Assessment of factors driving high fire severity potential and classification in a Mediterranean pine ecosystem

Mitsopoulos, Ioannis; Chrysafi, Irene; Bountis, Diamantis; Mallinis, Giorgos

doi:10.1016/j.jenvman.2019.01.056

Cited by 25 publications

(18 citation statements)

References 59 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Fire severity is a latent variable that can be quantified by the changes in manifest variables of ecosystems caused by fire. It can be investigated after the fire, or it can be predicted before the fire (Cram et al 2003;Fang et al 2018;Keyser and Westerling 2019;Mitsopoulos et al 2019;Sikkink and Keane 2012). Severity results from a combination of pre-fire ecosystems, fire intensity, and duration, whereas the current state of the ecosystem is the product of the pre-fire ecosystem, fire severity, temporal recovery, and ecosystem response.…”

Section: Challenges Common To All Assessments Of Fire Severity and Recommendations For Improvementmentioning

confidence: 99%

Quantifying fire severity: a brief review and recommendations for improvement

Han

Yang

et al. 2021

Ecosyst Health Sustain

View full text Add to dashboard Cite

Wildfire is an important factor in the evolution of terrestrial organisms, and fires assist in maintaining the structure and function of the global ecosystem. Knowledge of fire severity is indispensable for understanding the ecological significance of wildfire. Therefore, quantifying fire severity is an important aspect of studying the response mechanism of terrestrial ecosystems to wildfire, and it is of great significance to fire ecology. In this paper we comprehensively introduce and compare the classification and quantification methods for fire severity; we discuss the development and application status of various methods, and we elucidate their existing problems. We analyze each method in terms of the relevant ecosystem, the fire behavior, time, and the relationship between fire severity and ecosystem response in order to provide a reference basis for proposing and developing new severity measurement methods. The current methods for describing fire severity include four main types: 1) According to features of the burned area, fire severity can be classified as light, moderate, and heavy. 2) Using composite burn index (CBI) to quantify and record the fire severity. 3) In quantifying fire severity with vegetation change, there are certain limitations and theoretical problems to be solved. 4) Remote sensing could very well be an important means of measuring fire severity in the future, but there are still many problems that need to be solved before the remote sensing index can become a global fire severity indicator. Fire severity is a latent variable in ecosystem. Only by clarifying the relationship between fire behavior, fire severity, time related variables and the pre-and post-fire ecosystem can the existing models be perfected or new, better fire severity measurement models be proposed for broad applications.

show abstract

Section: Challenges Common To All Assessments Of Fire Severity and Recommendations For Improvementmentioning

confidence: 99%

Quantifying fire severity: a brief review and recommendations for improvement

Han

Yang

et al. 2021

Ecosyst Health Sustain

View full text Add to dashboard Cite

show abstract

“…The Random Forests (RF) algorithm is an ensemble learning method that builds multiple decision trees using random selections of the training data. It has shown excellent results in classification problems related to burn scar detection and severity mapping with Landsat images (Collins et al 2018;Long et al 2019;Mitsopoulos et al 2019;Vetrita and Cochrane 2019). The role of RF in our routine is to improve the classification accuracy that would result from a simple threshold cut from Phase 1.…”

Section: Phase 3 -Random Forests Classification (Rf)mentioning

confidence: 99%

Small fires, frequent clouds, rugged terrain and no training data: a methodology to reconstruct fire history in complex landscapes

Fornacca

Ren

Xiao

2021

Int. J. Wildland Fire

View full text Add to dashboard Cite

An automated burned area extraction routine that attempts to overcome the particular difficulties of remote sensing applications in complex landscapes is presented and tested in the mountainous region of northwest Yunnan, China. In particular, the lack of burned samples to use for training and testing, the rugged relief, the small size of fires and the constant presence of clouds during the rainy season heavily affecting the number of usable scenes within a year are addressed. The algorithm flows through five phases: creation of standardised difference vegetation indices time series; automatic extraction of multiclass training areas using adaptive z-score thresholds; Random Forests classification; Seeded Region Growing; and spatiotemporal clustering to form polygons representing fire events. A final database spanning the period 1987–2018 was created. Accuracy assessment of location and number of fire polygons using a stratified random sampling design showed satisfactory results with reduced omission and commission errors compared with global fire products in the same region (20 and 22% respectively). Mapping accuracy of single burned areas showed higher omission (27%) but reduced commission (13%) errors. This methodology takes a step forward towards the inclusion of regions characterised by small fires that are often poorly represented in impact assessments at the global scale.

