




































Georgian Geographical Journal 2023, Vol.3 (2) 

 

Analysis of Forest Cover Changes in the Dharchula Alpine 
Ecosystem, Western Himalayas, India: in the context of the 

governance of protected areas 
Bhanwar Vishvendra Raj Singh1*, Ravi Mishra 2, Shailesh Yadav3 

Abstract 

The heart of biodiversity conservation initiatives are biosphere reserves, national parks, wildlife reserves, and 

other protected areas (PAs). PAs are permanent institutions, according to protection policy and practice, although 

the fragmentation forest habitat indicator is downgrading, downsizing, and degazettement (PADDD). In the 

current context, India has 514 wildlife sanctuaries, while the Himalayas have approximately 35 wildlife 

sanctuaries. However, wildlife sanctuaries in the Himalayan range have not been extensively studied using space 

technology (such as geospatial techniques), so Dhar Chula Tehsil, which is part of the Pithoragarh district in 

Uttarakhand, India, was chosen as the study area due to the high density of forests and proximity to the Askot 

Wildlife Sanctuary. This research will aid in tracking changes in wildlife habitats and demonstrating options for 

greater biodiversity restoration. The examination of land cover and land-use change includes a substantial 

concentration of forest habitat change. This study seeks to examine changes in the LULC of Dharchula utilising 

multitemporal satellite data, e.g., Landsat 2 and Sentinel 2b for 1975 and 2020, respectively, to assess the 

understanding of forest changes at Dhar Chula Tehsil. The adjustments were validated using numerous indices, 

including the normalised difference vegetation index (NDVI), the normalised difference vegetation index (RDVI), 

and the weighted analysis method. As a result, the forest area has expanded marginally over the last five decades, 

from 130455 hectares to 157140.2 hectares. This is good news for the forest environment. 

Keywords: Forest Ecosystem, Protected Areas 

Introduction 

India is the 7th-largest country in the world. The vastness and complexity of Indian landscapes 

contribute to a diverse ecosystem that faces many problems. 

Forests are home to many species, mostly flora and fauna, and provide abundant resources to our 

environment [1]. The forest is not only a place to enjoy nature's beauty but also a phenomenon that 

provides essential needs for humans and society, such as fuelwood, edible materials such as fruits and 

vegetables, and housing for many herders and farmers [2]. Forest areas in India range from temperate 

to coniferous, evergreen to shrub and deciduous forests, and from tropical forests to cool temperate 

forests [3]. With different climatic conditions, India is home to a diverse range of flora and fauna, 

including lions, Bengal tigers, white tigers, panthers, one-horned rhinoceros, and lion-tailed macaques. 

The flora of India ranges from the western Himalayas to the country's plains and includes species such 

as chirping, blue pine, silver fir, and dwarf willow. 

India is dealing with enormous issues in the forest sector, such as deforestation, degradation, and 

loss of habitat for both flora and wildlife, as well as extreme climatic conditions and the consequences 

of forest fires across enormous areas, resulting in carbon escapes back into the atmosphere [4]. Forests 

are critical for ecosystem survival and biodiversity [5]. Because of the substantial asset externalities 

involved at both the global and local levels, deforestation and degradation in the Himalayan regions are 

of considerable concern to all policymakers [6]. 

The Himalayas are the only source of India's three major river systems, which include the Ganges, 

the Brahmaputra, and the Indus [7]. Along with the abovementioned main rivers, there are several 

tributaries that originate in the Himalayas. The Himalayas are among the most tectonically unstable, 

undeveloped, ecologically vulnerable, and dense mountains on the planet [8]. Because of continuous 

uplift, this mountain range is extremely vulnerable to large-scale tectonic movements and landslides [9, 

10]. They are the world's most unstable and vulnerable mountain ranges [6]. 

                                                           
1 Department of Geography Mohanlal Sukhadia University, Udaipur, India 
2 Kumaun University, Almora, Uttarakhand, India 
3 Sahariya Govt. College, Kaladera, Jaipur, India 

* Corresponding author: bhanwarsa28@gmail.com 

 



Singh et al. Georgian Geographical Journal 2023, Vol.3 (2) 

This study is based on locating areas in the Askot Wildlife Sanctuary, which are protected areas that 

serve as a reservoir of biodiversity in a biogeographical unit; they serve as a refuge for native plants, 

animals, and microorganisms; and they serve as a laboratory [11], where forest degradation and 

deforestation have occurred or are on the verge of occurring. Conservation of the world's wild genetic 

resources is becoming increasingly dependent on a limited percentage of land area in natural reserves, 

particularly as natural areas rapidly deplete [12, 13]. 

