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 Analysis of forest fire and climate variability using Geospatial Technology for the State of Telangana, India

2019 ◽  
Vol 7 (1) ◽  
pp. 24-37 ◽  
Author(s):  
Firoz Ahmad ◽  
Laxmi Goparaju
Keyword(s):  
Relative Humidity ◽  
Solar Radiation ◽  
Wind Velocity ◽  
Forest Fire ◽  
Forest Fires ◽  
Fire Regime ◽  
Fire Frequency ◽  
The State ◽  
Maximum Temperature ◽  
Hotspot Analysis

Abstract The dynamic changes in the regimes of forest fires are due to the severity of climate and weather factors. The aim of the study was to examine the trend of forest fires and to evaluate their relationship with climate parameters for the state of Telangana in India. The climate and forest fire data were used and uploaded to the GIS platform in a specified vector grid (spacing: 0.3° x 0.3°). The data were evaluated spatially and statistical methods were applied to examine any relationships. The study revealed that there was a 78% incidence of forest fires in the months of February and March. The overall forest fire hotspot analysis (January to June) of grids revealed that the seven highest forest fire grids retain fire events greater than 600 were found in the north east of Warangal, east of Khammam and south east of Mahbubnagar districts. The forest fire analysis significantly followed the month wise pattern in grid format. Ten grids (in count) showed a fire frequency greater than 240 in the month of March and of these, three grids (in count) were found to be common where the forest fire frequency was highest in the preceding month. Rapid seasonal climate/weather changes were observed which significantly enhanced the forest fire events in the month of February onwards. The solar radiation increased to 159% in the month of March when compared with the preceding month whereas the relative humidity decreased to 47% in the same month. Furthermore, the wind velocity was found to be highest (3.5 meter/sec.) in the month of February and precipitation was found to be lowest (2.9 mm) in the same month. The analysis of Cramer V coefficient (CVC) values for wind velocity, maximum temperature, solar radiation, relative humidity and precipitation with respect to fire incidence were found to be in increasing order and were in the range of 0.280 to 0.715. The CVC value for precipitation was found to be highest and equivalent to 0.715 and showed its strongest association/relationship with fire events. The significant increase in precipitation not only enhances the moisture in the soil but also in the dry fuel load lying on the forest floor which greatly reduces the fuel burning capacity of the forest. The predicted (2050) temperature anomalies data (RCP-6) for the month of February and March also showed a significant increase in temperature over those areas where forest fire events are found to be notably high in the present scenario which will certainly impact adversely on the future forest fire regime. Findings from this study have their own significance because such analyses/relationships have never be examined at the state level, therefore, it can help to fulfill the knowledge gap for the scientific community and the state forest department, and support fire prevention and control activities. There is a need to replicate this study in future by taking more climate variables which will certainly give a better understanding of forest fire events and their relationships with various parameters. The satellite remote sensing data and GIS have a strong potential to analyze various thematic datasets and in the visualization of spatial/temporal paradigms and thus significantly support the policy making framework.

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 INTEGRATED FLOOD STUDY OF BAGMATI RIVER BASIN WITH HYDRO PROCESSING, FLOOD INUNDATION MAPPING & 1-D HYDRODYNAMIC MODELING USING REMOTE SENSING AND GIS

2018 ◽  
Vol IV-5 ◽  
pp. 165-172 ◽  
Author(s):  
A. K. Rastogi ◽  
P. K. Thakur ◽  
G. S. Rao ◽  
S. P. Aggarwal ◽  
V. K. Dadhwal ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Water Level ◽  
Shuttle Radar Topography Mission ◽  
Remote Sensing And Gis ◽  
The State ◽  
Flood Inundation ◽  
Flood Inundation Mapping ◽  
Gis Environment ◽  
Elevation Model ◽  
Bagmati River

<p><strong>Abstract.</strong> Flood is one of the most the most re-occurring natural hazard in the state of Bihar, as well as in India. The major rivers responsible for flood in the state of Bihar are Kosi, Gandak, Ghagra and Bagmati, which are the tributary rivers of Ganges. The head water catchment area of these rivers lies in the Himalayan state of Nepal. The high rainfall in Nepal, siltation of hydraulic structures, rivers and low topography of North Bihar causes flood occurrence in these areas on regular basis. Remote sensing and GIS plays an important role in mapping, monitoring and providing spatial database for all flood related studies. The present work focuses on the use remote sensing based topography and images in GIS environment for integrated flood study of Bagmati River, which is one of the most flood prone rivers of North Bihar. The Digital Elevation Model (DEM) from shuttle radar topography mission (SRTM) was used to create detailed sub-basin and river network map of entire Bagmati basin. The floods of July–August 2002 were mapped using RADARSAT-1 data using threshold based method. The SRTM DEM and ground based river cross-section from Dheng to Benibad stretch of Bhagmati River were used to create 1-dimensional hydrodynamic (1-D HD) model for simulating flood water level, discharge and flood inundation. Validation of simulated flood flows was done using observed water level of central water commission (CWC) from Dheng to Runisaidpur stations, with coefficient of correlation of 0.85. Finally, an integrated framework for flood modelling and management system is proposed.</p>

scholarly journals Spatio-temporal modelling of the duration of the cotton cycle in the State Of Goiás, Brazil

2011 ◽  
Vol 31 (4) ◽  
pp. 652-662
Author(s):  
Jorge C. dos A. Antonini ◽  
Euzebio M. da Silva ◽  
Nori P. Griebeler ◽  
Edson E. Sano
Keyword(s):  
Pearson Correlation ◽  
The State ◽  
Coefficient Of Determination ◽  
Maximum Temperature ◽  
Model Parameters ◽  
Crop Fields ◽  
Time Space ◽  
Spatio Temporal ◽  
Temperature Records ◽  
Comparative Results

The objective of this work was to develop and validate a mathematical model to estimate the duration of cotton (Gossypium hirsutum L. r. latifolium hutch) cycle in the State of Goiás, Brazil, by applying the method of growing degree-days (GD), and considering, simultaneously, its time-space variation. The model was developed as a linear combination of elevation, latitude, longitude, and Fourier series of time variation. The model parameters were adjusted by using multiple-linear regression to the observed GD accumulated with air temperature in the range of 15°C to 40°C. The minimum and maximum temperature records used to calculate the GD were obtained from 21 meteorological stations, considering data varying from 8 to 20 years of observation. The coefficient of determination, resulting from the comparison between the estimated and calculated GD along the year was 0.84. Model validation was done by comparing estimated and measured crop cycle in the period from cotton germination to the stage when 90 percent of bolls were opened in commercial crop fields. Comparative results showed that the model performed very well, as indicated by the Pearson correlation coefficient of 0.90 and Willmott agreement index of 0.94, resulting in a performance index of 0.85.

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