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Remote Sensing in Forest Fire Monitoring and Post-fire Damage Analysis

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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 October 2022) | Viewed by 51391

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Special Issue Editors

Dr. Víctor Fernández-García

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Guest Editor

Institute of Geography and Environment, University of Lausanne, Lausanne, Switzerland
Interests: remote sensing; soil science; vegetation science; fire ecology
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Dr. Leonor Calvo

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Guest Editor

Area of Ecology, Department of Biodiversity and Environmental Management, Faculty of Biological and Environmental Sciences, Universidad de León, 24071 León, Spain
Interests: wildfire; resilience; post-fire regeneration
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Dr. Susana Suarez-Seoane

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Guest Editor

Organisms and Systems Department and Research Unit of Biodiversity, University of Oviedo, Oviedo and Mieres, Spain
Interests: biogeography; bird conservation; ecosystem services; fire-prone ecosystems; fire regime; heathlands; land use change; landscape ecology; mountain systems; perturbations; remote sensing; species distribution models; vegetation regeneration; wildland fires
Special Issues, Collections and Topics in MDPI journals

Dr. Elena Marcos

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Guest Editor

Area of Ecology, Department of Biodiversity and Environmental Management, Faculty of Biological and Environmental Sciences, Universidad de León, 24071 León, Spain
Interests: atmospheric pollution; effects of perturbations on soil and vegetation nutrients; forestry; soil burn severity; soil quality
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Special Issue Information

Dear Colleagues,

Forest fires are one of the most important disturbances across the world, which have produced negative impacts mainly in provisioning and regulating ecosystems services. Furthermore, during the last decade, the magnitude and extension of these fires have grown, making account management more difficult. In this context, remote sensing is a valuable tool to deal with the environmental challenges of fires and to drive solutions. Because of its versatility, the wealth of information it provides, and its rapid advances in technology, techniques, and platforms, remote sensing is an essential tool for forest management, monitoring, damage analysis, and result reporting with the aim to facilitate post-fire management.

This Special Issue invites studies covering new remote sensing technologies, sensors, data collections, and processing methodologies that can be successfully applied in post-fire damage mapping, ecosystems service recovery, and post-fire decision making after large forest fires. We welcome submissions that cover but are not limited to:

Predictive mapping of post-fire biodiversity patterns in forests using species distribution models and remote sensing data;
3D mapping by photogrammetry, LiDAR, and SAR in post-fire studies;
Using unmanned aerial vehicles (UAV) in post-fire studies;
Remote sensing methods to quantify biophysical parameters of vegetation;
Spectral unmixing models applied to the study of post-fire recovery of vegetation;
Hyperspectral imagery applied to the study of soil burn severity and the post-fire recovery of soils;
Analysis of fire impacts in the wildland-urban interface (WUI);
Estimation of carbon losses in soil and vegetation caused by fires;
Methods to estimate forest canopy status and vegetation recovery after fire;
Analysis of post-fire erosion, changes in water sediment loads, and water quality using remote sensing methods.

Dr. Víctor Fernández-García
Dr. Leonor Calvo
Dr. Susana Suarez-Seoane
Dr. Elena Marcos
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 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.

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Published Papers (11 papers)

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Editorial

Jump to: Research

2 pages, 197 KiB

Open AccessEditorial

Remote Sensing Advances in Fire Science: From Fire Predictors to Post-Fire Monitoring

by Víctor Fernández-García, Leonor Calvo, Susana Suárez-Seoane and Elena Marcos

Remote Sens. 2023, 15(20), 4930; https://doi.org/10.3390/rs15204930 - 12 Oct 2023

Viewed by 1533

Abstract

Fire activity has significant implications for ecological communities, biogeochemical cycles, climate, and human lives and assets [...] Full article

(This article belongs to the Special Issue Remote Sensing in Forest Fire Monitoring and Post-fire Damage Analysis)

