Estimate of land use land cover change and its impact on landscape structure through using Landsat Satellite data and techniques: The case of Bagmundi C.D. Block, Purulia District, West Bengal.

Estimate of land use land cover change and its impact on landscape structure through using Landsat Satellite data and techniques: The case of Bagmundi C.D. Block, Purulia District, West Bengal. | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 10 March 2025 V1 Latest version Share on Estimate of land use land cover change and its impact on landscape structure through using Landsat Satellite data and techniques: The case of Bagmundi C.D. Block, Purulia District, West Bengal. Author : Rajesh Hansda 0009-0002-5943-7584 [email protected] Authors Info & Affiliations https://doi.org/10.22541/au.174160655.53720400/v1 451 views 180 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract This research paper tried to find out the change in land use land cover and its impact on forest landscape pattern in Bagmundi C.D. block of Purulia district using Landsat satellite data for the years of 1990, 2000, 2010 and 2020. Supervised image classification techniques are used for image classification. Patch and landscape metrics are calculated from Landsat satellite data in the FRAGSTATS 4.2 software. The results show that the area of forest cover has decreased at the expense of the built-up and agricultural land. The area of built-up has continuously increased over time at the cost of agricultural and forest land. Agricultural land has continuously decreased over time because this land is being converted into a built-up area. Forest clearing outside the major forest areas does cannot affect the morphology, shape and configuration of the forest landscape. Deforestation is occurring in the form of patch clearing in small areas. So, the forest landscapes are becoming more compact, suggesting irregular felling of trees. This research paper has been able to point out some important findings only for a small area. However, this kind of research work can be extended to other areas with heavy deforestation and then suitable planning can be made in the future. Estimate of land use land cover change and its impact on landscape structure through using Landsat Satellite data and techniques: The case of Bagmundi C.D. Block, Purulia District, West Bengal. First author and Corresponding author: Rajesh Hansda Junior Research Fellow, Department of Geography, The University of Burdwan, Burdwan Pin-713104, West Bengal, India, E-mail address: [email protected] ORCID ID: 0009-0002-5943-7584 not-yet-known not-yet-known not-yet-known unknown Abstracts This research paper tried to find out the change in land use land cover and its impact on forest landscape pattern in Bagmundi C.D. block of Purulia district using Landsat satellite data for the years of 1990, 2000, 2010 and 2020. Supervised image classification techniques are used for image classification. Patch and landscape metrics are calculated from Landsat satellite data in the FRAGSTATS 4.2 software. The results show that the area of forest cover has decreased at the expense of the built-up and agricultural land. The area of built-up has continuously increased over time at the cost of agricultural and forest land. Agricultural land has continuously decreased over time because this land is being converted into a built-up area. Forest clearing outside the major forest areas does cannot affect the morphology, shape and configuration of the forest landscape. Deforestation is occurring in the form of patch clearing in small areas. So, the forest landscapes are becoming more compact, suggesting irregular felling of trees. This research paper has been able to point out some important findings only for a small area. However, this kind of research work can be extended to other areas with heavy deforestation and then suitable planning can be made in the future. Keyword: Land use land cover (LULC), Forest cover, Normalized difference vegetation index (NDVI), Patch area, Landscape structure. Introduction The forest is the driving energy of the entire ecosystem, but it is more degraded due to incidents of forest fires, changes in land use land cover, illegal tree cutting and population pressure in some place of developing countries. In some place agricultural land have decreased with expense of the built-up area (Kumar et al., 2018; Sam and Balasubramanian, 2023). Most of the forest area and grassland have decreased due to deforestation and the expanse of the impermeable surface (built up) (Wang et al., 2018; Roy et al., 2020; Shikary and Rudra, 2021; Kaiser et al., 2022). Most of the vegetation have decreased with expense of agricultural land, built-up area and high population density in the last few decades (Ghosh et al., 2021; San et al., 2021; Roy and Mandal, 2023). In some places, forest fragmentation has occurred by the anthropogenic activities such as industrialization, high population density, illegal tree cutting etc. (Biswas and Sengupta, 2022). Maximum likelihood classifier technique is mostly used for image