Mapping of vegetation cover using Sentinel-2 to estimate forest fire danger

Yankovich, Elena P.; Yankovich, Ksenia S.; Baranovskiy, Nikolay V.; Базаров, А. В.; Sychev, Roman Sergeevich; Badmaev, N. B.

doi:10.1117/12.2532509

Cited by 4 publications

(3 citation statements)

References 7 publications

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“…In recent years, significant efforts have been made to develop methods for forest fire danger monitoring, prediction and assessment, for example, in the Baikal basin [66][67][68]. Currently, a project is being implemented to develop methods for monitoring forest fire dangers caused by transport infrastructure, namely, railways.…”

Section: Figurementioning

confidence: 99%

Mathematical Simulation of Forest Fuel Pyrolysis in One-Dimensional Statement Taking into Account Soot Formation

Baranovskiy

Kirienko

2021

Processes

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Pyrolysis (thermal decomposition) is considered as the most important stage of a forest fire before direct forest fuel ignition. This process is accompanied by soot particle formation. Such particles have a negative impact on public health in the vicinity of forest fires. The purpose of this article was to investigate the heat and mass transfer process occurring in a typical forest fuel element (birch leaf). The pyrolysis and soot formation processes were taken into account, and various forest fire scenarios were considered. Computational experiments were carried out using the high-level programming language Delphi. Heat and mass transfer processes were described by nonlinear non-stationary differential heat conduction equations with corresponding initial and boundary conditions. The differential equations were solved by the finite difference method. Nonlinearity was resolved using a simple iteration. The main results of the research were (1) physical and mathematical models proposed for modeling forest fuel pyrolysis, taking into account soot formation and conditions corresponding to various forest fires; (2) a computer program coded in the high-level programming language Delphi; (3) the obtained temperature distributions over leaf thickness; (4) volume fractions obtained for various components dependent on time and space coordinates. The qualitative analysis of the dependencies showed that the temperature distributions in the birch leaf structure are similar for all forest fire types and differ only in absolute value. The intensity of the soot formation process directly depends on the forest fire type. The presented results should be useful in predicting and assessing forest fire danger, including near the facilities of the Russian Railways.

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Section: Figurementioning

confidence: 99%

Mathematical Simulation of Forest Fuel Pyrolysis in One-Dimensional Statement Taking into Account Soot Formation

Baranovskiy

Kirienko

2021

Processes

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“…Some sensors widely used for this purpose include the Landsat TM, ETM and OLI (He et al 2019;Stefanidou et al 2018;Van Wagtendonk and Root 2003), the Terra and Aqua MODIS (Bajocco et al 2015;Lanorte and Lasaponara 2008), the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER, Yamaguchi et al 1998) on board Terra (Lasaponara and Lanorte 2007), and even AVHRR (Nadeau et al 2005). More recently, Sentinel-2 data have also been used for forest characterization and fuel mapping (Franke et al 2018;Hościło and Lewandowska 2019;Yankovich et al 2019). High-resolution sensors, such as QuickBird, have also been used for fuel type classification in small areas (Arroyo et al 2006).…”

Section: Fuel Types Classificationmentioning

confidence: 99%

Fire Danger Observed from Space

Pettinari

Chuvieco

2020

Surv Geophys

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Biomass burning is one of the critical components of the Earth system, significantly affecting atmospheric emissions and carbon budgets. Fires occurring in the interface between wildland and urban areas also have important socioeconomic effects, affecting people's lives and resources. Even though fires are natural in many ecosystems, climate and societal changes have recently caused particularly severe fire seasons (Australia, California, Amazonia, Portugal…). Mitigating the negative impacts of fire requires further efforts to assess fire danger conditions. Satellite Earth observation provides considerable capabilities to evaluate the different variables involved in fire danger. Data obtained from remote sensors offer information on possible sources of fire ignition, on fuel status and abundance, and on the topography and the meteorological conditions that will affect fire spread. Satellite observations also provide near-real-time information on fire occurrence for early response teams. This article describes the different variables affecting fire danger and illustrates how satellite data can offer useful information to estimate these variables, focusing on global and continental fire danger systems.

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“…Currently, the majority of existing studies consider texture features as implicit features for algorithm learning and training, with only a minority of studies incorporating texture features in the explicit construction of detection indices [30,31]. To differentiate between burned and unburned areas, local adaptive algorithms such as the Support Vector Machines (SVM) [32], Maximum Likelihood (ML) [33], and Random Forest (RF) [34] algorithms have been widely employed in mapping burnt areas. These algorithms operate by maximizing interclass variation and minimizing intraclass variation.…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Index for Monitoring Burned Vegetation by Combining Image Texture Features with Vegetation Indices

Fan,

Yao,

Tang

et al. 2024

Remote Sensing

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The detection and monitoring of burned areas is crucial for vegetation recovery, loss assessment, and anomaly analysis. Although vegetation indices (VIs) have been widely used, accurate vegetation detection is challenging due to potential confusion in the spectra of different types of land cover and the interference of shadow effects caused by terrain. In this work, a novel Vegetation Anomaly Spectral Texture Index (VASTI) is proposed, which leverages the merits of both spectral and spatial texture features to identify abnormal pixels for extracting burned vegetation areas. The performance of the VASTI and its components, the Global Environmental Monitoring Index (GEMI), the Enhanced Vegetation Index (EVI), and the texture feature Autocorrelation (AC) were assessed based on a global dataset previously established, which contains 1774 pairs of samples from 10 different sites. The results illustrated that, compared with the GEMI and EVI, the VASTI improved the user’s accuracy (UA), producer’s accuracy (PA), and kappa coefficient across the ten study areas by approximately 5% to 10%. Compared to AC, the VASTI improved the accuracy of abnormal vegetation detection by 13% to 25%. The improvements were mainly caused by the fact that the incorporation of texture features can reduce spectral confusion between pixels. The innovation of the VASTI is that it considers the relationship between anomalous pixels and surrounding pixels by explicitly integrating spatial texture features with traditional spectral features.

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Mapping of vegetation cover using Sentinel-2 to estimate forest fire danger

Cited by 4 publications

References 7 publications

Mathematical Simulation of Forest Fuel Pyrolysis in One-Dimensional Statement Taking into Account Soot Formation

Mathematical Simulation of Forest Fuel Pyrolysis in One-Dimensional Statement Taking into Account Soot Formation

Fire Danger Observed from Space

A Hybrid Index for Monitoring Burned Vegetation by Combining Image Texture Features with Vegetation Indices

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