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Vegetation recovery after the large wildfires that occurred in central Portugal in 2017 is assessed in the present study. These wildfires had catastrophic consequences, among which were human losses and a vast extent of forest devastation. Landsat 8 OLI images were used to obtain the land use and cover (LUC) classification and to determine the Norm...
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... data used in this research are described in Table 1. Landsat 8 OLI images were selected instead of wildfire occurrence: a pre-fire image was used for the determination of land cover (LUC) of the previously existing unburned vegetation, in order to compare with the regeneration of vegetation after the fire. ...Similar publications
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Phyllostachys pubescens (Moso bamboo) is a large monopodial bamboo species, naturally distributed in China and cultivated in neighboring countries. Seed availability of the P. pubescens is limited due to its long gregarious flowering cycle. Hence, developing nursery techniques from rhizome cuttings is necessary to fulfill the demand for Moso bamboo...
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... The superior results obtained with the NBR and NBR2 in this work corroborate other studies where these indices also presented better discrimination of burnt areas (Escuín, Navarro, andFernández 2008, Sacramento, Michel, andSiqueira 2020;Veraverbeke, Harris, and Hook 2011). NBR is a fire index, designed specifically to detect changes in carbon signal after fire through the normalized differences of the NIR and SWIR bands (Figure 8) (Meneses 2021). Before the fire, the absorbance of vegetation in the NIR band is low, whereas reflectance and transmittance are high; in the SWIR wavelengths, the reflectance and transmittance of vegetation are low, and the absorbance is very high (Alcaras et al. 2022). ...
... Research on post-fire vegetation recovery in Mediterranean climates focuses on understanding the ecological processes and factors that influence post-fire regeneration, including the role of fire severity, climate conditions, plant traits, and landscape characteristics [30,[50][51][52][53][54][55]. Previous studies have typically used a combination of field measurements, remote sensing, and modeling approaches to assess post-fire vegetation recovery patterns, identify key drivers, and inform post-fire management strategies [56]. ...
... Previous studies have typically used a combination of field measurements, remote sensing, and modeling approaches to assess post-fire vegetation recovery patterns, identify key drivers, and inform post-fire management strategies [56]. Meneses [53] in his study about wildfires in Portugal, suggests that factors affecting regrowth are vegetation type, soil, climate, and continentality. Aguiar et al. [52] found that burn severity had no impact on the composition of flora or its richness on the stage of post-fire regrowth. ...
Wildfires are frequently observed in watersheds with a Mediterranean climate and seriously affect vegetation, soil, hydrology, and ecosystems as they cause abrupt changes in land cover. Assessing wildfire effects, as well as the recovery process, is critical for mitigating their impacts. This paper presents a geospatial analysis approach that enables the investigation of wildfire effects on vegetation, soil, and hydrology. The prediction of regeneration potential and the period needed for the restoration of hydrological behavior to pre-fire conditions is also presented. To this end, the catastrophic wildfire that occurred in August 2021 in the wider area of Varybobi, north of Athens, Greece, is used as an example. First, an analysis of the extent and severity of the fire and its effect on the vegetation of the area is conducted using satellite imagery. The history of fires in the specific area is then analyzed using remote sensing data and a regrowth model is developed. The effect on the hydrological behavior of the affected area was then systematically analyzed. The analysis is conducted in a spatially distributed form in order to delineate the critical areas in which immediate interventions are required for the rapid restoration of the hydrological behavior of the basin. The period required for the restoration of the hydrological response is then estimated based on the developed vegetation regrowth models. Curve Numbers and post-fire runoff response estimations were found to be quite similar to those derived from measured data. This alignment shows that the SCS-CN method effectively reflects post-fire runoff conditions in this Mediterranean watershed, which supports its use in assessing hydrological changes in wildfire-affected areas. The results of the proposed approach can provide important data for the restoration and protection of wildfire-affected areas.
... Various remote sensing products have been utilized to assess vegetation recovery trajectories over large territories. This has primarily been achieved by estimating spectral indices, such as the Normalized Difference Vegetation Index (NDVI), the differenced Normalized Burn Ratio (dNBR), the Enhanced Vegetation Index (EVI), the Leaf Area Index (LAI), and Fractional Vegetation Cover (FVC), due to their theoretical simplicity and efficiency [22][23][24][25][26][27]. The use of such spectral indices is ultimately based on establishing pre-fire baselines on the forest cover of remote sensing images in order to compare with the patterns of post-fire recovery. ...
