{"paper_id":"2f4fa9d2-17d2-48ea-b075-ce3b583e6c9b","body_text":"Population growth and its impact on land use land cover: Mokokchung district, Nagaland | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Population growth and its impact on land use land cover: Mokokchung district, Nagaland SENTIAKUM JAMIR, SANGYU YADEN This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6635027/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract 21 st century has seen unprecedented growth of population. The growing population has created lots of chaos as with the growing population expanding needs for resources also arise. The study of land use and land cover change is consequential in the current context as it helps in understanding as to what extend the resources are being utilized. Mokokchung district of Nagaland has vouch phenomenal growth of population over the past few decades. The unprecedented growth of population has appreciably impact on the land use and land cover pattern of the district. The districts population increases by approximately 86.52% over the past 4 decades (1991 – 2021). The total area under built – up areas has seen a major raise where as areas under agricultural land use has decline in area as per the detection analysis. For the purpose of the study Landsat image for the year 1991 and 2021 has been mapped. Data from Census of India and other statistical report were used for the study purpose. The study discloses a great interconnection between population growths on land use land cover change pattern in the study area. Land use population growth landsat image agriculture Figures Figure 1 Figure 2 Figure 3 Figure 4 1. Introduction Land is the most important natural resource, and it has evolved through time to accommodate the needs of humanity in its quest for a better life (khan et al 2022) . Human intervention has resulted in some of the world’s most significant landscape alterations ( Gogoi et al. 2019). Alteration of land occurs naturally or through anthropogenic activities, though the later is found to be the principal drives of land cover change. The burgeoning growth of human population had led to expanding – growing demand for land which led to indefensible pressure on the land. The lopsided increase in the pace of human population growth has led to the expansion of agricultural land and settlement at the expense of degradation of natural land cover, especially forest and water bodies (Brown et al 2012). Mans intervention on land – use has caused severe damage to the environment over the years. Environmental problems such as global warming, biodiversity loss, deforestation, floods, environmental pollution and ecological imbalances are many of such problems. Population growth and land use and land cover change are closely related as mankind has being exploiting the land for centuries for his own benefit. There is an increasing recognition of the linkages between rapid population increase and the quality of the environment. Population growth and the resultant human activities generate pressures to the natural and man-made environments (Barana Babiso et al 2020). Islam et.al. (2018) have stated that the triggering population growth creates a burden on the limited natural resources, which results in intensifying land surface transformation. Rapid population increase causes people to migrate to vulnerable ecosystems such as highlands, wetlands, and forests to fulfil rising demands on land resources which in turn exceeds the usage of land and leads to its degradation (khan et. al. 2022). Human activities which are mainly driven by socio-economic factors bring out changes in non-built-up and built-up land despite restrictions by physical conditions (Long et al. 2007). Human wants are unlimited, and in order to fulfil their needs human had to a great extend explored the inaccessible land which had bring a drastic change to the land use and land cover classes. Over the years built – up areas has seen a major change which is rapidly developing whereas all over the world dense forest areas has been declining in a accelerando manner. The rapid economic growth accompanied by population growth over the past two or three centuries has accelerated the change in earths land use and land cover, and all this evidence point out the future amplification of the same momentum. The study aims to understand the correlation between the growing population and the LULC change in Mokokchung district of Nagaland. Satellite imagery for the year 1991 to 2021 has been used for the purpose of the study. The image analysis shows that dense vegetation area has reduced over the years whereas built – up area has increased. Population growth has deemed a major impact on the land use and land cover change in the study area. With the growing population the change in socioeconomic pattern over the decades has brought tremendous shift in the LULC pattern in the study area. 1.1 Study area A fragmented extension of the eastern Himalayan Mountains, the Naga Hills are the hill ranges on which the district of Mokokchung is located. The hill ranges move from northeast to southwest, passing roughly parallel to one another. The hills' average elevation above mean sea level ranges from 1,000 to 1,200 meters. At 1,326 meters above mean sea level, Mokokchung serves as the district headquarters. The district is divided into six significant ranges. Asetkong, Jangpetkong, Japukong, Langpangkong, Ongpangkong, and Tsurangkong are among them. The paths of Jangpetkong and Japukong were nearly parallel. The easternmost range is Langpangkong, the southernmost is Ongpangkong, and the central range is Asetkong. On the eastern side of the district, the hill ranges are typically greater in elevation than those on the western side. The majority of the area is covered in dense jungles and deciduous trees, which provide significant firewood and timber. The primary activity of the inhabitants is farming, and the primary crop that is widely grown in the valley portion of the area is paddy. The major diet of the populace is rice. 2. Methodology Satellite image for the year 1991, 2001, 2011 