Forest fires

Forest fires

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An environment study

Dr. Saurabh Gupta
Dr. Saurabh Gupta, Syed Taha Owais
National Informatics Centre, Sikkim
Email: [email protected], [email protected]

Accidents do not make appointments. They occur anytime, under the most unfavourable circumstances and the consequences of such bizarre incidents can be intimidating. Forest Fire is one such common environmental disaster in Sikkim Himalayas..

The Himalayan state of Sikkim, situated between situated between 270 04′ to 280 07’ North latitudes and 880 01’to 880 55’ East longitudes, is primarily the catchment of Tista drainage system. Nearly 80% of the total area is forest land whose capital value has been estimated to more than Rs. 130 billion and their environment contribution is Rs. 540 crores annually. These verdant forests are storehouse of rare flora and fauna; vary from very dense to degraded forest and span over altitudes ranging from foothills to as high as 8500 mts. above MSL. These forests are vital resources and play a major role in the socio-economic setup of the state. However, wild fires have become common recurrent phenomena especially in the Sal Forest belts. Large stretches of greenery are lost to these raging fires and the present fire fighting and management techniques are ad hoc and outdated. Considering the high stakes involved, National Informatics Centre, Sikkim in collaboration with Forest Department, Government of Sikkim selected South district with the objective to delineate wild fire risk areas and develop complete spatial details using state-of-art GIS/RS technologies.


Figure 1: Altitude Map

The Study Area
The study area consisted of South district, which is the second most populas district. The area is rich in forest resources as 55% of the area is forest and its estimated capital value is Rs. 33 billion. There are two subdivisions namely Namchi and Ravogla and there are 144 inhabited revenue blocks. There is one-degree college and Jorethang (urban area) has the highest literacy in the whole state.

Table 1: Study Area

Latitude 27o0’8” N to 27o26’31” N
Longitude 88o15’0″E to 88o56’25″E
Total geographical Area 750.00
Area under Reserve Forest. 304.81
Area under Khasmal 97.78
Area under Gaucharan
(For Grazying)
10.82

The altitude of the area varies from 400 mts to more than 5000 mts above MSL and the average sloping is moderate (60-90%). Areas having higher altitude and steep slope are inaccessible and are generally forestlands. Streams of all orders are found and average proximity of settlements from water bodies is around 200 metres. The gully lines along with the ridges form watershed at micro-level and often form boundaries of revenue blocks. As all the channels are not perennial, Namchi areas especially suffers from severe water crisis during lean period. Geologically, the study area comes under Foothill belt and as such soil type is by and large loamy and soil depth is moderate with severe to moderate soil erosion.


Figure 2: Slope Map

The South district has all three kinds of roads viz. footpath, often forming the shortcut routes and are generally staircases, metalled and unmetalled roads. The road accessibility is satisfactory; however during rainy season due to landslides roads are often blocked.


Figure 3: Aspect Map

Forest Fire Incidents
Year 1998-1999

  • Namchi Range-12 places-Area damaged-150 hec.
  • Namthang Range-11 places-Area damaged-265 hec.
  • Ravongla Range-8 places-Area damaged-100hec.
  • Melli range-20 places-Area damaged-280 hec.
  • Lingmoo range-2 places-Area damaged-30 hec.

Year 1999-2001
As reported, 26 places in the Reserve Forest, Khasmal and Gaucharan (comprising sal, teak and other pole-sized timber forests) were damaged by fire under south division. Total 20 kms. has been done as fireline clearances. In March 2001, 8 cases of wild fires have been reported in the sal belt and areas around Ravangla.

Forest Fire Analysis
A fire causing incalculable damage to the ecosystem can be termed a forest fire. Such a fire is common in almost all types of forests barring some wet evergreen patches.

Table 2:Land use pattern
Land Use Pattern Area
(%)
Cumm
Area(%)
Area
(sq km)
Agriculture 41.48 41.48 291.19
Alpine Barren 4.60 46.08 32.29
Barren 0.11 46.19 0.77
Degraded Forest 2.49 48.68 17.49
Scrub Forest 1.20 49.87 8.42
Semi Evergreen Forest 50.13 100.00 351.91
Total 100.00   702.00

The current endevour is an attempt to develop a viable and area specific solution resultant to the identification of grey areas from wild fire hazard perspective. The GIS based model takes into account the contribution made by slope, aspect, elevation, landuse, acanopy and species of forest in the ignition and propagation of fire. For example, Sal forest having south to west aspect at lower altitude has every chance of catching fire. The weights are assigned to each of these parameters that signify their contribution to the dynamics of the event and the model classifies the risk areas into extremely high, high, low and extremely low potential. It found that the model developed here matches the ground reality.