Our study is based on the detection of changes in the forest region of Dhar Chula Tehsil (near Askot 

Wildlife Sanctuary), which is located in the Pithoragarh district of Uttarakhand, India, in the western 

Himalayan range. There are 279 kinds of fodder, including trees, shrubs, and herbs, according to Samant 

1998 [14, 15]. The research is carried out using multitemporal satellite data. The primary focus of the 

research is the application of remote sensing and GIS [16, 17]. 

Remote sensing (RS) data, in conjunction with geographic information systems (GIS), aid in the 

analysis, mapping, and monitoring of earth resources for effective and sustainable forest landscape 

management. Remote sensing is a method of gathering data or information about an area without 

physically coming into contact with an object or study area [17]. GIS is a computer system that has 

been expanded to gather data, store it, analyse it, and then present it as an output result to aid in the 

solution of a real-world problem [18, 19]. 

Methods and Materials 

Dharchula is a tehsil and a Nagar panchayat in the Uttarakhand district of Pithoragarh. In the 

mediaeval period, Dharchula was a key trade town for the Trans-Himalayan trade routes. The Dharchula 

is surrounded by Himalayan peaks and is located at an elevation of 915 metres above sea level. It is 

located in a valley on the Kali River's banks. This city is named Dharchula because it is situated on a 

hill that resembles a stove. Pithoragarh is 90 kilometres away. It is located at a distance and is 

surrounded by mountains. In regard to tourism, the most prominent tourist attraction in this city is 

Manas Lake, also known as Manasa Sarovar. 

 
 

Figure 1. Study Area. Source- Prepared by researchers, GOI. 



Singh et al. Georgian Geographical Journal 2023, Vol.3 (2) 

Askot Wildlife Sanctuary is also included in this tehsil and is located in Pithoragarh district on the 

longitude of 29°46'45"N to 30°27'45"N and latitude of 81°01'53"E to 80°16'25"E. Uttarakhand. 

Pithoragarh is approximately 1514 m tall, or 4967 ft [20]. In 1960, the district was separated from the 

Almora district. This is the easternmost district of Uttarakhand, which shares borders with Nepal in the 

east and Tibet in the north. Pithoragarh is another name for "Little Kashmir" [21]. It is a historical site 

since it served as the Chand Kings' seat of authority in the Kumaon region. The months of March to 

June and September to December are ideal for visiting the Pithoragarh district [22]. 

Askot was established to protect the endangered musk deer, but it is also rich in flora such as herbs 

and shrubs. "Green Paradise" is another name for the Askot Wildlife Sanctuary. This location is 3629 

feet above sea level. Along the different river streams, the region is covered in a dense forest of deodar 

and pine. It is also reported to be a significant terrain feature for pilgrims travelling to Bhanar, Nirikot, 

Kalapani, and Adi Kailash. The sanctuary region includes passes such as Lumpia Lekh, Lipu Lekh, and 

Mankshang Lekh. 

 
Chart 1: Methodology Chart 

Dharchula tehsil is our research study region because, despite its never-ending natural beauty, it is 

being degraded by the consequences of forest degradation and deforestation. Our research is based on 

identifying profiles of places where degradation and deforestation have occurred, as well as analysing 

the impact on biodiversity. 

Aster (DEM) 

Relief 

Slope 

Aspect 

Data 

Landsat 2 & Sentinel- 2A 

Pre - Processing 

Geometric 

Correction 

Radiometric 

Correction 

Post - Processing 

INDICES 

NDVI 

RDVI 

SAVI 

Image 

Classification 

Land Use/Land Cover 

LULC 1975 LULC 2020 

Accuracy Assessment 

Overlay/Weighted Analysis 

Analysis of Forest Change Final Result 

Ground Truthing 

Google Earth Satellite Imagery 



Singh et al. Georgian Geographical Journal 2023, Vol.3 (2) 

This study used a combination of quantitative and qualitative methods of research with geospatial 

technologies to explore the spatiotemporal dynamic change in the Askot Wildlife Sanctuary. 

