scholarly journals Investigation of Forest Fire Activity Changes over the Central India Domain using Satellite Observations during 2001‐2020

GeoHealth ◽  
2021 ◽  
Author(s):  
Madhavi Jain ◽  
Pallavi Saxena ◽  
Som Sharma ◽  
Saurabh Sonwani
Keyword(s):  
Forest Fire ◽  
Central India ◽  
Satellite Observations ◽  
Fire Activity

scholarly journals Identifying required model structures to predict global fire activity from satellite and climate data

2016 ◽  
Author(s):  
Matthias Forkel ◽  
Wouter Dorigo ◽  
Gitta Lasslop ◽  
Irene Teubner ◽  
Emilio Chuvieco ◽  
...  
Keyword(s):  
Satellite Observations ◽  
Burned Area ◽  
Human Society ◽  
Fire Emissions ◽  
Fire Models ◽  
Fire Activity ◽  
Process Oriented ◽  
Density And Biomass ◽  
Global Vegetation ◽  
Model Structures

Abstract. Vegetation fires affect human infrastructures, ecosystems, global vegetation distribution, and atmospheric composition. In particular, extreme fire conditions can cause devastating impacts on ecosystems and human society and dominate the year-to-year variability in global fire emissions. However, the climatic, environmental and socioeconomic factors that control fire activity in vegetation are only poorly understood and consequently it is unclear which components, structures, and complexities are required in global vegetation/fire models to accurately predict fire activity at a global scale. Here we introduce the SOFIA (Satellite Observations for FIre Activity) modelling approach, which integrates several satellite and climate datasets and different empirical model structures to systematically identify required structural components in global vegetation/fire models to predict burned area. Models result in the highest performance in predicting the spatial patterns and temporal variability of burned area if they account for a direct suppression of fire activity at wet conditions and if they include a land cover-dependent suppression or allowance of fire activity by vegetation density and biomass. The use of new vegetation optical depth data from microwave satellite observations, a proxy for vegetation biomass and water content, reaches higher model performance than commonly used vegetation variables from optical sensors. The SOFIA approach implements and confirms conceptual models where fire activity follows a biomass gradient and is modulated by moisture conditions. The use of datasets on population density or socioeconomic development do not improve model performances, which indicates that the complex interactions of human fire usage and management cannot be realistically represented by such datasets. However, the best SOFIA models outperform a highly flexible machine learning approach and the state-of-the art global process-oriented vegetation/fire model JSBACH-SPITFIRE. Our results suggest using multiple observational datasets on climate, hydrological, vegetation, and socioeconomic variables together with model-data integration approaches to guide the future development of global process-oriented vegetation/fire models and to better understand the interactions between fire and hydrological, ecological, and atmospheric Earth system components.

scholarly journals Risk of large-scale fires in boreal forests of Finland under changing climate

2016 ◽  
Vol 16 (1) ◽  
pp. 239-253 ◽  
Author(s):  
I. Lehtonen ◽  
A. Venäläinen ◽  
M. Kämäräinen ◽  
H. Peltola ◽  
H. Gregow
Keyword(s):  
Climate Change ◽  
Forest Fire ◽  
Forest Fires ◽  
Large Scale ◽  
Fire Danger ◽  
Short Period ◽  
Projected Change ◽  
Fire Activity ◽  
Model Variability ◽  
The Impact

Abstract. The target of this work was to assess the impact of projected climate change on forest-fire activity in Finland with special emphasis on large-scale fires. In addition, we were particularly interested to examine the inter-model variability of the projected change of fire danger. For this purpose, we utilized fire statistics covering the period 1996–2014 and consisting of almost 20 000 forest fires, as well as daily meteorological data from five global climate models under representative concentration pathway RCP4.5 and RCP8.5 scenarios. The model data were statistically downscaled onto a high-resolution grid using the quantile-mapping method before performing the analysis. In examining the relationship between weather and fire danger, we applied the Canadian fire weather index (FWI) system. Our results suggest that the number of large forest fires may double or even triple during the present century. This would increase the risk that some of the fires could develop into real conflagrations which have become almost extinct in Finland due to active and efficient fire suppression. However, the results reveal substantial inter-model variability in the rate of the projected increase of forest-fire danger, emphasizing the large uncertainty related to the climate change signal in fire activity. We moreover showed that the majority of large fires in Finland occur within a relatively short period in May and June due to human activities and that FWI correlates poorer with the fire activity during this time of year than later in summer when lightning is a more important cause of fires.

Climate Change and Forest Fire Activity in North American Boreal Forests

2000 ◽  
pp. 368-376 ◽  
Author(s):  
Brian J. Stocks ◽  
Michael A. Fosberg ◽  
Michael B. Wotton ◽  
Timothy J. Lynham ◽  
Kevin C. Ryan
Keyword(s):  
Climate Change ◽  
Forest Fire ◽  
North American ◽  
Boreal Forests ◽  
Fire Activity

scholarly journals A data-driven approach to identify controls on global fire activity from satellite and climate observations (SOFIA V1)

2017 ◽  
Vol 10 (12) ◽  
pp. 4443-4476 ◽  
Author(s):  
Matthias Forkel ◽  
Wouter Dorigo ◽  
Gitta Lasslop ◽  
Irene Teubner ◽  
Emilio Chuvieco ◽  
...  
Keyword(s):  
Machine Learning ◽  
Satellite Observations ◽  
Data Driven ◽  
Predictor Variables ◽  
Burned Area ◽  
Fire Models ◽  
Fire Activity ◽  
Process Oriented ◽  
Density And Biomass ◽  
Global Vegetation

