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26 pages, 10210 KiB  
Article
Research on the Simulation Model of Dynamic Shape for Forest Fire Burned Area Based on Grid Paths from Satellite Remote Sensing Images
by Xintao Ling, Gui Zhang, Ying Zheng, Huashun Xiao, Yongke Yang, Fang Zhou and Xin Wu
Remote Sens. 2025, 17(1), 140; https://doi.org/10.3390/rs17010140 - 3 Jan 2025
Viewed by 299
Abstract
The formation of forest fire burned area, influenced by a variety of factors such as meteorology, topography, vegetation, and human intervention, is a dynamic process of fire line burning that develops from the point of ignition to the boundary of the burned area. [...] Read more.
The formation of forest fire burned area, influenced by a variety of factors such as meteorology, topography, vegetation, and human intervention, is a dynamic process of fire line burning that develops from the point of ignition to the boundary of the burned area. Accurately simulating and predicting this dynamic process can provide a scientific basis for forest fire control and suppression decisions. In this study, five typical forest fires located in different regions of China were used as the study object. The straight path distances from the ignition point grid to each grid on fire line in Sentinel-2 imageries for each forest fire were used as the target variables. We obtained the values of 11 independent variables for each pathway, including wind speed component, Temperature, Relative Humidity, Elevation, Slope, Aspect, Degree of Relief, Normalized Difference Vegetation Index, Vegetation Type, Fire Duration, and Gross Domestic Product reflecting human intervention capacity for fires. The value of each target variable and that of its corresponding independent variable constituted a sample. Four machine learning models, such as Random Forest (RF), Gradient Boosting Decision Trees (GBDT), Support Vector Machine (SVM), and Multilayer Perceptron (MLP), were trained using 80% effective samples from four forest fires, and 20% used to verify the above models. The hyper-parameters of each model were optimized using grid search method. After analyzing the validation results of models which showed temperature as a non-significant variable, the training and validation process of models above was repeated after excluding temperature. The results show that RF is the optimal model with 49.55 m for root mean square error (RMSE), 29.19 m for mean absolute error (MAE) and 0.9823 for coefficient of determination (R2). This study used the RF model to construct the shape of burned areas by predicting lengths of all straight path distances from the ignition point to the fire line. The study can dynamically capture the development of forest fire scenes. Full article
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Figure 1

Figure 1
<p>Location of five typical forest fires in China.</p>
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<p>The technical route of this study.</p>
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<p>Simple schematics of four machine learning methods.</p>
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<p>The straight path in the forest fire area.</p>
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<p>The interrelationship diagram of <math display="inline"><semantics> <mrow> <mi>φ</mi> </mrow> </semantics></math>,<math display="inline"><semantics> <mrow> <mi>ϕ</mi> </mrow> </semantics></math>,<math display="inline"><semantics> <mrow> <mi>θ</mi> </mrow> </semantics></math>.</p>
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<p>The number of grids on a straight path from the ignition point to the fire line.</p>
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<p>Removes the special straight path.</p>
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<p>ROC curves prediction rates of four models.</p>
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<p>Extraction results of burned areas of five forest fires. (<b>a</b>–<b>d</b>) show the results of the burned area extraction for “Qinyuan 3.29” at two time points (2019-04-01 03:27 and 2019-04-04 05:00). (<b>e</b>–<b>h</b>) illustrate the results of the burned area extraction for “Gaoming 12.5” at two time points (2019-12-06 03:11 and 2019-12-08 10:30). (<b>i</b>–<b>l</b>) display the results of the burned area extraction for “Beibei 8.21” at two time points (2022-10-19 03:20 and 2022-08-26 00:30). (<b>m</b>–<b>p</b>) present the results of the burned area extraction for “Xintian 10.17” at two time points (2022-10-19 03:20 and 2022-10-26 13:00). Finally, (<b>q</b>–<b>t</b>) represent the results of the burned area extraction for “Muli 3.28” at two time points (2020-03-30 04:00 and 2020-04-08 09:00).</p>
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<p>The weight distribution of the independent variables of the four machine learning models.</p>
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<p>The scatter comparison between the predicted distance and the real distance of the four machine learning models.</p>
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<p>The residual error comparison of four machine learning models.</p>
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<p>Simulation results of the fire line for four forest fires using RF. (<b>a</b>,<b>b</b>) show the fire line simulation results for “Qinyuan 3.29” at two time points (2019-04-01 03:27 and 2019-04-04 05:00). (<b>c</b>,<b>d</b>) illustrate the fire line simulation results for “Gaoming 12.5” at two time points (2019-12-06 03:11 and 2019-12-08 10:30). (<b>e</b>,<b>f</b>) display the fire line simulation results for “Beibei 8.21” at two time points (2022-10-19 03:20 and 2022-08-26 00:30). Finally, (<b>g</b>,<b>h</b>) present the fire line simulation results for “Xintian 10.17” at two time points (2022-10-19 03:20 and 2022-10-26 13:00).</p>
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<p>The comparison of the simulated fire line and the real fire line for the “Muli 3.28” forest fire is shown. (<b>a</b>,<b>b</b>) represent the fire line simulation results for “Muli 3.28” at two time points (2020-03-30 04:00 and 2020-04-08 09:00).</p>
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19 pages, 13236 KiB  
Article
Permafrost Degradation and Vegetation Growth Beyond the Polar Circle in Siberia
by Viacheslav I. Kharuk, Sergei T. Im, Il’ya A. Petrov and Evgeny G. Shvetsov
Forests 2025, 16(1), 47; https://doi.org/10.3390/f16010047 - 30 Dec 2024
Viewed by 304
Abstract
Permafrost thawing is potentially a crucial but poorly investigated factor that influences vegetation dynamics in the Arctic. We studied the permafrost thaw rate beyond the Polar Circle in Siberia. We analyzed its influence on the larch (Larix spp.) growth and Arctic vegetation [...] Read more.
Permafrost thawing is potentially a crucial but poorly investigated factor that influences vegetation dynamics in the Arctic. We studied the permafrost thaw rate beyond the Polar Circle in Siberia. We analyzed its influence on the larch (Larix spp.) growth and Arctic vegetation (sparse larch forests, tundra, and forest–tundra communities) productivity (NPP). We checked the following hypotheses: (1) satellite gravimetry is valid for permafrost thawing analysis; (2) meltwater runoff stimulated trees’ growth and NPP. We used satellite (GRACE, Terra/MODIS) and field data, and larch tree radial growth index measurements. We found a continuous negative trend in the terrestrial water content (r2 = 0.67) caused by permafrost thawing beyond the Polar Circle. Runoff is maximal in West and Mid Siberia (9.7 ± 2.9 kg/m2/y) and decreases in the eastward direction with minimal values in the Chukotka Peninsula sector (−2.9 ± 3.2 kg/m2/y). We found that the growth increment of larch trees positively correlated with meltwater runoff (0.5…0.6), whereas the correlation with soil water content was negative (−0.55…−0.85). Permafrost thawing leads to an increase in the Arctic vegetation productivity. We found a positive trend in NPP throughout the Siberian Arctic (r2 = 0.30). NPP negatively correlated with soil water content (r = −0.55) and positively with meltwater runoff (West Siberia, r = 0.7). An increase in VPD (vapor pressure deficit) and air and soil temperatures stimulated the larch growth and vegetation NPP (r = 0.5…0.9 and r = 0.6…0.9, respectively). Generally, permafrost degradation leads to improved hydrothermal conditions for trees and vegetation growth and contributes to the preservation of the Arctic as a carbon sink despite the increase in burning rate. Full article
(This article belongs to the Section Forest Ecology and Management)
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Figure 1

Figure 1
<p>The study area is located within continuous permafrost mostly. Stars indicate on-ground study sites (1—Pyasino, 2—Ary-Mas, 3—Kotuy, 4—Emb). Background: vegetation landcover classes (according to VEGA PRO map <a href="http://pro-vega.ru/eng" target="_blank">http://pro-vega.ru/eng</a>, accessed on 19 November 2024) and permafrost types (adapted with permission from [<a href="#B23-forests-16-00047" class="html-bibr">23</a>]).</p>
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<p>Within the entire Siberian Arctic, a stable decreasing trend (<span class="html-italic">p</span> &lt; 0.01, grey line) of water anomalies (WAs) has been observed since 2007.</p>
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<p>Portion of the biomass remaining in the given year since the beginning of decomposition.</p>
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<p>A map of <span class="html-italic">WR<sub>r</sub></span> (raw data) changes beyond the Arctic Circle (Δ<span class="html-italic">G<sub>m</sub></span> = <span class="html-italic">G<sub>m</sub></span><sub>(2007–2009)</sub> − <span class="html-italic">G<sub>m</sub></span><sub>(2021–2023)</sub>). Mean Δ<span class="html-italic">G<sub>m</sub></span> is 52 kg/m<sup>2</sup> (σ = 38; min = −52, max = 175). Positive Δ<span class="html-italic">G<sub>m</sub></span> means runoff and negative Δ<span class="html-italic">Gm</span> means water accumulation. Insert is the study area location.</p>
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<p>The Arctic’s clusters with different values of meltwater runoff (<span class="html-italic">WR<sub>r</sub></span>). Legend: WestSib, MidSib, LenaRiver, EastSib, and ChukotkaPen are the West Siberia, Mid Siberia, Lena River, East Siberia, and Chukotka Peninsula clusters, respectively. Insert is the study area location.</p>
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<p>The <span class="html-italic">WA</span> (raw data) dynamics within Arctic sectors (<a href="#forests-16-00047-f005" class="html-fig">Figure 5</a>). On average, during 2007–2023, the mean water runoff rate was about ~3 kg/m<sup>2</sup>/y (<span class="html-italic">r</span><sup>2</sup> = 0.67). Vertical lines show the 95% confidence interval, grey lines indicate trends. The years 2017 and 2018 were excluded because &gt;25% of the data were missed. Abbreviations: WestSib, MidSib, LenaRiver, EastSib, and ChukotkaPen are the West Siberia, Mid Siberia, Lena River, East Siberia, and Chukotka Peninsula clusters, respectively.</p>
