Site‐scale drivers of post‐fire vegetation regrowth in gullies: A case study in Mediterranean Europe

Earth Surface Processes and Landforms, Journal Year: 2024, Volume and Issue: unknown

Published: Sept. 3, 2024

Abstract Mediterranean forests are very degraded, mainly due to the intensification of wildfires in recent decades, which, boosted by human activity, have contributed acceleration erosion processes and soil degradation. Under certain conditions, this also contributes formation gullies. The aim study is identify characterise gullies considering their morphological topographical aspects determine factors that control vegetation regrowth a environment after wildfire. were identified based on 2018 orthophotograph, large wildfire October 2017 affected entire area. To analyse regrowth, we used normalised difference index (NDVI) derived from seven Landsat 8 OLI/TIRS images (2017–2022). Spearman's rho correlation coefficient was selected estimate between gully characteristics regrowth. Before running model, multicollinearity test conducted ( VIF ≤ 10 tolerance ≥ 0.1). Stepwise multiple regression order independent variable has strong relationship with A marginal effects plot drawn up. 38 forest areas, composed pine Pinus pinaster ) trees (17 gullies) or combination broadleaf Eucalyptus globulus (eight gullies). In all, invasive species present 11 gullies, alone (one gully), together (four other (six). trees. channel recovered well year following years there growth at slower rate until it reached similar values NDVI 2022, 5 (SMR) produced solution three models. dimensions covered 66.8% variance, mean width, altitude flow accumulation. results can help devise more effective management strategies for areas where recurrence intensity effectively loss degradation erosion, view resilient sustainable territory.

Language: Английский

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Prediction of some soil properties in volcanic soils using random forest modeling: A case study at chinyero special nature reserve (Tenerife, canary islands)

Ecological Informatics, Journal Year: 2025, Volume and Issue: 86, P. 103054 - 103054

Published: Jan. 29, 2025

Language: Английский

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Fire severity shows limited dependence on fuel structure under adverse fire weather conditions: a case study of two extreme wildfire events

Fire Ecology, Journal Year: 2025, Volume and Issue: 21(1)

Published: May 7, 2025

Language: Английский

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Post-Fire Vegetation (Non-)Recovery across the Edges of a Wildfire: An Unexplored Theme

Fire, Journal Year: 2024, Volume and Issue: 7(7), P. 250 - 250

Published: July 13, 2024

Wildfires have a significant influence on ecosystems globally, shaping vegetation, biodiversity, landscapes, soil properties, and other ecosystem processes. Despite extensive research different aspects of wildfires, the edges burned areas remain understudied, even though they involve complex dynamics. In this study, we analyzed post-fire vegetation recovery across large wildfire in Mediterranean area. The investigations were focused patches woodlands that, previous showed normalized burn ratio (NBR) decline one year after fire. Field surveys carried out characterized by NBR rates outside area as controls. Five hypotheses tested, identifying delayed tree mortality key factor linked to decline, particularly low-severity fire zones proximity edges. Delayed mortality, observed predominantly near edges, may also affect unburned or less severely within main perimeter, highlighting need for ongoing monitoring. As these play crucial role succession dynamics, understanding second-order effects is imperative effective management. This study underscores importance long-term assessment impacts, emphasizing necessity field alongside remote sensing. Continued observation essential elucidate enduring impacts wildfires facilitate informed restoration strategies.

Language: Английский

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FIREMAP: Cloud-based software to automate the estimation of wildfire-induced ecological impacts and recovery processes using remote sensing techniques

Ecological Informatics, Journal Year: 2024, Volume and Issue: 81, P. 102591 - 102591

Published: April 7, 2024

The formulation and planning of integrated fire management strategies must be strengthened by decision support systems about fire-induced ecological impacts ecosystem recovery processes, particularly in the context extreme wildfire events that challenge land initiatives. Wildfire data collection analysis through remote sensing earth observations is utmost importance for this purpose. However, needs managers are not always met because exploitation full potential techniques requires a high level technical expertise. In addition, acquisition storage, database management, networking, computing requirements may present difficulties. Here, we FIREMAP software, which leverages Google Earth Engine (GEE) cloud-based platform, an intuitive graphical user interface (GUI), European Forest Fire Information System (EFFIS) analyses collections. software allows automatic (i) machine learning-based burned area (BA) detection algorithms to facilitate mapping (historical) perimeters, (ii) severity spectral indices, (iii) post-fire trajectories inversion physically-based radiative transfer models. We introduce platform architecture GUI, implementation well-established science GEE, validation algorithm fifteen case-study wildfires across western Mediterranean Basin, (iv) near-future long-term planned expansion features.

Language: Английский

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Fuel build-up promotes an increase in fire severity of reburned areas in fire-prone ecosystems of the western Mediterranean Basin

Fire Ecology, Journal Year: 2023, Volume and Issue: 19(1)

Published: Dec. 12, 2023

Abstract Background Fire-vegetation feedbacks can modulate the global change effects conducive to extreme fire behavior and high severity of subsequent wildfires in reburn areas by altering composition, flammability traits, spatial arrangement fuels. Repeated, high-severity at short return intervals may trigger long-term vegetation state transitions. However, empirical evidence about these is absent fire-prone ecosystems western Mediterranean Basin, where response activity has been enhanced contemporary socioeconomic land-use changes. Here, we evaluated whether differs between initial burns (fire-free periods = 10–15 years) maritime pine Aleppo forests, holm oak woodlands, shrublands there a relationship such interactive wildfire disturbances. We also tested how type ecosystem changes structure after influence relationships. leveraged Landsat-based estimates for last using Relativized Burn Ratio (RBR) Light Detection Ranging (LiDAR) data acquired before wildfire. Results Fire was significantly higher than that each dominant areas. These differences were very pronounced forests shrublands. For consistency, same patterns evidenced first-entry type. woodlands (particularly pine-dominated) raised with increasing previous greater extent Pre-fire fuel density lower strata (up 4 m as well shrublands, up 2 forests) Conclusions Our results suggest land managers should promote more fire-resistant landscapes minimizing build-up thus hazard through pre-fire reduction treatments prescribed burning.

