Forest Fire Hazards Vulnerability and Risk Assessment in Sirmaur District Forest of Himachal Pradesh (India): a Geospatial Approach

International Journal of Geo-Information

Article Forest Fire Hazards Vulnerability and Risk Assessment in Sirmaur District Forest of Himachal Pradesh (India): A Geospatial Approach

Jagpal Singh Tomar 1, Nikola Kranjˇci´c 2,* , Bojan Ðurin 3 , Shruti Kanga 1 and Suraj Kumar Singh 1

1 Centre for Climate Change and Water Research, Suresh Gyan Vihar University, Jaipur 302017, India; [email protected] (J.S.T.); [email protected] (S.K.); [email protected] (S.K.S.) 2 Faculty of Geotechnical Engineering, University of Zagreb, Hallerova aleja 7, 42000 Varaždin, Croatia 3 Department of Civil Engineering, University North, 42000 Varaždin, Croatia; [email protected] * Correspondence: [email protected]

Abstract: The Himachal Pradesh district’s biggest natural disaster is the forest fire. Forest fire threat evaluation, model construction, and forest management using geographic information system techniques will be important in this proposed report. A simulation was conducted to evaluate the driving forces of fires and their movement, and a hybrid strategy for wildfire control and geostatistics was developed to evaluate the impact on forests. The various methods we included herein are those based on information, such as knowledge-based AHP-crisp for figuring out forest-fire risk, using such variables as forest type, topography, land-use and land cover, geology, geomorphology, settlement, drainage, and road. The models for forest-fire ignition, progression, and action are built on various spatial scales, which are three-dimensional layers. To create a forest fire risk model using Citation: Tomar, J.S.; Kranjˇcić,N.; three different methods, a study was made to find out how much could be lost in a certain amount of Ðurin, B.; Kanga, S.; Singh, S.K. time using three samples. Precedent fire mapping validation was used to produce the risk maps, and Forest Fire Hazards Vulnerability and ground truths were used to verify them. The accuracy was highest in the form of using “knowledge Risk Assessment in Sirmaur District base” methods, and the predictive value was lowest in the use of an analytic hierarchy process or Forest of Himachal Pradesh (India): A AHP (crisp). Half of the area, about 53.92%, was in the low-risk to no-risk zones. Very-high- to Geospatial Approach. ISPRS Int. J. high-risk zones cover about 24.66% of the area of the Sirmaur district. The middle to northwest Geo-Inf. 2021, 10, 447. https:// doi.org/10.3390/ijgi10070447 regions are in very-high- to high-risk zones for forest fires. These effects have been studied for forest fire suppression and management. Management, planning, and abatement steps for the future were Academic Editor: Wolfgang Kainz offered as suitable solutions.

Received: 23 April 2021 Keywords: geospatial modeling; MCDA; AHP; risk zone; management Accepted: 25 June 2021 Published: 30 June 2021

Publisher’s Note: MDPI stays neutral 1. Introduction with regard to jurisdictional claims in India is one of the world’s mega-rich areas in biodiversity, both in terms of fauna and published maps and institutional affil- flora. The incredible richness of India’s biodiversity includes 6 million square kilometers of iations. forest cover. The forests of the country are both environmentally and economically valuable. In India, there are only around 1.7 million hectares of well-defined conifer forest, consisting of important species such as fir, teak, sal, and chir pine. The estimated timber growth in these forests is close to 2 billion cubic meters, which is worth around 60 million rupees Copyright: © 2021 by the authors. (100 million USD) [1]. Large forest fires impact the climate, human health, and property, as Licensee MDPI, Basel, Switzerland. well as resulting in danger to life. Global attention to forest fires has increased recently due This article is an open access article to their major long-term threat to forest habitats and public safety, as well as shorter-term distributed under the terms and risks to property and human lives [2]. Fire plays an important role in ecological processes, conditions of the Creative Commons including altering the composition of plant populations, conserving water, enhancing soil Attribution (CC BY) license (https:// quality, and promoting biodiversity. Due to their role in natural plant succession, forest creativecommons.org/licenses/by/ fires are an important mechanism for initiating new growth in the ecosystem. A region 4.0/).

ISPRS Int. J. Geo-Inf. 2021, 10, 447. https://doi.org/10.3390/ijgi10070447 https://www.mdpi.com/journal/ijgi ISPRS Int. J. Geo-Inf. 2021, 10, 447 2 of 19

equivalent to 6 million km2 of land in India has been destroyed over the past two centuries due to forest fires [3]. Throughout that time, the Forest Survey of India (FSI) has been carrying out fieldwork in the country’s different forest plots since 1965 in order to record fires. For statistical significance, long-term observations of forest conditions were analyzed, and the results were released in [1]. A total of 95% of the country’s forest fires are of human origin, according to FSI research. As per data from the Indian Forest Service, 50% of the forests are likely to be vulnerable to burning. A few national and foreign NGOs (non-governmental organizations), including the Survey of India and state agencies, are also conducting research on forest fires. “India has carried out several forest fire incident case studies in various locations and this booklet summarizes it all their findings” [4]. Despite all of these interventions, the forest-fire-danger data bank remains inadequate. New methods will be needed to circumvent this problem. Satellite data can be very valuable in terms of new fire management strategies. Some new innovations, such as the use of satellites, should be applied in conjunction with the country’s extensive field-based fire data-collection programmed. According to analysts, the research data and facts that have been published show that the country is in a very serious crisis that requires immediate action to be resolved. In India, there are no details on the area and value of the forests burnt, as well as the amount of forest devastated by fire, which makes it impossible to determine the losses. They are incomplete because the number and extent of fires is not available. The explanation given for this is the fear of having to bear responsibility. There is thought to be about 1 million hectares of forest loss due to fire annually. According to an assessment conducted by the Indian Forest Service, each year, there are 3.73 million hectares of wildfires in India that have an effect on forests [5]. There are very few fire outbreaks in India that are caused by natural factors. There is widespread (99%) agreement among observers that the majority these fires are of the people’s own doing, and there is a connection to their socioeconomic status. The chief sources of forest fires in India are grazing, shifting cultivation, and the collection of non-wood products. Forest-fire risk modeling uses remote sensing, and GIS, in 1990 [6], used an advanced very high-resolution radiometer to detect the fire risk for Rajaji National Park in Uttar Pradesh in India, which is considered to have a more than 50% higher fire risk. This paper also presents one of the earliest papers on this topic for areas in India. The authors of [7] used remote sensing to map vegetation types and using slope, proximity to settlements, and distance from roads to predict fire risk in Madhya Pradesh in India. The authors of [8] used neural networks, knowledge-intensive systems, in order to predict forest fires based on temperature, humidity, rainfall, and fire history. Similar to [8], the author in [9] used fuzzy sets and semi-triangular membership function in order to perform long-term prediction of forest-fire risk based on fire history and drought cycles. The authors in [10,11] also considered different areas for fire risk prediction. For areas in India, multiple authors considered estimating forest-fire risks [12–15]. The mentioned authors provide an overview of forest fire risk for different areas in India. The authors of [16] used multicriteria decision analysis and an analytical hierarchy process to estimate Bhajji forest fire risk in Himachal Pradesh in India. This paper presents only one forest part of Himachal Pradesh, while our paper presents the whole area of the Sirmaur district.