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<p>(<b>a</b>) The accumulated NPP (∑NPP) during 2007–2023. Mean ∑NPP is 2.6 kg/m<sup>2</sup> (σ = 1.1; min = 0, max = 8.5). (<b>b</b>) Map of the remaining biomass (<math display="inline"><semantics> <mrow> <mo>∑</mo> <msub> <mrow> <mi>V</mi> </mrow> <mrow> <mi>m</mi> </mrow> </msub> </mrow> </semantics></math>). Mean <math display="inline"><semantics> <mrow> <mo>∑</mo> <msub> <mrow> <mi>V</mi> </mrow> <mrow> <mi>m</mi> </mrow> </msub> </mrow> </semantics></math> is 2.1 kg/m<sup>2</sup> (σ = 0.8; min = 0.1, max = 6.6).</p>
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<p>Total fire-caused carbon loss (∑<span class="html-italic">F</span>) during 2007–2023. Mean ∑<span class="html-italic">F</span> is 0.06 kg/m<sup>2</sup> (σ = 0.35; min = 0, max = 11.16).</p>
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<p>(<b>a</b>) Map of the mean (2007–2023) annual water runoff within the Arctic. Mean <span class="html-italic">WR</span> is 7.7 kg/m<sup>2</sup>/year (σ = 4.4, min = −9.9, max = 19.1). Positive and negative <span class="html-italic">WR</span> corresponded to water runoff and water accumulation, respectively. (<b>b</b>) Meltwater runoff within given Arctic sectors. Whiskers show standard deviations.</p>
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<p>(<b>a</b>) Map of NPP trends, (<b>b</b>) and the mean NPP dynamics within the Siberian Arctic (dotted line, <span class="html-italic">p</span> &lt; 0.05). Positive and negative trends occurred within ~15% of the Arctic and ~1%, respectively.</p>
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<p>(<b>a</b>) Map of Spearman correlations (<span class="html-italic">p</span> &lt; 0.1) between NPP and water runoff (<span class="html-italic">WR</span>). Positive Spearman correlations are observed within 4.5% of the analyzed area, negative—1%, and insignificant—94%. (<b>b</b>) NPP positively correlated with meltwater runoff within the WestSib sector and, much lower but significantly, in the MidSib sector. Within the other sectors, positive correlations are local (<a href="#forests-16-00047-f0A2" class="html-fig">Figure A2</a>).</p>
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<p>NPP correlations with summer ((<b>a</b>–<b>e</b>); <span class="html-italic">p</span> &lt; 0.01) air and soil temperatures ((<b>f</b>–<b>j</b>); <span class="html-italic">p</span> &lt; 0.01), vapor pressure deficit (VPD; (<b>k</b>–<b>o</b>); <span class="html-italic">p</span> &lt; 0.01), and precipitation ((<b>p</b>–<b>t</b>); <span class="html-italic">p</span> &lt; 0.01) within the Arctic sectors.</p>
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<p>(<b>a</b>) Correlations between larch growth index (GI) and water anomalies (<span class="html-italic">WAs</span>) in JJA (red) and the hydrological year (August–September, blue) (since 2003). Grey columns: correlations between GI and water runoff (<span class="html-italic">WR</span>) (since 2007, <span class="html-italic">p</span> &lt; 0.2). (<b>b</b>) Correlations between GI and JJA air (TEMP) and soil temperatures (SoTEMP), precipitation (PRE), and vapor pressure deficit (VPD). Significances at <span class="html-italic">p</span> &lt; 0.01, <span class="html-italic">p</span> &lt; 0.05, and <span class="html-italic">p</span> &lt; 0.1 are indicated by one (*), two (**), and three (***) asterisks. Abbreviations: Ary-Mas, Pyasino, Kotuy, and Emb are the on-ground sites (<a href="#forests-16-00047-f001" class="html-fig">Figure 1</a>).</p>
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<p>Climate variables of ARY-MAS (<b>a</b>–<b>d</b>), PYASINO (<b>e</b>–<b>h</b>), KOTUY (<b>i</b>–<b>l</b>), and EMB (<b>m</b>–<b>p</b>) sites (based on ERA5 Land data): summer temperature (<b>a</b>,<b>e</b>,<b>i</b>,<b>m</b>); summer precipitation (<b>b</b>,<b>f</b>,<b>j</b>,<b>n</b>); summer VPD (<b>c</b>,<b>g</b>,<b>k</b>,<b>o</b>); summer soil temperature (<b>d</b>,<b>h</b>,<b>l</b>,<b>p</b>); dotted lines indicate trends.</p>
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<p>Correlations between NPP and WA (water anomalies) within the Arctic sectors. Significant correlations were observed in the WestSib and MidSib sectors.</p>
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22 pages, 7798 KiB  
Article
Soil Burn Severity Assessment Using Sentinel-2 and Radiometric Measurements
by Rafael Llorens, José Antonio Sobrino, Cristina Fernández, José M. Fernández-Alonso and José Antonio Vega
Fire 2024, 7(12), 487; https://doi.org/10.3390/fire7120487 - 23 Dec 2024
Viewed by 409
Abstract
The objective of this article is to create soil burn severity maps to serve as field support for erosion tasks after forest fire occurrence in Spain (2017–2022). The Analytical Spectral Device (ASD) FieldSpec and the CIMEL CE-312 radiometers (optical and thermal, respectively) were [...] Read more.
The objective of this article is to create soil burn severity maps to serve as field support for erosion tasks after forest fire occurrence in Spain (2017–2022). The Analytical Spectral Device (ASD) FieldSpec and the CIMEL CE-312 radiometers (optical and thermal, respectively) were used as input data to establish relationships between soil burn severity and reflectance or emissivity, respectively. Spectral indices related to popular forest fire studies and soil assessment were calculated by Sentinel-2 convolved reflectance. All the spectral indices that achieve the separability index algorithm (SI) were validated using specificity, sensitivity, accuracy (ACC), balanced accuracy (BACC), F1-score (F1), and Cohen’s kappa index (k), with 503 field plots. The results displayed the highest overall accuracy results using the Iron Oxide ratio (IOR) index: ACC = 0.71, BACC = 0.76, F1 = 0.63 and k = 0.50, respectively. In addition, IOR was the only spectral index with an acceptable k value (k = 0.50). It is demonstrated that, together with NIR and SWIR spectral bands, the use of blue spectral band reduces atmospheric interferences and improves the accuracy of soil burn severity mapping. The maps obtained in this study could be highly valuable to forest agents for soil erosion restoration tasks. Full article
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Figure 1
<p>Study area with all the forest fires studied (red color) and Sentinel−2 tiles used (represented by discontinuous red lines. The reference coordinate system is WGS84 (EPSG: 4326).</p>
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<p>Soil burn severity levels classified using a modified version [<a href="#B6-fire-07-00487" class="html-bibr">6</a>] of the soil burn severity index proposed by [<a href="#B43-fire-07-00487" class="html-bibr">43</a>]. Levels 1 and 2 correspond to low severity, level 3 to moderate severity, and level 4, 5, and 6 to high severity.</p>
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<p>Example of optical (ASD) and thermal (CIMEL CE-312) radiometric measurements carried out during Pedro Bernardo’s field campaign (2 August 2019).</p>
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<p>CIMEL CE-312 emissivity data obtained after TES algorithm for all soil burn severity levels measured on the field: Vilaza (10 October 2018), Nerva (4 October 2018), Pedro Bernardo (2 August 2019), and Real Sitio de San Ildefonso (5 September 2019).</p>
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<p>Reclassification of CIMEL CE-312 emissivity data into three new soil burn severity levels (Low = severity 2; Moderate = severity 3; and High = severity 4, 5, and 6), obtained after TES algorithm for all soil burn severity levels measured on the field: Vilaza (10 October 2018), Nerva (4 October 2018), Pedro Bernardo (2 August 2019), and Real Sitio de San Ildefonso (5 September 2019).</p>
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<p>(<b>a</b>) ASD reflectance data obtained for all soil burn severity levels measured on the field: Vilaza (10 October 2018), Nerva (4 October 2018), Pedro Bernardo (2 August 2019) and Real Sitio de San Ildefonso (5 September 2019); (<b>b</b>) ASD reflectance data obtained for all soil burn severity measured at the laboratory.</p>
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<p>(<b>a</b>) Reclassification of ASD reflectance data into three new soil burn severity levels (Low = severity 2; Moderate = severity 3 and High = severity 4, 5, and 6), for all soil burn severity levels measured on the field: Vilaza (10 October 2018), Nerva (4 October 2018), Pedro Bernardo (2 August 2019), and Real Sitio de San Ildefonso (5 September 2019); (<b>b</b>) Reclassification of ASD reflectance data into three new soil burn severity levels (Low = severity 2; Moderate = severity 3, and High = severity 4, 5, and 6) for all soil burn severity levels measured at laboratory.</p>
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<p>(<b>a</b>) Field ASD measurements convolved to Sentinel-2 spectral bands configuration using the spectral response function of Sentinel-2; (<b>b</b>) Laboratory ASD measurements convolved to Sentinel-2 spectral bands configuration using the spectral response function of Sentinel-2.</p>
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<p>Weighted separability index (SI<sub>w</sub>) obtained for all spectral indices used in this study (<a href="#fire-07-00487-t003" class="html-table">Table 3</a>). The green color represents SI<sub>w</sub> for all soil burn severity levels measured by ASD on the field. The blue color represents SI<sub>w</sub> for all soil burn severity levels measured by ASD in the laboratory.</p>
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<p>Example of soil burn severity map obtained by the optimal thresholds (<a href="#fire-07-00487-t004" class="html-table">Table 4</a>) of IOR spectral index (spectral index with highest accuracy results shown in <a href="#fire-07-00487-t005" class="html-table">Table 5</a> and <a href="#fire-07-00487-t006" class="html-table">Table 6</a>) focused on a San Millao forest fire (field date: 10 September 2020).</p>
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18 pages, 3550 KiB  
Article
Wildfire Severity to Valued Resources Mitigated by Prescribed Fire in the Okefenokee National Wildlife Refuge
by C. Wade Ross, E. Louise Loudermilk, Joseph J. O’Brien, Steven A. Flanagan, Grant Snitker and J. Kevin Hiers
Remote Sens. 2024, 16(24), 4708; https://doi.org/10.3390/rs16244708 - 17 Dec 2024
Viewed by 480
Abstract
Prescribed fire is increasingly utilized for conservation and restoration goals, yet there is limited empirical evidence supporting its effectiveness in reducing wildfire-induced damages to highly valued resources and assets (HVRAs)—whether natural, cultural, or economic. This study evaluates the efficacy of prescribed fire in [...] Read more.
