State University – UDESC, Lages, SC, 88520-000, Brazil
Interests: remote sensing; SAR; LiDAR, forest management; data integration; change detection; biomass modeling

Special Issue Information

Dear Colleagues,

LiDAR (light detection and ranging) remote sensing has emerged as a technology that is well-suited for providing accurate estimates of forest attributes in a wide variety of forest ecosystems at a variety of spatial scales. Wildland fires burn millions of hectares every year, and their impacts are of high interest for society, especially in the wildland urban interface.  

The purpose of this Special Issue is to bring together state-of-the-art of remote sensing for forest resource management and wildland fire science. Review papers, technical notes, and research contributions are suitable. In particular, novel contributions covering, but not limited to, the following subtopics described below are welcome:

  • Forest attribute estimation at individual tree and landscape levels using lidar and photogrammetry 3-D derived point cloud data applied to wildfire management;
  • Machine learning and deep learning approaches for estimating forest structure attributes. Fuel mapping and estimation of canopy characteristics across the landscape. Analysis of spatial and temporal changes of vegetation and associated attributes;
  • Use of LiDAR remote sensing data to assess fire/burn severity. Fire effects and post-wildfire landscape change and erosional processes. Quantification of biomass consumption and carbon release;
  • Integration and data fusion approaches using multiple remote sensing data sources to estimate fire progression and burned area. Additionally, fire simulation and fire behavior analysis based on remote sensing data;
  • New methodologies to estimate live and dead fuel moisture content;
  • LiDAR measurements of wildfire smoke over urban environments;
  • Characterization of the wildland fire exposure and risk. Wildfire prevention and planning based on remote sensing technologies;
  • Synergies among platforms (airborne, terrestrial, and spaceborne) for forest inventory and monitoring.

Dr. Adrian Cardil
Dr. Carlos Alberto Silva
Prof. Dr. Carine Klauberg
Dr. Veraldo Liesenberg
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com 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 papers will be 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 2000 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.

Published Papers (1 paper)

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Open AccessArticle
Direct Estimation of Forest Leaf Area Index based on Spectrally Corrected Airborne LiDAR Pulse Penetration Ratio
Remote Sens. 2020, 12(2), 217; https://doi.org/10.3390/rs12020217 - 08 Jan 2020
The leaf area index (LAI) is a crucial structural parameter of forest canopies. Light Detection and Ranging (LiDAR) provides an alternative to passive optical sensors in the estimation of LAI from remotely sensed data. However, LiDAR-based LAI estimation typically relies on empirical models, [...] Read more.
The leaf area index (LAI) is a crucial structural parameter of forest canopies. Light Detection and Ranging (LiDAR) provides an alternative to passive optical sensors in the estimation of LAI from remotely sensed data. However, LiDAR-based LAI estimation typically relies on empirical models, and such methods can only be applied when the field-based LAI data are available. Compared with an empirical model, a physically-based model—e.g., the Beer–Lambert law based light extinction model—is more attractive due to its independent dataset with training. However, two challenges are encountered when applying the physically-based model to estimate LAI from discrete LiDAR data: i.e., deriving the gap fraction and the extinction coefficient from the LiDAR data. We solved the first problem by integrating LiDAR and hyperspectral data to transfer the LiDAR penetration ratio to the forest gap fraction. For the second problem, the extinction coefficient was estimated from tiled (1 km × 1 km) LiDAR data by nonlinearly optimizing the cost function of the angular LiDAR gap fraction and simulated gap fraction from the Beer–Lambert law model. A validation against LAI-2000 measurements showed that the estimates were significantly correlated to the reference LAI with an R2 of 0.66, a root mean square error (RMSE) of 0.60 and a relative RMSE of 0.15. We conclude that forest LAI can be directly estimated by the nonlinear optimization method utilizing the Beer–Lambert model and a spectrally corrected LiDAR penetration ratio. The significance of the proposed method is that it can produce reliable remotely sensed forest LAI from discrete LiDAR and spectral data when field-measured LAI are unavailable. Full article
(This article belongs to the Special Issue LiDAR Remote Sensing of Forest Resources and Wildland Fires)
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