2. Study Area The Sirmaur district is in the outer Himalayas and is also commonly known as the Shivalik range. The district shown in Figure1 and is located between the latitudes of 31 ◦000 North and 77◦000 South, and 77◦000770 longitude, between the base latitudes of longitude and the Surveys of India sheet ‘A’ and ‘L’ (Figure1). It is located at an altitude ranging from 300 to 3647 m, with four major rivers. The forested area is around 48,682 hectares [17]. It is connected to the capital Shimla to the west and to the east. In the south is the state of Haryana and in the east is the Indian state of Uttar Pradesh. ISPRS Int. J. Geo-Inf. 2021, 10, x FOR PEER REVIEW 3 of 20

ISPRS Int. J. Geo-Inf. 2021, 10, x FOR PEER REVIEW 3 of 20 ISPRS Int. J. Geo-Inf. 2021, 10, 447 3 of 19

Figure 1. Study area.

It is connected to the capital Shimla to the west and to the east. In the south is the stateFigure of 1. HaryanaStudy area. and in the east is the Indian state of Uttar Pradesh. Figure 1. Study area. The Monsoon that generally starts in the Sirmaur district is from the third week of The Monsoon that generally starts in the Sirmaur district is from the third week of June and lasts up to the second week of September. April and May are also the rainiest JuneIt andis connected lasts up toto the the second capital weekShimla of to September. the west and April to andthe Mayeast. areIn the also south the rainiest is the months of the spring season in the Sirmaur district. In July and August, rainfall is more active. statemonths of Haryana of the spring and in season the east in is the the Sirmaur Indian state district. of Uttar In July Pradesh. and August, rainfall is more The early rainy season provides 80% of the rainfall and the average rainfall of the Sirmaur active.The TheMonsoon early rainy that generally season provides starts in 80% the of Si thermaur rainfall district and is the from average the third rainfall week of theof district is about 1324.3 mm. Figure 2 shows the average rainfall of the district from temporal JuneSirmaur and districtlasts up is to about the 1324.3second mm. week Figure of September.2 shows the April average and rainfallMay are of also the districtthe rainiest from variation between 2001 and 2020. monthstemporal of the variation spring season between in 2001the Sirmaur and 2020. district. In July and August, rainfall is more active. The early rainy season provides 80% of the rainfall and the average rainfall of the Sirmaur district is about 1324.3 mm. Figure 2 shows the average rainfall of the district from temporal variation between 2001 and 2020.

Figure 2. Rainfall (in mm) in the Sirmaur district. Figure 2. Rainfall (in mm) in the Sirmaur district. 3. Data and Methodology The following resources and equipment are needed to complete the analysis. Below Figureare the 2. Rainfall following (in mm) various in the data Sirmaur used district. for the forest fire analysis and methodology with their flow chart for analysis. The land cover layer was created by combining digital image classification and visual interpretation techniques to view satellite images. As

supplementary data, the region’s current land use/cover map was used. The key remotely

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3. Data and Methodology The following resources and equipment are needed to complete the analysis. Below are the following various data used for the forest fire analysis and methodology with their flow chart for analysis. The land cover layer was created by combining digital image classification and visual interpretation techniques to view satellite images. As supplementary data, the region’s current land use/cover map was used. The key remotely sensed data source in this study was Sentinel 2 imagery from 20 May 2019, resized to a spatial resolu- ISPRS Int. J. Geo-Inf. 2021, 10, 447 tion of 10 m [18]. Forest and wild land fire risks, including elevation,4 of slope 19 and aspect, population density and precipitation, were incorporated into ArcGIS for further analysis. The reasons for fires range greatly. The full data used for forest fire risk incorporates slope, sensedelevation, data sourceaspect, in land this study use and was land Sentinel cover, 2 imagery geology, from geomorphology, 20 May 2019, resized buffer to zone of road, adrainage spatial resolution buffer, ofbuilt 10 m up [18 area]. Forest buffer, and wildand landforest fire type. risks, Slope, including elevation, elevation, and aspect data slopewere and calculated aspect, population from the density Cartosat-1 and precipitation, digital elevation were incorporated model into[19]. ArcGIS Based for on [20], with the further analysis. The reasons for fires range greatly. The full data used for forest fire risk incorporatesincrease of slope, elevation, elevation, the aspect,probability land use of and fire land decreases cover, geology, and aspect geomorphology, is only significant on the bufferwarmer zone sides. of road, Land drainage use buffer, and land built upcover area is buffer, defined and forest with type. Sentinel-2 Slope, elevation, imagery [21]. Geology andand aspect geomorphology data were calculated data were from thedownloaded Cartosat-1 digital from elevation Geological model survey [19]. Based of India [22]. Geol- onogy [20 ],and with geomorphology the increase of elevation, data do the not probability directly of fireaffect decreases the risks and aspectof forest is only fires but wildfires significant on the warmer sides. Land use and land cover is defined with Sentinel-2 can cause problems such as landslides and erosions [23,24]. The roads were downloaded imagery [21]. Geology and geomorphology data were downloaded from Geological survey offrom India Geological [22]. Geology Survey and geomorphology of India and data the do not road directly and affect built-up the risks area of forest proximity fires of the 200-m butbuffer wildfires zone can can cause significantly problems such increase as landslides forest andfire erosions risk [25,26]. [23,24 ].Therefore, The roads were a 200 m buffer zone downloadedis included from around Geological roads Survey and built-up of India and areas. the road and built-up area proximity of the 200-mThe buffer moderate zone can resolution significantly imaging increase spectroradiometer forest fire risk [25,26]. (MODIS) Therefore, ahas 200 been m widely used buffer zone is included around roads and built-up areas. for Thewildfire moderate incidence resolution and imaging distribution spectroradiometer detecting (MODIS) and fire has risk been assessments. widely used To check the fordata wildfire distribution incidence and and distributionaccessibility detecting under andthe fireUn riskiversal assessments. Transverse To check Merc theator data (UTM) projection, distribution1:50,000 topographic and accessibility paper under maps the Universalwith the MODI TransverseS hotspot Mercator data (UTM) were projection, mapped to the 1:50,000 1:50,000topographic topographic paper paper maps maps of withthe WGS84 the MODIS datum. hotspot The data comparatively were mapped to thehigher 1:50,000 temperature on the topographic paper maps of the WGS84 datum. The comparatively higher temperature day of the fire was due to the burned and ash-covered pixels nearby, releasing the latent heat on the day of the fire was due to the burned and ash-covered pixels nearby, releasing the latentmore heat quickly more quicklythan usual, than usual,resulting resulting in a inhigher a higher temperature temperature for for ID11. ID11. TheThe full full workflow workflow is presented is presented in Figure in3. Figure 3.