Research

Jump to: Editorial

24 pages, 49457 KiB

Open AccessArticle

Global Patterns and Dynamics of Burned Area and Burn Severity

by Víctor Fernández-García and Esteban Alonso-González

Remote Sens. 2023, 15(13), 3401; https://doi.org/10.3390/rs15133401 - 4 Jul 2023

Cited by 6 | Viewed by 3469

Abstract

It is a widespread assumption that burned area and severity are increasing worldwide due to climate change. This issue has motivated former analysis based on satellite imagery, revealing a decreasing trend in global burned areas. However, few studies have addressed burn severity trends, rarely relating them to climate variables, and none of them at the global scale. Within this context, we characterized the spatiotemporal patterns of burned area and severity by biomes and continents and we analyzed their relationships with climate over 17 years. African flooded and non-flooded grasslands and savannas were the most fire-prone biomes on Earth, whereas taiga and tundra exhibited the highest burn severity. Our temporal analysis updated the evidence of a decreasing trend in the global burned area (−1.50% year⁻¹; p < 0.01) and revealed increases in the fraction of burned area affected by high severity (0.95% year⁻¹; p < 0.05). Likewise, the regions with significant increases in mean burn severity, and burned areas at high severity outnumbered those with significant decreases. Among them, increases in severely burned areas in the temperate broadleaf and mixed forests of South America and tropical moist broadleaf forests of Australia were particularly intense. Although the spatial patterns of burned area and severity are clearly driven by climate, we did not find climate warming to increase burned area and burn severity over time, suggesting other factors as the primary drivers of current shifts in fire regimes at the planetary scale. Full article

(This article belongs to the Special Issue Remote Sensing in Forest Fire Monitoring and Post-fire Damage Analysis)

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24 pages, 18855 KiB

Open AccessArticle

Post-Fire Forest Vegetation State Monitoring through Satellite Remote Sensing and In Situ Data

by Daniela Avetisyan, Emiliya Velizarova and Lachezar Filchev

Remote Sens. 2022, 14(24), 6266; https://doi.org/10.3390/rs14246266 - 10 Dec 2022

Cited by 12 | Viewed by 2796

Abstract

Wildfires have significant environmental and socio-economic impacts, affecting ecosystems and people worldwide. Over the coming decades, it is expected that the intensity and impact of wildfires will grow depending on the variability of climate parameters. Although Bulgaria is not situated within the geographical borders of the Mediterranean region, which is one of the most vulnerable regions to the impacts of temperature extremes, the climate is strongly influenced by it. Forests are amongst the most vulnerable ecosystems affected by wildfires. They are insufficiently adapted to fire, and the monitoring of fire impacts and post-fire recovery processes is of utmost importance for suggesting actions to mitigate the risk and impact of that catastrophic event. This paper investigated the forest vegetation recovery process after a wildfire in the Ardino region, southeast Bulgaria from the period between 2016 and 2021. The study aimed to present a monitoring approach for the estimation of the post-fire vegetation state with an emphasis on fire-affected territory mapping, evaluation of vegetation damage, fire and burn severity estimation, and assessment of their influence on vegetation recovery. The study used satellite remotely sensed imagery and respective indices of greenness, moisture, and fire severity from Sentinel-2. It utilized the potential of the landscape approach in monitoring processes occurring in fire-affected forest ecosystems. Ancillary data about pre-fire vegetation state and slope inclinations were used to supplement our analysis for a better understanding of the fire regime and post-fire vegetation damages. Slope aspects were used to estimate and compare their impact on the ecosystems’ post-fire recovery capacity. Soil data were involved in the interpretation of the results. Full article

(This article belongs to the Special Issue Remote Sensing in Forest Fire Monitoring and Post-fire Damage Analysis)

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13 pages, 2167 KiB

Open AccessArticle

Comparison of Physical-Based Models to Measure Forest Resilience to Fire as a Function of Burn Severity

by José Manuel Fernández-Guisuraga, Susana Suárez-Seoane, Carmen Quintano, Alfonso Fernández-Manso and Leonor Calvo

Remote Sens. 2022, 14(20), 5138; https://doi.org/10.3390/rs14205138 - 14 Oct 2022