Figure 4: Forest Type Map

Spatial Inputs
The spatial inputs required to develop the working fire model includes

  • Elevation
  • Slope
  • Aspect
  • Land use
  • Forest Type
  • Forest Density


Figure 5: Forest Density Map

In this study it is assumed that wind velocity is constant and the influence of wind direction in fire propagation is dependent upon the slope profile, thus it can also be ignored as slope effect is taken into account. The elevation, slope and aspect classification have been derived from contour map of 40 meters interval sourced from SOI toposheets. Forest details have been sourced form RS imagery of 1988.

Parameters Weight Assigned

Aspect

North to NE 1
NE to East 2
East to SE 3
SE to South 4
 South to SW 4
 SW to West 3
 West to NW 2
 NW to North 1
 Slope (in %)  
0-30 1
 30-45 2
 45-90 3
 >90 4

 Forest Density (canopy)

Agriculture 1
 Alpine Barren 1
 Alpine Scrub 1
 Barren 1
 Dense (>0.4 Canopy) 4
 Open Forest (0.2-0.4 Canopy) 3
 Scrub (<0.1 Canopy) 2

 Land Use (1988)

Agriculture 1
Alpine Barren 1
Barren 1
Degraded 3
Scrub Forest 2
Semi Evergreen 4
Forest Type  
Agriculture 1
Alpine Barren 1
Conifer 3
Mixed Broad Leaved Forest 3
Sal Forest 4
Scrub Forest 2

Altitude (in meters)

400-800 4
800-1200 4
1200-1600 4
1600-2000 3
2000-2400 3
2400-2800 3
2800-3200 2
3200-3600 2
3600-4000 1
4000-5000 1
>5000 1

Elevation: The elevation variable is an important variable as it determines the amount of oxygen in the atmosphere and the temperature variation.

Slope: Slope profile determines the intensity of fire and the direction of the spread. Here the slopes have been classified into four classes

  1. 0-30%
  2. 30-45%

Aspect: In what concerns the aspect, four classes will be created:

  • Slopes facing South – higher fire risk class (slopes are the warmest)
  • Slopes facing West – the slopes heat increases after noon until the sunset
  • Slopes facing East – the slopes heat increases from sunrise until noon
  • Slopes facing North – lower fire risk class

Forest Density: Forest density suggests the fuel matrix and their arrangement. Forest with 40% canopy suggests that forest is dense and as such lie close to each other. Here again weights have been assigned on the scale of 1 to 4.

Thus these parameters can now be used to develop a weighted model, which can delineate the fire risks zones. Table 3 presents the weight assigned to the parameters affecting wild fires.


Figure 6: Forest Fire Potential Map

Table 4:Forest Fire Risk
Sl. no. Forest Fire Risk Area (%) Cumm. Area (%) Area (Sq Km)
1 Extremely High 2.87 2.87 20.145
2 High 38.25 41.13 268.515
3 Low 45.70 86.82 320.814
4 Extremely low 13.18 100.00 92.524
      100.00 702

Conclusion
The analysis (Table 4) shows that around 41.43% of the study area has high fire susceptibility out of which 2.87% has extremely high index. These areas have been identified in the lower portion (southern) of the tract, which has Sal forests. In fact, these are reported to have forest fires as recently as 1999-2001. This conforms to the GIS model developed in this case study and thus enforces the validity of GIS based study on environment.

As mentioned earlier the capital value of the forests in the study area is estimated as 33 billion rupees, it is galore that high stakes are involved. Further the environmental contribution of these forests is Rs. 540 crores annually. Hence, suitable disaster management model taking care of preventive measures against visible forest fire hazards is all the more essential.

It is pertinent to point out that the road network within the forest acts as man-made fire-line. Simultaneously the road network also enhances the approachability within the forest areas thus making it more probabilistic for the occurrence of the fire incidence.