Methodology Charts 

This study employed remote sensing data to track changes in forest cover in the study area between 

1975 and 2020. ERDAS Imagine 2015 software and ArcGIS 3.1.0 were used for image processing. This 

study made use of both primary and secondary data. In the research process, every piece of data is useful 

and important. Landsat MSS data image acquisition date 06/12/1975 with four bands; Sentinel 2-A data 

image acquisition date 24/10/2020; and ASTER DEM data 30 m resolution were the major data used in 

the study. (Table 1). 
Table 1. Data Set Used 

Sr. No. Data type Source Spatial resolution (m) 

1 Landsat 2 MSS (1975) 

& 

Sentinel 2-A (2020) 

United States Geological Survey 

(USGS) 

80 

 

10 

2 Digital Elevation Model ASTER 30 

For forest cover change detection approaches, Landsat imaging preprocessing and supervised 

classification algorithms were used. Images were classified into five LULC groups (Table 2): dense 

forest, shrubland, grassland, companion land, and water body. 
Table 2. Land use and land cover classes 

Sr. No Land use/land cover type Description 

1 Dense Forest Area covered by dense forest with high pixels 

2 Shrub Land Open Forest and bushes, Agriculture, and grassland 

3 Fellow land Not in use lands, bank of rivers 

4 Barren Land Hilly features and low fertile areas 

5 Water bodies Every pixel contains water like rivers, lakes, streams & ponds, etc. 

 

Forest cover change detection technique 

The detection of change entails the use of multitemporal datasets to distinguish areas of land cover 

change between imaging dates. It is commonly utilised in remote sensing techniques that analyse 

multitemporal datasets. 

For any given period, remote sensing technology has the potential to identify numerous types of land 

cover change. Multitemporal Landsat data were gathered to track changes in forest ecosystems in 1975, 

1990, 2005, and 2020. 

The raster data were then transformed to vector layers with Arc GIS 10.2.2 software, and the LULC 

classes were identified. Following the classification of LULCs, maps were created, and the changes in 

forest cover in the research region were analysed. 

Normalised Difference Vegetation Index 

This process is used to calculate the NDVI value of the forest between 1975 and 2020 and to compare 

the NDVI value of each image. NDVI levels indicate forest quality and health. To assess the health of 

the forest, the NDVI process was used for both images. The difference between near-infrared (NIR) 

and visible red reflectance values normalised to reflectance is the NDVI. 

The vegetation category's NDVI value varies from 0.1 to 1. A dense woodland region is usually 

linked with an NDVI value of 0.4 to 1. The NDVI formula is as follows: 

 

                                                             NDVI =  
(𝑁𝐼𝑅−𝑅𝑒𝑑)

(𝑁𝐼𝑅+𝑅𝑒𝑑)
                                                                                       (1) 

Classification Accuracy Assessment using the Error Matrix 

An error matrix was used in the study to examine classification accuracy. It is a square array of 

integers organised in rows and columns that expresses the number of sample units assigned to each 

category relative to the real range as revealed by reference data. 



Singh et al. Georgian Geographical Journal 2023, Vol.3 (2) 

The accuracy assessment reflects the actual difference between the categorisation and the reference 

map or data. The evaluation may imply that the categorisation results are bad if the reference data are 

extremely erroneous. 

To compute the percentage of LULUC (%), Garai and Narayan (2018) compared the original and 

end LULC area coverage, as indicated in Eq. 2 [23]. 

                                                               % Change =
(𝑉2 − V1)

𝑉1
× 100                                                                     (2) 

where % change is the LULC change percentage, V2 represents the final value, and V1 represents 

the initial value. The kappa coefficient shows the percentage of agreement obtained after removing the 

chances of agreement that are likely to occur by chance [24]. The kappa statistic can be used to calculate 

the degree of agreement between value categories and reference data [8, 25]. 

Landis and Koch (1977) classified kappa values ranging from 1 to 1 into three categories: (1) greater 

than 0.80 indicates robust agreement, (2) between 0.40 and 0.80 indicates mild agreement, and (3) less 

than 0.40 indicates poor agreement [26]. There is agreement between the classification and reference 

data. 

 The kappa coefficient typically varies from 0 to 1.00, with the latter indicating sufficient agreement 

and often being multiplied by a hundred to produce a percentage degree of classification accuracy. 