Increased wildfire activity is the most significant natural disturbance affecting forest ecosystems as it has a strong impact on their natural recovery. This study aimed to investigate how burn severity (BS) levels and climate factors, including land surface temperature (LST) and precipitation variability (Pr), affect forest recovery in the Middle Volga region of the Russian Federation. It provides a comprehensive analysis of post-fire forest recovery using Landsat time-series data from 2000 to 2023. The analysis utilized the LandTrendr algorithm in the Google Earth Engine (GEE) cloud computing platform to examine Normalized Burn Ratio (NBR) spectral metrics and to quantify the forest recovery at low, moderate, and high burn severity (BS) levels. To evaluate the spatio-temporal trends of the recovery, the Mann–Kendall statistical test and Theil–Sen’s slope estimator were utilized. The results suggest that post-fire spectral recovery is significantly influenced by the degree of the BS in affected areas. The higher the class of BS, the faster and more extensive the reforestation of the area occurs. About 91% (40,446 ha) of the first 5-year forest recovery after the wildfire belonged to the BS classes of moderate and high severity. A regression model indicated that land surface temperature (LST) plays a more critical role in post-fire recovery compared to precipitation variability (Pr), accounting for approximately 65% of the variance in recovery outcomes.
... Various remote sensing products have been utilised to assess the vegetation recovery trajectories over large territories. This has primarily been achieved by estimating spectral indices, such as Normalised Difference Vegetation Index (NDVI), differenced Normalized Burn Ratio (dNBR), Enhanced Vegetation Index (EVI), Leaf Area Index (LAI), and Fractional Vegetation Cover (FVC) due to their theoretical simplicity and efficiency [22][23][24][25][26][27]. The use of such spectral indices is ultimately based on establishing pre-fire baselines on the forest cover of remote sensing images in order to compare with the patterns of the post-fire recovery. ...
... (www.preprints.org) | NOT PEER-REVIEWED | Posted: 24 September 2024 doi:10.20944/preprints202409.1781.v126 ...
Increased wildfire activity is the most significant natural disturbance affecting forest ecosystems, with a strong impact on their natural regeneration. This study presents a comprehensive analysis of post-fire forest recovery using Landsat time series data from 2000 to 2023 in the Middle Volga region of the Russian Federation. The analyses utilised the LandTrendr algorithm in Google Earth Engine (GEE) cloud computing platform to examine Normalized Burn Ratio (NBR) spectral metrics and quantify the forest recovery at low, moderate, and high burn severity (BS) levels. To assess the spatio-temporal trends of the recovery, the Mann–Kendall statistical test and Theil–Sen’s slope estimator was applied. The results suggested that the post-fire spectral recovery is significantly influenced by the degree of the BS on affected areas. The higher the class of BS, the faster and more extensive the reforestation of the area occurs. About 91% (40,446 ha) of the first 5-year forest recovery after the wildfire belong to the BS classes of moderate and high severity. A regression model showed that land surface temperature (LST) was more significant to post-fire recovery than the variability in precipitation (Pr), explaining about 65% of the variance in post-fire recovery. This study provides new insights into the post-fire forest recovery dynamics and scientific bases for the cost-effective management strategies under changing climate conditions.
... group, it will likely take decades to receive similar leaf litter inputs prior to the fire for recovery. Trees contribute large amounts of leaf litter in these ecosystems and their recovery and regeneration is slower than most shrub species (Meneses, 2021). However, this study targeted mineral soils, therefore we suspect other primary drivers of nutrient concentration reductions. ...
Climate change has increased drought frequency and duration that are exacerbated by increased temperatures globally. This e�ect has, and will continue, to increase fire occurrence across many regions of North America. In the southern Appalachian Mountains, wildfires with high burn severity occurred in 2016 due to increased drought and human activity. To investigate the e�ects of burn severity on soil physicochemical properties, microbial extracellular enzyme production, and microbial abundances in a temperate region, surface soils (0–15 cm) were collected from two sites (the Great Smoky Mountains National Park in Tennessee and the Nantahala National Forest in North Carolina, USA) spanning lightly, moderately, and severely burned areas, accompanied by adjacent unburned locations that act as controls. The soil samples were collected at three time points between 2017 and 2019 (i.e., 0.5, 1, and 2.5 years post-fire) among burn severity plots. Total hydrolytic enzyme production varied over time, with severe burn plots having significantly lower enzyme production at 2.5 years post-fire. Individual enzymes varied among burn severities and across time post-fire. Light burn plots showed greater carbon-specific (�-glucosidase and �-xylosidase) and phosphorus-specific (acid phosphatase) enzyme activities at 0.5 years post-fire, but this e�ect was transient. At 2.5 years post-fire, the �-xylosidase and acid phosphatase activities were lower in severe or moderate burn plots relative to the controls. In contrast, the activity of nitrogen-specific enzyme leucyl aminopeptidase was the lowest in severe burn plots at 0.5 years post-fire, but was the lowest in light burn plots at 2.5 years post-fire. The fungi:bacteria ratio declined with burn severity, indicating that fungi are sensitive or less resilient to high burn severity during recovery. These results suggest that wildfires alter trajectories for soil microbial structure and function within a 2.5-year timeframe, which potentially has long-term impacts on biogeochemical cycling.
