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Advances in Remote Sensing of Post-fire Environmental Damage and Recovery Dynamics

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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 (30 June 2021) | Viewed by 64381

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Special Issue Editors

Dr. Alfonso Fernández-Manso

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Guest Editor

Applied Ecology and Remote Sensing Group, Agrarian Science and Engineering Department, University of León, Av. Astorga s/n, 24400 Ponferrada, Spain
Interests: forestry; wildfire; forest management; remote sensing; LiDAR
Special Issues, Collections and Topics in MDPI journals

Dr. Carmen Quintano

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Guest Editor

1. Electronic Technology Department, School of Industrial Engineering, University of Valladolid, 47011 Valladolid, Spain
2. Sustainable Forest Management Research Institute, University of Valladolid-Spanish National Institute for Agriculture and Food Research and Technology (INIA), 34004 Palencia, Spain
Interests: fire damage (burned area, burn severity); multi and hyper-spectral remote sensing; unmixing; classification
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Understanding of forest fire regimes involves characterizing spatial distribution, recurrence, intensity, seasonality, size, and severity. In recent years, knowledge of damage levels can be directly related to the environmental impact of fire and, at the same time, it is a valuable estimator of fire intensity, when the data about it are not available. Remote sensing may be seen as a tool to accurately assess burn severity and to predict the potential effects of forest fires on ecosystems, thus making the prediction of the regeneration of the plant community and the effects on ecosystems easier. This information is basic to facilitate decision-making in the post-fire management of fire-prone ecosystems.

Nowadays, there has been intense research activity in relation to burned areas, burn severity, and vegetation regeneration because fires in many areas of the planet are becoming more severe and extensive. The environmental damage of affected ecosystems is also much more important, so their correct evaluation and follow-up pose great challenges to current scientists. The current advances in remote sensing and related sciences will allow us to evaluate the damage with greater precision and to know with greater reliability the dynamics of recovery.

This Special Issue aims at studies covering new remote sensing technologies, new sensors, data collections, and processing methodologies that can be successfully applied in burn severity mapping, vegetation revovery monitoring, and post-fire management of fire-prone ecosystems affected by large fires. We welcome submissions that cover but are not limited to:

Global trends in mapping burned and burn severity in local and regional areas using the remote sensing approach;
Wildfire severity evaluation and land monitoring with big data and artificial intelligence classification;
Remote sensing-based assessment of post-fire forest patterns monitoring successional stages;
3D mapping by photogrammetry, LiDAR, and SAR in post-fire studies;
New hyperspectral sensors applications in post-fire studies;
Ultra-high spatial resolution using unmanned aerial vehicles (UAV) in post-fire studies;
Improved methods of modeling image time-series for fire disturbance recovery;
Understanding wildfire behavior and ecology behavior within and around the wildland–urban interface (WUI);
Impact of climate change on forest fire severity and consequences for ecosystem recovery;
Fire severity and recovery dynamics in Reducing Emissions from deforestation and degradation programs (REDD+).

Dr. Alfonso Fernández-Manso
Dr. Carmen Quintano
Guest Editors

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Keywords

Wildfire severity evaluation and land monitoring
Big data and artificial intelligence
3D mapping post-fire Studies
Hyperspectral sensors post fire Studies
Ultra-high spatial resolution
Modeling image time-series for fire disturbance recovery
Wildland–urban interface (WUI)
Climate change on forest fire severity

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Published Papers (14 papers)

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21 pages, 27719 KiB

Open AccessEditor’s ChoiceArticle

The Potential of Multispectral Imagery and 3D Point Clouds from Unoccupied Aerial Systems (UAS) for Monitoring Forest Structure and the Impacts of Wildfire in Mediterranean-Climate Forests

by Sean Reilly, Matthew L. Clark, Lisa Patrick Bentley, Corbin Matley, Elise Piazza and Imma Oliveras Menor

Remote Sens. 2021, 13(19), 3810; https://doi.org/10.3390/rs13193810 - 23 Sep 2021