FigureFigure 3. Flow3. Flow chart chart of forest of forest ﬁre area fire calculation. area calculation.

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3.1. Analytical Hierarchy Process (AHP) As a multi-criteria approach, the analytical hierarchy process (AHP) is one of the best for defining forest fire locations. Many studies have explored the proof of AHP’s claimed forest fire effectiveness [27]. In AHP strategy, the enormous choices are made comprehensible by knowledge from math and experts. We have used the nine-point non- stop scale to review the two parameters. Both odd numbers (1, 3, 5, and 7) and even numbers (2, 4, and 6) refer to equal and humble characteristics, but the odd ones stand out (Table1). The AHP is a methodology focused on objective objectives branching into multiple criteria (objective branches) that is used for multiple-criteria problems. Table2 shows the random consistency index (RI).

Table 1. The ratio scale and deﬁnition of AHP [28].

Importance Deﬁnition Explanation 1 Equally Important Equally vital to the target 3 Moderately Important Compared to the overall proﬁt or damage 5 Strong Importance A strong preference for one factor over another The one thing that has gained preeminence, considered to be above all the others 7 Very Strong Importance and vastly superior in the real world, is the theory world of practice. If it is strongly proven with evidence and facts, then one element is favored in 9 Extreme Importance comparison to the other 2, 4, 6, 8 Inter values

Table 2. Random Consistency Index (RI) [28].

n 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 RI 0 0 0.58 0.9 1.12 1.24 1.32 1.41 1.45 1.49 1.51 1.53 1.56 1.57 1.59

3.2. Deriving the Weights Using AHP/ANP Analytic network processing (ANP) is a way of evaluating different parameters by prioritizing and compiling them [29]. Each layer is multilayered, indicating that the relationships between these interdependent groups are too complicated to understand without some effort. This results in their relationship to the different classes being calculated by ANP, so the relationship among these nine has been ﬁgured out thusly. The thematic layer weights using ANP and AHP can be derived by following these steps: 1. Construction of a model: many models have been found for the forest reserve map ability, based on a literature review. It is imperative that the problem is identiﬁed at both an abstract and thematic level before putting it into model layers. 2. Generation of pairwise comparison matrices: with this handy table—aside from arbitrary designations of importance as found in arbitrary scales—the relative values are assigned as follows: 1 means the same importance of the two themes, while a score of 9 represents an extreme priority of one of the themes [30]. In Table3, the order of the classes indicates the way we want to implement the priority. Saaty’s nine-scale levels for the delineation of groundwater capacity were used in a grid. It aims to capture what is unknown in opinions with AHP views [29]. Referring to the previous entry, the consistency index (CI) is a measure of the consistency by using Equation (1): CI = λmax − n/n − 1 (1) As a result, n, Lambda max or λmax is the most frequently occurring Eigenvalue of the pairwise comparison matrix. The measure of consistency of pairwise consistency (MC) is expressed as Equation (2): CR = CI/RI (2) ISPRS Int. J. Geo-Inf. 2021, 10, 447 6 of 19

In addition to the Empirical Tables, we have a Ranking Tables section that allows RIs to be obtained by reference to different measures and countries that apply the indexes. We have Ranking Tables that include the Empirical Table, ratios, and countries that use those indexes as measures. Table2 gives the value of RI for various values of n [ 31]. As long as the variability does not exceed 0.1, it is permissible. In cases where CR is higher than 10%, our judgments must be reﬁned. The 10 pairwise consistencies are presented in Table3. CR: 0.089; count value: 10.00; λmax: 11.179; CI: 0.131; CR: 0.09; constant: 1.49.

Table 3. Pairwise comparisons.

Item Number 1 2 3 4 5 6 7 8 9 10 Item Item Forest Road Built up Land Drainage Geomor- Num- Description Type Aspect Slope Elevation Use/Land Buffer phology Geology ber Buffer Land Cover 1 Forest Type 1.00 5.00 3.00 5.00 4.00 3.00 4.00 5.00 3.00 1.00 2 Aspect 0.20 1.00 2.00 1.00 0.50 4.00 1.00 5.00 1.00 2.00 3 Slope 0.33 0.50 1.00 1.00 1.00 3.00 1.00 6.00 3.00 1.00 4 Road Buffer 0.20 1.00 1.00 1.00 1.00 2.00 1.00 2.00 4.00 2.00 5 Elevation 0.25 2.00 1.00 1.00 1.00 1.00 1.00 2.00 2.00 2.00 6 Built up land 0.33 0.25 0.33 0.55 1.00 1.00 0.50 4.00 0.33 2.00

7 Land use/land 0.25 1.00 1.00 1.00 1.00 2.00 1.00 2.00 1.00 1.00 cover 8 Drainage Buffer 0.20 0.20 0.17 0.50 0.50 0.25 0.50 1.00 3.00 0.33 9 Geomorphology 0.33 1.00 0.33 0.25 0.50 3.00 1.00 3.00 1.00 1.00 10 Geology 1.00 0.50 1.00 0.50 0.50 0.50 1.00 3.00 1.00 1.00 Sum 4.10 12.45 10.83 11.75 11.00 19.75 12.00 30.33 19.33 13.33

The physical multi-criteria model includes the following variables: slope, elevation, aspect, land use and land cover, geology, geomorphology, buffer zone of road, drainage buffer, built up area buffer, and forest type. Electronic clinometers were used to measure the slope of the lines on the map. After that, it was cross-checked with the digital elevation model of Cartosat 1. The data source for the topography maps is the Cartosat 1 digital elevation model with a spectral resolution of the panchromatic band of 2.5 m [19]. Table4 shows the weighted value assigned to each class according to the ﬁre hazards.

Table 4. Weighted values assigned to each class.