Prescribed fire is increasingly utilized for conservation and restoration goals, yet there is limited empirical evidence supporting its effectiveness in reducing wildfire-induced damages to highly valued resources and assets (HVRAs)—whether natural, cultural, or economic. This study evaluates the efficacy of prescribed fire in reducing wildfire severity to LANDFIRE-defined vegetation classes and HVRAs impacted by the 2017 West Mims event, which burned across both prescribed-fire treated and untreated areas within the Okefenokee National Wildlife Refuge. Wildfire severity was quantified using the differenced normalized burn ratio (dNBR) index, while treatment records were used to calculate the prescribed frequency and post-treatment duration, which is defined as the time elapsed between the last treatment and the West Mims event. A generalized additive model (GAM) was fit to model dNBR as a function of post-treatment duration, fire frequency, and vegetation type. Although dNBR exhibited considerable heterogeneity both within and between HVRAs and vegetation classes, areas treated with prescribed fire demonstrated substantial reductions in burn severity. The beneficial effects of prescribed fire were most pronounced within approximately two years post-treatment with up to an 88% reduction in mean wildfire severity. However, reductions remained evident for approximately five years post-treatment according to our model. The mitigating effect of prescribed fire was most pronounced in Introduced Upland Vegetation-Shrub, Eastern Floodplain Forests, and Longleaf Pine Woodland when the post-treatment duration was within 12 months. Similar trends were observed in areas surrounding red-cockaded woodpecker nesting sites, which is an HVRA of significant ecological importance. Our findings support the frequent application of prescribed fire (e.g., one- to two-year intervals) as an effective strategy for mitigating wildfire severity to HVRAs. Full article
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Figure 1

Figure 1
<p>The West Mims wildfire. (<b>a</b>) Location of the Okefenokee National Wildlife Refuge (tan) and b, the West Mims wildfire perimeter (gray, 675 km<sup>2</sup>). The map also depicts the prescribed fire burn units (<b>b</b>, green) and red-cockaded woodpecker (<span class="html-italic">Picoides borealis</span>) clusters (black dots).</p>
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<p>The 2017 West Mims wildfire. (<b>a</b>), The West Mims fire perimeter illustrates the variation in wildfire severity (dNBR) with regions of low and high severity depicted in blues and yellows, respectively. (<b>b</b>), Unmanaged areas within the wildfire perimeter that have not been treated with prescribed fire, and (<b>c</b>) actively managed areas that have been treated with prescribed fire. (<b>d</b>) Violin plots illustrate the variability of wildfire severity within managed areas. The x-axis represents the duration of time (in months) between the prescribed fires and the wildfire event. The red curve was fit to the 97th percentile of data using the NLS exponential growth model to characterize the upper limits of wildfire severity in actively managed areas.</p>
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<p>Wildfire severity (dNBR) grouped by LANDFIRE-defined vegetation classes. The top panel corresponds to vegetation classes found within actively managed areas, while the bottom panel corresponds to unmanaged areas. The boxplots are color coded by LANDFIRE-defined physiognomy classes.</p>
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<p>Percent change between unmanaged and managed areas, grouped by post-treatment duration, for LANDFIRE-defined vegetation classes. Negative values indicate that dNBR was lower in areas treated with prescribed fire relative to untreated areas.</p>
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<p>Percent change between unmanaged and managed areas, grouped by post-treatment duration, for highly valued resources and assets (HVRAs). Negative values indicate that dNBR was lower in areas treated with prescribed fire relative to untreated areas.</p>
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<p>(<b>a</b>), The relationship between post-treatment duration in months and the NLS model fit for dNBR. (<b>b</b>) Residual plot showing the difference between observed and fitted response values. (<b>c</b>) Histogram indicates that the residuals are normally distributed. (<b>d</b>) Probability plot of the residuals is approximately linear supporting the condition that the error terms are normally distributed.</p>
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<p>(<b>a</b>) Probability plot of the residuals is approximately linear supporting the condition that the error terms are normally distributed. (<b>b</b>) Residual plot showing the difference between observed and fitted response values. (<b>c</b>) Histogram indicates that the residuals are normally distributed. (<b>d</b>) Actual vs. fitted dNBR values.</p>
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<p>Partial effects of LANDFIRE vegetation classes from the best-performing model (GAM). The zero line represents the overall mean of the response; negative values on the y-axis indicate where the effect of the covariate reduces the response below the average value, and positive values indicate those covariate values where the response is increased above the average (all conditional upon the other estimated model terms). The gray bands represent the 95% confidence intervals for the fitted values, indicating the range of uncertainty around the estimated trend.</p>
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29 pages, 8852 KiB  
Article
Assessment of Forest Fire Severity for a Management Conceptual Model: Case Study in Vilcabamba, Ecuador
by Fernando González, Fernando Morante-Carballo, Aníbal González, Lady Bravo-Montero, César Benavidez-Silva and Fantina Tedim
Forests 2024, 15(12), 2210; https://doi.org/10.3390/f15122210 - 16 Dec 2024
Viewed by 882
Abstract
Wildfires are affecting natural ecosystems worldwide, causing economic and human losses and exacerbated by climate change. Models of fire severity and fire susceptibility are crucial tools for fire monitoring. This case study analyses a fire event on 3 September 2019 in Vilcabamba parish, [...] Read more.