Cited by 14 | Viewed by 4371

Abstract

Wildfire shapes vegetation assemblages in Mediterranean ecosystems, such as those in the state of California, United States. Successful restorative management of forests in-line with ecologically beneficial fire regimes relies on a thorough understanding of wildfire impacts on forest structure and fuel loads. As these data are often difficult to comprehensively measure on the ground, remote sensing approaches can be used to estimate forest structure and fuel load parameters over large spatial extents. Here, we analyze the capabilities of one such methodology, unoccupied aerial system structure from motion (UAS-SfM) from digital aerial photogrammetry, for mapping forest structure and wildfire impacts in the Mediterranean forests of northern California. To determine the ability of UAS-SfM to map the structure of mixed oak and conifer woodlands and to detect persistent changes caused by fire, we compared UAS-SfM derived metrics of terrain height and canopy structure to pre-fire airborne laser scanning (ALS) measurements. We found that UAS-SfM was able to accurately capture the forest’s upper-canopy structure, but was unable to resolve mid- and below-canopy structure. The addition of a normalized difference vegetation index (NDVI) ground point filter to the DTM generation process improved DTM root-mean-square error (RMSE) by ~1 m with an overall DTM RMSE of 2.12 m. Upper-canopy metrics (max height, 95th percentile height, and 75th percentile height) were highly correlated between ALS and UAS-SfM (r > +0.9), while lower-canopy metrics and metrics of density and vertical variation had little to no similarity. Two years after the 2017 Sonoma County Tubbs fire, we found significant decreases in UAS-SfM metrics of bulk canopy height and NDVI with increasing burn severity, indicating the lasting impact of the fire on vegetation health and structure. These results point to the utility of UAS-SfM as a monitoring tool in Mediterranean forests, especially for post-fire canopy changes and subsequent recovery. Full article

(This article belongs to the Special Issue Advances in Remote Sensing of Post-fire Environmental Damage and Recovery Dynamics)

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24 pages, 5198 KiB

Open AccessArticle

A Framework for Multi-Dimensional Assessment of Wildfire Disturbance Severity from Remotely Sensed Ecosystem Functioning Attributes

by Bruno Marcos, João Gonçalves, Domingo Alcaraz-Segura, Mário Cunha and João P. Honrado

Remote Sens. 2021, 13(4), 780; https://doi.org/10.3390/rs13040780 - 20 Feb 2021

Cited by 9 | Viewed by 3530

Abstract

Wildfire disturbances can cause modifications in different dimensions of ecosystem functioning, i.e., the flows of matter and energy. There is an increasing need for methods to assess such changes, as functional approaches offer advantages over those focused solely on structural or compositional attributes. In this regard, remote sensing can support indicators for estimating a wide variety of effects of fire on ecosystem functioning, beyond burn severity assessment. These indicators can be described using intra-annual metrics of quantity, seasonality, and timing, called Ecosystem Functioning Attributes (EFAs). Here, we propose a satellite-based framework to evaluate the impacts, at short to medium term (i.e., from the year of fire to the second year after), of wildfires on four dimensions of ecosystem functioning: (i) primary productivity, (ii) vegetation water content, (iii) albedo, and (iv) sensible heat. We illustrated our approach by comparing inter-annual anomalies in satellite-based EFAs in the northwest of the Iberian Peninsula, from 2000 to 2018. Random Forest models were used to assess the ability of EFAs to discriminate burned vs. unburned areas and to rank the predictive importance of EFAs. Together with effect sizes, this ranking was used to select a parsimonious set of indicators for analyzing the main effects of wildfire disturbances on ecosystem functioning, for both the whole study area (i.e., regional scale), as well as for four selected burned patches with different environmental conditions (i.e., local scale). With both high accuracies (area under the receiver operating characteristic curve (AUC) > 0.98) and effect sizes (Cohen’s |d| > 0.8), we found important effects on all four dimensions, especially on primary productivity and sensible heat, with the best performance for quantity metrics. Different spatiotemporal patterns of wildfire severity across the selected burned patches for different dimensions further highlighted the importance of considering the multi-dimensional effects of wildfire disturbances on key aspects of ecosystem functioning at different timeframes, which allowed us to diagnose both abrupt and lagged effects. Finally, we discuss the applicability as well as the potential advantages of the proposed approach for more comprehensive assessments of fire severity. Full article