Item Forest Road Built up Land Drainage Geomor- Num- Variable Type Aspect Slope Elevation Use/Land Buffer phology Geology Weight ber Buffer Land Cover Forest 1 Type 0.24 0.40 0.28 0.43 0.36 0.15 0.33 0.16 0.16 0.08 25.9% 2 Aspect 0.05 0.08 0.18 0.09 0.05 0.20 0.08 0.16 0.05 0.15 11.0% 3 Slope 0.08 0.04 0.09 0.09 0.09 0.15 0.08 0.20 0.16 0.08 10.5% 4 Road 0.05 0.08 0.09 0.09 0.09 0.10 0.08 0.07 0.21 0.15 10.0% Buffer 5 Elevation 0.06 0.16 0.09 0.09 0.09 0.05 0.08 0.07 0.10 0.15 9.4% Built up 6 0.08 0.02 0.03 0.04 0.09 0.05 0.04 0.13 0.02 0.15 6.6% land Land 7 use/land 0.06 0.08 0.09 0.09 0.09 0.10 0.08 0.07 0.05 0.08 7.9% cover Drainage 8 0.05 0.02 0.02 0.04 0.05 0.01 0.04 0.03 0.16 0.03 4.4% Buffer Geomor- 9 phology 0.08 0.08 0.03 0.02 0.05 0.15 0.08 0.01 0.05 0.08 6.3% 10 Geology 0.24 0.04 0.09 0.04 0.05 0.03 0.08 0.10 0.05 0.08 8.0% ISPRS Int. J. Geo-Inf. 2021, 10, 447 7 of 19

According to the AHP, participation is encouraged for a community decision to be ensured. The theory of AHP is one of hierarchical structure. The hierarchy makes it possible to evaluate the quality of an individual’s contribution at lower levels of the hierarchy. The role of the AHP is to help the decision-makers to choose the alternative with the greatest overall impact on the different priorities when they have to choose among many alternatives. Nonetheless, multi-criteria spatial issues such as land suitability are not mentioned in the land analysis. Currently, GIS-based multi-criteria analysis can be used to pick sustainable sites for new development. Many alternate decisions may be present, so the AHP helps to focus on the best of them. A literature survey demonstrates that the use of GIS for spatial problems has increased steadily over the last three decades. It provides validation for researchers for the realization of the value of a GIS-based multiple-criteria decision analysis or MCDA. Nowadays, computer technology is being increasingly applied to various decision-making tasks. As a result, it is important for geographic information systems (GIS) to be incorporated into sustainable development decisions.

3.2.1. Forest Type Spatial data is the most important since it must be understood using a map. The radiometric maps are of the highest resolution for points far away from major landmasses due to reduced ground checking. The knowledge derived from remote sensing techniques has already been employed in India and abroad. The photo interpretation and georefer- encing was performed. The forest types were as depicted in Figure4, according to the ISPRS Int. J. Geo-Inf. 2021, 10, x FOR PEER REVIEW 8 of 20 visualization and translation of the data presented. Later on in the process, the layout was adjusted using topology.

FigureFigure 4. 4.Forest Forest type type map. map.

3.2.2.3.2.2. AspectAspect Index Index Map Map DueDue toto thethe greatergreater sunshinesunshine in the states states to to the the southwest southwest and and southeast southeast being being partic- par- ticularlyularly fire-prone, ﬁre-prone, as as well well as as direct direct sunlight sunlight in the southsouth andand southeastsoutheast (Figure(Figure5 ),5), those those regionsregions were were given given higher higher indices. indices. TheThe southsouth isis playedplayed upup toto fullfull (Appendix(AppendixA A Table Table A1 A1).). FiresFires erupt erupt and and move move faster faster in in the the southern southern regions regions where where sunshine sunshine is is prevalent. prevalent.

Figure 5. Aspect buffer map.

3.2.3. Slope Index Map As slopes grow steeper, the rate of fire spread increases due to convective pre-ignition and ignition. Larger slopes had more importance given to them in the mathematical

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Figure 4. Forest type map.

3.2.2. Aspect Index Map Due to the greater sunshine in the states to the southwest and southeast being partic- ISPRS Int. J. Geo-Inf. 2021, 10, 447 ularly fire-prone, as well as direct sunlight in the south and southeast8 of (Figure 19 5), those regions were given higher indices. The south is played up to full (Appendix A Table A1). Fires erupt and move faster in the southern regions where sunshine is prevalent.

Figure 5. Aspect buffer map. Figure 5. Aspect buffer map.

ISPRS Int. J. Geo-Inf. 2021, 10, x FOR 3.2.3.PEER REVIEW Slope Index Map 9 of 20 3.2.3. Slope Index Map As slopes growAs steeper, slopes the grow rate steeper, of ﬁre spread the rate increases of fire sp dueread to increases convective due pre-ignition to convective pre-igni- and ignition. Largertion and slopes ignition. had more Larger importance slopes had given more to im themportance in the given mathematical to them model.in the mathematical Slopesmodel. ranging Slopes ranging from 50 from to 60 50 degrees to 60 degrees and above and haveabove a have weight a weight of 5 as of the 5 upperas the upper limit. Thelimit. gradient The gradient of this of hose this goeshose up,goes so up, there so there is less is waterless water to lose to lose and and it is it more is more effective effective at convectiveat convective pre-heating pre-heating (Figure (Figure6). 6).

FigureFigure 6.6.Slope Slope map.map.

3.2.4.3.2.4. RoadRoad BufferBuffer IndexIndex MapMap DueDue toto thethe roadroad density,density, areaarea index index values values were were assigned assigned to to the the closer closer areas. areas. ItIt isis possiblepossible to to come come close close to to these these hotspots, hotspots, which whic canh can be abe ﬁre a fire hazard. hazard. The The trafﬁc traffic intersections intersections were set up at a distance of 500 m intervals (Figure 7). Approximately 200 miles of white markers were required to delineate the study area in order to cover the full extent of road lines at 1000 m (Appendix A).

Figure 7. Road buffer map.

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model. Slopes ranging from 50 to 60 degrees and above have a weight of 5 as the upper limit. The gradient of this hose goes up, so there is less water to lose and it is more effective at convective pre-heating (Figure 6).

Figure 6. Slope map.

ISPRS Int. J. Geo-Inf. 2021, 10, 447 9 of 19 3.2.4. Road Buffer Index Map Due to the road density, area index values were assigned to the closer areas. It is possible to come close to these hotspots, which can be a fire hazard. The traffic intersec- weretionsset were up set at aup distance at a distance of 500 of m 500 intervals m intervals (Figure (Figure7). Approximately 7). Approximately 200 miles 200 ofmiles white of markerswhite markers were required were required to delineate to delineate the study the areastudy in area order in to order cover to the cover full the extent full ofextent road linesof road at 1000lines m at (Appendix1000 m (AppendixA). A).