Land Use Dynamic Degree 

To evaluate the temporal and spatially shifting aspects of land use, this study used a single dynamic 

degree index. The single dynamic degree of land refers to the entire amount of change in certain forms 

of land use in the research area over a specific time period [27]. A single dynamic degree index can be 

used to quantify the changing rate of regional land usage. This allows for the comparison of regional 

differences in land-use change as well as the analysis of the changing trend of land use [28]. 

This can be calculated as follows: 

                                                 {𝐿𝐶(𝐾) =  
𝑢𝑏−𝑢𝑎

𝑢𝑎
∗

1

𝑇
∗ 100 %}      (3) 

 
In this formula, 𝑢𝑏 represents the area of a specific land use category at the end of the research period, 

and 𝑢𝑎 represents the area of a specific land use type at the start of the research period. T denotes the 

length of the research period. The dynamic degree of certain types of land use within the study period 

or time is represented by LC. If the dynamic degrees are positive, it indicates that the number of land 

use types is increasing with time. Similarly, if the dynamic degree is negative, the land use types 

decrease with time. 

Terrain Mapping 

Digital Elevation Model/Elevation: The elevation of the study area is depicted on the elevation map. 

The elevation ranges from 212 metres to 6461 metres. The elevation map is shown in Figure 2 below. 

Aspect: The study area's aspect map in the figure below indicates flat areas with values of 0-39, 40-

79 as North, 80-120 as North‒East, 130-160 as East, 170-200 as South‒East, 210-240 as South, 250-

280 as South‒West, 290320 as West, and 330-360 as North‒West. The slope's orientation is measured 

clockwise in degrees from 0-360, with 0 being north, 90 being east, 180 being south, and 270 being 

west. Aspect values determine the physical slope face direction. The aspect directions are supported by 

the slope angle. The aspect map was prepared with the help of the digital elevation model (DEM). 

Relief: The relief map depicts the contours of landmarks and landscape as a function of size and 

elevation. They are the most sophisticated type of topographic map. A location's relief is the difference 

between its highest and lowest altitudes. The study region relief map depicts the shaded region of the 

slopes using azimuth and sun angular directions. The relief map above displays a low value of 0 and a 

high value of 254 m. 

Drainage Pattern: The drainage map of the study region depicts the river network's streams and flow 

patterns. The topography of the land, whether a certain area is dominated by hard or soft rocks, as well 

as the slope of the ground, governs streams and networks. A system is said to conform if its pattern is 



Singh et al. Georgian Geographical Journal 2023, Vol.3 (2) 

tied to the structure and relief of the terrain, it flows through. The drainage map is created using DEM 

data and the Arc GIS software hydrology function. Five different stream orders have been discovered. 

(See Fig. 2(d)). 

  

  
Figure 2: Terrain Map of Askot Wildlife Sanctuary A. Digital Elevation Model, b. Aspect Map, c. Relief Map, d. Stream 

Orders 

Results 

Land Use/Land Cover 

The area naturally covered by the natural environment, such as forest, farmland, water bodies, and 

wetlands, is referred to as land cover. Whereas land use refers to the development of places by humans 

for their lives, whether for conservation, hospitals, schools, residential, or other purposes. Land cover 

analysis is the analysis of satellite and aerial data that aids in the identification of terrain across time. 

  
Figure 3: LULC of 1975 & 2020 



Singh et al. Georgian Geographical Journal 2023, Vol.3 (2) 

Figure 3 represents the land use and land cover area of the Dhar Chula Area for the years 1975 and 

2020. This figure represents all six classes’ areas and their changes. In 1975, the area of forest was 

approximately 92528.64 hectares, and it drastically changed in another image of 2020, with 82363.22 

hectares. 
Table 3. Area of classes and changes in hectares 

Sr. No. Class Name Area, 1975 Area, 2020 Change (2020 - 1975) 

1 Dense Forest 92528.64 82363.22 -10165.4 

2 Water Bodies 8642.88 24572.98 15930.1 

3 Fellow Land 56541.96 57057.61 515.65 

4 Barren Land 66653.28 43757.34 -22895.9 

5 Shrub Land 15746.76 13240.45 -2506.31 

6 Snow Area 457365.6 56986.81 -400379 

Shrubland also decreased by over 2506 hectares. Water bodies and neighbouring land have only 

witnessed good changes. 

