Climate induced variation in forest fire using Remote Sensing and GIS in Bilaspur District of Himachal Pradesh

2017 ◽  
Author(s):  
Shruti Kanga ◽  
◽  
Sumit Kumar ◽  
Suraj Kumar Singh ◽  
◽  
...  
Keyword(s):  
Remote Sensing ◽  
Forest Fire ◽  
Remote Sensing And Gis ◽  
Himachal Pradesh ◽  
Induced Variation

scholarly journals Climate change and its impact on Forest Fire in the state of Himachal Pradesh and Uttarakhand states of India: Remote Sensing and GIS Analysis

2018 ◽  
Vol 7 (2) ◽  
pp. 229-246 ◽  
Author(s):  
Firoz Ahmad ◽  
Laxmi Goparaju
Keyword(s):  
Remote Sensing ◽  
Forest Fire ◽  
Pearson Correlation ◽  
Fire Frequency ◽  
Remote Sensing And Gis ◽  
Himachal Pradesh ◽  
Significant Rise ◽  
The State ◽  
Annual Precipitation ◽  
Maximum Temperature

Abstract We have examined the climate and forest fire data using Remote Sensing and GIS in the state of Himachal Pradesh and Uttarakhand states of India. The significant high forest fire events were observed in district of Pauri Garhwal (22.4%) followed by Naini Tal (16.4%), Tehri Garhwal (8.5%), Almora (7.7%), Chamoli (5.8%), Dehra Dun (4.6%), Uttarkashi (4.3%), Champawat (4.2%), Haridwar (3.6%), Una (3.4%), Bageshwar (3.1%), Udham Singh Nagar (2.9%), Sirmaur (2.7%), Solan (2.3%), Kangra (2.1%), Pithoragarh (1.7%) and Shimla (1.2%). The LULC forest category “Deciduous Broadleaf Forest” occupied 17.2% of total forest area and retain significantly high forest fire percent equivalent to 44.7% of total forest fire events. The study revealed that 79% of forest fire incidence was found in the month of April and May. The fire frequency was found highest in the month of April (among all months) whereas it was spread over the five grids (in the count) where the fire frequencies were greater than 100. The average monthly analysis (from January to June) for maximum temperature (°C), precipitation (mm), solar radiation (MJ/m^2), wind velocity (meter/sec.), wet-days frequency (number of days) and evapotranspiration (mm/day) were found to be in the range of (9.90 to 26.44), (26.06 to 134.71), (11738 to 24119), (1.397 to 2.237), (1.46 to 5.12) and (3.96 to 8.46) respectively. Rapid climate/weather severities which significantly enhance the forest fire events were observed in the month of April and May. The analysis of the Pearson Correlation Coefficient (PCC) values of climate parameters showed a significant correlation with forest fire events. The analysis of predicted (2050) climate anomalies data (RCP-6) for the month of April and annual precipitation manifest the significant rise in April temperature and reduction in annual precipitation observed over large part of high forest fire grids will certainly impact adversely to the future forest fire scenario.

scholarly journals Advances in remote sensing and GIS applications in support of forest fire management

2014 ◽  
Vol 23 (5) ◽  
pp. 603 ◽  
Author(s):  
Ioannis Z. Gitas ◽  
Jesús San-Miguel-Ayanz ◽  
Emilio Chuvieco ◽  
Andrea Camia
Keyword(s):  
Remote Sensing ◽  
Forest Fire ◽  
Fire Management ◽  
Remote Sensing And Gis ◽  
Gis Applications ◽  
Forest Fire Management

scholarly journals Study On Forest Fire Spreading Model Based On Remote Sensing And GIS

2018 ◽  
Vol 199 ◽  
pp. 022017 ◽  
Author(s):  
Chen Qiao ◽  
Lili Wu ◽  
Tao Chen ◽  
Quanyi Huang ◽  
Zhipeng Li
Keyword(s):  
Remote Sensing ◽  
Forest Fire ◽  
Remote Sensing And Gis ◽  
Spreading Model ◽  
Model Based ◽  
Fire Spreading

scholarly journals Assessment of Threats to Forest Ecosystems Using Geospatial Technology in Jharkhand State of India

2017 ◽  
Vol 12 (2) ◽  
pp. 355-365 ◽  
Author(s):  
Firoz Ahmad ◽  
Laxmi Goparaju
Keyword(s):  
Remote Sensing ◽  
Forest Fire ◽  
Forest Ecosystem ◽  
Forest Fires ◽  
Satellite Remote Sensing ◽  
Remote Sensing And Gis ◽  
Critically Endangered ◽  
Landsat 8 ◽  
Grid Cells ◽  
Conservation Priority

Conservation of forest biodiversity is vital for mankind as it provides enormous benefits such as biological resources and ecosystem services. Of late, the forests are facing risk and threats such as fragmentation, degradation and forest fires which are responsible for the deteriorating condition. The progress in the field of science and technology like satellite remote sensing and GIS since the past few decades in India and the world provide an opportunity to track and monitor the changes taking place on the Earth’s surface. Besides, analysis of large spatial data in GIS can also provide insight into the various driving factors which lead to the loss of biodiversity in the threatened ecosystems i.e forests. This study has attempted to obtain information about the spatial extent of the three forest ecosystem degradation indicators viz. deforestation, fragmentation of forest and forest fires using methodical approach in the Jharkhand state of India. The satellite remote sensing data sets belonging to Landsat-8 were used to analyse the forest cover of Jharkhand state. To identify the areas of threat, grid cells (5KmX5Km) were generated in GIS domain. Analysis of deforestation was conducted using multi source data of the time periods 1935 and 2015. Evaluation of deforestation spanning over a time period reveals that vital changes have occurred in the forests of Jharkhand and determined 1224 extinct, 248 critically endangered, 318 endangered and 396 vulnerable ecosystem grid cells. The fragmentation analysis has determined 148 critically endangered, 296 endangered and 402 vulnerable ecosystem grid cells. Forest fire point’s data from the year 2005 to 2016 were utilized and analysis was executed. Further frequency of forest fires for each grid was noted. The result indicates that 67.3% of grid cell of Jharkhand forest was affected with forest fire. Conservation status has been evaluated based on the value of threat for each grid which was the fundamental criteria for conservation priority hotspot. About 2.1% of Jharkhand forest ecosystem grids are defined as extremely high ecosystem risk stage and have been designated in the category of conservation priority hotspot-1 followed by 19.7% conservation priority hotspot-2, 41.3% conservation priority hotspot-3, 27.8% conservation priority hotspot-4 and 9.1% lowest conservation priority hotspot-5. This study highlights the capability of integrating remote sensing and GIS data for mapping the forest degradation, which can be useful in formulating the strategies and policies for protection and conservation of forests.

scholarly journals Remote Sensing and GIS Based Forest Fire Vulnerability Assessment in Dachigam National Park, North Western Himalaya

2018 ◽  
Vol 11 (2) ◽  
pp. 98-114 ◽  
Author(s):  
Muzamil Ahmad Rather ◽  
Majid Farooq ◽  
Gowhar Meraj ◽  
Mudasir Ahmad Dada ◽  
Bashir Ahmad Sheikh ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Forest Fire ◽  
Vulnerability Assessment ◽  
National Park ◽  
Western Himalaya ◽  
Remote Sensing And Gis ◽  
Dachigam National Park ◽  
North Western
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