(This article belongs to the Special Issue Advances in Remote Sensing of Post-fire Environmental Damage and Recovery Dynamics)

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18 pages, 2787 KiB

Open AccessArticle

Wildland Fire Tree Mortality Mapping from Hyperspatial Imagery Using Machine Learning

by Dale A. Hamilton, Kamden L. Brothers, Samuel D. Jones, Jason Colwell and Jacob Winters

Remote Sens. 2021, 13(2), 290; https://doi.org/10.3390/rs13020290 - 15 Jan 2021

Cited by 11 | Viewed by 3536

Abstract

The use of imagery from small unmanned aircraft systems (sUAS) has enabled the production of more accurate data about the effects of wildland fire, enabling land managers to make more informed decisions. The ability to detect trees in hyperspatial imagery enables the calculation of canopy cover. A comparison of hyperspatial post-fire canopy cover and pre-fire canopy cover from sources such as the LANDFIRE project enables the calculation of tree mortality, which is a major indicator of burn severity. A mask region-based convolutional neural network was trained to classify trees as groups of pixels from a hyperspatial orthomosaic acquired with a small unmanned aircraft system. The tree classification is summarized at 30 m, resulting in a canopy cover raster. A post-fire canopy cover is then compared to LANDFIRE canopy cover preceding the fire, calculating how much the canopy was reduced due to the fire. Canopy reduction allows the mapping of burn severity while also identifying where surface, passive crown, and active crown fire occurred within the burn perimeter. Canopy cover mapped through this effort was lower than the LANDFIRE Canopy Cover product, which literature indicated is typically over reported. Assessment of canopy reduction mapping on a wildland fire reflects observations made both from ground truthing efforts as well as observations made of the associated hyperspatial sUAS orthomosaic. Full article

(This article belongs to the Special Issue Advances in Remote Sensing of Post-fire Environmental Damage and Recovery Dynamics)

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19 pages, 4774 KiB

Open AccessArticle

Mapping Burn Extent of Large Wildland Fires from Satellite Imagery Using Machine Learning Trained from Localized Hyperspatial Imagery

by Dale Hamilton, Enoch Levandovsky and Nicholas Hamilton

Remote Sens. 2020, 12(24), 4097; https://doi.org/10.3390/rs12244097 - 15 Dec 2020

Cited by 4 | Viewed by 2727

Abstract

Wildfires burn 4–10 million acres annually across the United States and wildland fire related damages and suppression costs have exceeded $13 billion for a single year. High-intensity wildfires contribute to post-fire erosion, degraded wildlife habitat, and loss of timber resources. Accurate and temporally adequate assessment of the effects of wildland fire on the environment is critical to improving the of wildland fire as a tool for restoring ecosystem resilience. Sensor miniaturization and small unmanned aircraft systems (sUAS) provide affordable, on-demand monitoring of wildland fire effects at a much finer spatial resolution than is possible with satellite imagery. The use of sUAS would allow researchers to obtain data with more detail at a much lower initial cost. Unfortunately, current regulatory and technical constraints prohibit the acquisition of imagery using sUAS for the entire extent of large fires. This research examined the use of sUAS imagery to train and validate burn severity and extent mapping of large wildland fires from various satellite images. Despite the lower resolution of the satellite image, the research utilized the advantages of satellite imagery such as global coverage, low cost, temporal stability, and spectral extent while leveraging the higher resolution of hyperspatial sUAS imagery for training and validating the mapping analytics. Full article

(This article belongs to the Special Issue Advances in Remote Sensing of Post-fire Environmental Damage and Recovery Dynamics)