Wildfires are affecting natural ecosystems worldwide, causing economic and human losses and exacerbated by climate change. Models of fire severity and fire susceptibility are crucial tools for fire monitoring. This case study analyses a fire event on 3 September 2019 in Vilcabamba parish, Loja province, Ecuador. This article aims to assess the severity and susceptibility of a fire through spectral indices and multi-criteria methods for establishing a fire action plan proposal. The methodology comprises the following: (i) the acquisition of Sentinel-2A products for the calculation of spectral indices; (ii) a fire severity model using differentiated indices (dNBR and dNDVI) and a fire susceptibility model using the Analytic Hierarchy Process (AHP) method; (iii) model validation using Logistic Regression (LR) and Non-metric Multidimensional Scaling (NMDS) algorithms; (iv) the proposal of an action plan for fire management. The Normalised Burn Ratio (NBR) index revealed that 10.98% of the fire perimeter has burned areas with moderate-high severity in post-fire scenes (2019) and decreased to 0.01% for post-fire scenes in 2021. The Normalised Difference Vegetation Index (NDVI) identified 67.28% of the fire perimeter with null photosynthetic activity in the post-fire scene (2019) and 5.88% in the post-fire scene (2021). The Normalised Difference Moisture Index (NDMI) applied in the pre-fire scene identified that 52.62% has low and dry vegetation (northeast), and 8.27% has high vegetation cover (southwest). The dNDVI identified 10.11% of unburned areas and 7.91% using the dNBR. The fire susceptibility model identified 11.44% of the fire perimeter with null fire susceptibility. These results evidence the vegetation recovery after two years of the fire event. The models demonstrated excellent performance for fire severity models and were a good fit for the AHP model. We used the Root Mean Square Error (RMSE) and area under the curve (AUC); dNBR and dNDVI have an RMSE of 0.006, and the AHP model has an RMSE of 0.032. The AUC = 1.0 for fire severity models and AUC = 0.6 for fire susceptibility. This study represents a holistic approach by combining Google Earth Engine (GEE), Geographic Information System (GIS), and remote sensing tools for proposing a fire action plan that supports decision making. This study provides escape routes that considered the most significant fire triggers, the AHP, and fire severity approaches for monitoring wildfires in Andean regions. Full article
(This article belongs to the Special Issue Forest Fires Prediction and Detection—2nd Edition)
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<p>Location of the study zone: (<b>a</b>) Representation on a macro-scale (Ecuador); (<b>b</b>) Vilcabamba parish including the delineation of the wildfire perimeter analysed, weather stations, and the wildfires recorded in the year 2019 (pre-fire scene) by the SNGRE and VIIRS.</p>
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<p>Methodological approach.</p>
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<p>Framework of the wildfire susceptibility analysis using the AHP method.</p>
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<p>A conceptual model for wildfire management in Vilcabamba parish.</p>
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<p>NBR index in fire perimeter with Sentinel-2A imagery: (<b>a</b>) Pre-fire scene (9 September 2019); (<b>b</b>) Post-fire scene (9 September 2019); and (<b>c</b>) Post-fire scene (4 August 2021).</p>
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<p>NDVI results of fire perimeter: (<b>a</b>) Pre-fire scene (25 August 2019); (<b>b</b>) Post-fire scene (9 September 2019); and (<b>c</b>) Post-fire scene (4 August 2021).</p>
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<p>NDMI results of Vilcabamba parish in pre-fire scene.</p>
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<p>Wildfire severity models with Sentinel-2A imagery. (<b>a</b>) dNDVI and (<b>b</b>) dNBR.</p>
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<p>Area Under Curve for the Logistic Regression model: (<b>a</b>) the AUC for the fire severity models and (<b>b</b>) the AUC for the fire susceptibility model.</p>
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<p>Variables for the wildfire susceptibility map: (<b>a</b>) slope angle; (<b>b</b>) elevation; (<b>c</b>) slope aspect; (<b>d</b>) isohyets); (<b>e</b>) isotherms; (<b>f</b>) land use in pre-fire scene; (<b>g</b>) land use in post-fire scene; (<b>h</b>) distance to water bodies (rivers); and (<b>i</b>) distance to roads. Source: Adapted from [<a href="#B60-forests-15-02210" class="html-bibr">60</a>,<a href="#B61-forests-15-02210" class="html-bibr">61</a>,<a href="#B64-forests-15-02210" class="html-bibr">64</a>].</p>
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<p>Analysis of fire susceptibility: (<b>a</b>) Wildfire susceptibility map through AHP method. (<b>b</b>) Access to water bodies (lagoons and lakes) by aerial transport for each parcel. (<b>c</b>) Access to rivers and streams by terrestrial transport.</p>
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<p>Proposal action plan in Vilcabamba parish where evacuation routes and fire refuge areas are outlined.</p>
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18 pages, 25764 KiB  
Article
Evaluating Landsat- and Sentinel-2-Derived Burn Indices to Map Burn Scars in Chyulu Hills, Kenya
by Mary C. Henry and John K. Maingi
Fire 2024, 7(12), 472; https://doi.org/10.3390/fire7120472 - 11 Dec 2024
Viewed by 607
Abstract
Chyulu Hills, Kenya, serves as one of the region’s water towers by supplying groundwater to surrounding streams and springs in southern Kenya. In a semiarid region, this water is crucial to the survival of local people, farms, and wildlife. The Chyulu Hills is [...] Read more.
Chyulu Hills, Kenya, serves as one of the region’s water towers by supplying groundwater to surrounding streams and springs in southern Kenya. In a semiarid region, this water is crucial to the survival of local people, farms, and wildlife. The Chyulu Hills is also very prone to fires, and large areas of the range burn each year during the dry season. Currently, there are no detailed fire records or burn scar maps to track the burn history. Mapping burn scars using remote sensing is a cost-effective approach to monitor fire activity over time. However, it is not clear whether spectral burn indices developed elsewhere can be directly applied here when Chyulu Hills contains mostly grassland and bushland vegetation. Additionally, burn scars are usually no longer detectable after an intervening rainy season. In this study, we calculated the Differenced Normalized Burn Ratio (dNBR) and two versions of the Relative Differenced Normalized Burn Ratio (RdNBR) using Landsat Operational Land Imager (OLI) and Sentinel-2 MultiSpectral Instrument (MSI) data to determine which index, threshold values, instrument, and Sentinel near-infrared (NIR) band work best to map burn scars in Chyulu Hills, Kenya. The results indicate that the Relative Differenced Normalized Burn Ratio from Landsat OLI had the highest accuracy for mapping burn scars while also minimizing false positives (commission error). While mapping burn scars, it became clear that adjusting the threshold value for an index resulted in tradeoffs between false positives and false negatives. While none were perfect, this is an important consideration going forward. Given the length of the Landsat archive, there is potential to expand this work to additional years. Full article
(This article belongs to the Special Issue Fire in Savanna Landscapes, Volume II)
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<p>Location of Chyulu Hills, Kenya, in East Africa. Protected areas are shown in hatch-filled areas with labels in legend. Study area falls within three counties, Kajiado, Makueni, and Taita Taveta, as shown in map. Elevation is also shown in map, with higher elevations in white. Major roads include Mombasa Road to east of study area.</p>
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<p>Flow chart showing methods used in this study. OLI = Operational Land Imager, OLI2 = Operational Land Imager 2; MSI = MultiSpectral Instrument; BOA = Bottom of Atmosphere Reflectance; NBR = Normalized Burn Ratio; dNBR = Differenced Normalized Burn Ratio; RdNBR = Relative Differenced Normalized Burn Ratio; RdNBR2 = Relative Differenced Normalized Burn Ratio alternate calculation. Boxes with bold outline indicate inputs to final analysis.</p>
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<p>Mapped burn scars for the 2021 fire season in Chyulu Hills, Kenya. Yellow shows areas mapped as burned using Landsat RdNBR with a threshold of 0.23. Clouds and cloud shadows are masked out and shown in black. Purple boundaries indicate protected areas in the Chyulu Hills.</p>
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<p>Mapped burn scars for the 2021 fire season in Chyulu Hills, Kenya. Yellow shows areas mapped as burned using Sentinel-2 RdNBR with a threshold of 0.22. Clouds and cloud shadows are masked out and shown in black. Purple boundaries indicate protected areas in the Chyulu Hills.</p>
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14 pages, 2304 KiB  
Article
Our Experience and Clinical Findings in Perineal Burns: Implications for Patient Prognosis—A 3 Year Retrospective Study
by Matei Iordache, Eliza-Maria Bordeanu-Diaconescu, Andreea Grosu-Bularda, Mihaela-Cristina Andrei, Adrian Frunza, Sabina Grama, Raducu Costache, Tiberiu-Paul Neagu, Ioan Lascar and Cristian-Sorin Hariga
Medicina 2024, 60(12), 2009; https://doi.org/10.3390/medicina60122009 - 5 Dec 2024
Viewed by 601
Abstract
Background and Objectives: Burn injury represents a very important public health problem that affects all age groups. Of all burns, of particular interest is that of the perineum. Despite the importance of the subject, unfortunately, the medical literature on this anatomical region is [...] Read more.