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FigureFigure 7.7.Road Road bufferbuffer map.map.

3.2.5.3.2.5. ElevationElevation Index Map ThereThere isis lessless riskrisk ofof firefire atat higherhigher altitudesaltitudes due to the effects of of climate. When When this this researchresearch waswas carriedcarried out,out, the possibility of fire fire was calculated. calculated. It It was was then then discovered discovered that that inin thisthis region,region, with with a a maximum maximum elevation elevation of of 4700 4700 m m and and lower lower elevations elevations averaging averaging 750 750 m, them, the chances chances of fire of fire were were greater greater (Figure (Figure8). Hence,8). Hence, the maximumthe maximum weight weight was was used used as theas basethe base for the for findings. the findings. The lastThe weight last weight for elevations for elevations lesser lesser than 1000 than m 1000 is set m to is fiveset poundsto five (Appendixpounds (AppendixA). A).

FigureFigure 8.8. ElevationElevation map.

3.2.6. Built Up Buffer Index Map Accidental and intentional forest fires were the origin of the wild land fires. A value assignment buffer of 200 m with five rings (Figure 9) has been developed with the index value mention in Appendix A according to their risk of forest fire. The buffer rings surrounding the homes were the most critical in containing a fire; thus, they were given greater weights near the citizens and lesser on the periphery.

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3.2.6. Built Up Buffer Index Map Accidental and intentional forest fires were the origin of the wild land fires. A value assignment buffer of 200 m with five rings (Figure9) has been developed with the index value mention in AppendixA according to their risk of forest fire. The buffer rings ISPRS Int. J. Geo-Inf. 2021, 10, x FOR PEER REVIEW 11 of 20 surrounding the homes were the most critical in containing a fire; thus, they were given greater weights near the citizens and lesser on the periphery.

FigureFigure 9. 9. BuiltBuilt up up buffer buffer risk risk index. index.

3.2.7.3.2.7. Land Land Use Use and and Land Land Cover Cover Initially,Initially, the the image image on on Sentinel Sentinel 2 2 [32] [32] was resembled at a 10-m spatial resolution. It It waswas attempted attempted to to correct correct the the findings, ﬁndings, and and it it was was visually visually compared compared to to the the forest forest manage- manage- mentment chart chart on on several several occasions. occasions. The The imagery imagery is is made made up up of of six six spectral spectral bands bands from from the approximateapproximate spectrum spectrum of of different different cent centralral wavelengths: wavelengths: band band 2 2 (blue (blue (0.490 (0.490 μµm)),m)), band band 3 3 (green(green (0.560 µμm)),m)), bandband 4 4 (red (red (0.665 (0.665µm)), μm)), band band 8 (NIR 8 (NIR (0.842 (0.842µm)), μ bandm)), band 11 and 11 12 and (SWIR 12 (SWIR(1.610 µ(1.610m and μm 2.190 andµ 2.190m)) andμm)) land and feature land feature are made are made accordingly accordingly as shown as shown in Figure in Fig- 10. ureThe 10. risk The analysis risk analysis is shown is shown in Table in A1 Table (Appendix A1 (AppendixA). A).

ISPRSISPRS Int.Int. J.J. Geo-Inf.Geo-Inf. 20212021,,10 10,, 447x FOR PEER REVIEW 1112 ofof 1920

Figure 10. Land use/land cover map. FigureFigure 10.10. LandLand use/landuse/land cover cover map. map.

3.2.8.3.2.8. DrainageDrainage BufferBuffer IndexIndex MapMap TheThe drainage,drainage, slope,slope, aspectaspect andand elevationelevation areare calculatedcalculated fromfrom thethe CartosatCartosat 11 datadata ofof 30-m30-m resolutionresolution downloadeddownloaded fromfrom thethe BhuvanBhuvan ISROISRO [[33].33]. WhenWhen assigningassigning indexindex valuesvalues toto thethe bufferbuffer class,class, areasareas thatthat werewere furtherfurther awayaway fromfrom thethe drainage pointspoints had higher indexes. ItIt isis possible to to come come close close to to these these hotspots hotspots that that can can be be a afire ﬁre hazard. hazard. Drainage Drainage was was in- inventedvented at at a aunit unit distance distance of of 200 200 m m (Figure (Figure 11).11). Drainage Drainage and and grout pipes ofof variousvarious sizessizes havehave beenbeen employed,employed, fromfrom 100100 mm upup toto 10001000 mm inin orderorder toto covercover thethe areaarea (Appendix(AppendixA A).).

FigureFigure 11.11. DrainageDrainage bufferbuffer riskrisk index.index.

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ISPRS Int. J. Geo-Inf. 2021, 10, 447 12 of 19 3.2.9. Geomorphology The area in the Sirmaur district presents a dazzling geographic mosaic of high moun- tain ranges, hills, and valleys with elevations ranging from 300 m to 3000 m and the source 3.2.9. Geomorphology of data from the Bhukosh site of Survey India in 1:50,000 scale (Geological Survey of India [22]). TheThe areamountain in the Sirmaurpeaks of districtdenudational presents hill a dazzlingremain snow-covered geographic mosaic all year of highround. moun- For thetain Siwalik ranges, range hills, andof hills, valleys which with takes elevations up the rangingsouthwestern from 300 part m of to the 3000 district, m and the the words source “lowof data denuded” from the would Bhukosh be appropriate. site of Survey Table India 5 shows in 1:50,000 the index scale value (Geological according Surveyto the risk of ofIndia forest [22 ]).fire The of mountainthe geomorphological peaks of denudational features. The hill remainPiedmont snow-covered zone of the all area year covered round. withFor the0.13% Siwalik is very range high of and hills, with which 88.31% takes is high up the(Figure southwestern 12). part of the district, the words “low denuded” would be appropriate. Table5 shows the index value according Tableto the 5. risk Forest of forestfire risk ﬁre area of distribu the geomorphologicaltion with validation features. points. The Piedmont zone of the area covered with 0.13% is very high and with 88.31% is high (Figure 12). Forest Fire Validation Brightness Range Area (in km2) Area (in %) Index Value TableRisk 5. Forest ﬁre risk area distribution with validation points. Points Value Very high 339.152 12.13 5 5 343.8–327.5 Forest Fire Risk Area (in km2) Area (in %) Index Value Validation Points Brightness Range Value High 350.442 12.53 4 7 327.4–323.9 Very high 339.152Moderately 12.13 5 5 343.8–327.5 High 350.442 12.53599.352 21.43 4 3 7 11 327.4–323.9 323.4–306.1 Moderately high 599.352high 21.43 3 11 323.4–306.1 Low risk 924.910Low risk 33.07 924.910 33.07 2 2 3 3 303.1–303.6 303.1–303.6 No risk 583.095No risk 20.85 583.095 20.85 1 1 1 1 301–303 301–303

Figure 12. Geomorphology map.