Cited by 8 | Viewed by 18323

Abstract

We aimed to compare the potential of physical-based models (radiative transfer and pixel unmixing models) for evaluating the short-term resilience to fire of several shrubland communities as a function of their regenerative strategy and burn severity. The study site was located within the perimeter of a wildfire that occurred in summer 2017 in the northwestern Iberian Peninsula. A pre- and post-fire time series of Sentinel-2 satellite imagery was acquired to estimate fractional vegetation cover (FVC) from the (i) PROSAIL-D radiative transfer model inversion using the random forest algorithm, and (ii) multiple endmember spectral mixture analysis (MESMA). The FVC retrieval was validated throughout the time series by means of field data stratified by plant community type (i.e., regenerative strategy). The inversion of PROSAIL-D featured the highest overall fit for the entire time series (R² > 0.75), followed by MESMA (R² > 0.64). We estimated the resilience of shrubland communities in terms of FVC recovery using an impact-normalized resilience index and a linear model. High burn severity negatively influenced the short-term resilience of shrublands dominated by facultative seeder species. In contrast, shrublands dominated by resprouters reached pre-fire FVC values regardless of burn severity. Full article

(This article belongs to the Special Issue Remote Sensing in Forest Fire Monitoring and Post-fire Damage Analysis)

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27 pages, 7468 KiB

Open AccessArticle

Quantifying Lidar Elevation Accuracy: Parameterization and Wavelength Selection for Optimal Ground Classifications Based on Time since Fire/Disturbance

by Kailyn Nelson, Laura Chasmer and Chris Hopkinson

Remote Sens. 2022, 14(20), 5080; https://doi.org/10.3390/rs14205080 - 11 Oct 2022

Cited by 4 | Viewed by 2317

Abstract

Pre- and post-fire airborne lidar data provide an opportunity to determine peat combustion/loss across broad spatial extents. However, lidar measurements of ground surface elevation are prone to uncertainties. Errors may be introduced in several ways, particularly associated with the timing of data collection and the classification of ground points. Ground elevation data must be accurate and precise when estimating relatively small elevation changes due to combustion and subsequent carbon losses. This study identifies the impact of post-fire vegetation regeneration on ground classification parameterizations for optimal accuracy using TerraScan and LAStools with airborne lidar data collected in three wavelengths: 532 nm, 1064 nm, and 1550 nm in low relief boreal peatland environments. While the focus of the study is on elevation accuracy and losses from fire, the research is also highly pertinent to hydrological modelling, forestry, geomorphological change, etc. The study area includes burned and unburned boreal peatlands south of Fort McMurray, Alberta. Lidar and field validation data were collected in July 2018, following the 2016 Horse River Wildfire. An iterative ground classification analysis was conducted whereby validation points were compared with lidar ground-classified data in five environments: road, unburned, burned with shorter vegetative regeneration (SR), burned with taller vegetative regeneration (TR), and cumulative burned (both SR and TR areas) in each of the three laser emission wavelengths individually, as well as combinations of 1550 nm and 1064 nm and 1550 nm, 1064 nm, and 532 nm. We find an optimal average elevational offset of ~0.00 m in SR areas with a range (RMSE) of ~0.09 m using 532 nm data. Average accuracy remains the same in cumulative burned and TR areas, but RMSE increased to ~0.13 m and ~0.16 m, respectively, using 1550 nm and 1064 nm combined data. Finally, data averages ~0.01 m above the field-measured ground surface in unburned boreal peatland and transition areas (RMSE of ~0.19 m) using all wavelengths combined. We conclude that the ‘best’ offset for depth of burn within boreal peatlands is expected to be ~0.01 m, with single point measurement uncertainties upwards of ~0.25 m (RMSE) in areas of tall, dense vegetation regeneration. The importance of classification parameterization identified in this study also highlights the need for more intelligent adaptative classification routines, which can be used in other environments. Full article

(This article belongs to the Special Issue Remote Sensing in Forest Fire Monitoring and Post-fire Damage Analysis)