classification and provide better accuracy level (Chughtai et al., 2021). The residential and commercial activities have increased in urban area with increased of the built-up area (Kafy et al., 2020).Early It is be mentioned that the vegetation and built-up area have increased with increased of the cost of barren land (Fatemi and Narangifard, 2019; Karakus, 2019; Mumtaz et al., 2020). The value of the normalized difference vegetation index (NDVI) is varying due to change in land use land cover change (Gondwe et al., 2018). any vacant and follow land of the urban area highly demand for housing, industry and other development activities in such way the built-up area increased over time (Jagtap et al., 2024). Euclidean nearest neighbour is most important indexing is used for measuring proximity of the patch fragmentation in landscape structure (Hernandez-Stefanoni, 2005; Matte et al., 2015; Frazier and Kedron, 2017; Roy and Mandal, 2023). FRAGSTATS software is used for analysis of change in landscape structure over time (Mc Garigal et al., 2002; Singh et al., 2014; Kumar et al.,2018). Landscape structure of the forest is degraded by the expansion of built-up area, illegal tree cutting, mining and infrastructure activities etc. (Southworth et al., 2002; Chust et al.,2003; Midha and Mathur, 2010; Styers et al., 2010; Kumar et al.,2018). The highest forest fragmentation has occurred in trans Himalayas area than the Eastern Himalayas of India due to natural calamities (Reddy et al., 2013). Ecosystem services of the forest is most vulnerable due to highest for anthropogenic causes than natural in some places of India (Hamrick, 2004; Desprez-Loustau et al., 2016; Mori et al., 2017). The major objectives of this paper are to estimate the change in land use land cover and its impact on forest landscape configuration. This paper also investigates the character of vegetation for selected years. Above, most of the research papers clearly mention that the forest is degraded by unscientific land-use planning and anthropogenic pressure, so for this purpose this research paper properly investigates the changing pattern of the forest landscape by using Landsat satellite images. Identification of the patch and landscape metric pattern over time either increased or decreased. Earlier, most of the research papers were used by FRAGSTATS software to identify change patterns of the patch metrics as well as landscape metrics from Landsat satellite images for different years. Here we use two types of Landsat images, such as Landsat 5 for the years of 1990, 2000 and 2010. On the other hand, Landsat 8 for the year of 2020. Before going to analysis of the patch metric and landscape metric of the forest landscape there are properly identify different types of land use land cover from the same database by using supervised image classification techniques. After classification of images for the above selected years validated the classification images for assessment of the accuracy level for current year land use land cover map. forest cover is derived from classified land use land cover map in all selected. the raster image of the forest cover is analysis by using FRAGSTATS software. Study area Bagmundi C.D. block is located in the western part and outer region of Ajodhya Pahar of Purulia district, West Bengal, which is chosen as study area of analysis, and it is shown in (Figure 1). The geographical extension of the block lies between 23°7´18´´ and 23°17´42´´ North of latitude and between 85°40´6´´ and 86°11´50´´ East of longitude. The area is covered by 427.95 sq. km out of 6257.78 sq. km of Purulia district (Census of India 2011). The height of topography lies from 180 to 700 meters from the mean sea level (MSL) of the block, which is located in the outer region of the Ajodhya Pahar. The rainfall of the block was recorded at 363.68 mm during the monsoon, but 44.89 mm during winter. The annual surface temperature occurs at 25.15 °C (Source: Prediction of Worldwide Energy Resource, 2021). So, it is evident that the climate pattern has occurred in tropical wet and dry or Savana (Aw) type climate. The population is distributed to 69520 by males (51%) and 66059 (49%) by females out of the 135579 population (Census of India, 2011). Most of the vegetation occurs in a subtropical deciduous forest, such as Sal (Shorea robusta), Palash (Butea monosperma), Bel (Aegle marmelos), Babla (Vachellia nilotica), Sonajhuri (Acacia auriculiformis) etc. (Sur et al., 1992). Most of the area is covered by laterite and red-yellow soil, which is located in the north, south and western parts of the block. In Bagmundi block, most of the local people collect different goods: fuel, wood, timber for domestic purposes, even they are used medical plant. Their population has gradually increased over time, so this is the reason why the forest is degraded. A lot of literature has been carried out on forest deforestation and fragmentation in Ajodhya Phar, so this literature clearly mentioned that most of the forest has been degraded by unscientific land use planning, plantation errors, population