scholarly journals Drivers and distribution of forest fires in Sikkim Himalaya: A maximum entropy-based approach to spatial modelling.

2020 ◽  
Author(s):  
Polash Banerjee
Keyword(s):  
Forest Fire ◽  
Forest Fires ◽  
Forest Cover ◽  
Spatial Modelling ◽  
Climatic Conditions ◽  
Mitigation Measures ◽  
Sikkim Himalaya ◽  
Environmental Features ◽  
Validation Criteria ◽  
Forest Fire Model

Abstract The recent episodes of forest fire in Brazil and Australia of 2019 are tragic reminders of the hazards of the forest fire. Globally incidents of forest fire events are in the rise due to human encroachment into wilderness and climate change. Sikkim with a forest cover of more than 47%, suffers seasonal instances of frequent forest fire during the dry winter months. To address this issue, a GIS-aided and MaxEnt machine learning-based forest fire prediction map has been prepared using forest fire inventory database and maps of environmental features. The study indicates that amongst the environmental features, climatic conditions and proximity to roads are the major determinants of the forest fire. Model validation criteria like ROC curve, correlation coefficient and Cohen’s Kappa show a good predictive capability (AUC = 0.95, COR = 0.78, κ = 0.78). The outcomes of this study in the form of a forest fire prediction map can aid the stakeholders of the forest in taking informed mitigation measures.

scholarly journals Maximum entropy-based forest fire likelihood mapping: analysing the trends, distribution, and drivers of forest fires in Sikkim Himalaya.

2021 ◽  
Author(s):  
Polash Banerjee
Keyword(s):  
Forest Fire ◽  
Forest Fires ◽  
Forest Cover ◽  
Predictive Ability ◽  
Climatic Conditions ◽  
Mitigation Measures ◽  
Sikkim Himalaya ◽  
Environmental Features ◽  
Validation Criteria ◽  
Likelihood Mapping

Abstract The recent episodes of forest fires in Brazil and Australia of 2019 are tragic reminders of the hazards of forest fire. Globally incidents of forest fire events are on the rise due to human encroachment into the wilderness and climate change. Sikkim with a forest cover of more than 47%, suffers seasonal instances of frequent forest fire during the dry winter months. To address this issue, a GIS-aided and MaxEnt machine learning-based forest fire prediction map has been prepared using a forest fire inventory database and maps of environmental features. The study indicates that amongst the environmental features, climatic conditions and proximity to roads are the major determinants of forest fires. Model validation criteria like ROC curve, correlation coefficient, and Cohen’s Kappa show a good predictive ability (AUC = 0.95, COR = 0.81, κ = 0.78). The outcomes of this study in the form of a forest fire prediction map can aid the stakeholders of the forest in taking informed mitigation measures.

scholarly journals Maximum entropy method-based forest fire prediction mapping of Sikkim Himalaya.

2020 ◽  
Author(s):  
Polash Banerjee
Keyword(s):  
Forest Fire ◽  
Maximum Entropy Method ◽  
Forest Cover ◽  
Entropy Method ◽  
Mitigation Measures ◽  
Sikkim Himalaya ◽  
Environmental Features ◽  
Validation Criteria ◽  
Forest Fire Model

Abstract The recent episodes of forest fire in Brazil and Australia of 2019 are tragic reminders of the hazards of the forest fire. Globally incidents of forest fire events are in the rise due to human encroachment into wilderness and climate change. Sikkim with a forest cover of more than 47%, suffers seasonal instances of frequent forest fire during the dry winter months. To address this issue, a GIS-aided and MaxEnt machine learning-based forest fire prediction map has been prepared using forest fire inventory database and maps of environmental features. The study indicates that amongst the environmental features, population density and proximity to roads are the major determinants of the forest fire. This indicates the role of human activities on the incidences of a forest fire. Model validation criteria like ROC curve, correlation coefficient and Cohen’s Kappa show a good predictive capability (AUC = 0.95, COR = 0.77, κ = 0.77). The outcomes of this study in the form of a forest fire prediction map can aid the stakeholders of the forest in taking informed mitigation measures.