and 2021 of Mokokchung district were used in order to detect LULC change in the study area. Landsat 4 (TM) image for the year 1991, Landsat 5 (TM) for 2001 and Landsat 8(OIL) image for the year 2011 and 2021 were used respectively. Landsat image were obtained from USGS Earth explorer website. Table 1 Satellite Image Source Year Sensor Date of acquisition Spatial resolution (m) 1991 Landsat – 4 TM December 14 1991 30 M 2001 Landsat – 5 TM October 15 2001 30 M 2011 Landsat – 8 OIL November 20 2011 30 M 2021 Landsat – 8 OIL December 21 2021 30 M Source: USGS Earth Explorer 2.1 Processing of satellite data Pre-processing step was done to remove the infelicitous misrepresentation of data or to improve the image attributes for later processing. Atmospheric correction was done to remove the atmospheric interference affecting image quality while geometric processing involve rectifying spatial distortion in satellite image were done. Cloud free satellite image was geo – referenced and configure apropos to UTM zone 46 0 N data using the WGS84 coordinate system. 2.2 Classification of image In a broad sense, image classification is defined as the process of categorizing all pixels in an image or raw remotely sensed satellite data to obtain a given set of labels or land cover themes (Lillesand, Keifer 1994 ). Supervised classification was used for the classification of image. Maximum Likelihood Classification (MLC) algorithm has been applied for all the study year i.e., 1991–2021. Sample data representing each LULC class were firstly selected, after that the image processing software uses those samples to learn and assign labels to all the pixels in the image based on their spectral characteristics. The study area is divided into six LULC classes including, agriculture, dense vegetation, low vegetation, barren land, water bodies and built – up. Table 2 Description of LULC class LULC class Class description Agriculture It includes, plantation, shifting cultivation area Dense vegetation Reserved and conserved area, semi evergreen and deciduous forest Low vegetation Sparse vegetation, ground cover percentage, scrub forest Built – up Rural – urban built – up areas, settlement etc. Water bodies Rivers, ponds, wetlands etc. Barren land Fallow land, extremely low vegetation cover. Classified by researcher 2.3 Accuracy assessment To determine the accuracy of the LULC map obtained from Landsat image, 120 reference points were selected through stratified random sampling method from the classified image of 1991, 2001, 2011, and 2021. The selected reference points were justified using Google Earth data. After rectifying the image Kappa coefficient statistic method was used to measure the inter – rater reliability between User Accuracy (UA), Producer Accuracy (PA) and overall accuracy of the generated land use and land cover image. For accuracy assessment the following equations were used Table 3 Accuracy Assessment Using Kappa Coefficient for LULC maps (1991, 2001, 2011 & 2021) Year 1991 2001 2011 2021 Kappa coefficient value 0.82 0.576 0.672 0.706 Source: Calculated by researcher 3. Results and discussion 3.1 Land use and land cover dynamic (1991–2021) Land use and land cover change, as dynamic as it is, there was multi temporal change detected in the study area. Six land use and land cover classes were identified for the purpose of the study which includes agricultural land, dense vegetation, low vegetation, built – up, barren land and water bodies. Throughout the study period forest cover dominates the LULC classes. Where low vegetation cover area has seen a surge over the decades but dense vegetation cover has decline in its area. Low vegetation area covers an area of 474 km 2 in 1991, 501km² in 2001, 268 km 2 in 2011 and 824 km 2 in 2021. Whereas dense vegetation area covers an area of 946 km 2 in 1991, 897 km² in 2001, 1059 km² in 2011 and 503 km² in 2021. Agricultural land use and built –up area shares a significant portion of LULC classes in the study area. The area under agricultural land use was 97 km² in 1991, 109 km² in 2001, 132 km² in 2011 and 91 km² in 2021. On the other hand, built – up area covers 32 km² in 1991, 42 km² in 2001, 85km² in 2011 and 129 km² in 2021 which shows consistent growth in the area over the past four decades. Barren land occupies a significant portion of the study area. it covers 21 km@ in 1991, 32 km² in 2001, 45 km² in 2011 and 41 km² in 2021. Another LULC class is water bodies which cover 39 km² in 1991, 34 km² in 2001, 25 km² in 2011 and 27 km² in 2021. In all the LULC classes built – up area shows the most consistence growth during the whole study period with a total growth of 75% in its area from 1991–2021. Low vegetation cover has shown a growth of 42.47% in its area from 1991–2021 on the contrary dense vegetation area shows a growth of -88.07% in its area during the study period. 3.2 Change detection The objective of change detection is to compare spatial representation of two points in time by controlling all variances caused by differences in variable that are not of interest and to measure change caused by differences in the variable of interest (Green et al. 1994). Change detection is important as it give us an overview of the LULC pattern of a given region. Change detection analysis of Mokokchung district over the past four decade i.e. 1991–2021 shows a variant pattern of LULC. The change detection analysis for the year 1991–2001 shows that, dense vegetation has decreased in area from 946 km² in 1991 to 897 km² in 2001, which shows a decadal growth of -5.46% in the area. Agricultural area has increased from 97 km² in 1991 to 109 km² in 2001 which shows an increase of 11% in the total agricultural area. During the same period (1991–2001) low vegetation cover show a growth of 5.38% in the total area, which change from 474 km² in 1991 to 501 km² in 2001. Built – up area has significantly increased from 32 km² in 1991 to 42 km² in 2001 (26.17% growth). Barren land has increased from 21 km² in 1991 to 32 km² in 2001 with a total growth of 11 km² (34.37%). Water bodies decline in its coverage by 5km² which shows a decadal growth