Search Results (28)

Remote sensing techniques that use spectral indices (SIs) are essential for monitoring vegetation recovery after wildfires. However, there is a critical gap in the comparability of SI responses across ecoregions due to ecological variability. In this study, a meta-analysis was conducted to evaluate the capacity of different SIs (GCI, MSI, NBR, NDVI, NDII, and EVI2) to reflect the effect of post-wildfire emergency works on early recovery of vegetation in four Spanish ecoregions. It compared vegetation regrowth between treated and untreated sites, identifying the most sensitive SI for monitoring this recovery. All indices except EVI2 detected significantly better recovery in treated areas; among these, GCI was the most sensitive and NDII the least. The effect of treatment on recovery measured through SI is influenced by site covariates (fire severity, physiography, post-fire action period, post-fire climate, and edaphic characteristics). Finally, random mixed models showed that annual precipitation lower than 700 mm, diurnal temperature over 21 °C, soils with finer texture, and water content under 33% are quantitative limits of the treatment effectiveness on vegetation recovery. Overall, the study highlighted the importance of immediate interventions after fires, especially in the first six months, and advocated context-specific management strategies based on fire severity, ecoregion, soil properties, and climate to optimize vegetation recovery. Full article

Hydrological processes in river systems are changing due to climate variability and human activities, making it crucial to understand and quantify these changes for effective water resource management. This study examines long-term trends in hydroclimate variables (1990–2022) and land use/land cover (LULC) changes (1988, 2002, and 2022) within the Continental Reach of the Gambia River Basin (CGRB). Trend analyses of the Standardized Precipitation-Evapotranspiration Index (SPEI) at 12-month and 24-month scales, along with river discharge at the Simenti station, reveal a shift from dry conditions to wetter phases post-2008, marked by significant increases in rainfall and discharge variability. LULC analysis revealed significant transformations in the basin. LULC analysis highlights significant transformations within the basin. Forest and savanna areas decreased by 20.57 and 4.48%, respectively, between 1988 and 2002, largely due to human activities such as agricultural expansion and deforestation for charcoal production. Post-2002, forest cover recovered from 32.36 to 36.27%, coinciding with the wetter conditions after 2008, suggesting that climatic shifts promoted vegetation regrowth. Spatial analysis further highlights an increase in bowe and steppe areas, especially in the north, indicating land degradation linked to human land use practices. Bowe areas, marked by impermeable laterite outcrops, and steppe areas with sparse herbaceous cover result from overgrazing and soil degradation, exacerbated by the region’s drier phases. A notable decrease in burned areas from 2.03 to 0.23% suggests improvements in fire management practices, reducing fire frequency, which is also supported by wetter conditions post-2008. Agricultural land and bare soils expanded by 14%, from 2.77 to 3.07%, primarily in the northern and central regions, likely driven by both population pressures and climatic shifts. Correlations between precipitation and land cover changes indicate that wetter conditions facilitated forest regrowth, while drier conditions exacerbated land degradation, with human activities such as deforestation and agricultural expansion potentially amplifying the impact of climatic shifts. These results demonstrate that while climatic shifts played a role in driving vegetation recovery, human activities were key in shaping land use patterns, impacting both precipitation and stream discharge, particularly due to agricultural practices and land degradation. Full article

This study utilizes the unique capabilities of Sentinel-1 C-band synthetic aperture radar (SAR) data to map post-fire burned areas and monitor vegetation recovery in a heath-dominated Queensland National Park. Sentinel-1 SAR data were used due to their cloud-penetrating capability and frequent revisit times. Using Google Earth Engine (GEE), a bitemporal ratio analysis was applied to SAR data from post-fire periods between 2021 and 2023. SAR backscatter changes over time captured fire impacts and subsequent vegetation regrowth. This differentiation was further enhanced with k-means clustering. Validation was supported by Sentinel-2 dNBR and official fire history records. The dNBR provided a quantitative assessment of burn severity and was used alongside the fire history data to evaluate the accuracy of the burned area classification. While Sentinel-2 false-colour composite (FCC) imagery was generated for visualisation and interpretation purposes, the primary validation relied on dNBR and QPWS fire history records. The results highlighted significant vegetation regrowth, with some areas returning to near pre-fire biomass levels by March 2023. This approach demonstrates the sensitivity of Sentinel-1 SAR, especially in VV polarization, for detecting subtle changes in vegetation, providing a cost-effective method for post-fire ecosystem monitoring and informing ecological management strategies amid increasing wildfire events. Full article

