Retrieval of vegetation moisture indicators for dynamic fire risk assessment with simulated MODIS radiance

Maffei, Carmine; Leone, Alessandro; Vella, Mauro; Meoli, Giuseppe; Tosca, M. G.; Menenti, Massimo

doi:10.1109/igarss.2007.4423894

Cited by 3 publications

(5 citation statements)

References 15 publications

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“…Additionally, there are some studies that focused on the both vegetation parameters derived from remotely sensed data and climate data or only one of them for detecting the forest fire probability (Maffei et al 2007;Gabban et al 2008;Sharples et al 2009). Although the results were acceptable, human effects were ignored due to study areas were located in rural places mainly.…”

Section: Discussionmentioning

confidence: 99%

“…Soil erosion increases, due to both loss of vegetation cover, which attenuates rainfalls and facilitates water percolation in the soil, and to the physicochemical alteration of the soil surface. For these reasons, prevention activities have become a major concern for policy makers, and in this context fire risk mapping is considered to be an important tool (Maffei et al 2007).…”

Section: Introductionmentioning

confidence: 99%

“…to calculate fire danger rating and fuel moisture content. Additionally, remotely sensed data such as normalized difference vegetation index (NDVI) time series and normalized difference water index (NDWI) were used to retrieve vegetation variables to evaluate fire hazard (Gabban et al 2008;Maffei et al 2007). Even if such techniques have good results, only climate or vegetation variables are not enough for forest fire hazard description alone in the regional scale.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Mapping regional forest fire probability using artificial neural network model in a Mediterranean forest ecosystem

Şatır

Berberoğlu

Dönmez

2015

Geomatics, Natural Hazards and Risk

183

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Forest fires are one of the most important factors in environmental risk assessment and it is the main cause of forest destruction in the Mediterranean region. Forestlands have a number of known benefits such as decreasing soil erosion, containing wild life habitats, etc. Additionally, forests are also important player in carbon cycle and decreasing the climate change impacts. This paper discusses forest fire probability mapping of a Mediterranean forestland using a multiple data assessment technique. An artificial neural network (ANN) method was used to map forest fire probability in Upper Seyhan Basin (USB) in Turkey. Multi-layer perceptron (MLP) approach based on back propagation algorithm was applied in respect to physical, anthropogenic, climate and fire occurrence datasets. Result was validated using relative operating characteristic (ROC) analysis. Coefficient of accuracy of the MLP was 0.83. Landscape features input to the model were assessed statistically to identify the most descriptive factors on forest fire probability mapping using the Pearson correlation coefficient. Landscape features like elevation (R D ¡0.43), tree cover (R D 0.93) and temperature (R D 0.42) were strongly correlated with forest fire probability in the USB region. KEYWORDSForest fire probability and hazard; landscape feature; weighting; artificial neural network; Mediterranean region; fire weather index

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Mapping regional forest fire probability using artificial neural network model in a Mediterranean forest ecosystem

Şatır

Berberoğlu

Dönmez

2015

Geomatics, Natural Hazards and Risk

183

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“…Additionally, wind speed has strong influence on fire probability and spread as a result of drying and directing effects. There are many forest fire risk evaluation methods including, remote sensing based methods, FFWIs (Gabban et al, 2008;Maffei et al, 2007) and integrated data methods in geographical information system (GIS) interface such as multi criteria evolution or artificial neural network (Dickson et al, 2006;Satir et al, 2016). Even if integrated methods are more accurate, FFWIs have two significant advantages over other approaches; i) Applicants are not required any extra computing knowledge and additional dataset so FFWIs application is more user friendly than other approaches.…”

Section: Introductionmentioning

confidence: 99%

Modelling Long Term Forest Fire Risk Using Fire Weather Index Under Climate Change in Turkey

Şatır¹

2016

Appl Ecol Env Res

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Abstract. Fire weather indices (FWIs) are among the most effective techniques to define real time or long term forest fire risk using meteorological data. In this research, long term forest fire risk of Turkey was modelled using a fire weather index called F index for present (1990 -2010) and future (2061 -2080) periods. Dry bulb temperature, relative humidity and maximum wind speed were mapped using 945 meteorological stations in Turkey, with a spatial resolution of 250 m. Long term mean F index values (from 1990 to 2010 and from 2061 to 2080) were calculated for 7 months representing fire seasons from April to October. Average fire occurrence of each month and monthly mean F index values of the forestlands were correlated using Pearson correlation statistic and determination coefficiency (R 2 ) was 0.82. Additionally, projected annual mean temperature and humidity based on HadGEM2-ES model RCP 4.5 scenario were used to derive future F index. Mean F index values of the forestlands were shown that forest fire risk of Turkey will have an increase of 21.1% in 2070s.

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“…They specifically formulated DC using GWL and concluded that it could be used to predict forest fires. In an earlier study, Maffei et al [95] wrote that forest fire danger models rely on FMC as a measure of vegetation moisture. They used MODIS in their research.…”

Section: Fuel Moisture Codesmentioning

confidence: 99%

Mutli-Modality Federate Learning with Multi-Source Data for Forest Fire Prediction

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Forest fires result in significant destruction of natural resources and human lives.They are commonly caused by humans or naturally occurring phenomena like lightning strikes. They may start due to lightning when necessary climatic conditions prevail for a the fire to ignite. The available forest fire data contains both fire and non-fire data and is highly imbalanced. To overcome this, the thesis provides a spatio-temporal agnostic subsampling (STAS) framework to subsample the highly imbalanced forest fire data. The proposed framework also works with limited variations between fire and non-fire data. Apart from lightning, weather and drought conditions are also significant factors in forest fires. Forest fire prediction which does not take these into consideration, may not be accurate. Therefore, the thesis is also focused on building and applying a multi-modality modeling approach for predicting forest fires. The multi-modality model is built using available datasets on forest fires, lightning, weather, and hydrometrics to predict the probability of forest fires. Using the proposed STAS framework and multi-modality model, the research aimed to generate a generalized model by applying multi-modality federated learning to predict forest fires independent of geographic location without the need for calibration.This research was able to successfully work with federated learning framework and multi-modality model to obtain high F 1Score (> 0.9) and R 2 Score (> 0.8).

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Retrieval of vegetation moisture indicators for dynamic fire risk assessment with simulated MODIS radiance

Cited by 3 publications

References 15 publications

Mapping regional forest fire probability using artificial neural network model in a Mediterranean forest ecosystem

Mapping regional forest fire probability using artificial neural network model in a Mediterranean forest ecosystem

Modelling Long Term Forest Fire Risk Using Fire Weather Index Under Climate Change in Turkey

Mutli-Modality Federate Learning with Multi-Source Data for Forest Fire Prediction

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