Background and Objectives: Burn injury represents a very important public health problem that affects all age groups. Of all burns, of particular interest is that of the perineum. Despite the importance of the subject, unfortunately, the medical literature on this anatomical region is sparse. With this study we aim to analyze the characteristics of burns affecting the perineal area, the physiopathologic implications of this injury, the influence of patient prognosis, possible complications and therapeutic guidelines. Materials and Methods: This study is formed by a retrospective analysis of cases that were admitted over a period spanning 3 years with a total of 258 burned patients. After inclusion criteria, we selected 49 patients who had perineal burns and compared this group to a non-perineal burns lot of 198 patients (11 were excluded). We studied their characteristics and the demographical aspects that we deemed most important to their condition: age, sex, burn percentage of total body surface area (TBSA), the percentage of third-degree lesions, comorbidities, and associated infections, inhalation injuries and we calculated the significant scores such as the Abbreviated Burn Severity Index score (ABSI). Results: The patients in our study mostly had severe extensive burns (64.9% mean TBSA) which were also underlined by the mean ABSI of 10.88 ± 2.46 thus having a poor prognosis considering their age, the percentage of burned area, the presence of third-degree burns and inhalation injuries. In our study, perineal burns were usually associated with burns of adjacent regions abdominal wall burns comprising 51% and thigh burns comprising 97.9% of the associated injuries. This relationship both explains their presence in mostly severe cases with higher TBSA and also underlines the issues that derive from the burns of the perineum and their several complications which lead to an unbalance of the patients. The treatment of perineal burns still remains much debated in the literature when considering their indications and can become rather complex in the sequelae setting. Conclusions: The issue of burns remains one of the most important subjects in plastic surgery. Being a region hard to treat but with a big influence on patient evolution and survival chances prevention remains a key factor. Full article
(This article belongs to the Section Surgery)
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<p>The Figure illustrates the clinical appearance of different degrees of severity of perineal burns, depending on their depth. This classification is based on the American Burn Association Criteria [<a href="#B10-medicina-60-02009" class="html-bibr">10</a>].</p>
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<p>Microorganisms identified in perineal burn wounds.</p>
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<p>Distribution of microorganisms in the 13 patients with positive urine cultures (CoNS = Coagulase-Negative Staphylococci).</p>
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<p>A case from our clinic with the classical association between the genital and perineal burns in the lower abdomen and the upper thigh regions. Upper left is the initial aspect with most of the burns being third-degree. Upper central is the excised segment of the necrotic tissue with the resulting defect (upper right and lower left). The last two pictures show the aspect of the grafted regions immediately postoperatively and 2 months away, respectively. Note the different aspects of the skin grafts, being thicker on the penis to prevent contractures compared to the other grafted regions.</p>
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20 pages, 2495 KiB  
Article
Monitoring Postfire Biodiversity Dynamics in Mediterranean Pine Forests Using Acoustic Indices
by Dimitrios Spatharis, Aggelos Tsaligopoulos, Yiannis G. Matsinos, Ilias Karmiris, Magdalini Pleniou, Elisabeth Navarrete, Eleni Boikou and Christos Astaras
Environments 2024, 11(12), 277; https://doi.org/10.3390/environments11120277 - 4 Dec 2024
Viewed by 1440
Abstract
In recent decades, climate change has significantly influenced the frequency and intensity of wildfires across Mediterranean pine forests. The loss of forest cover can bring long-term ecological changes that impact the overall biodiversity and alter species composition. Understanding the long-term impact of wildfires [...] Read more.
In recent decades, climate change has significantly influenced the frequency and intensity of wildfires across Mediterranean pine forests. The loss of forest cover can bring long-term ecological changes that impact the overall biodiversity and alter species composition. Understanding the long-term impact of wildfires requires effective and cost-efficient methods for monitoring the postfire ecosystem dynamics. Passive acoustic monitoring (PAM) has been increasingly used to monitor the biodiversity of vocal species at large spatial and temporal scales. Using acoustic indices, where the biodiversity of an area is inferred from the overall structure of the soundscape, rather than the more labor-intensive identification of individual species, has yielded mixed results, emphasizing the importance of testing their efficacy at the regional level. In this study, we examined whether widely used acoustic indicators were effective at capturing changes in the avifauna diversity in Pinus halepensis forest stands with different fire burning histories (burnt in 2001, 2009, and 2018 and unburnt for >20 years) on the Sithonia Peninsula, Greece. We recorded the soundscape of each stand using two–three sensors across 11 days of each season from March 2022 to January 2023. We calculated for each site and season the following five acoustic indices: the Acoustic Complexity Index (ACI), Acoustic Diversity Index (ADI), Acoustic Evenness Index (AEI), Normalized Difference Soundscape Index (NDSI), and Bioacoustic Index (BI). Each acoustic index was then assessed in terms of its efficacy at predicting the local avifauna diversity, as estimated via two proxies—the species richness (SR) and the Shannon Diversity Index (SDI) of vocal bird calls. Both the SR and SDI were calculated by having an expert review the species identification of calls detected within the same acoustic dataset by the BirdNET convolutional neural network algorithm. A total of 53 bird species were identified. Our analysis shows that the BI and NDSI have the highest potential for monitoring the postfire biodiversity dynamics in Mediterranean pine forests. We propose the development of regional-scale acoustic observatories at pine and other fire-prone Mediterranean habitats, which will further improve our understanding of how to make the best use of acoustic indices as a tool for rapid biodiversity assessments. Full article
(This article belongs to the Special Issue Interdisciplinary Noise Research)
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<p>Process workflow outlining the methodology used.</p>
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<p>Map of the Aleppo pine (<span class="html-italic">Pinus halepensis</span>) forests selected for the deployment of the acoustic sensors.</p>
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<p>Soundscape Chord Diagram [<a href="#B47-environments-11-00277" class="html-bibr">47</a>] showing the default frequency ranges in Kaleidoscope Pro for each of the five acoustic indices, and the adjusted values ultimately used following an examination of the calling frequencies of the bird species confirmed to be present in the dataset.</p>
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<p>Spectrograms of typical calls of the four most abundant bird species detected in the acoustic data.</p>
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<p>Spearman rank correlation coefficients (ρ) of acoustic indices (NDSI, ACI, BI, ADI, AEI) and (<b>a</b>) species richness and (<b>b</b>) Shannon Diversity Index across different areas, seasons, and time periods.</p>
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<p>Box plots of acoustic indices across different forest burning histories: (<b>a</b>) NDSI for dawn and spring (Kruskal–Wallis test <span class="html-italic">p</span>-value: &lt; 0.001); (<b>b</b>) BI for dawn and summer (Kruskal–Wallis test <span class="html-italic">p</span>-value: &lt; 0.001).</p>
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<p>Scatterplots of acoustic indices (ACI, ADI, AEI, BI, NDSI) in relation to biodiversity metrics (species richness (SR) and Shannon Diversity Index (SDI)).</p>
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27 pages, 29254 KiB  
Article
Identification of Anthropogenic Impact and Indicators of Landscape Transformation in the Fatala River Basin (Republic of Guinea)
by Ksenia Myachina, Roman Ryakhov, Anton Shchavelev and Svetlana Dubrovskaya
Sustainability 2024, 16(23), 10319; https://doi.org/10.3390/su162310319 - 25 Nov 2024
Viewed by 653
Abstract
The aim of this study is to identify the main factors of anthropogenic impact and indicators of landscape transformation in the Fatala River Basin in the Republic of Guinea. Our fieldwork in the Boke and Kindia regions was the main source of materials [...] Read more.
The aim of this study is to identify the main factors of anthropogenic impact and indicators of landscape transformation in the Fatala River Basin in the Republic of Guinea. Our fieldwork in the Boke and Kindia regions was the main source of materials and data. The landscape and ecological situation of nine key study plots were characterized. These key plots make up a representative series of transformed and natural landscapes. We complemented our fieldwork with Landsat satellite image analysis. We learned that the main factors of anthropogenic impact in the Fatala River Basin are the systematic burning of vegetation, mechanical disturbances of soil and vegetation cover, the depletion of fertile topsoil, grazing, and the littering of the landscape with household waste. The indicators of landscape transformation are deforestation, changes in the natural vegetation cover, and mechanically disturbed lands. We identified five main stages of agro-landscape development, starting from the clearing of a plot by burning vegetation (stage I) and ending with the completion of the agricultural activity in the plot and its abandonment to restore the topsoil (stage V). The limiting factors of nature management are elevation differences, the rapid restoration of vegetation cover, and rocky/gravelly substrate. It is possible to identify transformed landscapes in large or hard-to-reach regions using satellite images. Thus, natural or quasi-natural landscapes can be identified based on the lower surface temperature relative to the surrounding lands. The normalized difference vegetation index (NDVI) and normalized difference moisture index (NDMI) could be useful for identifying agricultural pasture plots within a tropical forest using long-term satellite data series. We revealed a tendency for landscape deterioration in the middle and upper parts of the Fatala River Basin, while vegetation cover is being restored in the lower part of the basin. Finally, we propose some measures to rehabilitate transformed landscapes and increase the efficiency of agricultural production in the study region. Full article
(This article belongs to the Section Environmental Sustainability and Applications)
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<p>The study region (Fatala River Basin) in the Republic of Guinea: (a) the location of the study region within the coastal part of the Republic of Guinea; (b) the location of the Republic of Guinea in the western part of Africa (basemap source: National Imagery and Mapping Agency [<a href="#B15-sustainability-16-10319" class="html-bibr">15</a>]).</p>
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<p>The dynamics of the average annual temperature of the surface air layer in the Republic of Guinea.</p>