Wildfires serve a paradoxical role in landscapes—supporting biodiversity and nutrient cycling while also threatening ecosystems and economies, especially as climate change intensifies their frequency and severity. This study investigates the impact of wildfires and vegetation recovery in the Bosco Difesa Grande forest in southern Italy, focusing on the 2017 and 2021 fire events. Using Google Earth Engine (GEE) accessed in January 2025, we applied remote sensing techniques to assess burn severity and post-fire regrowth. Sentinel-2 imagery was used to compute the Normalized Burn Ratio (NBR) and Normalized Difference Vegetation Index (NDVI); burn severity was derived from differenced NBR (dNBR), and vegetation recovery was monitored via differenced NDVI (dNDVI) and multi-year NDVI time series. We uniquely compare recovery across four zones with different fire histories—unburned, single-burn (2017 or 2021), and repeated-burn (2017 and 2021)—providing a novel perspective on post-fire dynamics in Mediterranean ecosystems. Results show that low-severity zones recovered more quickly than high-severity areas. Repeated-burn zones experienced the slowest and least complete recovery, while unburned areas remained stable. These findings suggest that repeated fires may shift vegetation from forest to shrubland. This study highlights the importance of remote sensing for post-fire assessment and supports adaptive land management to enhance long-term ecological resilience. Full article

At present, remote sensing serves as a key approach to track ecological recovery after fires. However, systematic and quantitative research on the research progress of post-fire remote sensing remains insufficient. This study presents the first global bibliometric analysis of post-fire remote sensing research (1994–2024), analyzing 1155 Web of Science publications and using CiteSpace to reveal critical trends and gaps. The key findings include the following: As multi-sensor remote sensing and big data technologies evolve, the research focus is increasingly pivoting toward interdisciplinary, multi-scale, and intelligent methodologies. Since 2020, AI-driven technologies such as machine learning have become research hotspots and continue to grow. In the future, more extensive time-series monitoring, holistic evaluations under compound disturbances, and enhanced fire management strategies will be required to addressing the global climate change challenge and sustainability. The USA, Canada, China, and multiple European nations work jointly on fire ecology research and technology development, but Africa, as a high wildfire-incidence area, currently lacks appropriate local research. Remote sensing of the environment and remote sensing and forests maintain a pivotal role in scholarly impact and information exchange. This work redefines post-fire remote sensing as a nexus of ecological urgency and social justice, demanding inclusive innovation to address climate-driven post-fire recovery regimes. Full article

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. Full article

Forest ecosystems in the Mediterranean basin are significantly affected by summer wildfires. Drought, extreme temperatures, and strong winds increase the fire risk in Greece. This study explores the potential of NDVI for assessing and forecasting post-fire regeneration in burnt areas of the Peloponnese (2007) and Evros (2011). NDVI data from Landsat 7 and 9 were analyzed to identify the stages of the regeneration process and the dominant vegetation species at each stage. Comparing pre-fire and post-fire values highlighted the recovery rate, while the trendline slope indicated the regeneration rate. This combined analysis forms a methodology that allows drawing conclusions about the vegetation type that prevails after the fire. Validation was conducted using photointerpretation techniques and CORINE land cover data. The findings suggest that sclerophyllous species regenerate faster, while fir forests recover slowly and may be replaced by sclerophylls. To predict vegetation regrowth, two time series models (ARMA, VARIMA) and two machine learning-based ones (random forest, XGBoost) were tested. Their performance was evaluated by comparing the predicted and actual numerical values, calculating error metrics (RMSE, MAPE), and analyzing how the predicted patterns align with the observed ones. The results showed the overperformance of multivariate models and the need to introduce additional variables, such as soil characteristics and the effect of climate change on weather parameters, to improve predictions. Full article

Large wildfires have increased in the Mediterranean region due to socio-economic and land-use changes. The most immediate and concerning consequence of the wildfires is the loss of vegetation. However, there are few studies on the relationship between wildfire and vegetation recovery, especially on the complex relationship between species composition, burn severity and geo-environmental context. This study focuses on the analysis of post-fire vegetation regrowth (RV) in Mediterranean forests. Therefore, two objectives were set: (i) to analyse the influence of pre-fire conditions, burn severity and topographic variables on growth rates for each stage of recovery and (ii) to identify the drivers of post-fire vegetation recovery. The results show that NDVI increases rapidly in the first two years after the wildfire and more slowly in the following years. Except for the first year, RV shows a positive relationship with burn severity. In the first year, the importance of topographical features, especially curvature and flow accumulation, stands out. In the fourth year, when NDVI values are highest, RV is mainly explained by the presence of pre-fire vegetation, followed by burn severity and altitude. These results can be an important step towards more effective local management strategies leading to a resilient and sustainable territory. Full article