Because there is no such high-resolution image available, it is probable that the classification does 

not clearly define it. As a result, the study selected another depiction method, such as indices, for further 

investigation and verification. 

Change Detection and Error Matrix 

Change detection aids in the observation of changes over time [29]. Table 5 compares the change 

detection to the years 1975-2020 (Figure 10) as well as its accuracy assessment. The study area covers 

a total of 277985.44 hectares. 

For accuracy testing, 4000 random points are generated to be checked against Google Earth for 

ground truthing. After calculation, it is discovered that the research has an accuracy of 92.85%, which 

is derived from the following formula: 

                                                    𝑶𝒗𝒆𝒓𝒂𝒍𝒍 𝑨𝒄𝒄𝒖𝒓𝒂𝒄𝒚 =
𝑫𝒊𝒂𝒈𝒐𝒏𝒂𝒍 𝒕𝒐𝒕𝒂𝒍

𝑶𝒗𝒆𝒓𝒂𝒍𝒍 𝒕𝒐𝒕𝒂𝒍
∗ 𝟏𝟎𝟎                                               (3) 

where the diagonal total is 3713 and the overall total is 4000. The kappa coefficient is also calculated 

by using producer accuracy and user accuracy. The Kappa value is 0.89 here, which is very much 

affected, as it is quite close to 1. 
Table 4: Accuracy Assessment of LULC (1975-2020) 

2020 

1
9

7
5
 

LULC Classes Dense 

Forest 

Water 

Bodies 

Fellow 

Land 

Barren 

Land 

Shrub 

Land 

Snow 

Area 

Row 

Total 

User 

Accuracy 

Dense Forest 1767 0 0 0 76 0 1843 95.87 

Water Bodies 0 75 0 0 0 0 75 100 

Fellow Land 16 0 682 0 0 0 698 97.707 

Barren Land 5 0 59 158 58 36 316 50 

Shrub Land 12 1 0 20 942 3 978 96.319 

Snow Area 0 1 0 0 0 89 90 98.88 

Column Total 1800 77 741 178 1076 128 4000 
 

Producer 

Accuracy 

98.16 97.402 92.03 88.76 87.54 69.53 Diagonal Total: 3713 

AC 0.31 
 

kappa 0.896 Overall Accuracy 0.92 (92.85%) 

 

 

Land Use/Land Cover Change % and Single Land Use Dynamic Degree (k %) 



Singh et al. Georgian Geographical Journal 2023, Vol.3 (2) 

Table 5. LULCC in % & Single Land Use Dynamic Degree (k %) 

Land use/Land cover categories Change Area (%) Single Land Use Dynamic Degree (k %) 

Dense Forest -10.9862 -0.24414 

Water Bodies 184.3147 4.095883 

Fellow Land 0.911978 0.020266 

Barren Land -34.3508 -0.76335 

Shrub Land -15.9164 -0.3537 

Snow Area -87.5402 -1.94534 

From LULCC% 1975 to 2020, forest changed by -10.98%, while shrubland and snow area also 

changed negatively by -15.91% and 87.54%, respectively. With 184.31% and 0.91%, respectively, other 

land and water bodies had a good positive rate. 

The declining rates of forest and shrubland over 45 years are slightly lower for the single land use 

dynamic degree, with -0.24% and -0.35%, respectively. Snow area has a significantly negative trend of 

approximately -1.94% every year, which is rather high when compared to other classes. Land and water 

bodies have increased at a healthy rate of 0.02% to 4.09% every year. 

 
Figure 4: Change over Land Use/Land Cover (1975-2020) 

To check and verify the land use land cover outcome, research has used other indices to rectify and 

be sure of the result. 

Normalised Difference Vegetation Index (NDVI) 

The normalised difference vegetation index (NDVI) is a popular metric. It has a value between +1 

and -1. The vegetation index is a satellite image indicator that describes the health of vegetation as well 

as the number of greens. The NDVI calculates the difference between the near-infrared light reflected 

by vegetation and the red light absorbed by vegetation. 



Singh et al. Georgian Geographical Journal 2023, Vol.3 (2) 

The highest vegetation index values decreased from 0.97 in 1975 to 0.82 in 2020 (Table 6). The 

darker the green hue in the image is, the higher the NDVI values and the greater the vegetation cover. 