pressure over grazing etc. One important thing in Ajodhya Pahar is an important tourist place in South West Bengal. But sometimes tourist activities have a negative impact on the forest. So, these are significant aspects of this research work for select in the Bagmundi block. Materials and Methods Entire work for this research paper has been done based on two types of Landsat satellite images, such as Landsat 5 (TM) for the years of 1990, 2000 and 2010 and Landsat 8 for the year of 2020 (OLI). All the Landsat satellite images were collected by the United States Geological Survey (USGS) (https://earthexplorer.usgs.gov). Landsat satellite images have been collected in May and December and cloud cover is very low. The detailed information about the Landsat satellite images is shown in (Table 1). By using these Landsat satellite images, the analysis of the patch and landscape pattern of the forest. The database of the forest landscape is used also the above-mentioned Landsat satellite images to identify change patterns of patch metrics as well as landscape metrics. Prepare the land use land cover (LULC) and normalized difference vegetation index (NDVI) with the help of the above Landsat satellite images for the years of 1990, 2000, 2010 and 2020. The analysis of the forest structure of an entire block in Bagmundi is most suitable for Landsat satellite data because of the vast area, rugged topography, protected forest, and most sensitive area for locating the boundary between West Bengal and Jharkhand state. This research mostly focuses on trends in forest structure over time and what are the prime factors of forest deforestation and fragmentation. I have visited different places to show ground truth and evident of the landscape structure across forest beats of the block. Protected forest has come under the Bagmudi forest range consists of three beats, such as Kalimati, Bagmundi and Burda, which come under the Purulia forest division are shown (Table 2). The area under forest cover has highest occurred in Bagmundi beat (8512.50 hectares) and is followed by Burda beat (3908.60 hectares) and Kalimati beat (1823.40 hectares) out of the 14244.5 hectares in Bagmundi forest range (Table 2). Most of the beats have occurred in the Sal tree (Shorea robusta) and other tree species such as Palash (Butea monosperma), Sonajhuri (Acacia auriculiformis), Kusum, Mahua, Neem, Kend etc. Method of Normalized difference vegetation index (NDVI) Earlier, it was mentioned that you identify the vegetation pattern over time by the normalized difference vegetation index (NDVI). NDVI is the ratio between positive and negative values of the near-infrared band and red band (Rouse et al., 1974; Deering et al., 1975; Huete et al., 2002). It is used to identify the biomass, leaf area and percentage of the vegetation (Babalola and Akinsanola, 2016). NDVI = \(\frac{Near\ infrared\ band\ -\ Red\ \ band}{Near\ infrared\ band\ +\ Red\ band}\) Method of land use land cover classification Here two types of Landsat satellite images have been used, such as Landsat 5 (1990, 2000 and 2010) and Landsat 8 (2020). The maximum likelihood algorithm technique is used for land use land cover (LULC) image classification. Arc GIS 10.8 software is used for the entire procedure of image classification. Accuracy assessment of the image classification is done with the help of 115 signature samples from the current year image and merged. Pixel and object-based image classification are the most standard techniques because they maintain spectral variability and heterogeneity of the land class at a fine scale (Pal and Ziaul, 2017). Most of the research work has been mapping of the land use land cover based on Satellite Landsat images. In India, there is no one standard technique of land use classification and some organizations provide land-use land use land cover maps, such as the National Atlas and Thematic Mapping Organization (NA&TMO), Directorate of Economics and Statistics, national remote sensing centre (NRSC) under the Indian Space Research Organization (ISRO) etc. (Halder, 2013). not-yet-known not-yet-known not-yet-known unknown 3.2.1 Method applied for accuracy assessment An accuracy assessment of the image classification has been prepared with the help of an error matrix. The error matrix lies between actual and predicted classification and which provides an image classification system (Prisley and Smith, 1987; Jupp, 1989; Czaplewski, 1992; Pal and Ziaul, 2017). The training sample categorized from the image is compared to the same training sample on the ground which was detected by Google Earth Pro map. Accuracy assessment mostly supports the user with an overall accuracy level of the image classification. Most of the analytical techniques, such as overall accuracy and Kappa coefficient, are used for the assessment of accuracy level in image classification (Richard, 1996; Stehman, 1997; Sam and Balasubramanian, 2023). The percentage of overall accuracy and Kappa coefficient were calculated using the following formula. OA = \(\frac{\sum D_{\text{ii}}}{N}\ \)*100 where: OA= User accuracy, Dii = total number of correctly classified pixels (in the diagonal cell), N=Total number of reference pixels in the error matrix. KC: = \(\ \frac{N(\sum_{i}^{r}{=1\ xii)-(\sum_{i}^{r}{=1\ (x\ i+\ .x+i)}}}{N^{2}-\ \sum_{i}^{r}{=1\ (x\ i+\ .x+i)}}\) Whereas, KC = Kappa Coefficient, N = the total number of pixels, r = number of rows in the error matrix, xii = number of observations in row I and column I (on the major diagonal), xi = total of observations in row I (shown as marginal total to right of the matrix), x+i = total of observations in column I (shown as marginal total at bottom of matrix). So, it is evident that the investigative accuracy level of image classification has 84.67%, which is almost agreement. 