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29 pages, 20307 KiB

Open AccessArticle

Contextualizing the 2019–2020 Kangaroo Island Bushfires: Quantifying Landscape-Level Influences on Past Severity and Recovery with Landsat and Google Earth Engine

by Mitchell T. Bonney, Yuhong He and Soe W. Myint

Remote Sens. 2020, 12(23), 3942; https://doi.org/10.3390/rs12233942 - 2 Dec 2020

Cited by 19 | Viewed by 8419

Abstract

The 2019–2020 Kangaroo Island bushfires in South Australia burned almost half of the island. To understand how to avoid future severe ‘mega-fires’ and how vegetation may recover from 2019–2020, we can utilize information from the bulk of historical fires in an area. Landsat time-series of vegetation change provide this opportunity, but there has been little analysis of large numbers of fires to build a landscape-level understanding and quantify drivers in an Australian context. In this study, we built a yearly cloud-free surface reflectance normalized burn ratio (NBR) time-series (1988–2020) using all available summer Landsat images over Kangaroo Island. Data were collected in Google Earth Engine and fitted with LandTrendr. Burn severity and post-fire recovery were quantified for 47 fires, with a new recovery metric facilitating comparison where fire frequency is high. Variables representing the current burn, fire history, vegetation structure, and topography were related to severity and yearly recovery with random forest and bivariate analysis. Results show that the 2019–2020 bushfires were the most widespread and severe, followed by 2007–2008. Vegetation recovers quickly, with NBR stabilizing ten years post-fire on average. Severity is most influenced by fire frequency, vegetation capacity and land use with more severe burns in nature conservation areas with dense vegetation and a history of frequent fires. Influence on recovery varied with time since fire, with initial (year 1–3) faster recovery observed in areas with less surviving vegetation. Later (year 6–10) recovery was most influenced by a variable representing burn year and further investigation indicates that precipitation increases in later post-fire years likely facilitated faster recovery. The relative abundance of eucalypt woodlands also has a positive influence on recovery in middle and later years. These results provide valuable information to land managers on Kangaroo Island and in similar environments, who should consider adjusting practices to limit future mega-fire risk and potential ecosystem shifts if severe fires become more frequent with climate change. Full article

(This article belongs to the Special Issue Advances in Remote Sensing of Post-fire Environmental Damage and Recovery Dynamics)

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23 pages, 8853 KiB

Open AccessArticle

Mapping Burned Areas of Mato Grosso State Brazilian Amazon Using Multisensor Datasets

by Yosio Edemir Shimabukuro, Andeise Cerqueira Dutra, Egidio Arai, Valdete Duarte, Henrique Luís Godinho Cassol, Gabriel Pereira and Francielle da Silva Cardozo

Remote Sens. 2020, 12(22), 3827; https://doi.org/10.3390/rs12223827 - 21 Nov 2020

Cited by 22 | Viewed by 4729

Abstract

Quantifying forest fires remain a challenging task for the implementation of public policies aimed to mitigate climate change. In this paper, we propose a new method to provide an annual burned area map of Mato Grosso State located in the Brazilian Amazon region, taking advantage of the high spatial and temporal resolution sensors. The method consists of generating the vegetation, soil, and shade fraction images by applying the Linear Spectral Mixing Model (LSMM) to the Landsat-8 OLI (Operational Land Imager), PROBA-V (Project for On-Board Autonomy–Vegetation), and Suomi NPP-VIIRS (National Polar-Orbiting Partnership-Visible Infrared Imaging Radiometer Suite) datasets. The shade fraction images highlight the burned areas, in which values are represented by low reflectance of ground targets, and the mapping was performed using an unsupervised classifier. Burned areas were evaluated in terms of land use and land cover classes over the Amazon, Cerrado and Pantanal biomes in the Mato Grosso State. Our results showed that most of the burned areas occurred in non-forested areas (66.57%) and old deforestation (21.54%). However, burned areas over forestlands (11.03%), causing forest degradation, reached more than double compared with burned areas identified in consolidated croplands (5.32%). The results obtained were validated using the Sentinel-2 data and compared with active fire data and existing global burned areas products, such as the MODIS (Moderate Resolution Imaging Spectroradiometer product) MCD64A1 and MCD45A1, and Fire CCI (ESA Climate Change Initiative) products. Although there is a good visual agreement among the analyzed products, the areas estimated were quite different. Our results presented correlation of 51% with Sentinel-2 and agreement of r² = 0.31, r² = 0.29, and r² = 0.43 with MCD64A1, MCD45A1, and Fire CCI products, respectively. However, considering the active fire data, it was achieved the better performance between active fire presence and burn mapping (92%). The proposed method provided a general perspective about the patterns of fire in various biomes of Mato Grosso State, Brazil, that are important for the environmental studies, specially related to fire severity, regeneration, and greenhouse gas emissions. Full article