Forest Fires

2017 ◽  
Author(s):  
Paul Charbonneau
Keyword(s):  
Forest Fire ◽  
Forest Fires ◽  
Model Simulation ◽  
Percolation Cluster ◽  
Natural Process ◽  
Wildfire Management ◽  
Fire Model ◽  
Self Organized ◽  
Forest Fire Model ◽  
Self Organized Criticality

This chapter explores how a “natural” process generates dynamically something that is conceptually similar to a percolation cluster by using the case of forest fires. It first provides an overview of the forest-fire model, which is essentially a probabilistic cellular automata, before discussing its numerical implementation using the Python code. It then describes a representative simulation showing the triggering, growth, and decay of a large fire in a representative forest-fire model simulation on a small 100 x 100 lattice. It also considers the behavior of the forest-fire model as well as its self-organized criticality and concludes with an analysis of the advantages and limitations of wildfire management. The chapter includes exercises and further computational explorations, along with a suggested list of materials for further reading.

Fractality and Self-Organized Criticality of Wars

Fractals ◽  
1998 ◽  
Vol 06 (04) ◽  
pp. 351-357 ◽  
Author(s):  
D. C. Roberts ◽  
D. L. Turcotte
Keyword(s):  
Forest Fire ◽  
Power Law ◽  
Forest Fires ◽  
Size Dependence ◽  
Fire Model ◽  
Self Organized ◽  
Battle Deaths ◽  
Forest Fire Model ◽  
Alternative Measures ◽  
Self Organized Criticality

This paper considers the frequency-size statistics of wars. Using several alternative measures of the intensity of a war in terms of battle deaths, we find a fractal (power-law) dependence of number on intensity. We show that the frequency-size dependence of forest fires is essentially identical to that of wars. The forest-fire model provides a basis for understanding the distribution of forest firest in terms of self-organized criticality. We extend the analogy to wars in terms of the initial ignition (outbreak of war) and its spread to a group of metastable countries.

scholarly journals THE IMPACT OF FOREST FIRE ON FOREST COVER TYPES IN MONGOLIA

2020 ◽  
Vol XLIII-B3-2020 ◽  
pp. 693-698
Author(s):  
N.-E. Geserbaatar ◽  
E. Nasanbat ◽  
O. Lkhamjav
Keyword(s):  
Forest Fire ◽  
Forest Fires ◽  
Forest Cover ◽  
Forest Type ◽  
Landsat Imagery ◽  
Object Oriented ◽  
Burnt Area ◽  
Current Classification ◽  
The Impact ◽  
Forest Cover Types

Abstract. The objective of this study was the impact of forest fire on forest cover types. This study has identified non-forest and forest area that has seven forest class are included with cedar, pine, larch, birch, birch-pine mixed, birch-larch mixed and cedar-larch mixed, additionally, remote sensing imagery is applied. In contrast, Landsat imagery has been used several classification approaches. Moreover, the current classification has developments in segmentation and object-oriented techniques offer the suitable analysis to classify satellite data. In the object-oriented classification approach, images cluster to homogenous area as forest types by suitable parameters in some level. The accuracy analysis revealed that overall accuracy showed a good accuracy of determination (86.33 percent in 2000 and 93.75 percent in 2011) with regard to identify of the forest cover and type. Furthermore, these results suggest that the Landsat TM and ETM+ data can reliable detect the forest type based upon the segmentation and object-oriented techniques. In generally, our study area is high-risky region to forest fires. It is higher influence to forest cover and tree species and other ecosystems. Overall, wildfire of impact results showed that 25239 ha of forests were changed to burnt area and 52603 ha forests were changed to grassland.

scholarly journals Automatic Forest Fire Detection and Suppression Smart Grid System

