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LiDAR Remote Sensing of Forest Resources and Wildland Fires

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

Deadline for manuscript submissions: closed (31 July 2021) | Viewed by 29447

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Dr. Adrian Cardil

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Guest Editor

Department of Vegetal Production and Forestry Science, Universitat de Lleida, Lleida, Spain
Interests: Wildfire; satellite remote sensing; extreme weather events; fire management; fire ecology; global change; burn severity
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Dr. Carlos Alberto Silva

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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
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Prof. Dr. Carine Klauberg

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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; data integration; change detection; fire ecology and fire behavior modeling
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Prof. Dr. Veraldo Liesenberg

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Guest Editor

Department of Forest Engineering, Santa Catarina State University (UDESC), Florianópolis 88035-901, SC, Brazil
Interests: remote sensing applications using AI; retrieval of biophysical properties using AI; environmental modeling; spatial data analysis
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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

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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 2700 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 (6 papers)

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Research

16 pages, 4817 KiB

Open AccessArticle

Moving to Automated Tree Inventory: Comparison of UAS-Derived Lidar and Photogrammetric Data with Manual Ground Estimates

by Luiz Felipe Ramalho de Oliveira, H. Andrew Lassiter, Ben Wilkinson, Travis Whitley, Peter Ifju, Stephen R. Logan, Gary F. Peter, Jason G. Vogel and Timothy A. Martin

Remote Sens. 2021, 13(1), 72; https://doi.org/10.3390/rs13010072 - 27 Dec 2020

Cited by 23 | Viewed by 3478

Abstract

Unmanned aircraft systems (UAS) have advanced rapidly enabling low-cost capture of high-resolution images with cameras, from which three-dimensional photogrammetric point clouds can be derived. More recently UAS equipped with laser scanners, or lidar, have been employed to create similar 3D datasets. While airborne lidar (originally from conventional aircraft) has been used effectively in forest systems for many years, the ability to obtain important tree features such as height, diameter at breast height, and crown dimensions is now becoming feasible for individual trees at reasonable costs thanks to UAS lidar. Getting to individual tree resolution is crucial for detailed phenotyping and genetic analyses. This study evaluates the quality of three three-dimensional datasets from three sensors—two cameras of different quality and one lidar sensor—collected over a managed, closed-canopy pine stand with different planting densities. For reference, a ground-based timber cruise of the same pine stand is also collected. This study then conducted three straightforward experiments to determine the quality of the three sensors’ datasets for use in automated forest inventory: manual mensuration of the point clouds to (1) detect trees and (2) measure tree heights, and (3) automated individual tree detection. The results demonstrate that, while both photogrammetric and lidar data are well-suited for single-tree forest inventory, the photogrammetric data from the higher-quality camera is sufficient for individual tree detection and height determination, but that lidar data is best. The automated tree detection algorithm used in the study performed well with the lidar data, detecting 98% of the 2199 trees in the pine stand, but fell short of manual mensuration within the lidar point cloud, where 100% of the trees were detected. The manually-mensurated heights in the lidar dataset correlated with field measurements at r = 0.95 with a bias of −0.25 m, where the photogrammetric datasets were again less accurate and precise. Full article

(This article belongs to the Special Issue LiDAR Remote Sensing of Forest Resources and Wildland Fires)

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17 pages, 4506 KiB

Open AccessArticle

Regional Level Data Server for Fire Hazard Evaluation and Fuel Treatments Planning

by Goran Krsnik, Eduard Busquets Olivé, Míriam Piqué Nicolau, Asier Larrañaga, Adrián Cardil, Jordi García-Gonzalo and José Ramón González Olabarría

Remote Sens. 2020, 12(24), 4124; https://doi.org/10.3390/rs12244124 - 17 Dec 2020