(This article belongs to the Special Issue Advances in Remote Sensing of Post-fire Environmental Damage and Recovery Dynamics)

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19 pages, 6866 KiB

Open AccessArticle

Remote Sensing of Burn Severity Using Coupled Radiative Transfer Model: A Case Study on Chinese Qinyuan Pine Fires

by Changming Yin, Binbin He, Xingwen Quan, Marta Yebra and Gengke Lai

Remote Sens. 2020, 12(21), 3590; https://doi.org/10.3390/rs12213590 - 2 Nov 2020

Cited by 9 | Viewed by 2759

Abstract

Burn severity mapping is critical to quantifying fire impact on key ecological processes and post-fire forest management. Satellite remote sensing has the advantages of high spatial-temporal resolution and large-scale monitoring and provides a more efficient way to evaluate forest fire burn severity than traditional field or aerial surveys. However, the proportion of tree canopy cover (TCC) affects the spectral signal received by remote sensing sensors from the background charcoal and ash. Consequently, not considering this factor normally leads a spectral confusion in burn severity retrieval. In this study, the burn severity of two Qinyuan forest fires was estimated using a coupled Radiative Transfer Model (RTM) and Sentinel-2A Multi-Spectral Instrument (MSI) reflectance data. A two-layer Canopy Reflectance Model (ACRM) RTM was coupled with the GeoSail RTM by replacing the spectra of the background input of GeoSail RTM to simulate the spectra of the three-layered forests for burn severity retrieval measured as the Composite Burn Index (CBI). The TCC data was then served to RTM parameterization and constrain the backward inversion procedure of the coupled RTM to alleviate spectral confusion. Finally, the inversion retrievals were evaluated using 163 field measured CBI. The coupled RTM can simulate the radiative transfer characteristics of three-layer vegetation and has greater potential to accurately estimate burn severity worldwide. To evaluate the merit of our proposed method, the CBI was estimated through coupled RTM inversion with TCC constraint (CP_RTM+TCC), coupled RTM inversion with global optimal search (CP-RTM+GOS), Forest Reflectance and Transmittance (FRT) RTM inversion with TCC constraint (FRT+TCC), and random forest (RF) algorithm. The results showed that the method proposed in this study (CP_RTM+TCC) yielded the highest estimation accuracy (R² = 0.92, RMSE = 0.2) among the four methods used as benchmark, indicating its reasonable ability to assist forest managers to better understand post-fire vegetation regeneration and forest management. Full article

(This article belongs to the Special Issue Advances in Remote Sensing of Post-fire Environmental Damage and Recovery Dynamics)

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21 pages, 4798 KiB

Open AccessArticle

Repeat Oblique Photography Shows Terrain and Fire-Exposure Controls on Century-Scale Canopy Cover Change in the Alpine Treeline Ecotone

by David McCaffrey and Chris Hopkinson

Remote Sens. 2020, 12(10), 1569; https://doi.org/10.3390/rs12101569 - 15 May 2020

Cited by 14 | Viewed by 3312

Abstract

Alpine Treeline Ecotone (ATE), the typically gradual transition zone between closed canopy forest and alpine tundra vegetation in mountain regions, displays an elevational range that is generally constrained by thermal deficits. At landscape scales, precipitation and moisture regimes can suppress ATE elevation below thermal limits, causing variability in ATE position. Recent studies have investigated the relative effects of hydroclimatic variables on ATE position at multiple scales, but less attention has been given to interactions between hydroclimatic variables and disturbance agents, such as fire. Advances in monoplotting have enabled the extraction of canopy cover information from oblique photography. Using airborne lidar, and repeat photography from the Mountain Legacy Project, we observed canopy cover change in West Castle Watershed (Alberta, Canada; ~103 km²; 49.3° N, 114.4° W) over a 92-year period (1914–2006). Two wildfires, occurring 1934 and 1936, provided an opportunity to compare topographic patterns of mortality and succession in the ATE, while factoring by exposure to fire. Aspect was a strong predictor of mortality and succession. Fire-exposed areas accounted for 83.6% of all mortality, with 72.1% of mortality occurring on south- and east-facing slope aspects. Succession was balanced between fire-exposed and unburned areas, with 62.0% of all succession occurring on north- and east-facing slope aspects. The mean elevation increase in closed canopy forest (i.e., the lower boundary of ATE) on north- and east-facing undisturbed slopes was estimated to be 0.44 m per year, or ~44 m per century. The observed retardation of treeline advance on south-facing slopes is likely due to moisture limitation. Full article