3.2.10.3.2.10. Geology Geology TheThe Sirmaur Sirmaur district district is is located located in the Shiwalik and lesser Himalayan ranges and the sourcesource of of data data from from the Bhukosh site of Survey India is presented on a 1:50,000 scale. The rocksrocks in the region areare allall overover 600600 million million years years old old and and were were deposited deposited in in a successiona succession of ofsandstone, sandstone, shale, shale, limestone, limestone, and and schist, schist, with wi bothth both limestone limestone and schistand schist forming forming at the at same the sametime duringtime during that timethat period.time period. These These geological geological formations formations are noted are noted for their for their limestone lime- stonedeposits deposits from from the Krol the epoch.Krol epoch. The southernThe southe regionrn region is occupied is occupied largely largely by the by Oligocenethe Oligo- ceneSea deposit. Sea deposit. Himalayan Himalayan districts districts span span intothe into southern the southern area of area the of rivers’ the rivers’ main ﬂowmain to flow the Indus Brahmaputra. A total of 26.42% of the area is considered to be in the low-risk zone and 0.62% of the area is in the very high-risk zone mentioned in AppendixA and Figure 13.

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to the Indus Brahmaputra. A total of 26.42% of the area is considered to be in the low-risk ISPRS Int. J. Geo-Inf. 2021, 10, 447 13 of 19 zone and 0.62% of the area is in the very high-risk zone mentioned in Appendix A and Figure 13.

Figure 13. GeologyGeology map. map.

4. Results and and Discussion Discussion Appendix A shows the index value value according according to to the the risk risk of of forest forest fire fire of of the the geomor- geomor- phological features (Figure 12).12). The The geomorph geomorphologyology shows shows that that the the Piedmont Piedmont zone zone of of area area covering 0.13% 0.13% is is very very high high and and 88.31% 88.31% is is hi high.gh. Geology Geology features features of of the the Sirmaur Sirmaur district district show 26.42% ofof thethe areaarea inin thethe low-risk low-risk As–sb As–sb deposits deposits zone zone and and 0.62% 0.62% in in the the very very high-risk high- riskgranites granites area area zone. zone. The The most most important important variable variable is theis the forest forest type, type, in whichin which pine pine has has the thegreatest greatest risk risk of fires of fires as it hasas it an has abundance an abundance of flammable of flammable materials materials as 18.38% as 18.38% of the forestedof the forestedland in this land area in this is madearea is up made of grass up of andgrass litter. and litter. It is highly It is highly vulnerable vulnerable to wildfires. to wildfires. Pine Pineforests, forests, because because of the of high the oilhigh content oil content of turbine of turbine resins, resins, are more are more liable liable to be damagedto be dam- by agedfires thanby fires other than types other of forests.types of After forests. examination After examination of the nature of the of nature the forest, of the the forest, density the of densitythe wood, of the physicalwood, the characteristics physical characterist of the forest,ics of thethe animals,forest, the and animals, the duration and the of dura- fires to tiondevelop of fires a forest to develop fire risk, a forest the findings fire risk, are the found findings in Figure are found 12 and in Figure Appendix 12 andA, with Appendix Table4 A,showing with Table the area 4 showing in percent the under area in the percent no- to low-riskunder the zone no- andto low-risk the total zone is classified and the as total very ishigh-risk, classified 14.28% as very as high-risk, moderate, 14.28% and10.44% as moderate, as low-risk. and 10.44% The elevation as low-risk. index The map elevation shows indexthat 28.40% map shows of the that area 28.40% comes of under the area an elevationcomes under ranging an elevation from 0 toranging 700 m from and 13.39%0 to 700 of mthe and area 13.39% is under of the 700 area to 1000 is under m. Figure 700 to9 and1000 Appendix m. FigureA 9 show and Appendix a comparison A show of the a com- risks parisonand return of the areas risks in variousand return elevations, areas in respectively.various elevations, The slope respectively. variables The show slope that varia- 17.02% ◦ ◦ ◦ blesand show 23.60% that of 17.02% all falls and are 23.60% from 0 of to all 10 fallsand are 10 from to 20 0 toangles, 10° and rather 10 to than20° angles, just 10 rather to 30 ◦ ◦ (moderatethan just 10 risk), to 30° 30 (moderate to 40 (strong risk), risk) 30 to and 40° >40 (strong(very risk) high and risk). >40° There (very is high a steepness risk). There to the isgradient a steepness that isto crucial the gradient to the fire’sthat is spread, crucial visibleto the infire’s the spread, table and visible Figures in the5 and table 10 .and The Figuresaspect index 5 and shows10. The the aspect direction index of shows elevation the direction in which of the elevation area exhibits in which about the 14.00%area ex- in hibitsthe south, about 14.16% 14.00% in in the the south-west, south, 14.16% 11.97% in the facing south-west, the south-east, 11.97% 11.53% facing towardsthe south-east, the east, 11.53%23.88% towards under the the north-west east, 23.88% and under west, the and north-west 24.47% under and west, the north and 24.47% and the under north-east. the Thenorth risk and index the north-east. area chart The data risk is shown index area in Figure chart9 data. The is closest-to-the-road shown in Figure 9. buffersThe closest- were to-the-roadgiven a 200 buffers m limit. were Figure given8 depicts a 200 m the limit. road Figure network 8 depicts and buffer the road location network for the and purpose buffer locationof analysis. for the Table purpose4 shows of anal dataysis. according Table 4 toshows their data risk accordin of a forestg to fire. their The risk buffer of a forest rings surrounding the streams had no risk for containing a fire; thus, they were given lower weights near the drainage and higher on the periphery. Figure7 depicts the results of the drainage and buffer planning calculations for forest fire risk. A value assignment buffer of 200 m with five rings has been developed with the index value shown in Table4 according to their risk of a forest fire. The buffer rings surrounding the homes were the most critical in ISPRS Int. J. Geo-Inf. 2021, 10, x FOR PEER REVIEW 15 of 20

