scholarly journals Identifying the decadal forest fire effects on conversion of forest cover to grassland in Bandipur Tiger Reserve through geospatial technology

2021 ◽  
Vol 11 (9) ◽  
pp. 1-9
Author(s):  
Ashwatha K. N ◽  
Tejaswini J. S ◽  
Dr. A. S. Rayamane
Keyword(s):  
Forest Fire ◽  
Forest Cover ◽  
Fire Effects ◽  
Geospatial Technology ◽  
Tiger Reserve

PENILAIAN DAMPAK KEBAKARAN PADA TEGAKAN AKASIA DI BKPH PARUNG PANJANG KPH BOGOR, PERUM PERHUTANI UNIT III JAWA BARAT DAN BANTEN Fire Severity Assessment on Akasia stand at BKPH Parung Panjang KPH Bogor, Perum Perhutani III West Java and Banten

2017 ◽  
Vol 8 (1) ◽  
pp. 55-62
Author(s):  
Lailan Syaufina ◽  
Vera Linda Purba
Keyword(s):  
Growth Performance ◽  
Forest Fire ◽  
Health Monitoring ◽  
Fire Severity ◽  
Forest Health ◽  
Chemical Properties ◽  
Fire Effects ◽  
Burned Areas ◽  
Forest Health Monitoring ◽  
Physical And Chemical

Forest fire is one of the problem in forest management. The objectives of the study was to measure the forest fire severity based on soil physical and chemical properties. The forest fire effects were assessed using fire severity method and forest health monitoring plot. The study indicated that the burned areas at BKPH Parung Panjang after two years included in low fire severity. The site properties and growth performance analysis showed that the fire has only affected on pH, Mg and tree diameter significantly, whereas the other parameters such as bulk density, P, N, Na, K, Ca and height were not significantly affected. In addition, both burned and unburned areas are classified as in health condition.Key words : fire severity, forest health monitoring, growth performance, site properties

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.

Forest Fire Effects on Soil Phyllosilicates in California

1996 ◽  
Vol 60 (1) ◽  
pp. 309-315 ◽  
Author(s):  
A. L. Ulery ◽  
R. C. Graham ◽  
L. H. Bowen
Keyword(s):  
Forest Fire ◽  
Fire Effects

scholarly journals Comparison of approaches for reporting forest fire-related biomass loss and greenhouse gas emissions in southern Europe

2013 ◽  
Vol 22 (6) ◽  
pp. 730 ◽  
Author(s):  
Maria Vincenza Chiriacò ◽  
Lucia Perugini ◽  
Dora Cimini ◽  
Enrico D'Amato ◽  
Riccardo Valentini ◽  
...  
Keyword(s):  
Land Use ◽  
Greenhouse Gas ◽  
Forest Fire ◽  
Forest Fires ◽  
Fire Effects ◽  
Southern Europe ◽  
Gas Emissions ◽  
Fire Emissions ◽  
Biomass Loss ◽  
Mediterranean Forest Ecosystems

Wildfires are the most common disturbances in Mediterranean forest ecosystems that cause significant emissions of greenhouse gases as a result of biomass burning. Despite this, there is reasonably high uncertainty regarding the actual fraction of burnt biomass and the related CO2 and non-CO2 gas emissions released during forest fires. The aim of this paper is to compare existing methodologies adopted in the National Greenhouse Gas Inventory reports of five of the most fire-affected countries of southern Europe (Italy, Spain, Greece, Portugal, France) with those proposed in the literature, to operationally estimate forest fire emissions, and to discuss current perspectives on reducing uncertainties in reporting activities for the Land Use, Land Use Change and Forestry sector under the United Nations Framework Convention on Climate Change and the Kyoto Protocol. Five selected approaches have been experimentally applied for the estimation of burnt biomass in forest fire events that occurred in Italy in the period 2008–2010. Approaches based on nominal rates of biomass loss can lead to an overly conservative value or, conversely, to underestimation of the fraction of burnt biomass. Uncertainties can be greatly reduced by an operational method able to assess inter-annual and local variability of fire effects on fire-affected forest types.

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.

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