Language: Английский

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Comparative Study of Random Forest and Support Vector Machine for Land Cover Classification and Post-Wildfire Change Detection

Land, Journal Year: 2024, Volume and Issue: 13(11), P. 1878 - 1878

Published: Nov. 10, 2024

The land use cover (LULC) map is extensively employed for different purposes. Machine learning (ML) algorithms applied in remote sensing (RS) data have been proven effective image classification, object detection, and semantic segmentation. Previous studies shown that random forest (RF) support vector machine (SVM) consistently achieve high accuracy classification. Considering the important role of Portugal’s Serra da Estrela Natural Park (PNSE) biodiversity nature conversation at an international scale, availability timely on PNSE emergency evaluation periodic assessment crucial. In this study, application RF SVM classifiers, object-based (OBIA) pixel-based (PBIA) approaches, with Sentinel-2A imagery was evaluated using Google Earth Engine (GEE) platform classification a burnt area PNSE. This aimed to detect change closely observe vegetation recovery after 2022 wildfire. combination OBIA achieved highest all metrics. At same time, comparison Normalized Difference Vegetation Index (NDVI) Conjunctural Land Occupation Map (COSc) 2023 year indicated PBIA resembled maps better.

Language: Английский

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The Effects of Fire Severity on Vegetation Structural Complexity Assessed Using SAR Data Are Modulated by Plant Community Types in Mediterranean Fire-Prone Ecosystems

Fire, Journal Year: 2023, Volume and Issue: 6(12), P. 450 - 450

Published: Nov. 24, 2023

Vegetation structural complexity (VSC) plays an essential role in the functioning and stability of fire-prone Mediterranean ecosystems. However, we currently lack knowledge about effects increasing fire severity on VSC spatial variability, as modulated by plant community type complex post-fire landscapes. Accordingly, this study explored, for first time, effect different communities one year after leveraging field inventory Sentinel-1 C-band synthetic aperture radar (SAR) data. The field-evaluated retrieved scenarios from γ0 VV VH backscatter data featured high fit (R2 = 0.878) low predictive error (RMSE 0.112). Wall-to-wall estimates showed that types strongly response to severity, with linked regenerative strategies dominant species community. Moderate severities had a strong impact, fire, broom shrublands Scots pine forests, dominated facultative obligate seeder species, respectively. In contrast, fire-induced impacts were not significantly between moderate fire-severity resprouter i.e., heathlands Pyrenean oak forests.

Language: Английский

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A fast spectral recovery does not necessarily indicate post-fire forest recovery

Published: Feb. 19, 2024

Background Climate change has increased wildfire activity in the western USA and limited capacity for forests to recover post-fire, especially areas burned at high severity. Land managers urgently need a better understanding of spatiotemporal variability natural postfire forest recovery plan implement active projects. In areas, post-fire ‘spectral recovery’, determined by examining trajectory multispectral indices (e.g., normalized burn ratio) over time, generally corresponds with multiple vegetation types, including trees shrubs. Field data are essential deciphering types reflected spectral recovery, yet few studies validate metrics field or incorporate into spatial models recovery. We investigated relationships between measurements (16 27 years post-fire) from 99 plots mixed-conifer Blue Mountains, USA. Additionally, using generalized linear mixed effects models, we assessed relative capacities multispectral, climatic, topographic predict Results We found that did not necessarily coincide density regenerating seedlings, saplings, young % juvenile conifer cover). Instead, rapid often coincided increases shrub cover. primarily attributed this relationship response snowbrush ceanothus, an evergreen vigorously resprouts post-fire. However, non-trailing edge – where it was cooler wetter fast-growing conifers were more common both cover Otherwise, showed potential identify transitions grasslands, as grass-dominated sites showcased distinctly slow trajectories. Lastly, best predicted when climate predictive models. Conclusions Despite disconnect faster our results suggest improved predicting likelihood Improving would aid land identifying reforestation

Language: Английский

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Integrating Physical-Based Models and Structure-from-Motion Photogrammetry to Retrieve Fire Severity by Ecosystem Strata from Very High Resolution UAV Imagery

Fire, Journal Year: 2024, Volume and Issue: 7(9), P. 304 - 304

Published: Aug. 27, 2024

We propose a novel mono-temporal framework with physical basis and ecological consistency to retrieve fire severity at very high spatial resolution. First, we sampled the Composite Burn Index (CBI) in 108 field plots that were subsequently surveyed through unmanned aerial vehicle (UAV) flights. Then, mimicked methodology for CBI assessment remote sensing framework. strata identified individual tree segmentation geographic object-based image analysis (GEOBIA). In each stratum, wildfire effects estimated following methods: (i) vertical structural complexity of vegetation legacies was computed from 3D-point clouds, as proxy biomass consumption; (ii) biophysical variables retrieved multispectral data by inversion PROSAIL radiative transfer model, direct link remaining after canopy scorch torch. The scores predicted UAV ecologically related metrics level featured fit respect field-measured (R2 > 0.81 RMSE < 0.26). Conversely, conventional retrieval using battery spectral predictors (point height distribution indices) plot provided much worse performance = 0.677 0.349).

Language: Английский

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