2021 ◽  
pp. 666-669
Author(s):  
Gopalakrishnan G ◽  
Arul Mozhi Varman S ◽  
Dinessh T C ◽  
Divayarupa S ◽  
Benazir Begam R
Keyword(s):  
Forest Fire ◽  
Forest Fires ◽  
Fire Detection ◽  
Radical Change ◽  
Climatic Conditions ◽  
Grid System ◽  
The Past ◽  
Initial Stage ◽  
Smart Grid System ◽  
Forest Fire Detection

Over the past years, a radical change in Earth’s temperature has been recorded. It has caused global warming and severe changes in climatic conditions. Naturally, this has resulted in many natural disasters. Forest fire is one such calamity that harms the environment to a great extent. The traditional methods of controlling and suppressing the fires are ineffective as the fires spread too rapidly if it is not contained at the initial stage. Hence this paper proposes a system that aims to automatically detect forest fires and suppress them. This system will suppress and contain the forest fires long enough for the firefighters to arrive.

scholarly journals An Assessment of Forest Cover Change and Its Driving Forces in the Syrian Coastal Region during a Period of Conflict, 2010 to 2020

Land ◽  
2021 ◽  
Vol 10 (2) ◽  
pp. 191
Author(s):  
Mohamed Ali Mohamed
Keyword(s):  
Forest Fires ◽  
Forest Cover ◽  
Driving Forces ◽  
Forest Resources ◽  
Climatic Conditions ◽  
Forest Area ◽  
Forest Cover Change ◽  
Landsat Images ◽  
Syrian Coast ◽  
Coast Region

In Syria, 76% of the forests are located in the Syrian coast region. This region is witnessing a rapid depletion of forest cover during the conflict that broke out in mid-2011. To date, there have been no studies providing accurate, reliable, and comprehensive data on the qualitative and quantitative aspects of forest change dynamics and the underlying drivers behind this change. In this study, changes in the dynamics of forest cover and its density between 2010 and 2020 were detected and analyzed using multi-temporal Landsat images. This study also analyzed the relationship between changes in forest cover and selected physical and socio-demographic variables associated with the drivers of change. The results revealed that the study area witnessed a significant decrease in the total forest area (31,116.0 ha, 24.3%) accompanied by a considerable decrease in density, as the area of dense forests decreased by 11,778.0 ha (9.2%) between 2010 and 2020. The change in forest cover was driven by a variety of different factors related to the conflict. The main drivers were changes in economic and social activities, extensive exploitation of forest resources, frequent forest fires, and weakness of state institutions in managing natural resources and environmental development. Forest loss was also linked to the expansion of cultivated area, increase in population and urban area. Fluctuating climatic conditions are not a major driver of forest cover dynamics in the study area. This decrease in forest area and density reflects sharp shifts in the natural environment during the study period. In the foreseeable future, it is not possible to determine whether the changes in forest cover and its density will be permanent or temporary. Monitoring changes in forest cover and understanding the driving forces behind this change provides quantitative and qualitative information to improve planning and decision-making. The results of this study may draw the attention of decision-makers to take immediate actions and identify areas of initial intervention to protect current the forests of the Syrian coast region from loss and degradation, as well as develop policies for the sustainable management of forest resources in the long term.

scholarly journals MAPPING OF FOREST FIRE BURNED SEVERITY USING THE SENTINEL DATASETS

2018 ◽  
Vol XLII-5 ◽  
pp. 469-474 ◽  
Author(s):  
K. V. Suresh Babu ◽  
A. Roy ◽  
R. Aggarwal
Keyword(s):  
Forest Fire ◽  
Forest Fires ◽  
Forest Products ◽  
Mitigation Measures ◽  
Fire Season ◽  
Burned Area ◽  
Active Fire ◽  
Major Loss ◽  
Sentinel 2A ◽  
Normalized Burn Ratio