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21 pages, 2404 KiB

Open AccessArticle

Live Fuel Moisture Content Mapping in the Mediterranean Basin Using Random Forests and Combining MODIS Spectral and Thermal Data

by Àngel Cunill Camprubí, Pablo González-Moreno and Víctor Resco de Dios

Remote Sens. 2022, 14(13), 3162; https://doi.org/10.3390/rs14133162 - 1 Jul 2022

Cited by 15 | Viewed by 3609

Abstract

Remotely sensed vegetation indices have been widely used to estimate live fuel moisture content (LFMC). However, marked differences in vegetation structure affect the relationship between field-measured LFMC and reflectance, which limits spatial extrapolation of these indices. To overcome this limitation, we explored the potential of random forests (RF) to estimate LFMC at the subcontinental scale in the Mediterranean basin wildland. We built RF models (LFMC_RF) using a combination of MODIS spectral bands, vegetation indices, surface temperature, and the day of year as predictors. We used the Globe-LFMC and the Catalan LFMC monitoring program databases as ground-truth samples (10,374 samples). LFMC_RF was calibrated with samples collected between 2000 and 2014 and validated with samples from 2015 to 2019, with overall root mean square errors (RMSE) of 19.9% and 16.4%, respectively, which were lower than current approaches based on radiative transfer models (RMSE ~74–78%). We used our approach to generate a public database with weekly LFMC maps across the Mediterranean basin. Full article

(This article belongs to the Special Issue Remote Sensing in Forest Fire Monitoring and Post-fire Damage Analysis)

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32 pages, 25095 KiB

Open AccessArticle

Evaluating a New Relative Phenological Correction and the Effect of Sentinel-Based Earth Engine Compositing Approaches to Map Fire Severity and Burned Area

by Adrián Israel Silva-Cardoza, Daniel José Vega-Nieva, Jaime Briseño-Reyes, Carlos Ivan Briones-Herrera, Pablito Marcelo López-Serrano, José Javier Corral-Rivas, Sean A. Parks and Lisa M. Holsinger

Remote Sens. 2022, 14(13), 3122; https://doi.org/10.3390/rs14133122 - 29 Jun 2022

Cited by 6 | Viewed by 2849

Abstract

The remote sensing of fire severity and burned area is fundamental in the evaluation of fire impacts. The current study aimed to: (i) compare Sentinel-2 (S2) spectral indices to predict field-observed fire severity in Durango, Mexico; (ii) evaluate the effect of the compositing period (1 or 3 months), techniques (average or minimum), and phenological correction (constant offset, c, against a novel relative phenological correction, rc) on fire severity mapping, and (iii) determine fire perimeter accuracy. The Relative Burn Ratio (RBR), using S2 bands 8a and 12, provided the best correspondence with field-based fire severity (FBS). One-month rc minimum composites showed the highest correspondence with FBS (R² = 0.83). The decrease in R² using 3 months rather than 1 month was ≥0.05 (0.05–0.15) for c composites and <0.05 (0.02–0.03) for rc composites. Furthermore, using rc increased the R² by 0.05–0.09 and 0.10–0.15 for the 3-month RBR and dNBR compared to the corresponding c composites. Rc composites also showed increases of up to 0.16–0.22 and 0.08–0.11 in kappa values and overall accuracy, respectively, in mapping fire perimeters against c composites. These results suggest a promising potential of the novel relative phenological correction to be systematically applied with automated algorithms to improve the accuracy and robustness of fire severity and perimeter evaluations. Full article

(This article belongs to the Special Issue Remote Sensing in Forest Fire Monitoring and Post-fire Damage Analysis)