of -14.7% from 1991–2001. During the year 2001–2011 the area under dense cover increased from 897 km² (2001) to 1059 km² (2011) which was a growth of 15.29% in the dense vegetation cover. On the contrary during the same study period low vegetation cover decline by 233 km² (-86.94%) which was a significant development. Built – up area has shown a significant increased from 42 km² in 2001 to 85 km² in 2011 which shows 50.58% growth. Agricultural land covers 109 km² in 2001 and 132 km² in 2011 which shows a growth of 17.42% in agricultural land. Water bodies decline in area from 34 km² (2001) to 25 km² (2011) which shows a decline of 9 km² (-26.47%). Barren land shows a growth of 13 km² from 2001–2011. The year 2011–2021 sees a significant change in the LULC pattern. Dense vegetation covers drastically decline from 1059 km² in 2011 to 503 km² in 2021, whereas low vegetation cover increases from 268 km² (2011) to 824 km² (2021) which is 67.47% growth in the low vegetation cover. Change in built – up land use from 2011–2021 was notable as it increased by 34.1% (44km²). Agricultural land and barren land have seen a down growth in its area (2011–2021). Agricultural area shows a growth of -45.05% whereas barren land detected a growth of -9.75%. The change detection analysis shows certain alteration in LULC classes. Agricultural area has been converted to dense vegetation by 7.17km² from 1991–2001, whereas it is converted to low vegetation by 11.75km², water bodies by 6.43km², built – up by 9.18km² during the same period. The area under dense vegetation cover is converted to agricultural area by 26.32km², low vegetation by 114.42 km², water bodies by 1.23km², built – up by 4.8km² from 1991–2001. Whereas low vegetation cover has been converted to agricultural area by 10.12 km², dense vegetation cover by 29.89 km², water bodies by 1.09 km² and built up by 4.47 km². Moreover built – up area is converted to agricultural land by 5.39 km², dense vegetation by 0.77 km², low vegetation by 5.36 km² and water bodies by 0.18 km². Water bodies’ area is converted to agricultural land by 2.93 km², dense vegetation cover by 3.16 km², low vegetation by 1.67 km², built – up area by 6.97 km² and barren land to .56 km². during the same study period (1991–2001) barren land had converted to agricultural land by 3.2 km², dense vegetation by 3.45 km², low vegetation by 2.39 km², water bodies by 0.41 km², and built – up by 1.48 km². The LULC change analysis for the year 2001–2011 and 2011–2021 also shows a significant conversion of LULC classes. Agriculture area conversion to dense vegetation cover was 11.7 km² in 2001–2001 and by 18 km² in 2011–2021. Agriculture area conversion to low vegetation cover was 12.6 km² in 2001–2011 and by 6.1 km² in 2011–2021. Agricultural area conversion to built – up area by 8.1 km² in 2001–2011 and by 17 km² in 2011–2021. The area under dense vegetation has converted to agricultural by 14.84 km² and by 5.6 km² in the year 2001–2011 and 2011–2021 respectively. The conversion of dense forest to low vegetation was 34 km² in 2001–2011 and by 367 km² in 2011–2021. Dense vegetation area to water bodies was 0.56 km² in 2001–2011 and 2.1 km² in 2011–2021, furthermore from dense vegetation to built – up area was 10. 3 km² in 2001–2011 and 25 km² in 2011–2021. Low vegetation cover is shifted to agricultural land by 25 km² during 2001–2011 and by 7 km² in 2011–2021. Low vegetation area to dense vegetation cover was 197 km² in 2001–2011 and 12 km² in 2011–2021. Low vegetation cover to water bodies has converted by .51 km² in 2001–2011and by .12 km² in 2011–2021, whereas the conversion of low vegetation to built – up was 19 km² in 2001–2011 and 4.1 km² in 2011–2021. Water bodies to agricultural land conversion was 2.2 km² in 2001–2011 and 1.3 km² in 2011–2021. Water bodies are converted to dense vegetation by 1.26 km² in 2001–2011 and 1.06 km² in 2011–2021 and again the conversion from water bodies to low vegetation cover was 0.61 km² in 2001–2011 and 1.2 km² in 2011–2021. Water bodies to built – up area is 1.7 km² and 0.96 km² during 2001–2011 and 2011–2021. Water bodies’ conversion to barren land is 0.67 km² and 1.04 km² from 2001–2011 and 2011–2021. During the year 2001–2011 and 2011–2021 the area under built –up has converted to agricultural land by 8 km² (2001–2011) and 5 km² (2011–2021). Adair from built – up to dense vegetation cover is 5 km² in 2001–2011 and 1.29 km² in 2011–2021. Built – up area to low vegetation cover is 4 km² in 2001–2011 and 3.65 km² in 2011–2021. Furthermore the area under built – up shift to water bodies by 0.19 km² and 1.37 km² in 2001–2011 and 2011–2021 and from built – up to barren land was 2.76 km² in 2001–2011 and 3.1 km² in 2011–2021. Furthermore, barren land is converted to agricultural land by 1.1 km² and 2.5 km² in 2001–2011 and 2011–2021 and barren land to low vegetation cover is 2.69 km² and 3.2 km² in 2001–2011 and 2011–2021. The conversion of barren land to built – up area is 3.45 km² in 2001–2011 and 4.93 km² in 2011–2021, and further from barren land to water bodies was 0.59 km² in 2001–2011 and 1.23 km² in 2011–2021. 3.3 Population growth and changing LULC Population growth and environmental degradation goes hand in hand. Population growth alter the pattern of land use and land cover as with the growing population need for more resources arise which has significant impact on the environment, economy and society. Table 4 Population data from 1991–2021 Year Population 1991 158374 2001 232085 2011 194622 2021* 227989 Source: Census of India Estimated Population* Over the past few decades Mokokchung district as a whole has experience drastic population growth. In the year 1991 the population of Mokokchung district was 158,374, which increased to 232085 in 2001 which shows a decadal growth of 46.5%. A significant development in the population structure occurred between 2001–2011 census years. During this census period (2001–2011) the district experienced a negative growth in the population. In 2001 the population of Mokokchung was 232,085 which changed to 194622 in 2021 which shows a decadal growth of -16.14%. The