3.3 Method of patch and landscape parameter Earlier it was mentioned that analysis of the patch and landscape metrics of the forest is calculated in FRAGSTATS 4.2 software. FRAGSTATS software is the most familiar and reliable software for the analysis of landscape structures (McGarigal et al., 2009). Seven patch metrics and thirteen landscape metrics of the landscape have been selected to identify patch morphology, shape and configuration of the forest. An aggregation index is most popular for identifying aggregated or disaggregated of the patch. However, no change in patch size indicates more aggregation, and it gets reversed of the highest patch size (He et al., 2000). Some of the indexes, such as shape, area, connectivity index, Euclidean nearest neighbour etc., have been used to identify the shape, size and morphology of the landscape, Ripple et al., 1991. The Shannon and Weiner diversity index is the most popular index to identify the species diversity of species and landscape (Mandal et al., 2019). Patch area, patch perimeter and perimeter area ratio are used to quantify the pattern, extension and compactness of the patch. Most of the landscape metrics, such as contiguity, Euclidean nearest neighbour, contagion, patch cohesion etc. have been estimates of the spatial connectedness, clumsiness, clumping or aggregation of the landscape structure. The following procedure has been applied for calculation of the patch and landscape metrics of the forest for the specific years. 1) initially prepare land use land cover (LULC) map from Landsat satellite images, 2) identify the forest landscape from the LULC map for the targeted year, 3) the raster image of the forest landscape is put in the FRAGSTATS 4.2 software and set require parameter of the patch and landscape metrics, 4) finally the calculation table of the patch and landscape metrics is exported in the Microsoft Excel and analysis of the data. Results 4.1 Change in land use land cover (LULC) The spatial distribution of different types of land types, such as barren land, waterbody, agricultural land, forest land and settlement of the Bagmundi block by land use land cover (LULC) map of the years 1990, 2000, 2010 and 2020 is shown in (Figure 2). The area of the built-up has continuously increased over time at the cost of the agricultural land and forest land, which is located in the entire part of the Bagmundi block except for the protected forest area and hills. The area of the barren land occurs very lowest among the other land types, which is located in the western part (along the river bank). The waterbody is located in the central part (Khairabera Dem) and the west part (Subarnarekha River). The highest proportion of the agricultural land is located in the central, south and west parts of the block. The area under forest cover (Protected Forest) is located in the north, south, east and central parts of the block. Areas of the individual land use type of selected years are shown in (Table 3) and (Figure 3). Change detection of the different land types over three decades, such as 1990-2000, 2000-2010 and 2010-2020 in the Bagmundi block, is shown in (Table 4) and (Figure 4). The change in area of the waterbody and barren land is the lowest among the other land types in the last 30 years. The area of the built-up has continuously increased at different rates over time whereas 7.57 % from 2010 to 2020 and followed by 7.49 % from 1990 to 2000 and 3.36 % from 2000 to 2010 respectively. The area of forest cover has increased by 4.31% from 2010 to 2020 and followed by 1.07% from 1990 to 2000. However, it has decreased by 1.06 % from 2000 to 2010. The area of the agricultural land has continuously decreased over time in the last 30 years. The area of agricultural land is highest decreased by 13.68 % from 2010 to 2020 and followed by 7.02 % from 1990 to 2000 and 0.90 % from 2000 to 2010. It is evident that the area of forest cover has decreased at the expense of the built-up and agricultural land. Barren land has increased from the years 1990 to 2010. The area of agricultural land has continuously decreased due to this land being converted into built-up from the years 1990 to 2020. The area of the built-up continuously increased at the cost of the forest land and agricultural land from the years of 1990 till 2020 in Bagmundi block. It is evident that the small area of forest cover has witnessed a negative increment from 2000 to 2010. Between 2000 and 2010, the government of India adopted many afforestation schemes to fulfill the forest gap, so this scheme is not properly implemented in the Bagmundi block. The area of built-up has continuously increased over time at the cost of agricultural land and forest land. Some important factors such as well-connected transport, tourist places, and a forest-based economy have attracted of population in Bagmundi block. 