Cited by 5 | Viewed by 2798

Abstract

Both fire risk assessment and management of wildfire prevention strategies require different sources of data to represent the complex geospatial interaction that exists between environmental variables in the most accurate way possible. In this sense, geospatial analysis tools and remote sensing data offer new opportunities for estimating fire risk and optimizing wildfire prevention planning. Herein, we presented a conceptual design of a server that contained most variables required for predicting fire behavior at a regional level. For that purpose, an innovative and elaborated fuel modelling process and parameterization of all needed environmental and climatic variables were implemented in order to enable to more precisely define fuel characteristics and potential fire behaviors under different meteorological scenarios. The server, open to be used by scientists and technicians, is expected to be the steppingstone for an integrated tool to support decision-making regarding prevention and management of forest fires in Catalonia. Full article

(This article belongs to the Special Issue LiDAR Remote Sensing of Forest Resources and Wildland Fires)

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15 pages, 6127 KiB

Open AccessArticle

A Live Fuel Moisture Content Product from Landsat TM Satellite Time Series for Implementation in Fire Behavior Models

by Mariano García, David Riaño, Marta Yebra, Javier Salas, Adrián Cardil, Santiago Monedero, Joaquín Ramirez, M. Pilar Martín, Lara Vilar, John Gajardo and Susan Ustin

Remote Sens. 2020, 12(11), 1714; https://doi.org/10.3390/rs12111714 - 27 May 2020

Cited by 18 | Viewed by 4888

Abstract

Live Fuel Moisture Content (LFMC) contributes to fire danger and behavior, as it affects fire ignition and propagation. This paper presents a two layered Landsat LFMC product based on topographically corrected relative Spectral Indices (SI) over a 2000–2011 time series, which can be integrated into fire behavior simulation models. Nine chaparral sampling sites across three Landsat-5 Thematic Mapper (TM) scenes were used to validate the product over the Western USA. The relations between field-measured LFMC and Landsat-derived SIs were strong for each individual site but worsened when pooled together. The Enhanced Vegetation Index (EVI) presented the strongest correlations (r) and the least Root Mean Square Error (RMSE), followed by the Normalized Difference Infrared Index (NDII), Normalized Difference Vegetation Index (NDVI) and Visible Atmospherically Resistant Index (VARI). The relations between LFMC and the SIs for all sites improved after using their relative values and relative LFMC, increasing r from 0.44 up to 0.69 for relative EVI (relEVI), the best predictive variable. This relEVI served to estimate the herbaceous and woody LFMC based on minimum and maximum seasonal LFMC values. The understory herbaceous LFMC on the woody pixels was extrapolated from the surrounding pixels where the herbaceous vegetation is the top layer. Running simulations on the Wildfire Analyst (WFA) fire behavior model demonstrated that this LFMC product alone impacts significantly the fire spatial distribution in terms of burned probability, with average burned area differences over 21% after 8 h burning since ignition, compared to commonly carried out simulations based on constant values for each fuel model. The method could be applied to Landsat-7 and -8 and Sentinel-2A and -2B after proper sensor inter-calibration and topographic correction. Full article

(This article belongs to the Special Issue LiDAR Remote Sensing of Forest Resources and Wildland Fires)

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18 pages, 1994 KiB

Open AccessArticle

Impact of Calibrating Filtering Algorithms on the Quality of LiDAR-Derived DTM and on Forest Attribute Estimation through Area-Based Approach

by Diogo N. Cosenza, Luísa Gomes Pereira, Juan Guerra-Hernández, Adrián Pascual, Paula Soares and Margarida Tomé