estimated population for the year 2021 is 227989 which indicate a estimated growth of 17.14% (2011–2021). Table 5 Decadal growth Rate (1991–2021) Year 1991–2001 2001–2011 2011–2021* Population growth (in %) 46.5 -16.14 17.14 Source: Census of India Estimated Population* Table 6 Pearson correlation between population and LULC classes. Population Rho(q) value Agriculture Dense Vegetation Low Vegetation Built – Up Barren Land Water Bodies 0.056 -0.53 0.47 0.48 0.55 -0.48 Source: calculated by researcher In order to determine the correlation between population growth and land use and land cover change Person’s correlation coefficient rho (q) has been used. The calculation matrix shows significant relationship between the two variables. Population growth and agricultural area shows a positive connection with rho (q) 0.056. population growth and built – up show a rho (q) 0.48 which shows a strong interconnection between population growth and built – up area. This strongly indicates that with the growing population more human induced activities are stimulated. In addition, a strong relation is found between population growth and barren land with Rho (q) 0.55. the reason might be due to deforestation (especially inclined with jhum cultivation, road construction and for the purpose of firewood) and urbanization. Low vegetation cover and population growth shows a correlation of Rho (q) 0.47, this positive correlation is associated with the jhum cycle, deforestation and the increase in built – up area. Other LULC classes such as dense vegetation cover and water bodies show a Rho (q) -0.53 and − 0.48. The negative correlation between population and this two LULC class is due to human encroachment. The decline in dense forest cover is related with the clearing of unexplored dense forest cover for infrastructural development and for agricultural land use. on the other hand, the negative correlation between population growth and water bodies is related with the filling up of wetland and ponds for construction of new infrastructure and in addition climate change has played a significant role which led to drying up of many streams. The cumulative result reflects the impact of growing anthropogenic activities on the LULC of the study area. 4. Conclusion The research was conducted in Mokokchung district of Nagaland which is one of the most rapidly growing districts in the state of Nagaland. To assess the impact of growing pressure of human population on LULC, geospatial techniques were use for the study. The study focus to study the relationship between human population growth and its implication on LULC in Mokokchung district. The study demonstrates that there was detectable change in the LULC during the study period (1991–2021). The study shows that with the growing population the LULC classes under built – up, agriculture, barren land and low vegetation cover shows a positive correlation in the land use pattern and dense vegetation cover and water bodies shows a negative correlation. Barren land and built – up shows a strong correlation with the growing population with Rho (q) 0.56 and 0.48 which depict that with the growing human population demand for more, housing and other infrastructural development increases which led to environmental destruction which eventually leave the area as barren land. Population growth is dynamic in nature. Though there are lots of efforts made to control the human population growth it is impossible to put an end to its growth. In order to bring a balance between population growth and LULC change a proper LULC change detection analysis should be done timely and precise information about the same should be updated which will help the policy makers and resource planners to act accordingly which will help in a more sustainable management of land resources. Declarations Author Contribution Authors Contribution All the authors significantly contributed in preparing the study design. Data collection and analysis was done by Sentiakum Jamir. The first draft of the paper was devise by Sentiakum Jamir. Both author has revised the manuscript and commented on the previous draft. Sangyu Yaden supervises the study. Both author has read and approved the final manuscript. Acknowledgement The authors would like to thank Nagaland University, Lumami and the Ministry of Tribal Affairs, Government Of India for providing assistance to conduct the present study. The authors also thank the Hon’ble Vice Chancellor of Nagaland University Prof. Jagadish K. Patnaik for his constant encouragement and guidance throughout the completion of the study. Supports from family, friends and fellow scholar are also highly acknowledged. References Barana Babiso, Senbetie Toma, Aklilu Bajigo. Population Growth and Environmental Changes: Conclusions Drawn from the Contradictory Experiences of Developing Countries. International Journal of Environmental Monitoring and Analysis. Vol. 8, No. 5, 2020, pp. 161-169. doi: 10.11648/j.ijema.20200805.15 Bora R, Bora AK (2024) Population Pressure and Changing Land Use Land Cover in Morigaon District, Assam, India - A Geospatial Analysis. Indian Journal of Science and Technology 17(24): 2547-2556. https://doi.org/ 10.17485/IJST/v17i24.899 Brown DG, Walker R, Manson S and Seto K (2012) Modeling land use and land cover change. In Land change science (pp. 395–409). Springer, Dordrecht. Gogoi PP, Vinoj V, Swain D, Roberts G, Dash J, Tripathy S (2019) Land use and land cover change efect on surface temperature over Eastern India. Sci Rep 9(1):1–10 Green. K, Kempka. D, and Lackey. L, 1994, Using RS to detect and monitor land – cover and land – use change. (Photogrammetric Engineering and Remote Sensing), 60, 331 – 337. Islam K, Jashimuddin M, Nath B, Nath TK (2018) Land use classifcation and change detection by using multi-temporal remotely sensed imagery: The case of Chunati wildlife sanctuary, Bangladesh. Egyptian J Remote Sens Space Sci 21(1):37–47 Khan, N., Raza, M., Shakoor, M.S.A. et al. Dynamic of population growth and its effect on land use/land cover of bahraich district in Uttar Pradesh. J Environ Stud Sci 13 , 124–140 (2023). https://doi.org/10.1007/s13412-022-00805-6 Lillesand, T.M. & Keifer, R.W. 1994, Remote Sensing and Image Interpretation, . Long, H., Tang, G., Li, X., & Heilig, G. K. (2007). Socio-economic driving forces of land-use change in Kunshan, the Yangtze River Delta economic area of China. Journal of Environmental management, 83(3), 351-364. Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {\"props\":{\"pageProps\":{\"initialData\":{\"identity\":\"rs-6635027\",\"acceptedTermsAndConditions\":true,\"allowDirectSubmit\":true,\"archivedVersions\":[],\"articleType\":\"Research Article\",\"associatedPublications\":[],\"authors\":[{\"id\":461535293,\"identity\":\"a6ed63fe-f5f1-4d05-abe8-a1eb822f1b40\",\"order_by\":0,\"name\":\"SENTIAKUM JAMIR\",\"email\":\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA/UlEQVRIiWNgGAWjYDACHiCWgFBAYGADJBgbDxCv5YBBGkhLA2EtcHCA4TCUxgP4e86YbrCo2SZj3n/42OMPBeft1rYfBtpSYxONS4vE2R6zGxLHbvPI3EhLNzhgcDt525lEoJZjabkNuPSc5wFqYbvNIyHBYyYB0mJ2AKiFseEwTi3yYC3/gFr4z38DajmXbHb+IX4tBiCHSbYBtTDksAG1HLAzu0HAFsMzx8puSPaBHJZmJnHGIDnB7AbQlgQ8fpE7k7zttsS32/YS/IefSVT8sbM3O5/+8MGHGhvc3gcCZgkkTiJYZQIe5SDA+AGJY09A8SgYBaNgFIxAAADH8mPicmjqVAAAAABJRU5ErkJggg==\",\"orcid\":\"\",\"institution\":\"Nagaland University\",\"correspondingAuthor\":true,\"prefix\":\"\",\"firstName\":\"SENTIAKUM\",\"middleName\":\"\",\"lastName\":\"JAMIR\",\"suffix\":\"\"},{\"id\":461535294,\"identity\":\"b6858818-7983-4ffc-af2b-398c69f55970\",\"order_by\":1,\"name\":\"SANGYU YADEN\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Nagaland University\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"SANGYU\",\"middleName\":\"\",\"lastName\":\"YADEN\",\"suffix\":\"\"}],\"badges\":[],\"createdAt\":\"2025-05-10 13:23:21\",\"currentVersionCode\":1,\"declarations\":{\"humanSubjects\":false,\"vertebrateSubjects\":false,\"conflictsOfInterestStatement\":false,\"humanSubjectEthicalGuidelines\":false,\"humanSubjectConsent\":false,\"humanSubjectClinicalTrial\":false,\"humanSubjectCaseReport\":false,\"vertebrateSubjectEthicalGuidelines\":false},\"doi\":\"10.21203/rs.3.rs-6635027/v1\",\"doiUrl\":\"https://doi.org/10.21203/rs.3.rs-6635027/v1\",\"draftVersion\":[],\"editorialEvents\":[],\"editorialNote\":\"\",\"failedWorkflow\":false,\"files\":[{\"id\":83601340,\"identity\":\"1bc8c5b6-5c8b-4697-b72b-bb56fe22de22\",\"added_by\":\"auto\",\"created_at\":\"2025-05-29 09:21:21\",\"extension\":\"png\",\"order_by\":1,\"title\":\"Figure 1\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":108994,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eStudy area Map\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cem\\u003eSource: Created by Author\\u003c/em\\u003e\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"1.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-6635027/v1/60407366d8248c37e2ba8d9d.png\"},{\"id\":83601338,\"identity\":\"299bbb33-e65e-462b-a8da-6f4a4d821069\",\"added_by\":\"auto\",\"created_at\":\"2025-05-29 09:21:21\",\"extension\":\"png\",\"order_by\":2,\"title\":\"Figure 2\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":50451,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003eMethodological flowchart\\u003c/strong\\u003e\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"2.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-6635027/v1/91964b26be949a7249508e52.png\"},{\"id\":83601344,\"identity\":\"403f459e-c371-4b08-b67a-80ac288d723c\",\"added_by\":\"auto\",\"created_at\":\"2025-05-29 09:21:21\",\"extension\":\"png\",\"order_by\":3,\"title\":\"Figure 3\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":276239,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003eLULC Map 1991, 2001, 2011 \\u0026amp; 2024\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cem\\u003eSource: Created by researcher\\u003c/em\\u003e\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"3.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-6635027/v1/926ae95a3e9e3c8463032310.png\"},{\"id\":83602193,\"identity\":\"0390b89d-7ab1-4201-90ae-3e8c8e022f0b\",\"added_by\":\"auto\",\"created_at\":\"2025-05-29 09:37:21\",\"extension\":\"png\",\"order_by\":4,\"title\":\"Figure 4\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":494877,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003eLULC change detection map 1991 - 2021\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cem\\u003eSource: Created by researcher\\u003c/em\\u003e\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"4.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-6635027/v1/252f546c2c48a892d7dca3e1.png\"},{\"id\":83602778,\"identity\":\"32f405bb-fe47-4ba3-a6f1-355cf760edb1\",\"added_by\":\"auto\",\"created_at\":\"2025-05-29 09:45:22\",\"extension\":\"pdf\",\"order_by\":0,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"manuscript-pdf\",\"size\":1460613,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"manuscript.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-6635027/v1/dcb6ca87-416f-4961-bb31-75d7cc2fa97a.pdf\"}],\"financialInterests\":\"The authors declare no competing interests.\",\"formattedTitle\":\"Population growth and its impact on land use land cover: Mokokchung district, Nagaland\",\"fulltext\":[{\"header\":\"1. Introduction\",\"content\":\"\\u003cp\\u003eLand is the most important natural resource, and it has evolved through time to accommodate the needs of humanity in its quest for a better life (khan et al 2022) . Human intervention has resulted in some of the world’s most significant landscape alterations \\u003cem\\u003e(\\u003c/em\\u003eGogoi et al. 2019). Alteration of land occurs naturally or through anthropogenic activities, though the later is found to be the principal drives of land cover change. The burgeoning growth of human population had led to expanding – growing demand for land which led to indefensible pressure on the land. The lopsided increase in the pace of human population growth has led to the expansion of agricultural land and settlement at the expense of degradation of natural land cover, especially forest and water bodies (Brown et al 2012). Mans intervention on land – use has caused severe damage to the environment over the years. Environmental problems such as global warming, biodiversity loss, deforestation, floods, environmental pollution and ecological imbalances are many of such problems.