Similarly, the minimum values increased from -0.97 in 1975 to -0.56 in 2020. This suggests that the 

vegetation conditions in 1975 were better than those in 2020. The outcome can be seen as a dark red 

colour on the map (Fig. 5). The lower the NDVI values and the less vegetation, the darker the red colour, 

and vice versa. According to this comparison, the highest vegetation cover was recorded in 1975, while 

the average vegetation cover was observed in 2020. 

  
Figure 5: NDVI of 1975 & 2020 

Table 6: NDVI statistics of 1975 and 2020 

Sr. No. NDVI 1975 2020 

1 Minimum -0.976 -0.568 

2 Maximum 0.97 0.827 

3 Mean -0.171 0.209 

4 Standard deviation 0.277 0.247 

The mean and standard deviation of the two-period images can better explain this comparison. Table 

6 shows that the mean values grew from -0.171 in 1975 to 0.209 in 2020. This means that the lowest 

NDVI value in the 1975 satellite image has the least vegetation coverage, while the highest mean NDVI 

value in the 2018 image has the most vegetation. The standard deviation of the NDVI score, on the 

other hand, indicates a minor reduction from 0.27 in 1975 to 0.24 in 2020. This suggests that the 

condition of vegetation changes from its mean NDVI value varies more in the 1975 image than in the 

2020 satellite image. Hence, the change in vegetation is more average in the 2020 satellite image than 

the change in vegetation in 1975, according to the NDVI. 

 

  
Figure 6: RDVI of 1975 & 2020 

Renormalised Difference Vegetation Index (RDVI) 



Singh et al. Georgian Geographical Journal 2023, Vol.3 (2) 

The Renormalised Difference Vegetation Index (RDVI) uses the difference between near-infrared 

and therefore red wavelengths with the NDVI to focus on healthy vegetation. It is used for the most 

effective result, which is applied to heavily vegetated areas. 

The maximum renormalised difference vegetation index values grew from 9.6 in 1975 to 59.6 in 

2020 (Table 7). The darker the green hue in the image is, the higher the RDVI values and the greater 

the vegetation cover. Similarly, the minimum values fell from -12.1 in 1975 to -23.9 in 2020. This 

suggests that the state of the environment in 2020 will be better than it was in 1975. The outcome is 

also visible in the map (Fig. 6) as a dark purple colour. The darker the purple is, the lower the RDVI 

and the less vegetation there is, and vice versa. According to this comparison, the highest vegetation 

cover was observed in 2020, while the average vegetation cover was observed in 1975. 
Table 7: RDVI statistics of 1975 and 2020 

Sr. No. RDVI 1975 2020 

1 Minimum -12.1 -23.9 

2 Maximum 9.59 59.6 

3 Mean -0.89 10.6 

4 Standard deviation 1.33 12.8 

 

The mean and standard deviation of the two period images can better explain this comparison. Table 

7 shows that the mean values grew from -0.89 in 1975 to 10.6 in 2020. This means that the lowest RDVI 

value in the 1975 image has the least vegetation coverage, while the highest mean RDVI value in the 

2020 image has the most vegetation. The standard deviation of the RDVI value, on the other hand, 

shows an increase from 1.33 in 1975 to 12.8 in 2020. This signifies that the state of vegetation changed 

from its mean RDVI value in 1975 to 2020. In general, the RDVI values show that vegetation cover has 

increased in general, and forests in particular have increased throughout time. Even if the change was 

minor, the trend indicated an increase in natural vegetation. 

Soil Adjusted Vegetation Index 

SAVI is an abbreviation for the soil-adjusted vegetation index, which depicts the unequal variation 

within the saturation effect caused by soil moisture, soil colour, and high-density vegetation. Huete, A. 

R. (1988) developed a vegetation index that took into consideration changes in red and near-infrared 

destruction via the forest canopy [30]. 