fire. The buffer rings surrounding the streams had no risk for containing a fire; thus, they were given lower weights near the drainage and higher on the periphery. Figure 7 depicts ISPRS Int. J. Geo-Inf. 2021, 10, 447 the results of the drainage and buffer planning calculations for forest fire risk. A14 value of 19 assignment buffer of 200 m with five rings has been developed with the index value shown in Table 4 according to their risk of a forest fire. The buffer rings surrounding the homes were the most critical in containing a fire; thus, they were given greater weights near containing a fire; thus, they were given greater weights near the citizens and lesser on the the citizens and lesser on the periphery. Figure 6 depicts the results of the built-up land and periphery. Figure6 depicts the results of the built-up land and buffer planning calculations. buffer planning calculations. Figure 4 shows the land use and land cover classification risk Figure4 shows the land use and land cover classification risk zone; Figure 12 shows the areazone; covered Figure in12 theshows risk the zone. area A covered total of 51.86%in the risk of thezone. area A comestotal of under 51.86% forest of the cover, area whichcomes isunder under forest the riskcover, zone, which 2.81% is under of the the area risk is coveredzone, 2.81% under of the no-risk area zone,is covered and 22.52%under no-risk of the areazone, comes and 22.52% under of the the low-risk area comes zone. under the low-risk zone. TheThe finalfinal result, result, according according to theto the flow flow chart chart (Figure (Figure3) methodology, 3) methodology, shows thatshows 12.13% that of12.13% the area of the has area a very has high-risk a very high-risk of forest of fire forest and fire 12.53% and of12.53% the area of the has area a high has risk. a high A total risk. ofA 53.92%total of of53.92% the area of the is covered area is covered under the under low- the to no-risklow- to zoneno-risk (Table zone5). (Table Red zones 5). Red present zones inpresent the map in the (Figure map 14 (Figure) are very 14) high-risk-proneare very high-risk-prone areas for areas forest for fires. forest Hence, fires. the Hence, Sirmaur the districtSirmaur should district be should considered be considered accordingly accordingly under this under forest this fire forest risk managementfire risk management plan by theplan government, by the government, local bodies, local bodies, and NGOs, and NGOs, respectively. respectively These. These classes classes shown shown in in Table Table5 could5 could be be validated validated using using the the data data on on wildfires wildfires for for each each category categoryof of forestforest firefire riskrisk index.index. ValidationValidation pointspoints areare shownshown onon FigureFigure 15 15.. AA falsefalse alarmalarm waswas detecteddetected inin thethe north;north; itit waswas oneone ofof 4343 (or(or 2.33%)2.33%) ofof thethe locationslocations inin thethe group,group, withwith anan accuracyaccuracy ofof 97.67%.97.67%. MODISMODIS datadata isis downloadeddownloaded fromfrom thethe EarthdataEarthdata FirmsFirms site,site, fromfrom theirtheir archival archival data. data. ByBy downloading downloading MODISMODIS datadata of 2019, the brightness brightness value value was was converted converted into into a ashaped shaped file. file. About About 96% 96% of ofthe the data data shows shows accurate accurate findings findings in in the the calculation calculation of of the the forest forest fire fire risk zone.zone. TableTable5 5 showsshows thatthat mostmost ofof thethe pointspoints areare shownshown inin thethe moderatelymoderately high-high- toto veryvery high-riskhigh-risk zone.zone. FigureFigure 1515 showsshows thethe validationvalidation pointspoints onon thethe forestforest firefire riskrisk zonezone andand FigureFigure 16 16 shows shows thethe brightnessbrightness valuevalue rangerange ofof thethe MODISMODIS data.data. TheThe threethree MODISMODIS testtest sitesite validationvalidation exercisesexercises completedcompleted inin 20072007 allall hadhad aa finalfinal accuracyaccuracy ofof 93.06%93.06% inin thethe identificationidentification ofof forestforest fires.fires. Additionally,Additionally, thethe 2007–2008–20092007–2008–2009 finalfinal organizationalorganizational assessmentassessment inin ThailandThailand revealedrevealed aa 95.64%95.64% levellevel ofof confidence.confidence. InIn thethe IndiaIndia studystudy ofof thethe UttarakhandUttarakhand statestate throughthrough MODIS,MODIS, thethe brightnessbrightness valuevalue givesgives aa 97%97% accuracyaccuracy ofof wildfirewildfire zone zone assessment. assessment.

FigureFigure 14.14. ForestForest ﬁrefire riskrisk map.map.

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FigureFigure 15.15. ForestForest ﬁrefire validationvalidation hotspotshotspots ininforest forestﬁre fire risk risk zone zone map. map.

FigureFigure 16.16. ModisModis pointpoint withwith itsits brightnessbrightness values.values. 5. Conclusions 5. Conclusions From the aforementioned result and discussion, we can conclude that geospatial From the aforementioned result and discussion, we can conclude that geospatial technology can be used to quantify biomass, loss of forest, and loss of biodiversity due technology can be used to quantify biomass, loss of forest, and loss of biodiversity due to to forest fires in the future. Using high-resolution ground-satellite data, the forest fire forest fires in the future. Using high-resolution ground-satellite data, the forest fire riskrisk-assessment will assist fire fighters serving in the location of administrative bodies assessment will assist fire fighters serving in the location of administrative bodies such as such as local municipalities. We will measure the carrying capacity of herby habitat and local municipalities. We will measure the carrying capacity of herby habitat and use geo- uselocal geospatial municipalities. modeling We will to measure measure where the carr theying migratory capacity animals of herby reside habitat in orderand use to geo- see spatial modeling to measure where the migratory animals reside in order to see whether

ISPRS Int. J. Geo-Inf. 2021, 10, 447 16 of 19

whether there are any issues with these corridors being adequate. Such a model will work in the initial stages and will subsequently have to be devised to apply, for example, biomass density or degradation ratios, to the total human activity and biophysical parameters. Another kind of model could be created, allowing for both the total biomass loss and forest area change to be calculated including human-caused fire danger that exists in the region. Due to this, we have carried out research on the effect of climate changes on the current population, and socioeconomic location on people’s vulnerability. Census data will be used to produce a socio-economic analysis of the area, as well as an analysis of fire hazards through the computation of population density, agricultural laborers’ age demographics, and the number of children in the zero- to six-year-old age group. Every year in India, global warming has caused a rise in forest fires. There is an expected correlation between increased carbon emissions and boosts in burnt area and the number of fires. It can be shown that 12.13% of the area is under a very high risk of forest fires and 12.53% of the area is under a high risk. A total of 53.92% of the area is considered to be in the low- to no-risk zone. Validation of the final result by the MODIS brightness value shows the calculation of the forest fire risk zone and a 96% accuracy rate, which can be further utilized in these zones by not being affected from forest fire. It has been shown that policies and activities aimed at empowering community participation are safer and produce significantly reduced risk than those previous wildfire preparations and executions. Research increasingly suggests that scientific research and planning also help communities in the West to deal with the danger of wildfires. In addition to the wildfire problems increasing, as more citizens migrate to riskier locations, it is important to address these inadequacies between the best practice and the importance of integrating wildfires in community decision-making. Thus, this research supports the notion that community development and advanced preparation strategies will help to deal with wildfire incidents. An adapted geospatial model was employed in the development of the forest fire hazard in the Sirmaur district of the Himalayan provinces of India. The greatest risk is seen in areas where the fire threat is moderate to high, and we need to be expeditious to prevent it from spiraling of control.

