Fire Risk Zonation Through Geospatial Modeling

1. Fire Risk Zonation Through Geospatial Modeling
(A case study of Makwanpur District)
PRESENTED BY
Group Pixel
Bishow Babu Shrestha, Naresh Mahato, Rajan Rai, Jaman Sing
Moktan, Pramod Kumar Rimal, Sandhya Dhakal, Sandip
Pokhrel and Aashish Pathak
M.Sc. Forestry 1st year 3rd Semester
FoF, Hetauda
SUBMITTED TO
ASSISTANT PROFESSOR JEETENDRA GAUTAM
FACULTY OF FORESTRY
AGRICULTURE AND FORESTRY UNIVERSITY
HETAUDA
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A Term Paper Submitted for the Partial Fulfillment of M.Sc. Forestry
(FSE 701 Geospatial Technology & Application in NRM)

2. OUTLINE OF PRESENTATION
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INTRODUCTION
OBJECTIVE
STUDY AREA
PROCEDURE
CONCLUSION
REFERENCES

3.  Fire risk zone is an area where fires are likely to occur that has direct impact on overall
ecological balance (Jaiswal et al., 2002).
 Hence, need to generate accurate fire risk zone maps through geospatial modeling
becomes prominent in order to indicate the zonation of the areas which are susceptible
to high, medium or low fire risk (Chandra and Arora, 2006).
 GIS and remote sensing techniques have been widely used to assess and predict fire
frequency (Roy et al., 2002; Giglio et al., 2006).
 Geospatial modeling is an analytical procedure that simulate real-world conditions
within a GIS using the spatial relationships of geographic features.
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1. INTRODUCTION

4.  Remote sensing and GIS has significantly advanced over the conventional method to map
forest fire hazard, because of its continuity and coverage over large areas (Roy and
Porwal, 2008).
 Therefore , Satellite data are widely used to detect forest fires in different land use and
GIS is considered widely accepted geospatial model to assess and predict fire frequency
(Giglio et al, 2006).
 In this study, we have used data from the FIRMS with data source: SUOMI VIIRS C2 to
generate fire risk zones in Makawanpur district .
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5.  General objective of this study was to
find out spatial distribution of fire risk
zones within Makawanpur district
through geospatial modelling.
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2. OBJECTIVE

6. Map of Study Area
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3. STUDY AREA

7. 4. PROCEDURE
(I) To start fire risk zonation mapping three literatures were followed namely,
Forest Fire Hazards Vulnerability and Risk Assessment in Sirmaur District Forest of
Himachal Pradesh (India): A Geospatial Approach by Tomar et. al., 2021.
Understanding forest fire patterns and risk in Nepal using remote sensing, geographic
information system and historical fire data by Mir A. Matin et al., 2017.
Wildfire Risk Zonation of Sudurpaschim Province, Nepal by Bikram Singh et al., 2020.
(II) Necessary files were downloaded from following websites.
Landsat image from earthexplorer.usgs.gov.
DEM data downloaded from data.humdata.org
Boundary of Makawanpur downloaded from opendatanepal.com
Fire hotspot of Makawanpur from earthdata.nasa.gov
(III) Finally, steps for mapping fire risk zones of study area using Arc Map and its modeling
tools were followed to be mentioned later on.
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Step 1 – Preparation of map of the study area isolating the fire events within given time period.
(i) Procedure (ii) Outcome

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Step 2 – Addition of DEM file to Arc Map and clipping the study area
(i) Procedure (ii) Outcome
DEM of
Makwanpur
District

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Step 3 – Creation of Slope Map of study area using slope (spatial analyst)(tool) in ArcMap.
(i) Procedure (ii) Outcome

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Step 4 – Creation of Aspect Map using Aspect (spatial analyst) (tool) in ArcMap.
(i) Procedure (ii) Outcome

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Step 5 - Creating LULC map using Landsat 8 following supervised image classification
(ii) Outcome
(i) Procedure

13.  This Tool was used to find out statistical frequency of fire occurrence of elevation,
slope, aspect and LULC of study area.
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Step 6 - Extract Multi Values to Points from search box in Arc Map.