show abstract

“…These regions have been exposed for millennia to the effects of fire, which has modified the landscape and endowed many species with adaptive mechanisms that allow them to persist and regenerate after recurrent fires (Pausas 2004; Alcañiz et al 2020). Despite the adaptation of Mediterranean forests to fire, excessive fire frequency, and severe events may overcome the resistance and resilience of the region's plants and soils, resulting in ecosystem degradation (Mitsopoulos et al 2019; Moreira et al 2020). Fire alters vegetation cover and its biodiversity (Pausas & Keeley 2014; Heydari et al 2016; Moya et al 2019; Moradizadeh et al 2020), and can affect the physico‐chemical and biological properties of soils, depending on severity, intensity, or recurrence (DeBano 2000; Certini 2005; Ginzburg & Steinberger 2012; Heydari et al 2012).…”

Section: Introductionmentioning

confidence: 99%

“…Fire alters vegetation cover and its biodiversity (Pausas & Keeley 2014; Heydari et al 2016; Moya et al 2019; Moradizadeh et al 2020), and can affect the physico‐chemical and biological properties of soils, depending on severity, intensity, or recurrence (DeBano 2000; Certini 2005; Ginzburg & Steinberger 2012; Heydari et al 2012). Direct and indirect effects of fire on soils and plants can be critical for the functioning of forest ecosystems (Mitsopoulos et al 2019). Thus, promoting post‐fire recovery of forests is fundamental for adequate management and planning of these ecosystems (Grau‐Andrés et al 2019; Muñoz‐Rojas & Pereira 2019).…”

Section: Introductionmentioning

confidence: 99%

Post‐fire restoration with contour‐felled log debris increases early recruitment of Spanish black pine (Pinus nigra Arn. ssp. salzmannii) in Mediterranean forests

Lucas‐Borja

Stan

Heydari

et al. 2021

Restoration Ecology

View full text Add to dashboard Cite

Post-fire environmental conditions can heavily influence the natural regeneration of pine species in Mediterranean forests. Therefore, enhancing post-fire recovery of pine species is fundamental for effective ecological restoration of Mediterranean forests. In this study, the effects of a post-fire restoration treatment on the seedling emergence and survival of Spanish black pine (Pinus nigra Arn. ssp. salzmannii) were investigated under a treatment consisting of manual cut of burnt tree canopies placed on the soil surface with their tree branches, following contour lines (contour-felled log debris, CFD) in comparison with a control site at plot scale. Both CFD and control plots were tested on three slope gradients and two experimental conditions, that is, protected vs. non-protected seeds. The initial seedling recruitment of Spanish black pine was improved by CFD treatment and seed protection, specifically through increased survival of emergent seedlings by about 10 and 15 times, respectively, compared to control. Seedling emergence was not significantly different between the treatments or controls; however, the highest seedling emergence in the study (18.9 ± 14.9%) was recorded under the least severe drought conditions. The study demonstrates that post-fire CFD and seed protection treatments can be favorable for supporting ecological restoration of these pine forests. However, the environmental conditions are important drivers for the success of these strategies. Since droughts are expected to be more frequent in the upcoming years, post-fire management strategies that include treatments like CFD and seed protection can be useful in the ecological restoration of Mediterranean pine forests.

show abstract

Assessment of factors driving high fire severity potential and classification in a Mediterranean pine ecosystem

Cited by 25 publications

References 59 publications

Quantifying fire severity: a brief review and recommendations for improvement

Quantifying fire severity: a brief review and recommendations for improvement

Small fires, frequent clouds, rugged terrain and no training data: a methodology to reconstruct fire history in complex landscapes

Post‐fire restoration with contour‐felled log debris increases early recruitment of Spanish black pine (Pinus nigra Arn. ssp. salzmannii) in Mediterranean forests

Contact Info

Product

Resources

About