Remote Sens. 2020, 12(6), 918; https://doi.org/10.3390/rs12060918 - 12 Mar 2020

Cited by 10 | Viewed by 2652

Abstract

Ground point filtering of the airborne laser scanning (ALS) returns is crucial to derive digital terrain models (DTMs) and to perform ALS-based forest inventories. However, the filtering calibration requires considerable knowledge from users, who normally perform it by trial and error without knowing the impacts of the calibration on the produced DTM and the forest attribute estimation. Therefore, this work aims at calibrating four popular filtering algorithms and assessing their impact on the quality of the DTM and the estimation of forest attributes through the area-based approach. The analyzed filters were the progressive triangulated irregular network (PTIN), weighted linear least-squares interpolation (WLS) multiscale curvature classification (MCC), and the progressive morphological filter (PMF). The calibration was established by the vertical DTM accuracy, the root mean squared error (RMSE) using 3240 high-accuracy ground control points. The calibrated parameter sets were compared to the default ones regarding the quality of the estimation of the plot growing stock volume and the dominant height through multiple linear regression. The calibrated parameters allowed for producing DTM with RMSE varying from 0.25 to 0.26 m, against a variation from 0.26 to 0.30 m for the default parameters. The PTIN was the least affected by the calibration, while the WLS was the most affected. Compared to the default parameter sets, the calibrated sets resulted in dominant height equations with comparable accuracies for the PTIN, while WLS, MCC, and PFM reduced the models’ RMSE by 6.5% to 10.6%. The calibration of PTIN and MCC did not affect the volume estimation accuracy, whereas calibrated WLS and PMF reduced the RMSE by 3.4% to 7.9%. The filter calibration improved the DTM quality for all filters and, excepting PTIN, the filters increased the quality of forest attribute estimation, especially in the case of dominant height. Full article

(This article belongs to the Special Issue LiDAR Remote Sensing of Forest Resources and Wildland Fires)

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15 pages, 3955 KiB

Open AccessArticle

Measuring Individual Tree Diameter and Height Using GatorEye High-Density UAV-Lidar in an Integrated Crop-Livestock-Forest System

by Ana Paula Dalla Corte, Franciel Eduardo Rex, Danilo Roberti Alves de Almeida, Carlos Roberto Sanquetta, Carlos A. Silva, Marks M. Moura, Ben Wilkinson, Angelica Maria Almeyda Zambrano, Ernandes M. da Cunha Neto, Hudson F. P. Veras, Anibal de Moraes, Carine Klauberg, Midhun Mohan, Adrián Cardil and Eben North Broadbent

Remote Sens. 2020, 12(5), 863; https://doi.org/10.3390/rs12050863 - 07 Mar 2020

Cited by 112 | Viewed by 10425

Abstract

Accurate forest parameters are essential for forest inventory. Traditionally, parameters such as diameter at breast height (DBH) and total height are measured in the field by level gauges and hypsometers. However, field inventories are usually based on sample plots, which, despite providing valuable and necessary information, are laborious, expensive, and spatially limited. Most of the work developed for remote measurement of DBH has used terrestrial laser scanning (TLS), which has high density point clouds, being an advantage for the accurate forest inventory. However, TLS still has a spatial limitation to application because it needs to be manually carried to reach the area of interest, requires sometimes challenging field access, and often requires a field team. UAV-borne (unmanned aerial vehicle) lidar has great potential to measure DBH as it provides much higher density point cloud data as compared to aircraft-borne systems. Here, we explore the potential of a UAV-lidar system (GatorEye) to measure individual-tree DBH and total height using an automatic approach in an integrated crop-livestock-forest system with seminal forest plantations of Eucalyptus benthamii. A total of 63 trees were georeferenced and had their DBH and total height measured in the field. In the high-density (>1400 points per meter squared) UAV-lidar point cloud, we applied algorithms (usually used for TLS) for individual tree detection and direct measurement of tree height and DBH. The correlation coefficients (r) between the field-observed and UAV lidar-derived measurements were 0.77 and 0.91 for DBH and total tree height, respectively. The corresponding root mean square errors (RMSE) were 11.3% and 7.9%, respectively. UAV-lidar systems have the potential for measuring relatively broad-scale (thousands of hectares) forest plantations, reducing field effort, and providing an important tool to aid decision making for efficient forest management. We recommend that this potential be explored in other tree plantations and forest environments. Full article

(This article belongs to the Special Issue LiDAR Remote Sensing of Forest Resources and Wildland Fires)

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17 pages, 3803 KiB

Open AccessArticle

Direct Estimation of Forest Leaf Area Index based on Spectrally Corrected Airborne LiDAR Pulse Penetration Ratio

by Yonghua Qu, Ahmed Shaker, Lauri Korhonen, Carlos Alberto Silva, Kun Jia, Luo Tian and Jinling Song

Remote Sens. 2020, 12(2), 217; https://doi.org/10.3390/rs12020217 - 08 Jan 2020

Cited by 12 | Viewed by 3927

Abstract

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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