 

  
Figure 7: SAVI of 1975 & 2020 

The maximum soil-adjusted vegetation index values declined significantly from 1.44 in 1975 to 1.24 

in 2020 (Table 8). The darker the green colour in the photograph is, the higher the SAVI values and the 

greater the vegetation cover. However, the minimum values also decreased, from -1.46 in 1975 to -

0.851 in 2020. This signifies that the status of vegetation in 2020 is better than the status of vegetation 

in 1975. The outcome is also visible in the map (Fig. 7) as a dark purple colour. The lower the SAVI 

values and the lesser the vegetation, the darker the purple colour, and vice versa. According to this 

comparison, the highest vegetation cover was recorded in 2020, while the average vegetation cover was 

reported in 1975. 
Table 8: SAVI statistics of 1975 and 2020 



Singh et al. Georgian Geographical Journal 2023, Vol.3 (2) 

Sr. No. SAVI 1975 2020 

1 Minimum -1.46 -0.851 

2 Maximum 1.44 1.24 

3 Mean -0.246 0.314 

4 Standard deviation 0.397 0.37 

The mean and standard deviation of the two-period images can better explain this comparison. Table 

8 shows that the mean values grew from -0.246 in 1975 to 0.314 in 2020. This means that the lowest 

SAVI value in the 1975 image has the least plant coverage, while the highest mean SAVI value in the 

2020 image has the most vegetation. The standard deviation of the SAVI value, on the other hand, 

shows a minor reduction from 0.39 in 1975 to 0.37 in 2020. This signifies that the status of vegetation 

changed from 1975 to 2020 based on the SAVI mean value. In general, the SAVI values show that 

vegetation cover has increased in general, and forests in particular have increased throughout time. Even 

if the change was minor, the trend indicated an increase in natural vegetation. 

Because there have been some conflicting results, researchers have decided to aggregate all of the 

indicators using weighted methods and examine the real forest cover change. 

Weighted Analysis 

The Weightage Sum Tool in ArcGIS was used to produce the weighted analysis of the indices and 

variables indicated above. The weighted sum tool allows weighing variables by combining various 

inputs and integrated analysis. Weightage sum analysis, such as overlay analysis, includes numerous 

inputs with various components for combined weighing analysis. The multiplication of field values for 

each input in the raster is designed using weighted sum analysis. We aggregate all of the raster inputs 

to generate the output data for certain weights. 

  
Figure 8: Weighted Sum Analysis of Indices 

  
Figure 9: Final Forest Cover Map 

Table 9: Forest Cover of 1975 and 2020 



Singh et al. Georgian Geographical Journal 2023, Vol.3 (2) 

 1975 2020 

Forest Cover 130455 157140.2 

For weighted sum analysis, the LULC classification, normalised difference vegetation index, 

normalised difference vegetation index, soil adjusted vegetation index, and digital elevation model were 

also utilised. Following the weighted sum computation, the result is computed using a raster calculator 

and the output's mean value. After re-examining other criteria, it is determined to be a forest area if it 

is more than the mean value. 

 

Discussion 

Community composition patterns, as well as forest composition and diversity, are significant for 

ecological and anthropogenic variables [31-32]. It is also tied to the current environment. The nature of 

the forest community is determined by ecological variables such as location, species variety, and the 

renaissance state of various species [33-34]. 

The main issues that forests face are erosion and deforestation, which are the primary causes of 

reduced forest cover and biodiversity loss. As a result, the necessity for a wildlife sanctuary is critical 

for the protection of endangered species as well as the prevention of natural resources from becoming 

extinct in the future, which is critical for both animals and humans [35-36]. 

The Himalayas, a global biodiversity hotspot, have undergone substantial changes in forest cover in 

recent decades, necessitating the establishment of numerous protected areas (PAs) to prevent forest 

degradation. The spatiotemporal characteristics of this forest cover change throughout the region, as 

well as the effectiveness of its PAs, are unknown. 

Governance can be defined as a set of processes, procedures, resources, institutions, and stakeholders 

that govern how decisions in protected areas (PAs) are made and implemented. Forest PA governance 

is currently multilayered and convoluted, encompassing a diverse set of actors, many levels of power 

sharing, a plethora of statutory and informal regulations, and entrenched interests. However, there is 

little extensive data on how different local governance structures and day-to-day decision-making 

processes inside forest PAs affect PA performance in terms of attaining targeted conservation outcomes. 

Governance mechanisms and structures that drive social-ecological systems and in situ forest 

conservation measures such as protected areas (PAs) can be crucial for effective management and 

development of conservation outcomes. Despite this, little is known about how different types of local 

administration and decision-making mechanisms affect the conservation outcomes of forest-protected 

areas. This is mostly due to a dearth of studies on the relationship between governance regimes and 

environmental or social effects, and understanding is based on case studies. 