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<p>A diagram of carbon dioxide emissions into the atmosphere over the past 10 years (<b>top</b>, thousand tons) and the dynamics of carbon dioxide emissions from 1990 to 2020 (<b>bottom</b>, thousand tons) in the Republic of Guinea.</p>
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<p>The decrease in forest area (%) in Guinea: a diagram of the afforestation index of the country from 2013 to 2020 (<b>top</b>) and the dynamics of forest area from 1990 to 2020 (<b>bottom</b>).</p>
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<p>The agricultural land area increase (%) in Guinea: a diagram of the share of agricultural land in the country from 2014 to 2021 (<b>top</b>) and the dynamics of the agricultural land area from 1961 to 2021 (<b>bottom</b>).</p>
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<p>Land use/land cover map in the Fatala River Basin and adjacent territories. Symbols: 1—key plot; 2—cities; 3—road network; 4—Fatala River Basin boundary; 5—administrative boundary. Classes of LULC: 6—river network/water object; 7—wetlands; 8—trees; 9—crops; 10—built area; 11—bare ground; 12—rangeland.</p>
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<p>Analysis scheme of the study (NDVI—normalized difference vegetation index; NDMI—normalized difference moisture index; LULC—land use/land cover; LST—Landsat surface temperature).</p>
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<p>Mean NDVI values for land cover in the Fatala River Basin from 1986 to 2023 (gradient on a red–green scale). Symbols: 1—key plot; 2—cities; 3—road network; 4—Fatala River Basin boundary; 5—administrative boundary; 6—river network/water object; 7—wetlands.</p>
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<p>The NDVI trend in the Fatala River Basin based on calculations from 1986 to 2023 (gradient on a red–green scale, where I (red) is a stable negative trend, II (orange) is a significant negative trend, III (yellow) is no trend, IV (light green) is a significant positive trend, and V (dark green) is a steady positive trend). Symbols: 1—key plot; 2—cities; 3—road network; 4—Fatala River Basin boundary; 5—administrative boundary; 6—river network/water object; 7—wetlands.</p>
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<p>The classification of the key plots’ landscapes according to the values of the surface temperature, where 1–9 are the numbers of the key plots. The surface temperatures are displayed on a purple–yellow scale in degrees Celsius.</p>
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<p>The classification of the study area landscapes according to the values of the normalized difference moisture index (NDMI), where 1–9 are the numbers of the key study plots. The gradient of NDMI values is represented on a brown–purple scale from −1 to 1. NDMI values: (−1)–(−0.8)—vegetation is absent; (−0.8)–(−0.6)—vegetation is almost absent; (−0.6)–(−0.4)—the vegetation density is low; (−0.4)–(−0.2)—the vegetation cover is insignificant and moisture is absent, or the vegetation density is very low; (−0.2)–0— the vegetation density is below average with a high level of water stress, or vegetation cover is insignificant with a low level of water stress; 0–0.2—vegetation density is average with a high water stress level, or vegetation density is below average with a low water stress level; 0.2–0.4—vegetation density is above average with a high water stress level, or vegetation density is average with a low water stress level; 0.4–0.6—vegetation density is high, with no water stress observed; 0.6–0.8—vegetation density is very high, with no water stress; 0.8–1—the territory is completely covered with dense vegetation, with no water stress.</p>
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<p>The dynamics of the NDVI, NDMI, and LST for each key plot; 1–9 are the numbers of the key plots.</p>
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<p>A steep slope with rice and sorghum crops in the northern part of the Fatala River Basin: (<b>a</b>) a view from afar; (<b>b</b>) a point within an agricultural area on a slope.</p>
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<p>The stages of agro-landscape development in the Fatala River Basin in the Republic of Guinea, where unidirectional processes are indicated by blue arrows and bidirectional ones by purple arrows.</p>
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24 pages, 3462 KiB  
Article
Underutilized Feature Extraction Methods for Burn Severity Mapping: A Comprehensive Evaluation
by Linh Nguyen Van and Giha Lee
Remote Sens. 2024, 16(22), 4339; https://doi.org/10.3390/rs16224339 - 20 Nov 2024
Viewed by 734
Abstract
Wildfires increasingly threaten ecosystems and infrastructure, making accurate burn severity mapping (BSM) essential for effective disaster response and environmental management. Machine learning (ML) models utilizing satellite-derived vegetation indices are crucial for assessing wildfire damage; however, incorporating many indices can lead to multicollinearity, reducing [...] Read more.
Wildfires increasingly threaten ecosystems and infrastructure, making accurate burn severity mapping (BSM) essential for effective disaster response and environmental management. Machine learning (ML) models utilizing satellite-derived vegetation indices are crucial for assessing wildfire damage; however, incorporating many indices can lead to multicollinearity, reducing classification accuracy. While principal component analysis (PCA) is commonly used to address this issue, its effectiveness relative to other feature extraction (FE) methods in BSM remains underexplored. This study aims to enhance ML classifier accuracy in BSM by evaluating various FE techniques that mitigate multicollinearity among vegetation indices. Using composite burn index (CBI) data from the 2014 Carlton Complex fire in the United States as a case study, we extracted 118 vegetation indices from seven Landsat-8 spectral bands. We applied and compared 13 different FE techniques—including linear and nonlinear methods such as PCA, t-distributed stochastic neighbor embedding (t-SNE), linear discriminant analysis (LDA), Isomap, uniform manifold approximation and projection (UMAP), factor analysis (FA), independent component analysis (ICA), multidimensional scaling (MDS), truncated singular value decomposition (TSVD), non-negative matrix factorization (NMF), locally linear embedding (LLE), spectral embedding (SE), and neighborhood components analysis (NCA). The performance of these techniques was benchmarked against six ML classifiers to determine their effectiveness in improving BSM accuracy. Our results show that alternative FE techniques can outperform PCA, improving classification accuracy and computational efficiency. Techniques like LDA and NCA effectively capture nonlinear relationships critical for accurate BSM. The study contributes to the existing literature by providing a comprehensive comparison of FE methods, highlighting the potential benefits of underutilized techniques in BSM. Full article
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<p>Visualization of dimensionality reduction techniques. Each plot represents a 3D data projection using three main components.</p>
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<p>Location of the 2014 Carlton Complex wildfire used in this study.</p>
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<p>Heatmaps representing the performance of thirteen feature extraction methods on four different metrics, namely (<b>a</b>) overall accuracy (OA), (<b>b</b>) precision, (<b>c</b>) recall, and (<b>d</b>) F1-score, across six machine learning classifiers. The x-axis of each heatmap lists the FR methods, while the y-axis lists the classifiers. The color intensity in each heatmap indicates the mean performance score of 1000 simulations.</p>
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<p>Relationship between the number of components used in thirteen feature reduction methods and the performance (overall accuracy, OA) of six classifiers—RF, LR, KNN, SVM, AB, and MLP. The x-axis in each plot shows the number of components, while the y-axis represents the OA. Each line corresponds to one of the classifiers fitted by quadratic polynomial regression models.</p>
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<p>Performance comparison of PCA, LDA, and NCA across four wildfire severity categories: (<b>a</b>) no burn, (<b>b</b>) low, (<b>c</b>) moderate, and (<b>d</b>) high severity. The performance is evaluated using six classifiers, and the y-axis shows the F1-score value. Error bars representing interquartile ranges indicate the variability in model performance across each severity level.</p>
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11 pages, 712 KiB  
Review
Life Satisfaction After Burn Injury—A Comprehensive Review
by Maria Fernanda Hutter, Christian Smolle, Julia Kleinhapl and Lars-Peter Kamolz
Eur. Burn J. 2024, 5(4), 418-428; https://doi.org/10.3390/ebj5040037 - 20 Nov 2024
Viewed by 652
Abstract
Burn injuries can have long-lasting effects not only on a person’s bodily integrity but also on their psychosocial well-being. Since medical advancements have increased survival from burn injuries, improving psychosocial health has become a pivotal goal for burn rehabilitation. Besides health-related quality of [...] Read more.
Burn injuries can have long-lasting effects not only on a person’s bodily integrity but also on their psychosocial well-being. Since medical advancements have increased survival from burn injuries, improving psychosocial health has become a pivotal goal for burn rehabilitation. Besides health-related quality of life, life satisfaction has become an important parameter for evaluating long-term outcomes after burns. We reviewed life satisfaction after burns among adult burn patients to evaluate the current assessment methods and gain insight into recovery patterns. PubMed, EMBASE, Medline, and Cochrane Library were searched systematically for studies in the English language covering life satisfaction after burns, resulting in the inclusion of 18 studies. The Satisfaction With Life Scale (SWLS) was the most commonly used assessment tool. Others included the Life Satisfaction Index-A (LSI-A) and a non-standardized tool. Most studies’ recovery patterns showed a decreased life satisfaction post-burn injury. There was strong agreement that inhalation injury, body dysfunction, an extended hospital stay, and psychological illness before the injury are possible determinants of post-burn life satisfaction and have shown a negative correlation. There seems to be a consistent use of assessment tools, which opens up the possibility of a further comparative investigation to better understand factors that influence life satisfaction after a burn so that this knowledge can be used to improve patients’ recovery. Full article
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<p>Flowchart of screening strategy.</p>
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<p>Number of publications per year.</p>
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16 pages, 10577 KiB  
Article
Designing a Multitemporal Analysis of Land Use Changes and Vegetation Indices to Assess the Impacts of Severe Forest Fires Before Applying Control Measures
by Casandra Muñoz-Gómez and Jesús Rodrigo-Comino
Forests 2024, 15(11), 2036; https://doi.org/10.3390/f15112036 - 18 Nov 2024
Viewed by 968
Abstract
Forest fires represent a significant intersection between nature and society, often leading to the loss of natural resources, soil nutrients, and economic opportunities, as well as causing desertification and the displacement of communities. Therefore, the objective of this work is to analyze the [...] Read more.