4.2 Pattern of the vegetation The pattern of the vegetation is analyzed by the Normalized difference vegetation index (NDVI) in May and December of the years 1990, 2000, 2010 and 2020 are shown (Figure 5). The highest value of the NDVI has occurred in May than December, which is located in the north, east and central parts of the Bagmundi block from 1990-2020. The NDVI value of December occurred in a limited area which is located in the central and east parts from 1990-2020. The higher NDVI value indicates healthy vegetation. However, it gets reversed for unhealthy vegetation in block. The mean value of the NDVI has continuously decreased from 1990 to 2010, but it has suddenly increased in 2020 in the month of April. The mean value of the NDVI has been recorded as 0.18 in 1990, 0.13 in 2000, 0.12 in 2010 and 0.28 in 2020 in the month of April. The mean value of the NDVI has changed at different rates in December, such as 0.14 in 1990, 0.25 in 2000, 0.18 in 2010 and 0.22 in 2020. It is evident that the proportion or percentage of the NDVI value has changed over time and season, so, there is a deciduous pattern of the forest cover. Here vegetation includes protected forest and local vegetation which provides different direct services such as fuel wood, timber, leaf and tourist places. In Bagmundi, the block consists of higher amounts of biomass and carbon stock because a vast area of protected forest is occurred. not-yet-known not-yet-known not-yet-known unknown 4.3 Change in patch area and shape Changes in patch area and shape of the forest analysis using some patch metrics for the years 1990, 2000, 2010 and 2020 are shown (Figure 6 a, b) and (Table 5). Some patch metrics of the forest, such as patch perimeter area ratio, Euclidean nearest neighbor, gyrate, fractal dimension index, contiguity index, shape index and related circumscribing circle have not changed from 1990 to 2020. The value of the patch perimeter has increased from 1990 to 2000 and 2010 to 2020, whereas it has decreased from 2000 to 2010. The area of the patch has increased to 1721.07 ha from 2010 to 2020 and is followed by 428.40 ha from 1990 to 2000, whereas it has decreased to -423.27 ha from 2000 to 2010. The area of the patch has increased from 1990-2000, but it has suddenly decreased from 2000 to 2010, which suggests that deforestation is in the form of patch clearing in small areas. The patch perimeter has gradually increased from 1990 to 2020, whereas 299700 m from 2010 to 2020 and followed by 146700 m from 1990 to 2000 and 104880 m from 2000 to 2010. GYRATE is highest, increasing to 9318.39 m from 2000 to 2010 and followed by 2187.36 m from 2010 to 2020 and 1664.45 m from 1990 to 2000. The perimeter area ratio is highest, increasing to 482026.79 m from 2000 to 2010 and followed by 112477.46 m from 1990 to 2000. However, it has decreased to -30188.22 m from 2010 to 2020. The value of the shape index is highest, increasing to 502.51 m from 2000 to 2010 and followed by 92.33 m from 1990 to 2000; however, it has decreased by 15.50 m from 2010 to 2020. The contiguity index value (CONTIG) has increased to 71.04 m from 2000 to 2010; however, it has decreased to -16.99 m from 1990 to 2000 and is followed by -5.40 m from 2010 to 2020. The value of the Euclidean nearest neighbor (ENN) has increased by 60544.32 m from 2000 to 2010 and followed by 325.75 m from 1990 to 2000. However, it has decreased to -11037.30 m from 2010 to 2020. 4.4. Change in landscape structure and configuration Earlier, it was mentioned that some landscape metrics are used for the analysis of the landscape structure and configuration of forests, which is shown (Figure 7 a, b) and (Table 6). The number of patches has continuously increased from 1990-2020. Some landscape metrics of forest edge density, landscape shape index, and patch density have small changed from 1990 to 2020. The value of the largest patch index has gradually increased from 1990 to 2010, and will decrease by 2020. The value of Euclidian’s nearest neighbour has slowly decreased from 1990 to 2000 and 2010 to 2020, and it has increased from 2000 to 2010. The landscape division index has decreased from 1990 to 2010, and it has increased from 2010 to 2020. The patch number has increased to 510 from 1990 to 2020 and followed by 387 from 2010 to 2020 and 177 from 2000 to 2010. Patch density (PD) has increased by 1.28 from 1990 to 2000 and was followed by 0.97 from 2010 to 2020, and 0.44 from 2000 to 2010. Edge density has increased by 7.37 (m/ha) from 2010-2020 and followed by 3.64 (m/ha) from 1990-2000 and 2.58 (m/ha) from 2000 