(This article belongs to the Special Issue Advances in Remote Sensing of Post-fire Environmental Damage and Recovery Dynamics)

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25 pages, 6421 KiB

Open AccessArticle

Identifying Post-Fire Recovery Trajectories and Driving Factors Using Landsat Time Series in Fire-Prone Mediterranean Pine Forests

by Alba Viana-Soto, Inmaculada Aguado, Javier Salas and Mariano García

Remote Sens. 2020, 12(9), 1499; https://doi.org/10.3390/rs12091499 - 8 May 2020

Cited by 49 | Viewed by 6287

Abstract

Wildfires constitute the most important natural disturbance of Mediterranean forests, driving vegetation dynamics. Although Mediterranean species have developed ecological post-fire recovery strategies, the impacts of climate change and changes in fire regimes may endanger their resilience capacity. This study aims at assessing post-fire recovery dynamics at different stages in two large fires that occurred in Mediterranean pine forests (Spain) using temporal segmentation of the Landsat time series (1994–2018). Landsat-based detection of Trends in Disturbance and Recovery (LandTrendr) was used to derive trajectory metrics from Tasseled Cap Wetness (TCW), sensitive to canopy moisture and structure, and Tasseled Cap Angle (TCA), related to vegetation cover gradients. Different groups of post-fire trajectories were identified through K-means clustering of the Recovery Ratios (RR) from fitted trajectories: continuous recovery, continuous recovery with slope changes, continuous recovery stabilized and non-continuous recovery. The influence of pre-fire conditions, fire severity, topographic variables and post-fire climate on recovery rates for each recovery category at successional stages was analyzed through Geographically Weighted Regression (GWR). The modeling results indicated that pine forest recovery rates were highly sensitive to post-fire climate in the mid and long-term and to fire severity in the short-term, but less influenced by topographic conditions (adjusted R-squared ranged from 0.58 to 0.88 and from 0.54 to 0.93 for TCA and TCW, respectively). Recovery estimation was assessed through orthophotos, showing a high accuracy (Dice Coefficient ranged from 0.81 to 0.97 and from 0.74 to 0.96 for TCA and TCW, respectively). This study provides new insights into the post-fire recovery dynamics at successional stages and driving factors. The proposed method could be an approach to model the recovery for the Mediterranean areas and help managers in determining which areas may not be able to recover naturally. Full article

(This article belongs to the Special Issue Advances in Remote Sensing of Post-fire Environmental Damage and Recovery Dynamics)

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24 pages, 3451 KiB

Open AccessFeature PaperArticle

Can Landsat-Derived Variables Related to Energy Balance Improve Understanding of Burn Severity From Current Operational Techniques?