\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003e\\u0026nbsp;Population growth and land use and land cover change are closely related as mankind has being exploiting the land for centuries for his own benefit.\\u0026nbsp;There is an increasing recognition of the linkages between rapid population increase and the quality of the environment. Population growth and the resultant human activities generate pressures to the natural and man-made environments (Barana Babiso et al 2020). Islam et.al. (2018) have stated that the triggering population growth creates a burden on the limited natural resources, which results in intensifying land surface transformation. Rapid population increase causes people to migrate to vulnerable ecosystems such as highlands, wetlands, and forests to fulfil rising demands on land resources which in turn exceeds the usage of land and leads to its degradation (khan et. al. 2022).\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003eHuman activities which are mainly driven by socio-economic factors bring out changes in non-built-up and built-up land despite restrictions by physical conditions\\u0026nbsp;(Long et al. 2007). Human wants are unlimited, and in order to fulfil their needs human had to a great extend explored the inaccessible land which had bring a drastic change to the land use and land cover classes. Over the years built – up areas has seen a \\u0026nbsp;major change which is rapidly developing whereas all over the world dense forest areas has been declining in a accelerando manner. The rapid economic growth accompanied by population growth over the past two or three centuries has accelerated the change in earths land use and land cover, and all this evidence point out the future amplification of the same momentum.\\u0026nbsp;The study aims to understand the correlation between the growing population and the LULC change in Mokokchung district of Nagaland. Satellite imagery for the year 1991 to 2021 has been used for the purpose of the study. The image analysis shows that dense vegetation area has reduced over the years whereas built – up area has increased. Population growth has deemed a major impact on the land use and land cover change in the study area. With the growing population the change in socioeconomic pattern over the decades has brought tremendous shift in the LULC pattern in the study area.\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003e\\u003cem\\u003e1.1\\u0026nbsp;Study area\\u003c/em\\u003e\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eA fragmented extension of the eastern Himalayan Mountains, the Naga Hills are the hill ranges on which the district of Mokokchung is located. The hill ranges move from northeast to southwest, passing roughly parallel to one another. The hills' average elevation above mean sea level ranges from 1,000 to 1,200 meters. At 1,326 meters above mean sea level, Mokokchung serves as the district headquarters. The district is divided into six significant ranges. Asetkong, Jangpetkong, Japukong, Langpangkong, Ongpangkong, and Tsurangkong are among them. The paths of Jangpetkong and Japukong were nearly parallel. The easternmost range is Langpangkong, the southernmost is Ongpangkong, and the central range is Asetkong. On the eastern side of the district, the hill ranges are typically greater in elevation than those on the western side. The majority of the area is covered in dense jungles and deciduous trees, which provide significant firewood and timber. The primary activity of the inhabitants is farming, and the primary crop that is widely grown in the valley portion of the area is paddy. The major diet of the populace is rice.\\u003c/p\\u003e\"},{\"header\":\"2. Methodology\",\"content\":\"\\u003cp\\u003eSatellite image for the year 1991, 2001, 2011 and 2021 of Mokokchung district were used in order to detect LULC change in the study area. Landsat 4 (TM) image for the year 1991, Landsat 5 (TM) for 2001 and Landsat 8(OIL) image for the year 2011 and 2021 were used respectively. Landsat image were obtained from USGS Earth explorer website.\\u003c/p\\u003e\\n\\u003cdiv class=\\\"gridtable\\\"\\u003e\\u0026nbsp;\\u003ctable id=\\\"Tab1\\\" border=\\\"1\\\"\\u003e\\n \\u003ccaption language=\\\"En\\\"\\u003e\\n \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 1\\u003c/div\\u003e\\n \\u003cdiv class=\\\"CaptionContent\\\"\\u003e\\n \\u003cp\\u003eSatellite Image Source\\u003c/p\\u003e\\n \\u003c/div\\u003e\\n \\u003c/caption\\u003e\\n \\u003ccolgroup cols=\\\"4\\\"\\u003e\\u003c/colgroup\\u003e\\n \\u003cthead\\u003e\\n \\u003ctr\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eYear\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eSensor\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eDate of acquisition\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eSpatial resolution (m)\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/thead\\u003e\\n \\u003ctbody\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1991\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eLandsat \\u0026ndash; 4 TM\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eDecember 14 1991\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e30 M\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e2001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eLandsat \\u0026ndash; 5 TM\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eOctober 15 2001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e30 M\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e2011\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eLandsat \\u0026ndash; 8 OIL\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eNovember 20 2011\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e30 M\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e2021\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eLandsat \\u0026ndash; 8 OIL\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eDecember 21 