Author Contributions: Conceptualization, J.S.T. and N.K.; methodology, J.S.T.; software, S.K.; validation, N.K., B.Ð. and J.S.T.; formal analysis, S.K. and S.K.S.; investigation, J.S.T.; resources, J.S.T.; data curation, J.S.T.; writing—original draft preparation, J.S.T.; writing—review and editing, N.K.; visualization, S.K.; supervision, J.S.T.; funding acquisition, N.K. and B.Ð. All authors have read and agreed to the published version of the manuscript. Funding: This research received no external funding. Institutional Review Board Statement: Not applicable. Informed Consent Statement: Not applicable. Data Availability Statement: Data available at: European Space Agency web page Available online: http://www.esa.int/ (accessed on 24 May 2021). CARTOSAT-1 Available online: https://www.isro. gov.in/Spacecraft/cartosat-1 (accessed on 24 May 2021). Sentinel-2 Available online: https://sentinels. copernicus.eu/web/sentinel/missions/sentinel-2 (accessed on 24 May 2021). Geological map of India available online: https://www.gsi.gov.in/webcenter/portal/OCBIS/pageMAPS/pageMapsSeries? _adf.ctrl-state=eldqgtyzy_9&_afrLoop=22186242333769660#! (accessed on 24 May 2021). Acknowledgments: The authors would like to express their gratitude to the reviewers for greatly improving the quality of the manuscript. The authors would like to thank Shruti and Suraj for improving the quality of the manuscript. Conﬂicts of Interest: The authors declare no conﬂict of interest. ISPRS Int. J. Geo-Inf. 2021, 10, 447 17 of 19

Appendix A

Table A1. Fire hazard classes.

Intensity Area in Area in Variable Classes of Impor- Fire Hazards Classes 2 tance km % Agriculture 5 High 605.85 21.42 Built-up Land 2 Low Risk 12.11 0.43 Land Forest 7 Very High 1466.94 51.86 Use/Land Grass Land/Grazing Land 4 Moderately High 39.33 1.39 cover Waste Land 3 Low Risk 624.68 22.09 Water Bodies 1 No Risk 79.49 2.81 As–Sb Deposits 2 Low Risk 372.961 13.17 BlainiManjir formation 1 No Risk 123.61 4.37 Granitoids 7 Very High 17.6021 0.62 Geology Jatog Groups 4 Moderately High 257.035 9.08 Shimla and Jaunsar groups 3 Moderately High 375.189 13.25 Siwalik Groups 6 High 1461 51.60 Tal Kunzaml Thango 5 High 120.419 4.25 Unconsolidated Glacial 1 No Risk 103.671 3.66 Alluvial Plain 2 Low Risk 187.052 6.63 Denudational Hills 3 Low Risk 67.0437 2.38 Geomorphology Flood Plain 1 No Risk 72.0214 2.55 Piedment Zone 7 Very High 3.73777 0.13 Structural Hills 5 Moderately High 2492.39 88.31 Plantation/TOF 4 Moderately High 2.71 0.10 Lower or Siwalik chir pine forest 9 Very High 518.39 18.38 Non-Forest 1 No Risk 1386.66 49.16 Moist deodar forest 2 Low Risk 178.89 6.34 Oak scrub 3 Low Risk 103.48 3.67 Forest Type Northern dry mixed deciduous forest 5 High 203.46 7.21 Water 1 No Risk 11.06 0.39 Moist temperate deciduous forest 2 Low Risk 11.89 0.42 West Himalayan sub-alpine forest 3 Low Risk 0.39 0.01 West Himalayan high-level dry blue pine 7 Very High 6.73 0.24 forest Bhabar-dun sal forest 4 Moderately High 397.12 14.08 <200 m 1 No Risk 483.243 17.09 400 m 2 Low Risk 444.229 15.71 Drainage 600 m 3 Moderately High 400.589 14.16 Buffer 800 m 4 Moderately High 354.951 12.55 1000 m 5 High 311.513 11.01 >1000 m 7 Very High 833.783 29.48 <200 m 7 Very High 907.499 32.09 400 m 5 High 582.693 20.60 600 m 3 Moderate 405.018 147.61 Road Buffer 800 m 3 Moderately High 274.392 9.70 1000 m 1 Low Risk 185.152 6.55 >1000 m 1 No Risk 473.344 16.74 <200 m 7 Very High 70.3744 2.49 400 m 5 High 78.2607 2.77 Built-Up 600 m 4 Moderately High 96.1169 3.40 Buffer 800 m 3 Moderately High 107.646 3.81 1000 m 2 Low Risk 113.995 4.03 >1000 m 1 No Risk 2362.05 83.51 <10 2 Low Risk 506.46 17.02 10.1–20 3 Moderately High 702.186 23.60 Slope 20.1–30 4 Moderately High 960.044 32.27 30.1–40 5 High 646.021 21.71 40> 7 Very High 160.628 5.40 ISPRS Int. J. Geo-Inf. 2021, 10, 447 18 of 19

Table A1. Cont.

Intensity Area in Area in Variable Classes of Impor- Fire Hazards Classes 2 tance km %

North (0–22.5); North (337.5–360); 1 727.494 24.47 Northeast (22.5–67.5) No Risk East (67.5–112.5) 3 Moderately High 342.679 11.53 Aspects Southeast (112.5–157.5) 4 Moderately High 355.775 11.97 South (157.5–202.5) 7 Very High 416.389 14.00 Southwest (202.5–247.5) 5 High 420.902 14.16 West (247.5–292.5); Northwest 2 Low Risk 710.092 23.88 (292.5–337.5) 0–700 6 Very High 844.955 28.40 700.1–1000 4 High 398.448 13.39 Elevation 1000.1–1500 3 Moderately High 927.634 31.18 1500.1–2000 2 Low Risk 570.535 19.18 2000.1–3536 1 No Risk 233.768 7.86

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