Methodology of post-fire tree mortality monitoring  and prediction using remote sensing data

Барталев, С.А.; Stytsenko, F.V.; Khvostikov, S.A.; Loupian, Е.А.

doi:10.21046/2070-7401-2017-14-6-176-193

Cited by 13 publications

(5 citation statements)

References 5 publications

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“…Satellite images do not provide an opportunity to characterize the type of fire and fully determine the damage caused to the forest fund. They can also overestimate the area of fires as they capture the heat flux that is reflected in heated soil and smoke (Bartalev et al 2017).…”

Section: Pyrological Characteristics Of the Plant Formations And Their Combustion Indexmentioning

confidence: 99%

Characteristics of the spatial and temporal distribution of fire regime in ONE OF the most fire prone Region Of The Russian Far East

Zubareva

Глаголев

Grigorieva

2021

GES

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Wildfires affect the structure and distribution of vegetation all over the globe and have their own specifics in different regions. In this study, we considered the spatial and temporal distribution of fires in the Jewish Autonomous Region (JAR), which is the most fire-prone area of the Russian Far East. Using data from the Department of Natural Resources of the Jewish Autonomous Region, fires and burned areas for more than 40 years were analyzed. The average annual number of fires is near 100, and the average area of one fire is 134 hectares, which is significantly higher compared to other regions of Russia. The largest number of fires and fires with the greatest extent took place in 1975. The intra-annual distribution of fires is bimodal and depends on the climate characteristics of the region. Mapping of burning areas showed that most of the fires occurred near settlements and along roads. The main centers of fire ignition were areas with a large number of small fires (no more than 5 hectares), located within several types of locations: (1) asphalt and dirt roads, railroads and river valleys near settlements; (2) areas of former logging that have several large burned spots of more than 300 hectares; (3) plains with a high concentration of fires over a large region; and (4) small burned spots on the mountain slopes, along the field roads and small rivers. Regions with different degree of fire exposure were identified. Sedge-reed mixed grassy meadows and Agricultural land with shaded meadows are the plant formations most prone to wildfires. At the same time, more fires were detected in Cedar-deciduous forests as well as Oak and black birch forests. The findings are useful for environmental protection agencies in planning fire management strategies, optimizing the fire services and firefighting actions.

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Section: Pyrological Characteristics Of the Plant Formations And Their Combustion Indexmentioning

confidence: 99%

Characteristics of the spatial and temporal distribution of fire regime in ONE OF the most fire prone Region Of The Russian Far East

Zubareva

Глаголев

Grigorieva

2021

GES

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“…But due to problems with inventory actualization very often it is necessary to use others sources of information on wood stock values. VEGA services implement its own methodology on wood stock retrieval by using multiyear EO (Bartalev et al, 2017). Figure 5 reveals an example of VEGA application for wood stock estimations in Primorskiy Kray (Far-East Russia).…”

Section: Summer Imagementioning

confidence: 99%

“…The fire development model is implemented in the Rosleskhoz information system , which exemplifies the possibility of predicting pyrogenic forest deaths using VEGA services. The model is described in detail in previously published papers (see (Khvostikov et al, 2012;Khvostikov et al, 2016;Bartalev et al, 2017)). Figure 8 presents the principal modeling scheme that describes spreading of forest fire.…”

Section: Summer Imagementioning

confidence: 99%

“…The main content of the work is the upgrade of services and corresponding software (SW) web tools of the VEGA system, which allows to quickly estimate the direct damage of observed fire and the potential damage accounting future (after observation) fire developments calculated by IKI RAS methodology (Bartalev et al, 2017). Both estimations are presumed to be expressed in monetary form.…”

Section: Introductionmentioning

confidence: 99%

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Geoinformation Tools Providing Estimations of Vegetation Areal Damages Caused by Wild Fire Disasters