Forest fires represent a significant intersection between nature and society, often leading to the loss of natural resources, soil nutrients, and economic opportunities, as well as causing desertification and the displacement of communities. Therefore, the objective of this work is to analyze the multitemporal conditions of a sixth-generation forest fire through the use and implementation of tools such as remote sensing, photointerpretation with geographic information systems (GISs), thematic information on land use, and the use of spatial indices such as the Normalized Difference Vegetation Index (NDVI), the Normalized Burned Ratio (NBR), and its difference (dNBR) with satellite images from Sentinel-2. To improve our understanding of the dynamics and changes that occurred due to the devastating forest fire in Los Guájares, Granada, Spain, in September 2022, which affected 5194 hectares and had a perimeter of 150 km, we found that the main land use in the study area was forest, followed by agricultural areas which decreased from 1956 to 2003. We also observed the severity of burning, shown with the dNBR, reflecting moderate–low and moderate–high levels of severity. Health and part of the post-fire recovery process, as indicated by the NDVI, were also observed. This study provides valuable information on the spatial and temporal dimensions of forest fires, which will favor informed decision making and the development of effective prevention strategies. Full article
(This article belongs to the Topic Application of Remote Sensing in Forest Fire)
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<p>Localization of the study area and photographs during the fieldwork campaign.</p>
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<p>Maps of elevation and inclination of the study area.</p>
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<p>Land use maps showing the changes among selected dates.</p>
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<p>Maps considering land use changes between specific intervals of years.</p>
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<p>Satellite images with natural color from March 2022 to September 2022.</p>
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<p>Satellite images with natural color from October 2022 to March 2023.</p>
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<p>Satellite images with natural color from August to October in 2021 and 2023.</p>
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<p>Normalize Difference Vegetation Index (NDVI) from March 2022 to September 2022.</p>
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<p>Normalize Difference Vegetation Index (NDVI) from October 2022 to March 2023.</p>
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<p>Normalized Difference Vegetation Index (NDVI) from August to October in 2021 and 2023.</p>
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<p>Normalized Burn Ratio (NBR) from August and October 2022.</p>
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<p>Difference Normalized Burn Ratio (dNBR) from August and October 2022.</p>
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8 pages, 628 KiB  
Article
Predicting Mortality in Severe Burns: A Comparison of Four Mortality Prediction Scores and the Role of Organizational Changes in the Croatian Burn Center
by Agata Skunca, Ana Mesic, Dorotea Zagorac, Mirela Dobric, Vedran Lokosek, Morana Banic, Aleksandra Munjiza and Aisa Muratovic
Eur. Burn J. 2024, 5(4), 410-417; https://doi.org/10.3390/ebj5040036 - 15 Nov 2024
Viewed by 523
Abstract
Background: The primary aim of this study was to evaluate the performance of four burn prognostic scores—Abbreviated Burn Severity Index (ABSI), Ryan, Belgium Outcome Burn Injury (BOBI), and revised Baux score (rBaux) in a Croatian burn center. A secondary aim was to compare [...] Read more.
Background: The primary aim of this study was to evaluate the performance of four burn prognostic scores—Abbreviated Burn Severity Index (ABSI), Ryan, Belgium Outcome Burn Injury (BOBI), and revised Baux score (rBaux) in a Croatian burn center. A secondary aim was to compare patient outcomes before and after the organizational and protocol changes. Methods: A retrospective study and comparison of four prediction scores was conducted over a nine-year period in burn patients with ≥20% total body surface area (TBSA) burned. Additionally, outcomes before and after organizational changes were compared. Results: A total of 149 patients were included, with the mean patient age of 54.62 ± 19.38 years, the mean of TBSA of 42.98 ± 19.90, and an overall mortality rate of 48.99%. The area under the ROC curve (AUROC) was 0.79 for the rBaux and ABSI score, 0.77 for the BOBI score, and 0.76 for the Ryan score. The duration of mechanical ventilation and length of stay (LOS) in burn intensive care units (BICU) decreased after the organizational changes, though survival rates remained similar. Conclusions: Prognostic scores are good predictors of mortality but with moderate predictive accuracy. Continuity of care in intensive care could be important for better outcomes. Full article
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<p>AUROC for all four scores.</p>
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<p>Outcomes in two different time periods; first from 2016 to 2022, and second from 2023 to 2024: (<b>a</b>) Mechanical ventilation; (<b>b</b>) Length of stay in hospital; (<b>c</b>) Length of stay in burn intensive care unit.</p>
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29 pages, 4900 KiB  
Article
Forest Fire Severity and Koala Habitat Recovery Assessment Using Pre- and Post-Burn Multitemporal Sentinel-2 Msi Data
by Derek Campbell Johnson, Sanjeev Kumar Srivastava and Alison Shapcott
Forests 2024, 15(11), 1991; https://doi.org/10.3390/f15111991 - 11 Nov 2024
Viewed by 765
Abstract
Habitat loss due to wildfire is an increasing problem internationally for threatened animal species, particularly tree-dependent and arboreal animals. The koala (Phascolartos cinereus) is endangered in most of its range, and large areas of forest were burnt by widespread wildfires in [...] Read more.
Habitat loss due to wildfire is an increasing problem internationally for threatened animal species, particularly tree-dependent and arboreal animals. The koala (Phascolartos cinereus) is endangered in most of its range, and large areas of forest were burnt by widespread wildfires in Australia in 2019/2020, mostly areas dominated by eucalypts, which provide koala habitats. We studied the impact of fire and three subsequent years of recovery on a property in South-East Queensland, Australia. A classified Differenced Normalised Burn Ratio (dNBR) calculated from pre- and post-burn Sentinel-2 scenes encompassing the local study area was used to assess regional impact of fire on koala-habitat forest types. The geometrically structured composite burn index (GeoCBI), a field-based assessment, was used to classify fire severity impact. To detect lower levels of forest recovery, a manual classification of the multitemporal dNBR was used, enabling the direct comparison of images between recovery years. In our regional study area, the most suitable koala habitat occupied only about 2%, and about 10% of that was burnt by wildfire. From the five koala habitat forest types studied, one upland type was burnt more severely and extensively than the others but recovered vigorously after the first year, reaching the same extent of recovery as the other forest types. The two alluvial forest types showed a negligible fire impact, likely due to their sheltered locations. In the second year, all the impacted forest types studied showed further, almost equal, recovery. In the third year of recovery, there was almost no detectable change and therefore no more notable vegetative growth. Our field data revealed that the dNBR can probably only measure the general vegetation present and not tree recovery via epicormic shooting and coppicing. Eucalypt foliage growth is a critical resource for the koala, so field verification seems necessary unless more-accurate remote sensing methods such as hyperspectral imagery can be implemented. Full article
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Graphical abstract
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<p>Map showing the regional study extent and the location of the local study area (LSA) therein (centre panel and right-hand legend and inset). The forest types comprising 70% or greater of the vegetation in an area are as follows: GBS = grey gum, mountain blue gum, and stringybark; IPR = ironbark on ridges; IBM = ironbark and mountain blue gum on microgranite; BFA = blue gum flats on alluvium inland; BCF = blue gum flats on alluvium closer to the coast. The upper left-hand panel shows the LSA with the locations of the ‘Plotless sites’ used to identify forest types (Regional Ecosystems) and collect the GeoCBI samples. Raw GeoCBI continuous values range from 0 to 3 and were derived from GeoCBI values from the sites, but they were reclassified in this study into classes 0–5. The lower left-hand panel shows the nearest towns and road network.</p>
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<p>Typical burn severities of eucalypt forest three months after the fire in the local study area. Ratings range from 0 to 5. (0) None. (1) Low (&lt;2 m scorch)—note recovery of the ground layer. (2) Moderate low (2–5 m scorch)—note some epicormic shooting. (3) Moderate high (just trunks scorched &gt;5 m OR a minority of crowns scorched). (4) High (most crowns scorched)—note recovery via epicormic shooting. Also note recovery of ground layer, which may look like canopy recovery from remote sensing. (5) Severe (crown foliage gone, ground bare)—note topkill and recovery via coppicing.</p>