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24 pages, 8904 KiB

Open AccessArticle

Satellite Observations of Fire Activity in Relation to Biophysical Forcing Effect of Land Surface Temperature in Mediterranean Climate

by Julia S. Stoyanova, Christo G. Georgiev and Plamen N. Neytchev

Remote Sens. 2022, 14(7), 1747; https://doi.org/10.3390/rs14071747 - 5 Apr 2022

Cited by 6 | Viewed by 2485

Abstract

The present work is aimed at gaining more knowledge on the nature of the relation between land surface temperature (LST) as a biophysical parameter, which is related to the coupled effect of the energy and water cycles, and fire activity over Bulgaria, in the Eastern Mediterranean. In the ecosystems of this area, prolonged droughts and heat waves create preconditions in the land surface state that increase the frequency and intensity of landscape fires. The relationships between the spatial–temporal variability of LST and fire activity modulated by land cover types and Soil Moisture Availability (SMA) are quantified. Long-term (2007–2018) datasets derived from geostationary MSG satellite observations are used: LST retrieved by the LSASAF LST product; fire activity assessed by the LSASAF FRP-Pixel product. All fires in the period of July–September occur in days associated with positive LST anomalies. Exponential regression models fit the link between LST monthly means, LST positive anomalies, LST-T2 (as a first proxy of sensible heat exchange with atmosphere), and FRP fire characteristics (number of detections; released energy FRP, MW) at high correlations. The values of biophysical drivers, at which the maximum FRP (MW) might be expected at the corresponding probability level, are identified. Results suggest that the biophysical index LST is sensitive to the changes in the dynamics of vegetation fire occurrence and severity. Dependences are found for forest, shrubs, and cultivated LCs, which indicate that satellite IR retrievals of radiative temperature is a reliable source of information for vegetation dryness and fire activity. Full article

(This article belongs to the Special Issue Remote Sensing in Forest Fire Monitoring and Post-fire Damage Analysis)

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19 pages, 72280 KiB

Open AccessArticle

Multilabel Image Classification with Deep Transfer Learning for Decision Support on Wildfire Response

by Minsoo Park, Dai Quoc Tran, Seungsoo Lee and Seunghee Park

Remote Sens. 2021, 13(19), 3985; https://doi.org/10.3390/rs13193985 - 5 Oct 2021

Cited by 20 | Viewed by 3700

Abstract

Given the explosive growth of information technology and the development of computer vision with convolutional neural networks, wildfire field data information systems are adopting automation and intelligence. However, some limitations remain in acquiring insights from data, such as the risk of overfitting caused by insufficient datasets. Moreover, most previous studies have only focused on detecting fires or smoke, whereas detecting persons and other objects of interest is equally crucial for wildfire response strategies. Therefore, this study developed a multilabel classification (MLC) model, which applies transfer learning and data augmentation and outputs multiple pieces of information on the same object or image. VGG-16, ResNet-50, and DenseNet-121 were used as pretrained models for transfer learning. The models were trained using the dataset constructed in this study and were compared based on various performance metrics. Moreover, the use of control variable methods revealed that transfer learning and data augmentation can perform better when used in the proposed MLC model. The resulting visualization is a heatmap processed from gradient-weighted class activation mapping that shows the reliability of predictions and the position of each class. The MLC model can address the limitations of existing forest fire identification algorithms, which mostly focuses on binary classification. This study can guide future research on implementing deep learning-based field image analysis and decision support systems in wildfire response work. Full article

(This article belongs to the Special Issue Remote Sensing in Forest Fire Monitoring and Post-fire Damage Analysis)

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27 pages, 15625 KiB

Open AccessArticle

A Burned Area Mapping Algorithm for Sentinel-2 Data Based on Approximate Reasoning and Region Growing

by Matteo Sali, Erika Piaser, Mirco Boschetti, Pietro Alessandro Brivio, Giovanna Sona, Gloria Bordogna and Daniela Stroppiana