The aim of this study is to identify and characterize gullies considering their morphological and topographical aspects and determine the factors that control vegetation regrowth (VR) in gullies in Alva Basin after the wildfire of 2017. The use of hierarchical clustering identified two groups of gullies. Multiple regression produced three models (R-Square = 81.3%) for gullies group 1, considering the explanatory factors mean width, slope, and burn severity. Group 2 also produced three models (R-Square = 71.8%) but considering the explanatory variables mean width, slope, and flow accumulation. VR mainly depends on post-fire recovery strategies for vegetation, the remaining soil, and site humidity. Full article

Accurate burn severity mapping is essential for understanding the impacts of wildfires on vegetation dynamics in arid savannas. The frequent wildfires in these biomes often cause topkill, where the vegetation experiences above-ground combustion but the below-ground root structures survive, allowing for subsequent regrowth post-burn. Investigating post-fire regrowth is crucial for maintaining ecological balance, elucidating fire regimes, and enhancing the knowledge base of land managers regarding vegetation response. This study examined the relationship between bush burn severity and woody vegetation post-burn coppicing/regeneration events in the Kalahari Desert of Botswana. Utilizing UAV-derived RGB imagery combined with a Random Forest (RF) classification algorithm, we aimed to enhance the precision of burn severity mapping at a fine spatial resolution. Our research focused on a 1 km² plot within the Modisa Wildlife Reserve, extensively burnt by the Kgalagadi Transfrontier Fire of 2021. The UAV imagery, captured at various intervals post-burn, provided detailed orthomosaics and canopy height models, facilitating precise land cover classification and burn severity assessment. The RF model achieved an overall accuracy of 79.71% and effectively identified key burn severity indicators, including green vegetation, charred grass, and ash deposits. Our analysis revealed a >50% probability of woody vegetation regrowth in high-severity burn areas six months post-burn, highlighting the resilience of these ecosystems. This study demonstrates the efficacy of low-cost UAV photogrammetry for fine-scale burn severity assessment and provides valuable insights into post-fire vegetation recovery, thereby aiding land management and conservation efforts in savannas. Full article

Savannas play a key role in estimating emissions. Climate change has impacted the Cerrado savanna carbon balance. We used the burned area product and long-term field inventories on post-fire vegetation regrowth to estimate the impact of the fire on greenhouse gas emissions and net carbon dioxide (CO₂) emissions in the Cerrado savanna between 1985 and 2020. We estimated the immediate emissions from fires, CO₂ emissions by plant mortality, and CO₂ removal from vegetation regrowth. The burned area was 29,433 km²; savanna fires emitted approximately 2,227,964 Gg of CO₂, 85,057 Gg of CO, 3010 Gg of CH₄, 5,103 Gg of NO_x, and 275 Gg of N₂O. We simulated vegetation regrowth according to three fire regime scenarios: extreme (high fire frequency and short fire interval), intermediate (medium fire frequency and medium fire interval), and moderate (low fire frequency and long fire interval). Under the extreme and intermediate scenarios, the vegetation biomass decreased by 2.0 and 0.4% (ton/ha-year), while the biomass increased by 2.1% under a moderate scenario. We converted this biomass into CO₂ and showed that the vegetation regrowth removed 63.5% of the total CO₂ emitted (2,355,426 Gg), indicating that the Cerrado savanna has been a source of CO₂ to the atmosphere. Full article

Climate change has intensified the need for robust fire prevention strategies. Sustainable forest fuel management is crucial in mitigating the occurrence and rapid spread of forest fires. This study assessed the impact of vegetation clearing and/or grazing over a three-year period in the herbaceous and shrub parts of a Mediterranean oak forest. Using high-resolution multispectral data from an unmanned aerial vehicle (UAV), four flight surveys were conducted from 2019 (pre- and post-clearing) to 2021. These data were used to evaluate different scenarios: combined vegetation clearing and grazing, the individual application of each method, and a control scenario that was neither cleared nor purposely grazed. The UAV data allowed for the detailed monitoring of vegetation dynamics, enabling the classification into arboreal, shrubs, herbaceous, and soil categories. Grazing pressure was estimated through GPS collars on the sheep flock. Additionally, a good correlation (r = 0.91) was observed between UAV-derived vegetation volume estimates and field measurements. These practices proved to be efficient in fuel management, with cleared and grazed areas showing a lower vegetation regrowth, followed by areas only subjected to vegetation clearing. On the other hand, areas not subjected to any of these treatments presented rapid vegetation growth. Full article