Abstract. Vegetation fires affect human infrastructures, ecosystems, global vegetation distribution, and atmospheric composition. However, the climatic, environmental, and socioeconomic factors that control global fire activity in vegetation are only poorly understood, and in various complexities and formulations are represented in global process-oriented vegetation-fire models. Data-driven model approaches such as machine learning algorithms have successfully been used to identify and better understand controlling factors for fire activity. However, such machine learning models cannot be easily adapted or even implemented within process-oriented global vegetation-fire models. To overcome this gap between machine learning-based approaches and process-oriented global fire models, we introduce a new flexible data-driven fire modelling approach here (Satellite Observations to predict FIre Activity, SOFIA approach version 1). SOFIA models can use several predictor variables and functional relationships to estimate burned area that can be easily adapted with more complex process-oriented vegetation-fire models. We created an ensemble of SOFIA models to test the importance of several predictor variables. SOFIA models result in the highest performance in predicting burned area if they account for a direct restriction of fire activity under wet conditions and if they include a land cover-dependent restriction or allowance of fire activity by vegetation density and biomass. The use of vegetation optical depth data from microwave satellite observations, a proxy for vegetation biomass and water content, reaches higher model performance than commonly used vegetation variables from optical sensors. We further analyse spatial patterns of the sensitivity between anthropogenic, climate, and vegetation predictor variables and burned area. We finally discuss how multiple observational datasets on climate, hydrological, vegetation, and socioeconomic variables together with data-driven modelling and model–data integration approaches can guide the future development of global process-oriented vegetation-fire models.

Satellite-Based Analysis of Fire Events and Transport of Emissions in the Arctic

2020 ◽  
Author(s):  
Anu-Maija Sundström ◽  
Tomi Karppinen ◽  
Antti Arola ◽  
Larisa Sogacheva ◽  
Hannakaisa Lindqvist ◽  
...  
Keyword(s):  
Greenhouse Gases ◽  
Forest Fire ◽  
Forest Fires ◽  
Arctic Region ◽  
Satellite Observations ◽  
The Arctic ◽  
Radiative Balance ◽  
Fire Emissions ◽  
Smoke Plumes ◽  
Absorbing Aerosols

<p>Climate change is proceeding fastest in the Arctic region. During past years Arctic summers have been warmer and drier elevating the risk for extensive forest fire episodes. In fact, satellite observations show, that during past two summers (2018, 2019) an increase is seen in the number of fires occurring above the Arctic Circle, especially in Siberia. While human-induced emissions of long-lived greenhouse gases are the main driving factor of global warming, short-lived climate forcers or pollutants emitted from the forest fires are also playing an important role especially in the Arctic. Absorbing aerosols can cause direct arctic warming locally. They can also alter radiative balance when depositing onto snow/ice and decreasing the surface albedo, resulting in subsequent warming. Aerosol-cloud interaction feedbacks can also enhance warming. Forest fire emissions also affect local air quality and photochemical processes in the atmosphere. For example, CO contributes to the formation of tropospheric ozone and affects the abundance of greenhouse gases such as methane and CO<sub>2</sub>.</p><p>This study focuses on analyzing fire episodes in the Arctic for the past 10 years, as well as investigating the transport of forest fire CO and smoke aerosols to the Arctic. Smoke plumes and their transport are analyzed using Absorbing Aerosol Index (AAI) from several satellite instruments: GOME-2 onboard Metop A and B, OMI onboard Aura, and TROPOMI onboard Copernicus Sentinel-5P satellite. Observations of CO are obtained from IASI (Metop A and B) as well as from TROPOMI, while the fire observations are obtained from MODIS instruments onboard Aqua and Terra, as well as from VIIRS onboard Suomi NPP.  In addition, observations e.g. from a space-borne lidar, CALIPSO, is used to obtain vertical distribution of smoke and to estimate plume heights.</p>

A 1000-yr record of forest fire activity from Eclipse Icefield, Yukon, Canada

The Holocene ◽  
2006 ◽  
Vol 16 (2) ◽  
pp. 200-209 ◽  
Author(s):  
Kaplan Yalcin ◽  
Cameron P. Wake ◽  
Karl J. Kreutz ◽  
Sallie I. Whitlow
Keyword(s):  
Forest Fire ◽  
Fire Activity

Forest fire activity in Sweden: Climatic controls and geographical patterns in 20th century

2012 ◽  
Vol 154-155 ◽  
pp. 174-186 ◽  
Author(s):  
Igor Drobyshev ◽  
Mats Niklasson ◽  
Hans W. Linderholm
Keyword(s):  
20Th Century ◽  
Forest Fire ◽  
Geographical Patterns ◽  
Fire Activity

Forest Fires Mapping and Monitoring of current and past forest fire activity from Meteosat Second Generation Data

2010 ◽  
Vol 25 (12) ◽  
pp. 1909-1914 ◽  
Author(s):  
L.C. Carvalheiro ◽  
S.O. Bernardo ◽  
M.D.M. Orgaz ◽  
Y. Yamazaki
Keyword(s):  
Forest Fire ◽  
Forest Fires ◽  
Second Generation ◽  
Fire Activity
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