<p><strong>Abstract.</strong> Forest fires are frequent phenomena in Uttarakhand Himalayas especially in the months of April to May, causing major loss of valuable forest products and impact on humans through the emissions and therefore effects the climate change. The major forest fire was started on May 19, 2018 and spread in 10 districts out of 13 districts of Uttarakhand state till the fire was suppressed after May 30, 2018. The burned area mapping is essential for the forest officials to plan for mitigation measures and restoration activities after the fire season. In this study, sentinel 2A &amp;amp; 2B satellite datasets were used to map burned severity over Uttarakhand districts. Differenced Normalized Burn Ratio (dNBR) and Relativized Burn Ratio (RBR) were calculated and compared with the active fire points. Results shows that both the dNBR and RBR are in good agreement with the actual occurence of forest fires.</p>

scholarly journals Waldbrandmodellierung - Möglichkeiten und Grenzen | Forest fire modeling - limits and possibilities

2010 ◽  
Vol 161 (11) ◽  
pp. 433-441
Author(s):  
Patrick Weibel ◽  
Ché Elkin ◽  
Björn Reineking ◽  
Marco Conedera ◽  
Harald Bugmann
Keyword(s):  
Human Activity ◽  
Forest Fire ◽  
Forest Fires ◽  
Fire Regimes ◽  
Fire Frequency ◽  
Climatic Conditions ◽  
Forest Composition ◽  
General Validity ◽  
Landscape Models ◽  
Dynamic Landscape

Models make it possible to investigate the factors which influence forest fires and to measure their importance. Using various forest fire models, the works presented here examine the influence of weather, forest composition, human activity and changes in legislation on the likelihood of forest fire ignitions in Ticino and Valais. A distinction was made between forest fires started by flash of lightning, and those resulting from human activity. The results show that the weather has the greatest influence where lightning starts, whereas in fires caused by people, the weather takes a subordinate place to human activities. Depending on the ignition causes, different weather indices best represent the danger of forest fires: for those caused by lightning, the Duff Moisture Code (DMC) drought index, and for those started by human activity, the Angstrom Index. In order to test the general validity of forest fire ignition models these were applied to Ticino and to Valais over two different periods of time. Results show that transferability of the models is limited, and that their use for the assessment of the future risk of forest fire is difficult under changing climatic conditions. The landscape model LandClim was used in order to simulate the observed patterns of fire frequency and size in Ticino and in Valais. Thanks to further development of the forest fire module, LandClim achieved a marked improvement of modelquality. Such dynamic landscape models should have an important role to play in assessing future forest fire regimes.

scholarly journals Forest fire spatial modelling using ordered weighted averaging multi-criteria evaluation

2021 ◽  
Vol 67 (No. 2) ◽  
pp. 87-100
Author(s):  
Hassan Faramarzi ◽  
Seyed Mohsen Hosseini ◽  
Hamid Reza Pourghasemi ◽  
Mahdi Farnaghi
Keyword(s):  
Forest Fire ◽  
Forest Fires ◽  
Fire Hazard ◽  
Fire Risk ◽  
Spatial Modelling ◽  
Weighted Averaging ◽  
Relative Operating Characteristic ◽  
Human Environment ◽  
Ordered Weighted Averaging ◽  
Multi Criteria Evaluation

Forest fires are a major environmental issue because they are increasing as a consequence of climate change and global warming. The present study was aimed to model forest fire hazard using the ordered weighted averaging (OWA) multi-criteria evaluation algorithm and to determine the role of human, climatic, and environmental factors in forest fire occurrence within the Golestan National Park (GNP), Iran. The database used for the present study was created according to daily classification of climate changes, environmental basic maps, and human-made influential forest fire factors. In the study area, the forest fires were registered using GPS. Expert opinions were applied through the analytic hierarchy process (AHP) to determine the importance of effective factors. Fuzzy membership functions were used to standardize the thematic layers. The fire risk maps were prepared using different OWA scenarios for man-made, climatic, and environment factors. The findings revealed that roads (weight = 0.288), rainfalls (weight = 0.288), and aspects (weight = 0.255) are the major factors that contribute to the occurrence of forest fire in the study area. The forest fire maps prepared from different scenarios were validated using the relative operating characteristic (ROC) curve. Values of forest fire maps acquired from scenarios of human, environment, climate factors and their combination were 0.87, 0.731, 0.773 and 0.819, respectively.

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