Wildfires are a common disturbance factor worldwide, especially over the last decade due to global climate change. Monitoring postfire forest regrowth provides fundamental information needed to enhance the management and support of ecosystem recovery after fires. The purpose of this study is to propose an algorithm for postfire forest regrowth monitoring using tasseled-cap-derived indices. A complex approach is used for its implementation, for which a model is developed based on three components—Disturbance Index (DI), Vector of Instantaneous Condition (VIC), and Direction Angle (DA). The final product—postfire regrowth (PFIR)—allows for a quantitative assessment of the intensity of regrowth. The proposed methodology is based on the linear orthogonal transformation of multispectral satellite images—tasseled cap transformation (TCT)—that increases the degree of identification of the three main components that change during a fire—soil, vegetation, and water/moisture—and implies a higher accuracy of the assessments. The results provide a thematic raster representing the intensity of the regrowth classes, which are defined after the PFIR threshold values are determined (HRI—high regrowth intensity; MRI—moderate regrowth intensity; and LRI—low regrowth intensity). The accuracy assessment procedure is conducted using very-high-resolution (VHR) aerial and satellite data from World View (WV) sensors, as well as multispectral Sentinel 2A images. Three different forest test sites affected by fire in Bulgaria are examined. The results show that the classified thematic raster maps are distinguished by a good performance in monitoring the regrowth dynamics, with an average overall accuracy of 62.1% for all three test sites, ranging from 73.9% to 48.4% for the individual forests. Full article

Although wildfires are a common disturbance factor to the environment, some of them can cause significant environmental and socioeconomic losses, affecting ecosystems and people worldwide. The wildfire identification and assessment of their effects on damaged forest areas is of great importance for provision of effective actions on their management and preservation. Forest regrowth after a fire is a continuously evolving and dynamic process, and the accuracy assessment of different remote sensing indices for its evaluation is a complicated task. The implementation of this task cannot rely on the standard procedures. Therefore, we suggested a method involving delineation of dynamic boundaries between conditional categories within burnt forest areas by application of spectral reflectance characteristics (SRC). This study compared the performance of firmly established for fire monitoring differenced vegetation indices—Normalized Difference Vegetation Index (dNDVI) and Normalized Burn Ratio (dNBR) and tested the capabilities of tasseled cap-derived differenced Disturbance Index (dDI) for post-fire monitoring purposes in different forest environments (Boreal Mountain Forest (BMF), Mediterranean Mountain Forest (MMF), Mediterranean Hill Forest (MHF)). The accuracy assessment of the tree indices was performed using Very High Resolution (VHR) aerial and satellite data. The results show that dDI has an optimal performance in monitoring post-fire disturbances in more difficult-to-be-differentiated classes, whereas, for post-fire regrowth, the more appropriate is dNDVI. In the first case, dDI has an overall accuracy of 50%, whereas the accuracy of dNBR and dNDVI is barely 35% and 36%. Moreover, dDI shows better performance in 16 accuracy metrics (from 17). In the second case, dNDVI has an overall accuracy of 59%, whereas those of dNBR and dDI are 55% and 52%, and the accuracy metrics in which dNDVI shows better performance than the other two indices are 11 (from 13). Generally, the studied indices showed higher accuracy in assessment of post-fire disturbance rather than of the post-fire forest regrowth, implicitly at test areas—BMF and MMF, and contrary opposite result in the accuracy at MHF. This indicates the relation of the indices’ accuracy to the heterogeneity of the environment. Full article

Slash-and-burn of Amazon Forest (AF) for pasture establishment has increased the occurrence of AF wildfires. Recent studies emphasize soil organic matter (SOM) molecular composition as a principal driver of post-fire forest regrowth and restoration of AF anti-wildfire ambience. Nevertheless, SOM chemical shifts caused by AF fires and post-fire vegetation are rarely investigated at a molecular level. We employed pyrolysis–gas chromatography–mass spectrometry to reveal molecular changes in SOM (0–10, 40–50 cm depth) of a slash-burn-and-20-month-regrowth AF (BAF) and a 23-year Brachiaria pasture post-AF fire (BRA) site compared to native AF (NAF). In BAF (0–10 cm), increased abundance of unspecific aromatic compounds (UACs), polycyclic aromatic hydrocarbons (PAHs) and lipids (Lip) coupled with a depletion of polysaccharides (Pol) revealed strong lingering effects of fire on SOM. This occurs despite fresh litter deposition on soil, suggesting SOM minimal recovery and toxicity to microorganisms. Accumulation of recalcitrant compounds and slow decomposition of fresh forest material may explain the higher carbon content in BAF (0–5 cm). In BRA, SOM was dominated by Brachiaria contributions. At 40–50 cm, alkyl and hydroaromatic compounds accumulated in BRA, whereas UACs accumulated in BAF. UACs and PAH compounds were abundant in NAF, possibly air-transported from BAF. Full article