to 2010. Euclidian’s nearest neighbour has increased by 8.24 m from 2000 to 2010; however, it has decreased to 9.26 m from 1990-2000 and followed by 6.64 m from 2010 to 2020. The landscape shape index (LSI) has increased to 3.68 from 2010 to 2020 and was followed by 1.29 from 2000 to 2010 and 1.82 from 1990 to 2000. The largest patch index (LPI) has increased to 9.74 from 1990-2000 and was followed by 2.35 from 2000 to 2010, whereas it has decreased to 14.048 from 2010 to 2020. Contagion value (CONTAG) has increased by 0.07 % from 2000 to 2010 whereas it has decreased to 5.278% from 2010 to 2020, followed by 1.98% from 1990 to 2000. Aggregation index value (AI) has decreased, followed by 1.1 from 2010 to 2020, 0.55 from 1990 to 2000, and 0.39 from 2000 to 2010. It is evident that the number of patches has continuously increased over time, which suggests deforestation has occurred in small areas. Some of the landscape metrics, such as edge density, landscape shape index, and patch density etc. have changed in the last 30 years, which indicates deforestation has not affected the shape and structure of the landscape. Some landscape indexes of the forest, such as the contagion index, aggregation index, patch cohesion index and contiguity index, have changed slightly from 1990 to 2020, which indicates the forest is becoming more compact, suggesting irregular felling of trees. Deforestation has occurred predominantly along irregular patches. The value of Shannon’s diversity index (SHDI) and modified Simpson’s diversity index (MSIDI) occurred at 0.61 and 0.55 respectively, which suggests the low diversity of the landscape in the Bagmundi block. Discussions Changes in land use land cover are not a new thing. However, it mostly occurs in urban areas, but it also occurs in local areas of India. It is evident that the area of the built-up has continuously changed over time at the cost of agricultural and forest land, which has a negative impact on the environment as well as the livelihood of the population. In our country, most of the population directly depends on agricultural activities and forest ecosystem services. In the research paper finding that agricultural land has continuously decreased over time at the expense of built-up area. However, those people directly involved in agricultural activities parallel depend on forest and, ultimately, there are occurring deforestation occurring. This kind of issue is available in Bagmundi C.D. block, so scientific land use planning should be applied to urban areas as well as local areas. In most cases, the built-up area gradually increased over time at the cost of agricultural land and waterbodies, which ensures surface temperature (Das et al.,2021). In large cities, population density has increased over a short period of time, which has an impact on changes in land use, land cover and heat flux (Neog,2021). The economy of India is dependent on agriculture and industries so, this purpose rapidly developed different types of industry in the last few decades in the forest land (Palit,2021). Earlier, it has been mentioned that deforestation has developed different problems, such as pollution, enhanced surface temperature, declining ecological balance etc. (Ahmed Azeez,2022). In some places of India, forest fragmentation is occurred due to deforestation and unscientific activities, which ultimately decrease ecological quality. Forest fragmentation has been analysed by different landscape domains such as connectivity, core area index, edge density, nearest neighbour distance, patch area etc. (Mandal and Das Chatterjee,2021). The patch perimeter and perimeter area ratio is used to identify the shape of the landscape because the higher value of the perimeter area ratio is associated with an irregular shape, and it gets reversed for the circular shape (Helzer and Jelinski,1999). Deforestation is the result of diversion of the river channel in the lower course, farming case crops, pond excavation, illegal tree cutting, changes in land use, land cover etc. (Bera et al., 2020). Landscape metrics are used to identify landscape variability such as ecosystem functionality, degradation, and habitat distribution (Singh et al.,2018). The highest sensitive value of the ecosystem occurs in forest than other lands use land cover (Saha et al., 2022). It is expected the area of forest cover will decrease at the expense of the mining area, but it is possible to recover if afforestation can be held in wasteland (Mandal et al.,2019). The result of this research paper is observed that the all-parameter value of patch and landscape metrics has small change over time, which suggests that forest clearing held in small areas. All the landscape parameter values no more change over time, which indicates the forest is becoming more compact, suggesting irregular felling of the trees and deforestation occurring predominantly along irregular patches. Finally, suggesting that forest clearing does not affect the morphology, shape and size of the landscape due to it occurring in the outer region of the major forest areas. In