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Step 7 – Manipulation of attribute tables to query statistical frequency distribution of
fire occurrence of different attributes of study area
(a) Elevation (b) Slope

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Step 7 - Contd...............
(c) Aspect (d) LULC

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Step 8 - Reclassifying Slope with respect to fire risk occurrence zone.
Tools used :
Reclassify(spatial analyst)
Reclassify as :
 High Risk zone – below 45 degree
 Low Risk zone - above 45 degree
(i) Procedure (ii) Outcome

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Step 9 - Reclassifying elevation with respect to fire risk occurrence zone.
Tools used :
Reclassify(spatial analyst)
Reclassify as :
Low risk (above 1600 m),
Moderate Risk (700 - 1600m)
High Risk (below 700 m)
(i) Procedure (ii) Outcome

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Step 10 - Reclassifying LULC with respect to fire risk occurrence zone.
Tools used :
Reclassify(spatial analyst)
Reclassify as :
 Forest & settlement - High risk
 Agricultural Land - Moderately risk
 Water Bodies - Low risk
(i) Procedure (ii) Outcome

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Step 11- Reclassifying Aspect with respect to fire risk occurrence zone.
Tools used :
Reclassify(spatial analyst)
Reclassify as :
 Low risk - N, NE, NW, W, F
 Moderate risk - E, SE
 High Risk - S, SW
(i) Procedure (ii) Outcome
(Tomar, 2021)

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Step 12 - Overlaying - Weighted overlay
(i) Procedure
(Tomar, 2021)
Tools used :
Weighted Overlay (spatial analyst)

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(ii) Outcome
Continued…

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5. CONCLUSION
 In this study, we analyzed fire incidence data to identify the spatial distribution of fire risk
zonation in study area.
 Elevation, slope, aspect and LULC of study area are determinant to Fire risk prominence.
 Determining the result of this spatial pattern, forest is very high detrimental to fire risk
zone in study area.
 Lower elevation is more detrimental to fire risk than higher one in the study area.
 Southern aspect is prominently risky than northern aspect with respect to fire zone.
 From this study, we can conclude that in Makwanpur district 0.65% of the total area have
low risk of fire occurrence where as 66.99% and 32.37% of the total area lie on moderate
fire risk zone and high fire risk zone respectively which depicts that majority of
Makwanpur district seems to be vulnerable to fire disaster.

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6. REFERENCES
Chandra, S & Arora, M.K., (2006). Forest fire risk zonation mapping using remote sensing technology . Proc. SPIE 6412, Disaster
Forewarning Diagnostic Methods and Management.
Giglio, L., G. R, van der Werf., J. T, Randerson., G. J, Collatz and P. Kasibhatla ., (2006). Global estimation of burned area using MODIS
active fire observations, Atmos. Chem. Phys., 6, 957– 974.
Jaiswal, R.K., Mukherjee, S., Raju, K.D., Saxena, R., (2002). Forest fire risk zone mapping from satellite imagery and GIS. Int. J. Appl.
Earth Obs. Geoinf., 4, 1–10.
Matin, M. A., Chitale, V. S., Murthy, M. S., Uddin, K., Bajracharya, B., & Pradhan, S. (2017). Understanding forest fire patterns and risk
in Nepal using remote sensing, geographic information system and historical fire data. International journal of wildland fire, 26(4),
276-286.
Roy. N. and Porwal. M.C. (2008). Forest Fire Risk Zonation using Geo-spatial Modeling in Part of Rajaji National Park, India. Indian
Institute of Remote Sensing, (NRSA), Department of Space 4 Kalidas Road, Dehradun-248001, India.
https://www.researchgate.net/publication/247778066.
Singh, B., Maharjan, M., & Thapa, M. S. (2020). Wildfire Risk Zonation of Sudurpaschim Province, Nepal. Forestry: Journal of Institute
of Forestry, Nepal, 17, 155-173.
Tomar, J. S., Kranjčić, N., Đurin, B., Kanga, S., & Singh, S. K. (2021). Forest fire hazards vulnerability and risk assessment in Sirmaur
district forest of Himachal Pradesh (India): A geospatial approach. ISPRS International Journal of Geo-Information, 10(7), 447.

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