... The normalized burn ratio index (NBRI) is used to identify the occurrence and severity of natural or human-caused fires in vegetation areas [68,69]. ...
It is crucial to monitor algal blooms in freshwater reservoirs through an examination of chlorophyll-a (Chla) concentrations, as they indicate the trophic condition of these waterbodies. Traditional monitoring methods, however, are expensive and time-consuming. Addressing this hindrance, we conducted a comprehensive investigation using several machine learning models for Chla modeling. To this end, we used in situ collected water sample data and remote sensing data from the Sentinel-2 satellite, including spectral bands and indices, for large-scale coverage. This approach allowed us to conduct a comprehensive analysis and characterization of the Chla concentrations across 149 freshwater reservoirs in Ceará, a semi-arid region of Brazil. The implemented machine learning models included k-nearest neighbors, random forest, extreme gradient boosting, the least absolute shrinkage, and the group method of data handling (GMDH); in particular, the GMDH approach has not been previously explored in this context. The forward stepwise approach was used to determine the best subset of input parameters. Using a 70/30 split for the training and testing datasets, the best-performing model was the GMDH model, achieving an R² of 0.91, an MAPE of 102.34%, and an RMSE of 20.4 μg/L, which were values consistent with the ones found in the literature. Nevertheless, the predicted Chla concentration values were most sensitive to the red, green, and near-infrared bands.
... The spectral indices are mathematical formulations that combine different satellite bands to extract more information from the satellite data. The usage of spectral indices can be found in the literature with applications in various areas of knowledge, such as monitoring crops [42,43], vegetation [44,45], and also water quality [46,47], to cite a few. ...
Turbidity is an important indicator of water quality in hydrology. More traditional ways to monitor turbidity can provide reliable results. However, they are prone to human error, have elevated costs, and lack real-time monitoring capacity. Addressing these hindrances, in this work we combine spectral bands and indices from Sentinel-2 with several machine learning paradigms, namely XGBoost, Random Forests, GMDH, Support Vector Regression, k-Nearest Neighbors and Least Absolute Shrinkage and Selection Operator to model turbidity, using data from twelve monitoring stations encompassing the Mississippi River, USA. Results show that considering the individual monitoring stations, the ML paradigms for turbidity modeling were satisfactory at locations with a larger range and standard deviation values, achieving a mean R2 value of 59.5%. Tree-based models were the best overall approach, often ranking as the best or second-best performing model. When all the samples from the monitoring stations were used, the XGBoost provided superior output for turbidity modeling, reaching an R2 equal to 75.7%. A comprehensive comparison with the literature found values showed that the models implemented using this study’s methodology could provide competitive results, deeming it as an alternative for turbidity modeling from remote sensing data.
... Furthermore, burning on steep slopes could have more pronounced environmental impacts due to the potential for increased fire severity (Scotland's Moorland Forum, 2017;Scottish Government, 2013), higher post-fire soil erosion (DeBano, 2000) and reduced rainfall infiltration (Doerr et al., 2009). It has also been suggested that post-fire vegetation recovery is inhibited on steeper slopes (Meneses, 2021). ...
In the UK uplands, prescribed burning of unenclosed heath, grass and blanket bog (‘moorland’) is used to support game shooting and grazing. Burning on moorland is contentious due to its impact on peat soils, hydrology and habitat condition. There is little information on spatial and temporal patterns of burning, the overlap with soil carbon and sensitive habitats and, importantly, whether these patterns are changing. This information is required to assess the sustainability of burning and the effectiveness of new legislation. We developed a method for semi‐automated detection of burning using satellite imagery – our best performing model has a balanced accuracy of 84.9%. We identified annual burn areas in Great Britain in five burning seasons from 2017/18 to 2021/22 of 8333 to 20 974 ha (average 15 250 ha year⁻¹). Annual extent in England in 2021/22 was 73% lower than the average of the four previous seasons. Burning was identified over carbon‐rich soils (mean 5150 ha or 34% by area of all burning annually) and on steep slopes – 915 ha across the five seasons (1.3%), contravening guidance. Burning (>1 ha) was recorded in 14% of UK protected areas (PAs) and, within these, the percentage area of moorland burned varied from 2 to 31%. In England in some years, the percentage area of moorland burned inside PAs was higher than outside, while this was not the case in Scotland. Burning in sensitive alpine habitats totalled 158 ha across the five seasons. The reduction in burned area in England in 2021/22 could relate to England‐specific legislation, introduced in May 2021, to prohibit burning on deep peat in PAs. This suggests that regulation can be effective. However, the continued overlap with sensitive features suggests that burning falls short of sustainable practices. Our method will enable repeatable re‐assessment of burning extents and overlap with ecosystem services.