India, most people believe God lives in the sacred tree. However, it is ensuring forest conservation from the overgrazing, extraction of fuel wood, forest fire etc. (Ganguli et al.,2016). The most important opportunity for forest conservation of social forestry and agroforestry is enhancing agricultural land and the benefit of acquiring maximum profit parallel ecological balance and sustainability (Halder,2013), by using insect movement, identify patch connectivity in buffer regions, whereas insect movement stops when stand forest is cut (Rigot et al.,2014). Earlier, it was mentioned that the forest area has decreased over time, and is also losing the ecological importance of the environment. Nobody lives in a world that is not dependent on forest, but this forest is degraded by different natural as well as anthropogenic causes. So, for this purpose, proper investigation of the forest degradation, fragmentation and ecological importance of the forest is necessary for management and conservation. This vast forest area typically provides different services and hosts a large amount of biodiversity. Most people are direct or indirect depends on forest ecosystem services. Most local people use medical plants for treatment purposes of disease and any injuries, so this plant disappears due to overextraction (Ghosh et al., 2021). However, forest cover has not only cooling effects in urban areas, but parallel effects in rural areas and gets different services (Verma & Kundapura, 2020). One important ecological importance of the forest is the large carbon reservoir of the terrestrial ecosystem in especially tropical dry deciduous dense forest (ISFR, 2019). Finally, this research paper suggests that we directly or indirectly depend on the forest in our daily life, so, for this purpose, we need to think about the forest for the future. The government of India as well as West Bengal immediately took action to conserve and manage the forest in Bagmundi C.D. block as well as other areas. Conclusion The result of the research reveal that all such parameters have changed over time. It is concluded that the area of the built-up has continuously increased from 1990-2020 at the cost of the agricultural land and forest land. The area of forest cover has decreased at the expense of the built-up and agricultural land. The area of agricultural land has decreased due to this land being converted into built-up from the years 1990 to 2020. The proportion of the normalized difference vegetation index (NDVI) value has changed over time and season, so, there is a deciduous pattern of the forest cover. Most of the patch metrics value have smaller changed from 1990-2020, which suggests that deforestation is in the form of patch clearing in small areas. Most of the landscape metrics indicating deforestation are occurring in the outer regions of the major forest areas, which does cannot affect the patch morphology or shape of the forest landscape. So, the forest landscapes are becoming more compact, suggesting irregular felling of trees. Deforestation has occurred predominantly along irregular patches. The value of the Shannon’s Diversity Index (SHDI) and Modified Simpson’s Diversity Index (MSIDI) is 0.61 and 0.55 respectively, which suggests the low diversity of the forest landscape in the studied block. Nobody lives in a world not dependent on a forest, but this forest is degraded. So, for this purpose, proper investigation of forest degradation is necessary for management and conservation for the future. Acknowledgements I would like to gratefully acknowledge the financial support granted to me by the University Grants Commission (UGC) (NTA UGC NET JRF), Government of India, for conducting this research paper. Thanks to my supervisor, Dr. Somasis Sengupta (Assistant Professor, Department of Geography, The University of Burdwan, West Bengal), for his valuable suggestion and guidance during the manuscript preparation. not-yet-known not-yet-known not-yet-known unknown Conflict of Interest The author declares that I have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this research paper. Data Availability Statement Entire work for this research paper has been done based on two types of Landsat satellite images, such as Landsat 5 (TM) for the years of 1990, 2000 and 2010 and Landsat 8 for the year of 2020 (OLI). All the Landsat satellite images were collected by the United States Geological Survey (USGS) (https://earthexplorer.usgs.gov). Landsat satellite images have been collected in May and December and cloud cover is very low. 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Estimate of land use land cover change and its impact on landscape structure through using Landsat Satellite data and techniques: The case of Bagmundi C.D. Block, Purulia District, West Bengal.. Authorea . 10 March 2025. DOI: https://doi.org/10.22541/au.174160655.53720400/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download. For more information or tips please see 'Downloading to a citation manager' in the Help menu . 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