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<p>Regional fire severity percentages of total area for each forest type tested (GBS, IPR, IBM, BFA, BCF) for 2019, the year of the fire. The regional study area defined by the Sentinel-2 scene is 12,056.04 km<sup>2</sup>. Forest types: GBS = grey gum, mountain blue gum, and stringybark (RE 12.12.23); IPR = ironbark on ridges (RE 12.12.12); IBM = ironbark and mountain blue gum on microgranite (RE 12.8.16); BFA = blue gum flats on alluvium inland (RE 12.3.3); BCF = blue gum flats on alluvium closer to the coast (RE 12.3.11). GeoCBI burn classes: 0 = none, 1 = low, 2 = moderate low, 3 = moderate high, 4 = high, and 5 = severe.</p>
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<p>Koala habitat areas suitable for refuge from fire. View covers the entire Sentinel-2 scene. Criteria are (1) koala habitat forest type; (2) fire severity class 0—unburnt; (3) proximity to burnt areas with moderate, high, and severe ratings—classes 2, 3, 4, and 5; and (4) proximity to recorded koala sightings.</p>
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<p>Koala habitat areas suitable for refuge from fire. View is local study area and its surrounds. Criteria are (1) koala habitat forest type; (2) fire severity class 0—unburnt; (3) proximity to burnt areas with moderate, high, and severe ratings—classes 2, 3, 4, and 5; and (4) proximity to recorded koala sightings.</p>
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<p>Normalised Difference Vegetation Index (NDVI) measured for two years prior to the fire in 2019, and three years after the fire, across 88 plotless sites in the local study area.</p>
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<p>Mean dNBR values for each forest type in each year. A negative dNBR indicates no burning, a dNBR of 0 is neutral (with no change between two years), and a dNBR &gt;= 1 in this study indicates severe burning. Forest types: GBS = grey gum, mountain blue gum, and stringybark (RE 12.12.23); IPR = ironbark on ridges (RE 12.12.12); IBM = ironbark and mountain blue gum on microgranite (RE 12.8.16); BFA = blue gum flats on alluvium inland (RE 12.3.3); BCF = blue gum flats on alluvium closer to the coast (RE 12.3.11).</p>
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<p>Burn severity classes for koala habitat forest types (Regional Ecosystems) at the regional scale across one whole Sentinel-2 scene (covering an area of 100 km × 100 km) in 2019, immediately after the fire. Negative dNBR indicates unburnt, a dNBR of 0 means neutral (with no change between two years), and dNBR ≥ 1 in this study indicates severely burnt. Forest types: GBS = grey gum, mountain blue gum, and stringybark (RE 12.12.23); IPR = ironbark on ridges (RE 12.12.12); IBM = ironbark and mountain blue gum on microgranite (RE 12.8.16); BFA = blue gum flats on alluvium inland (RE 12.3.3); BCF = blue gum flats on alluvium closer to the coast (RE 12.3.11).</p>
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<p>Burn severity classes for koala habitat forest types (Regional Ecosystems) at the regional scale across one whole Sentinel-2 scene (an area of 100 km × 100 km) in 2020, one year after the fire. Negative dNBR indicates unburnt, an dNBR of 0 means neutral (with no change between two years), and dNBR ≥ 1 in this study indicates severely burnt. Forest types: GBS = grey gum, mountain blue gum, and stringybark (RE 12.12.23); IPR = ironbark on ridges (RE 12.12.12); IBM = ironbark and mountain blue gum on microgranite (RE 12.8.16); BFA = blue gum flats on alluvium inland (RE 12.3.3); BCF = blue gum flats on alluvium closer to the coast (RE 12.3.11).</p>
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<p>Burn severity and recovery trend for forest type GBS (RE 12.12.23) and forest type IPR (RE 12.12.12) in the study region. Colour coding: red indicates year of fire, and other colours are years of recovery. Negative dNBR indicates unburnt, an dNBR of 0 means neutral (with no change between two years), and dNBR ≥ 1 in this study indicates severely burnt.</p>
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<p>Tree recovery responses from epicormic shooting versus dNBR value for each of 88 plotless sites within the local study area over three years for all forest types.</p>
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19 pages, 7466 KiB  
Article
Study on Flame Retardancy of Cotton Fabric Modified by Sulfonic Groups Chelated with Ba2+
by Lingling Guo, Hongqin Lin, Zhenming Qi, Jiang Pan, Haiyan Mao, Chunmei Huang, Guoqiang Li and Chunxia Wang
Molecules 2024, 29(22), 5306; https://doi.org/10.3390/molecules29225306 - 10 Nov 2024
Viewed by 960
Abstract
A simple and innovative method was introduced for the production of green and recoverable flame-retardant cotton fabrics, where sulfonated cotton fabric (COT-SC) was synthesized by oxidizing cotton fabric with sodium periodate, followed by a sulfonation step with sodium bisulfite to provide active sites, [...] Read more.
A simple and innovative method was introduced for the production of green and recoverable flame-retardant cotton fabrics, where sulfonated cotton fabric (COT-SC) was synthesized by oxidizing cotton fabric with sodium periodate, followed by a sulfonation step with sodium bisulfite to provide active sites, which further chelated barium ions (Ba2+) to achieve flame retardancy. The morphological and structural characterizations of the fabricated cotton fabrics (COT-SC-Ba) demonstrated that the cleavage of C2-C3 free hydroxy groups within the cellulose macromolecule was chemically modified for grafting a considerable number of sulfonic acid groups, and Ba2+ ions were effectively immobilized on the macromolecule of the cotton fabric through a chelation effect. Results from cone calorimeter tests (CCTs) revealed that COT-SC-Ba became nonflammable, displayed a delayed ignition time, and decreased the values of the heat release rate (HRR), total smoke release (TSR), effective heat of combustion (EHC), and CO/CO2 ratio. TG/DTG analysis demonstrated that COT-SC-Ba possessed greater thermal stability, fewer flammable volatiles, and more of a char layer during burning than that of the original cotton fabric. Its residual mass was increased from 0.02% to 26.9% in air and from 8.05% to 26.76% in N2, respectively. The COT-SC-Ba not only possessed a limiting oxygen index (LOI) of up to 34.4% but could also undergo vertical burning tests evidenced by results such as the non-afterflame, non-afterglow, and a mere 75 mm char length. Those results demonstrated that the combination of SO3 and Ba2+ promoted the formation of a char layer. Moreover, cotton fabric regained its superior flame retardancy after being washed and re-chelated with Ba2+. Additional characteristics of the cotton fabric, such as the rupture strength, white degree, and hygroscopicity, were maintained at an acceptable level. In conclusion, this research can offer a fresh perspective on the design and development of straightforward, efficient, eco-friendly, and recoverable fire-retardant fabrics. Full article
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<p>FTIR spectra (<b>on the left</b>) and their partial magnifications (<b>on the right</b>): (<b>a</b>) COT, (<b>b</b>) COT-M, (<b>c</b>) COT-DAC, (<b>d</b>) COT-SC, (<b>e</b>) COT-SC-Ba, and (<b>f</b>) the residual carbon of COT-SC-Ba after burning.</p>
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<p>Wide-scan XPS spectra of the cotton fabric and its various treatment stages: (<b>a</b>) COT-M, (<b>b</b>) COT-DAC, (<b>c</b>) COT-SC, and (<b>d</b>) COT-SC-Ba.</p>
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<p>Deconvoluted high-resolution C 1s, S 2p and Ba 3d XPS spectra, from (<b>a</b>–<b>d</b>) for C 1s, (<b>e</b>) for S 2p, and (<b>f</b>) for Ba 3d, respectively.</p>
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<p>X-ray Diffraction profiles: (<b>a</b>) COT, (<b>b</b>) COT-M, (<b>c</b>) COT-DAC, (<b>d</b>) COT-SC, and (<b>e</b>) COT-SC-Ba, along with (<b>f</b>) the residual char of COT-SC-Ba.</p>
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<p>SEM images and EDS analysis before and after the fireproof modification of cotton fabric and residual carbon: (<b>a</b>) COT, (<b>b</b>) COT-SC-Ba, and (<b>c</b>) the char layer of COT-Glu-Ba fabric, which are presented with 500× and 5000× magnification in the SEM images, respectively.</p>
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<p>Results of the cone calorimeter test including photographs of (<b>a</b>) COT and (<b>b</b>) COT-SC-Ba, along with graphs showing (<b>c</b>) the heat release rate (HRR), (<b>d</b>) total heat release (THR), (<b>e</b>) smoke production rate (SPR), and (<b>f</b>) total smoke release (TSR).</p>
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<p>TG and DTG curves for COT and COT-Glu-Ca in (<b>a</b>) air and (<b>b</b>) N<sub>2</sub> (TG), and (<b>c</b>) air and (<b>d</b>) N<sub>2</sub> (DTG).</p>
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<p>The vertical burning tests conducted on four different types of fabric at various time points: (<b>a</b>) raw cotton, (<b>b</b>) flame-retardant cotton fabric, (<b>c</b>) COT-SC-Ba after 5 washing cycles, and (<b>d</b>) washed and re-chelated COT-SC-Ba.</p>
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<p>The vertical burning tests conducted on four different types of fabric at various time points: (<b>a</b>) raw cotton, (<b>b</b>) flame-retardant cotton fabric, (<b>c</b>) COT-SC-Ba after 5 washing cycles, and (<b>d</b>) washed and re-chelated COT-SC-Ba.</p>
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<p>Analysis of washing resistance for flame-retardant fabrics by EDS results: (<b>a</b>) COT-SC-Ba fabric washed once, (<b>b</b>) COT-SC-Ba fabric washed three times, (<b>c</b>) COT-SC-Ba fabric washed five times, (<b>d</b>) washed and re-chelated COT-SC-Ba.</p>
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<p>Stress–strain curves of COT and a series of modified cotton fabrics.</p>
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<p>Water contact angle images: (<b>a</b>) COT and (<b>b</b>) COT-SC-Ba.</p>
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<p>Diagrammatic illustration of the procedure for altering cotton fabrics.</p>
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