Remote Sens. 2021, 13(11), 2214; https://doi.org/10.3390/rs13112214 - 5 Jun 2021

Cited by 12 | Viewed by 4245

Abstract

Sentinel-2 (S2) multi-spectral instrument (MSI) images are used in an automated approach built on fuzzy set theory and a region growing (RG) algorithm to identify areas affected by fires in Mediterranean regions. S2 spectral bands and their post- and pre-fire date (Δ_post-pre) difference are interpreted as evidence of burn through soft constraints of membership functions defined from statistics of burned/unburned training regions; evidence of burn brought by the S2 spectral bands (partial evidence) is integrated using ordered weighted averaging (OWA) operators that provide synthetic score layers of likelihood of burn (global evidence of burn) that are combined in an RG algorithm. The algorithm is defined over a training site located in Italy, Vesuvius National Park, where membership functions are defined and OWA and RG algorithms are first tested. Over this site, validation is carried out by comparison with reference fire perimeters derived from supervised classification of very high-resolution (VHR) PlanetScope images leading to more than satisfactory results with Dice coefficient > 0.84, commission error < 0.22 and omission error < 0.15. The algorithm is tested for exportability over five sites in Portugal (1), Spain (2) and Greece (2) to evaluate the performance by comparison with fire reference perimeters derived from the Copernicus Emergency Management Service (EMS) database. In these sites, we estimate commission error < 0.15, omission error < 0.1 and Dice coefficient > 0.9 with accuracy in some cases greater than values obtained in the training site. Regression analysis confirmed the satisfactory accuracy levels achieved over all sites. The algorithm proposed offers the advantages of being least dependent on a priori/supervised selection for input bands (by building on the integration of redundant partial burn evidence) and for criteria/threshold to obtain segmentation into burned/unburned areas. Full article

(This article belongs to the Special Issue Remote Sensing in Forest Fire Monitoring and Post-fire Damage Analysis)

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25 pages, 31757 KiB

Open AccessArticle

Generating a Baseline Map of Surface Fuel Loading Using Stratified Random Sampling Inventory Data through Cokriging and Multiple Linear Regression Methods

by Chinsu Lin, Siao-En Ma, Li-Ping Huang, Chung-I Chen, Pei-Ting Lin, Zhih-Kai Yang and Kuan-Ting Lin

Remote Sens. 2021, 13(8), 1561; https://doi.org/10.3390/rs13081561 - 17 Apr 2021

Cited by 10 | Viewed by 3385

Abstract

Surface fuel loading is a key factor in controlling wildfires and planning sustainable forest management. Spatially explicit maps of surface fuel loading can highlight the risks of a forest fire. Geospatial information is critical in enabling careful use of deliberate fire setting and also helps to minimize the possibility of heat conduction over forest lands. In contrast to lidar sensing and/or optical sensing based methods, an approach of integrating in-situ fuel inventory data, geospatial interpolation techniques, and multiple linear regression methods provides an alternative approach to surface fuel load estimation and mapping over mountainous forests. Using a stratified random sampling based inventory and cokriging analysis, surface fuel loading data of 120 plots distributed over four kinds of fuel types were collected in order to develop a total surface fuel loading model (lntSFL-BioTopo model) and a fine surface fuel model (lnfSFL-BioTopo model) for generating tSFL and fSFL maps. Results showed that the combination of topographic parameters such as slope, aspect, and their cross products and the fuel types such as pine stand, non-pine conifer stand, broadleaf stand, and conifer–broadleaf mixed stand was able to appropriately describe the changes in surface fuel loads over a forest with diverse terrain morphology. Based on a cross-validation method, the estimation of tSFL and fSFL of the study site had an RMSE of 3.476 tons/ha and 3.384 tons/ha, respectively. In contrast to the average loading of all inventory plots, the estimation for tSFL and fSFL had a relative error of 38% (PRMSE). The reciprocal of estimation bias of both SFL-BioTopo models tended to be an exponential growth function of the amount of surface fuel load, indicating that the estimation accuracy of the proposed method is likely to be improved with further study. In the regression modeling, a natural logarithm transformation of the surface fuel loading prevented the outcome of negative estimates and thus improved the estimation. Based on the results, this paper defined a minimum sampling unit (MSU) as the area for collecting surface fuels for interpolation using a cokriging model. Allocating the MSUs at the boundary and center of a plot improved surface fuel load prediction and mapping. Full article

(This article belongs to the Special Issue Remote Sensing in Forest Fire Monitoring and Post-fire Damage Analysis)

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