This research study examines changes in the forest environment in Dhar Chula Tehsil, which is close 

to the Askot Wildlife Sanctuary. According to the findings of the research study, the southern and 

southwest parts of this area are the most vulnerable, while the eastern and northeast parts are less 

vulnerable. The study's findings revealed that a holistic and complete approach can lessen 

anthropogenic strain by expanding the usage of eco-friendly technology and raising local people's 

knowledge. 

The maps were created using remote sensing and GIS techniques for the years 1975 and 2020. 

Various indicators, such as LULC, SAVI, RDVI, NDVI, relief, slope, and aspect maps, have been 

employed in research. These maps were developed and examined to improve scientific outcomes. 

Others explain the forest covering, height, features, and overall environmental health of this wildlife 

sanctuary, while others highlight improved utilisation of forest resources for biodiversity richness. 

This area's forest ecosystem provides a variety of services as well as a significant function in human 

welfare and subsistence. This research project relied on satellite-collected statistical data. The data 

analysis demonstrates that the adjacent areas have a tribal and rural population that is directly and 

indirectly dependent on forest resources. They are almost in touch with nature. 

It is insufficient to be pleased with a positive result in one region. There are many more areas that 

are under threat and must be protected. At the government, forest department, and local levels, a three-

tier sustainable, holistic, and inclusive management plan should be adopted and interconnected. With 

the collective effort of governments and nongovernmental organisations, the awareness, 



Singh et al. Georgian Geographical Journal 2023, Vol.3 (2) 

socioeconomic, and employment levels should be raised so that people understand how important forest 

resources are in the overall development of humans, which will aid in the achievement of sustainable 

development goals. 

Meanwhile, there are numerous advantages to protecting forest ecosystems. Forest conservation 

includes promoting cultural services, increasing carbon storage and segregation, lowering greenhouse 

gas emissions, poverty alleviation, watershed protection, natural hazard regulation, maintaining food 

security and agricultural services, improving medical services, and ecotourism. 

Conclusion 

Forests offer many farmers and herders shelter, food items such as fruits and vegetables, fuelwood, 

and other essentials for human existence. Natural forests are used for mining, oil extraction, dam 

construction, logging, farming, housing, manufacturing, cattle grazing, and the purchase of wood for 

fuel and furniture. 

Although determining the specific extent of changes in a hilly terrain such as the study location can 

be difficult, past research has revealed a favorable trend in vegetation. Despite the initial trend of falling 

taxonomic research in LULC, it was determined after combining all the indicators and classification 

methodologies that high-density forests still exist in the study region and are developing with rising 

afforestation. Time-series Landsat and Sentinel images show that the area's forest density has increased 

over time in terms of NDVI, indicating a positive trend across all regions of study. A 45-year 

investigation revealed a significant rise in vegetated areas. The amount of forest cover increased over 

the five decades between 1975 and 2020. From 130455 hectares in 1975 to 157140.2 hectares in 2020, 

the forest area has risen. 

The findings of this study are based on an initial assessment enabled by time-series data analysis and 

other relevant and well-known indicators. In-depth surveys and fieldwork are needed to corroborate the 

findings and improve the study's accuracy. 

Competing interests 

The authors declare that they have no known competing financial interests or personal relationships 

that could have appeared to influence the work reported in this paper. 

Authors’ contribution 

The offered concept was considered by Bhanwar Vishvendra Raj Singh. Ravi Mishra and Shailesh 

Yadav led the composition of the manuscript and performed the analytic computations, methodology, 

and results. All other authors contributed constructive criticism and assisted in shaping the research, 

analysis, and paper. 

Acknowledgements 

It is my proud privilege to express my gratitude to many people who helped me directly or indirectly 

conduct this scientific research work. I express my heartfelt indebtedness to ness and owe a deep sense 

of gratitude to my colleagues and my well-wishers for their encouragement. I am extremely thankful to 

Mr. Ravi Mishra and every member of my family for their sincere guidance and inspiration in 

completing this project. I also thank all my friends who have more or less contributed to the preparation 

of this project report. I will always be indebted to them. 

The study has indeed helped me to explore more knowledgeable avenues associated with my topic, 

and I am sure it will help me to grow more in the future. 

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