... In this study, we analysed the effects of postfire emergency stabilization on deciduous oak forest recovery in Portugal. Forest recovery was defined as the increase in vegetation greenness quantified with spectral indices through remote sensing (Gitas et al. 2012;Meneses 2021;Pérez-Cabello et al. 2021;White et al. 2022). Remote sensing techniques have been widely used to assess different types of restoration (e.g. ...
... Hence, NDVI increase under high burn severity in our study area may be partially explained by the fast recovery of the understory vegetation (Castro Rego et al. 2021). Such differences in the type of vegetation that recovers cannot be detected by NDVI measurements, which show vegetation greenness and do not guarantee that the same type of prefire vegetation is being regenerated (Gouveia et al. 2010;Meneses 2021). In agreement with our results, postfire NDVI values in pine forests mixed with oaks and other woody species under moderate/high burn severities increased quickly over time, achieving those of the lightly burned areas two years after the fire (Lee and Chow 2015). ...
Mediterranean Europe is experiencing a rise in severe wildfires, resulting in growing socioeconomic and ecological impacts. Postfire restoration has become a crucial approach to mitigate these impacts and promote ecosystem recovery. However, the ecological effects of such interventions are still not well understood. We employed remote sensing techniques to evaluate the impact of postfire emergency stabilization on the recovery of deciduous oak forests in Portugal. Our study encompassed 3013 sampling points located in areas with and without postfire interventions. We chose the Normalized Difference Vegetation Index (NDVI) as an indicator of oak forest recovery over a four-year period following wildfires that took place in 2016 and 2017. We used a Generalized Additive Mixed Model (GAMM) to assess how NDVI changed over time as a function of postfire restoration, fire characteristics, topography, and postfire drought events. We found that postfire restoration had a significant positive effect on NDVI recovery over time, although this effect was small. Severe drought and fire recurrence up to six fires had a negative effect on the recovery of NDVI. Conversely, severe wetness and either low or high burn severities had a positive effect on recovery. Our study emphasizes the importance of monitoring postfire restoration effects on forest recovery to guide restoration planning and improve forest management in burned areas. This becomes even more relevant under increased wildfire severity predicted for the Mediterranean region interacting with other climate-driven disturbances, which will further negatively affect forest recovery.
... The RS of forest BA mapping is still an active research topic employing advanced techniques that integrate geo-statistics and machine learning methods [37,45]. Many studies of post-fire vegetation responses are based on the discrimination of spectral bands and vegetation indices (mostly NDVI, dNBR, and EVI) by MODIS, Landsat, SPOT, and Sentinel multitemporal imagery in different regions and forest ecosystems of the world [46][47][48][49][50]. Classification of forest succession patterns after the wildfires is also an important research direction, that can be used to predict the dynamics of future forest cover [51][52][53]. ...
Wildfires are important natural drivers of forest stands dynamics, strongly influencing on their natural regeneration and ecosystem services. This paper presents a comprehensive analysis of spatiotemporal burnt area (BA) patterns over the period 2000–2022 in the Middle Volga region of the Russian Federation on the base of remote sensing time series, considering the impact of cli-matic factors on forest fires. The temporal trends were assessed with the Mann-Kendall nonpara-metric statistical test and Theil-Sen’s slope estimator using the LandTrendr algorithm on the Google Earth Platform (GEE). The accuracy assessment indicated a high overall accuracy (> 84%) and F-score value (> 82%) for forest burnt area detection as evaluated against 581 test sites of ref-erence data. The results revealed that the fire occurrences in the region were mainly irregular with the highest frequency of 7.3 over a 22-year period. The total forest BA was about 280 thousand ha, which equals to 1.7% of the land surface area or 4.0% of the total forested area under study in the Middle Volga region. The coniferous forest stands are the most fire-prone ecosystems accounting for 59.0 % of the total BA; deciduous stands accounts for 25.1%; and insignificant fire occurrences were registered in young forests and shrub lands. On a seasonal scale, temperature generally has a greater impact on the BA than precipitation and wind speed.