by Alfonso Fernández-Manso, Carmen Quintano and Dar A. Roberts

Remote Sens. 2020, 12(5), 890; https://doi.org/10.3390/rs12050890 - 10 Mar 2020

Cited by 6 | Viewed by 3659

Abstract

Forest managers rely on accurate burn severity estimates to evaluate post-fire damage and to establish revegetation policies. Burn severity estimates based on reflective data acquired from sensors onboard satellites are increasingly complementing field-based ones. However, fire not only induces changes in reflected and emitted radiation measured by the sensor, but also on energy balance. Evapotranspiration (ET), land surface temperature (LST) and land surface albedo (LSA) are greatly affected by wildfires. In this study, we examine the usefulness of these elements of energy balance as indicators of burn severity and compare the accuracy of burn severity estimates based on them to the accuracy of widely used approaches based on spectral indexes. We studied a mega-fire (more than 450 km² burned) in Central Portugal, which occurred from 17 to 24 June 2017. The official burn severity map acted as a ground reference. Variations induced by fire during the first year following the fire event were evaluated through changes in ET, LST and LSA derived from Landsat data and related to burn severity. Fisher’s least significant difference test (ANOVA) revealed that ET and LST images could discriminate three burn severity levels with statistical significance (uni-temporal and multi-temporal approaches). Burn severity was estimated from ET, LST and LSA using thresholding. Accuracy of ET and LST based on burn severity estimates was adequate (κ = 0.63 and 0.57, respectively), similar to the accuracy of the estimate based on dNBR (κ = 0.66). We conclude that Landsat-derived surface energy balance variables, in particular ET and LST, in addition to acting as useful indicators of burn severity for mega-fires in Mediterranean ecosystems, may provide critical information about how energy balance changes due to fire. Full article

(This article belongs to the Special Issue Advances in Remote Sensing of Post-fire Environmental Damage and Recovery Dynamics)

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18 pages, 7736 KiB

Open AccessArticle

A New Model for Transfer Learning-Based Mapping of Burn Severity

by Zhong Zheng, Jinfei Wang, Bo Shan, Yongjun He, Chunhua Liao, Yanghua Gao and Shiqi Yang

Remote Sens. 2020, 12(4), 708; https://doi.org/10.3390/rs12040708 - 21 Feb 2020

Cited by 14 | Viewed by 3456

Abstract

In recent years, global forest fires have occurred more frequently, seriously destroying the structural functions of forest ecosystem. Mapping the burn severity after forest fires is of great significance for quantifying fire’s effects on landscapes and establishing restoration measures. Generally, intensive field surveys across burned areas are required for the effective application of traditional methods. Unfortunately, this requirement could not be satisfied in most cases, since the field work demands a lot of personnel and funding. For mapping severity levels across burned areas without field survey data, a semi-supervised transfer component analysis-based support vector regression model (SSTCA-SVR) was proposed in this study to transfer knowledge trained from other burned areas with field survey data. Its performance was further evaluated in various eco-type regions of southwestern United States. Results show that SSTCA-SVR which was trained on source domain areas could effectively be transferred to a target domain area. Meanwhile, the SSTCA-SVR could maintain as much spectral information as possible to map burn severity. Its mapped results are more accurate (RMSE values were between 0.4833 and 0.6659) and finer, compared to those mapped by ∆NDVI-, ∆LST-, ∆NBR- (RMSE values ranged from 0.7362 to 1.1187) and SVR-based models (RMSE values varied from 1.7658 to 2.0055). This study has introduced a potentially efficient mechanism to map burn severity, which will speed up the response of post-fire management. Full article

(This article belongs to the Special Issue Advances in Remote Sensing of Post-fire Environmental Damage and Recovery Dynamics)

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18 pages, 5039 KiB

Open AccessArticle

Field-Validated Burn-Severity Mapping in North Patagonian Forests

by María Guadalupe Franco, Ignacio A. Mundo and Thomas T. Veblen

Remote Sens. 2020, 12(2), 214; https://doi.org/10.3390/rs12020214 - 8 Jan 2020

Cited by 34 | Viewed by 4698

Abstract

Burn severity, which can be reliably estimated by validated spectral indices, is a key element for understanding ecosystem dynamics and informing management strategies. However, in North Patagonian forests, where wildfires are a major disturbance agent, studies aimed at the field validation of spectral indices of burn severity are scarce. The aim of this work was to develop a field validated methodology for burn-severity mapping by studying two large fires that burned in the summer of 2013–2014 in forests of Araucaria araucana and other tree species. We explored the relation between widely used spectral indices and a field burn-severity index, and we evaluated index performance by examining index sensitivity in discriminating burn-severity classes in different vegetation types. For those indices that proved to be suitable, we adjusted the class thresholds and constructed confusion matrices to assess their accuracy. Burn severity maps of the studied fires were generated using the two most accurate methods and were compared to evaluate their level of agreement. Our results confirm that reliable burn severity estimates can be derived from spectral indices for these forests. Two severity indices, the delta normalized burn ratio (dNBR) and delta normalized difference vegetation index (dNDVI), were highly related to the fire-induced changes observed in the field, but the strength of these associations varied across the five different vegetation types defined by tree heights and tree and tall shrub species regeneration strategies. The thresholds proposed in this study for these indices generated classifications with global accuracies of 82% and Kappa indices of 70%. Both the dNBR and dNDVI classification approaches were more accurate in detecting high severity, but to a lesser degree for detecting low severity burns. Moderate severity was poorly classified, with producer and user errors reaching 50%. These constraints, along with detected differences in separability, need to be considered when interpreting burn severity maps generated using these methods. Full article