Savorskiy

Bartalev

Kashnitskiy

et al. 2018

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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<p><strong>Abstract.</strong> The object of this work is to develop web tools, which provide remote services necessary for disaster management purposes in domain of satellite Earth observation (EO) of vegetation areal, primarily forest or agricultural areas damaged by wild fires. The main idea for application of developed web tools is to provide targeted users with estimation instrument that allows not only to predict future or to detect existed perturbations in vegetation canopy but to estimate damage extent caused by disasters as well. To estimate damage extent of future possible disaster the developed services use historical data on vegetation canopy parameter both with hydrometeorology data on current weather conditions. Specialized EO data routines produce a set of products, which are required to make reliable prediction of possible damage extent subject to current weather conditions. In addition, the tools for estimation consequences of actual disaster event in vegetation areal are developed. In this case, special service compares actual state of vegetation canopy, which is retrieved from actual EO information, with historical information, which is extracted from special database that contains information on vegetation canopy status immediately before disaster event. This comparison allows not only to estimate the extent of actual damage but also to forecast disaster effects on future consequences, i.e. to estimate finally expected damage extent. Developed tools incorporate both forecasting and nowcasting possibilities to estimate damage extents. All necessary supporting services are implemented in active satellite data information system (IS) incorporated with long-term EO data archive. This incorporation allows upgrading possibilities of information system in disaster management support, namely in fast and reliable estimation of current and eventual damages.</p>

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“…-Institute of Space Research of the Russian Academy of Sciences (RAS) developed technologies for improving the Remote Monitoring Information System of the Russian Federal Forestry Agency, which is aimed at predicting pyrogenic tree mortality (Bartalev et al 2017), stochastic simulation of fire ignition and propagation (Khvostokov et al 2016) and mapping of fire danger classes based on remote sensing products (Plotnikova and Ershov 2015) for the Central and European parts of Russia as well as its entire territory;…”

Section: Introductionmentioning

confidence: 99%

Prediction of Wildfires Based on the Spatio-Temporal Variability of Fire Danger Factors

Gizatullin

Алексеенко

2022

GES

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Most methods in the field of wildfire prevention are based on expert assessment of fire danger factors. However, their weights are usually assumed constant for the entire application area despite the geographical and seasonal changes of factors. This study aimed to develop a wildfire prevention method based on partial and general fire danger ratings taking into account their spatio-temporal variability. The study was conducted for Krasnoyarsk territory, Orenburg region and the Meschera lowland as the most forest, steppe and peat fire dangerous regions of Russia respectively. Surface temperature, moisture, vegetation structure, anthropogenic load, topography and their variation over subzones and in time were used as fire danger factors. They were evaluated by measuring parameters such as radiobrightness temperature, Normalized Difference Water Index (NDWI), Normalized Difference Vegetation Index (NDVI), Soil-Adjusted Vegetation Index (SAVI), distance to settlements and roads, elevation, slope and aspect. Materials from the Terra/Aqua, Sentinel-3, Landsat-8, Sentinel-2 satellites, ASTER Global Digital Elevation Model and Open Street Maps vector layers were used in the study. Correlation between these parameters and the actual fires in 2016-2018 was analyzed. Linear relationships were established, and correlation coefficients, equations of partial ratings and prevention 90%-threshold values were identified. On their basis, the parameter weights were computed to integrate them into the general fire danger rating. The developed method was validated using data over 2019. The results showed 67% confidence and 61% reliability of fire prevention along with the spatio-temporal patterns of fire danger factors. The method is recommended for preventing wildfires within the study areas and can be extend to similar regions.

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Methodology of post-fire tree mortality monitoring and prediction using remote sensing data

Cited by 13 publications

References 5 publications

Characteristics of the spatial and temporal distribution of fire regime in ONE OF the most fire prone Region Of The Russian Far East

Characteristics of the spatial and temporal distribution of fire regime in ONE OF the most fire prone Region Of The Russian Far East

Geoinformation Tools Providing Estimations of Vegetation Areal Damages Caused by Wild Fire Disasters

Prediction of Wildfires Based on the Spatio-Temporal Variability of Fire Danger Factors

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