(This article belongs to the Special Issue Advances in Remote Sensing of Post-fire Environmental Damage and Recovery Dynamics)

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14 pages, 7089 KiB

Open AccessArticle

Using Long-Term SAR Backscatter Data to Monitor Post-Fire Vegetation Recovery in Tundra Environment

by Zhiwei Zhou, Lin Liu, Liming Jiang, Wanpeng Feng and Sergey V. Samsonov

Remote Sens. 2019, 11(19), 2230; https://doi.org/10.3390/rs11192230 - 25 Sep 2019

Cited by 25 | Viewed by 4669

Abstract

Wildfires could have a strong impact on tundra environment by combusting surface vegetation and soil organic matter. For surface vegetation, many years are required to recover to pre-fire level. In this paper, by using C-band (VV/HV polarization) and L-band (HH polarization) synthetic aperture radar (SAR) images acquired before and after fire from 2002 to 2016, we investigated vegetation change affected by the Anaktuvuk River Fire in Arctic tundra environment. Compared to the unburned areas, C- and L-band SAR backscatter coefficients increased by up to 5.5 and 4.4 dB in the severely burned areas after the fire. Then past 5 years following the fire, the C-band SAR backscatter differences decreased to pre-fire level between the burned and unburned areas, suggesting that vegetation coverage in burned sites had recovered to the unburned level. This duration is longer than the 3-year recovery suggested by optical-based Normalized Difference Vegetation Index (NDVI) observations. While for the L-band SAR backscatter after 10-year recovery, about 2 dB higher was still found in the severely burned area, compared to the unburned area. The increased roughness of the surface is probably the reason for such sustained differences. Our analysis implies that long records of space-borne SAR backscatter can monitor post-fire vegetation recovery in Arctic tundra environment and complement optical observations. Full article

(This article belongs to the Special Issue Advances in Remote Sensing of Post-fire Environmental Damage and Recovery Dynamics)

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15 pages, 8070 KiB

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Quantitative Analysis of Forest Fires in Southeastern Australia Using SAR Data

by Aqil Tariq, Hong Shu, Qingting Li, Orhan Altan, Mobushir Riaz Khan, Muhammad Fahad Baqa and Linlin Lu

Remote Sens. 2021, 13(12), 2386; https://doi.org/10.3390/rs13122386 - 18 Jun 2021

Cited by 35 | Viewed by 6360

Abstract

Prescribed burning is a common strategy for minimizing forest fire risk. Fire is introduced under specific environmental conditions, with explicit duration, intensity, and rate of spread. Such conditions deviate from those encountered during the fire season. Prescribed burns mostly affect surface fuels and understory vegetation, an outcome markedly different when compared to wildfires. Data on prescribed burning are crucial for evaluating whether land management targets have been reached. This research developed a methodology to quantify the effects of prescribed burns using multi-temporal Sentinel-1 Synthetic Aperture Radar (SAR) imagery in the forests of southeastern Australia. C-band SAR datasets were specifically used to statistically explore changes in radar backscatter coefficients with the intensity of prescribed burns. Two modeling approaches based on pre- and post-fire ratios were applied for evaluating prescribed burn impacts. The effects of prescribed burns were documented with an overall accuracy of 82.3% using cross-polarized backscatter (VH) SAR data under dry conditions. The VV polarization indicated some potential to detect burned areas under wet conditions. The findings in this study indicate that the C-band SAR backscatter coefficient has the potential to evaluate the effectiveness of prescribed burns due to its sensitivity to changes in vegetation structure. Full article

(This article belongs to the Special Issue Advances in Remote Sensing of Post-fire Environmental Damage and Recovery Dynamics)

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