2021\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e30 M\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/tbody\\u003e\\n \\u003ctfoot\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd colspan=\\\"4\\\"\\u003e\\u003cem\\u003eSource: USGS Earth Explorer\\u003c/em\\u003e\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/tfoot\\u003e\\n \\u003c/table\\u003e\\n\\u003c/div\\u003e\\n\\u003cdiv id=\\\"Sec4\\\" class=\\\"Section2\\\"\\u003e\\n \\u003ch2\\u003e2.1 Processing of satellite data\\u003c/h2\\u003e\\n \\u003cp\\u003ePre-processing step was done to remove the infelicitous misrepresentation of data or to improve the image attributes for later processing. Atmospheric correction was done to remove the atmospheric interference affecting image quality while geometric processing involve rectifying spatial distortion in satellite image were done. Cloud free satellite image was geo \\u0026ndash; referenced and configure apropos to UTM zone 46\\u003csup\\u003e0\\u003c/sup\\u003e N data using the WGS84 coordinate system.\\u003c/p\\u003e\\n\\u003c/div\\u003e\\n\\u003cdiv id=\\\"Sec5\\\" class=\\\"Section2\\\"\\u003e\\n \\u003ch2\\u003e2.2 Classification of image\\u003c/h2\\u003e\\n \\u003cp\\u003eIn a broad sense, image classification is defined as the process of categorizing all pixels in an image or raw remotely sensed satellite data to obtain a given set of labels or land cover themes (Lillesand, Keifer\\u0026nbsp;\\u003cspan class=\\\"CitationRef\\\"\\u003e1994\\u003c/span\\u003e). Supervised classification was used for the classification of image. Maximum Likelihood Classification (MLC) algorithm has been applied for all the study year i.e., 1991\\u0026ndash;2021. Sample data representing each LULC class were firstly selected, after that the image processing software uses those samples to learn and assign labels to all the pixels in the image based on their spectral characteristics. The study area is divided into six LULC classes including, agriculture, dense vegetation, low vegetation, barren land, water bodies and built \\u0026ndash; up.\\u003c/p\\u003e\\n \\u003cdiv class=\\\"gridtable\\\"\\u003e\\u0026nbsp;\\u003ctable id=\\\"Tab2\\\" border=\\\"1\\\"\\u003e\\n \\u003ccaption language=\\\"En\\\"\\u003e\\n \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 2\\u003c/div\\u003e\\n \\u003cdiv class=\\\"CaptionContent\\\"\\u003e\\n \\u003cp\\u003eDescription of LULC class\\u003c/p\\u003e\\n \\u003c/div\\u003e\\n \\u003c/caption\\u003e\\n \\u003ccolgroup cols=\\\"2\\\"\\u003e\\u003c/colgroup\\u003e\\n \\u003cthead\\u003e\\n \\u003ctr\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eLULC class\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eClass description\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/thead\\u003e\\n \\u003ctbody\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eAgriculture\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eIt includes, plantation, shifting cultivation area\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eDense vegetation\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eReserved and conserved area, semi evergreen and deciduous forest\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eLow vegetation\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eSparse vegetation, ground cover percentage, scrub forest\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eBuilt \\u0026ndash; up\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eRural \\u0026ndash; urban built \\u0026ndash; up areas, settlement etc.\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eWater bodies\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eRivers, ponds, wetlands etc.\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eBarren land\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eFallow land, extremely low vegetation cover.\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/tbody\\u003e\\n \\u003ctfoot\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd colspan=\\\"2\\\"\\u003e\\u003cem\\u003eClassified by researcher\\u003c/em\\u003e\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/tfoot\\u003e\\n \\u003c/table\\u003e\\n \\u003c/div\\u003e\\n\\u003c/div\\u003e\\n\\u003cdiv id=\\\"Sec6\\\" class=\\\"Section2\\\"\\u003e\\n \\u003ch2\\u003e2.3 Accuracy assessment\\u003c/h2\\u003e\\n \\u003cp\\u003eTo determine the accuracy of the LULC map obtained from Landsat image, 120 reference points were selected through stratified random sampling method from the classified image of 1991, 2001, 2011, and 2021. The selected reference points were justified using Google Earth data. After rectifying the image Kappa coefficient statistic method was used to measure the inter \\u0026ndash; rater reliability between User Accuracy (UA), Producer Accuracy (PA) and overall accuracy of the generated land use and land cover image.\\u003c/p\\u003e\\n \\u003cp\\u003eFor accuracy assessment the following equations were used\\u003c/p\\u003e\\n \\u003cp\\u003e\\u003cimg 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\\\"\\u003e\\u003cbr\\u003e\\u003c/p\\u003e\\n \\u003cdiv class=\\\"gridtable\\\"\\u003e\\n \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\"\\u003e\\u003cbr\\u003e\\u003c/div\\u003e\\u0026nbsp;\\u003ctable id=\\\"Tab3\\\" border=\\\"1\\\" class=\\\"fr-table-selection-hover\\\"\\u003e\\n \\u003ccaption language=\\\"En\\\"\\u003e\\n \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 3\\u003c/div\\u003e\\n \\u003cdiv class=\\\"CaptionContent\\\"\\u003e\\n \\u003cp\\u003eAccuracy Assessment Using Kappa Coefficient for LULC maps (1991, 2001, 2011 \\u0026amp; 2021)\\u003c/p\\u003e\\n \\u003c/div\\u003e\\n \\u003c/caption\\u003e\\n \\u003ccolgroup cols=\\\"5\\\"\\u003e\\u003c/colgroup\\u003e\\n \\u003cthead\\u003e\\n \\u003ctr\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eYear\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1991\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e2001\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e2011\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e2021\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/thead\\u003e\\n \\u003ctbody\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eKappa coefficient value\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.82\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.576\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.672\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.706\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/tbody\\u003e\\n \\u003ctfoot\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd colspan=\\\"5\\\"\\u003e\\u003cem\\u003eSource: Calculated by researcher\\u003c/em\\u003e\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/tfoot\\u003e\\n \\u003c/table\\u003e\\n \\u003c/div\\u003e\\n\\u003c/div\\u003e\"},{\"header\":\"3. Results and discussion\",\"content\":\"\\u003cdiv id=\\\"Sec8\\\" class=\\\"Section2\\\"\\u003e\\n \\u003ch2\\u003e3.1 Land use and land cover dynamic (1991\\u0026ndash;2021)\\u003c/h2\\u003e\\n \\u003cp\\u003eLand use and land cover change, as dynamic as it is, there was multi temporal change detected in the study area. Six land use and land cover classes were identified for the purpose of the study which includes agricultural land, dense vegetation, low vegetation, built \\u0026ndash; up, barren land and water bodies. Throughout the study period forest cover dominates the LULC classes. Where low vegetation cover area has seen a surge over the decades but dense vegetation cover has decline in its area. Low vegetation area covers an area of 474 km\\u003csup\\u003e2\\u003c/sup\\u003e in 1991, 501km\\u0026sup2; in 2001, 268 km\\u003csup\\u003e2\\u003c/sup\\u003e in 2011 and 824 km\\u003csup\\u003e2\\u003c/sup\\u003e in 2021. Whereas dense vegetation area covers an area of 946 km\\u003csup\\u003e2\\u003c/sup\\u003e in 1991, 897 km\\u0026sup2; in 2001, 1059 km\\u0026sup2; in 2011 and 503 km\\u0026sup2; in 2021. Agricultural land use and built \\u0026ndash;up area shares a significant portion of LULC classes in the study area. The area under agricultural land use was 97 km\\u0026sup2; in 1991, 109 km\\u0026sup2; in 2001, 132 km\\u0026sup2; in 2011 and 91 km\\u0026sup2; in 2021. On the other hand, built \\u0026ndash; up area covers 32 km\\u0026sup2; in 1991, 42 km\\u0026sup2; in 2001, 85km\\u0026sup2; in 2011 and 129 km\\u0026sup2; in 2021 which shows consistent growth in the area over the past four decades. Barren land occupies a significant portion of the study area. it covers 21 km@ in 1991, 32 km\\u0026sup2; in 2001, 45 km\\u0026sup2; in 2011 and 41 km\\u0026sup2; in 2021. Another LULC class is water bodies which cover 39 km\\u0026sup2; in 1991, 34 km\\u0026sup2; in 2001, 25 km\\u0026sup2; in 2011 and 27 km\\u0026sup2; in 2021. In all the LULC classes built \\u0026ndash; up area shows the most consistence growth during the whole study period with a total growth of 75% in its area from 1991\\u0026ndash;2021. Low vegetation cover has shown a growth of 42.47% in its area from 1991\\u0026ndash;2021 on the contrary dense vegetation area shows a growth of -88.07% in its area during the study period.\\u003c/p\\u003e\\n\\u003c/div\\u003e\\n\\u003cdiv id=\\\"Sec9\\\" class=\\\"Section2\\\"\\u003e\\n \\u003ch2\\u003e3.2 Change detection\\u003c/h2\\u003e\\n \\u003cp\\u003eThe objective of change detection is to compare spatial representation of two points in time by controlling all variances caused by differences in variable that are not of interest and to measure change caused by differences in the variable of interest (Green et al. 1994). Change detection is important as it give us an overview of the LULC pattern of a given region. Change detection analysis of Mokokchung district over the past four decade i.e. 1991\\u0026ndash;2021 shows a variant pattern of LULC.\\u003c/p\\u003e\\n \\u003cp\\u003eThe change detection analysis for the year 1991\\u0026ndash;2001 shows that, dense vegetation has decreased in area from 946 km\\u0026sup2; in 1991 to 897 km\\u0026sup2; in 2001, which shows a decadal growth of -5.46% in the area. Agricultural area has increased from 97 km\\u0026sup2; in 1991 to 109 km\\u0026sup2; in 2001 which shows an increase of 11% in the total agricultural area. During the same period (1991\\u0026ndash;2001) low vegetation cover show a growth of 5.38% in the total area, which change from 474 km\\u0026sup2; in 1991 to 501 km\\u0026sup2; in 2001. Built \\u0026ndash; up area has significantly increased from 32 km\\u0026sup2; in 1991 to 42 km\\u0026sup2; in 2001 (26.17% growth). Barren land has increased from 21 km\\u0026sup2; in 1991 to 32 km\\u0026sup2; in 2001 with a total growth of 11 km\\u0026sup2; (34.37%). Water bodies decline in its coverage by 5km\\u0026sup2; which shows a decadal growth of -14.7% from 1991\\u0026ndash;2001. During the year 2001\\u0026ndash;2011 the area under dense cover increased from 897 km\\u0026sup2; (2001) to 1059 km\\u0026sup2; (2011) which was a growth of 15.29% in the dense vegetation cover. On the contrary during the same study period low vegetation cover decline by 233 km\\u0026sup2; (-86.94%) which was a significant development. Built \\u0026ndash; up area has shown a significant increased from 42 km\\u0026sup2; in 2001 to 85 km\\u0026sup2; in 2011 which shows 50.58% growth. Agricultural land covers 109 km\\u0026sup2; in 2001 and 132 km\\u0026sup2; in 2011 which shows a growth of 17.42% in agricultural land. Water bodies decline in area from 34 km\\u0026sup2; (2001) to 25 km\\u0026sup2; (2011) which shows a decline of 9 km\\u0026sup2; (-26.47%). Barren land shows a growth of 13 km\\u0026sup2; from 2001\\u0026ndash;2011. The year 2011\\u0026ndash;2021 sees a significant change in the LULC pattern. Dense vegetation covers drastically decline from 1059 km\\u0026sup2; in 2011 to 503 km\\u0026sup2; in 2021, whereas low vegetation cover increases from 268 km\\u0026sup2; (2011) to 824 km\\u0026sup2; (2021) which is 67.47% growth in the low vegetation cover. Change in built \\u0026ndash; up land use from 2011\\u0026ndash;2021 was notable as it increased by 34.1% (44km\\u0026sup2;). Agricultural land and barren land have seen a down growth in its area (2011\\u0026ndash;2021). Agricultural area shows a growth of -45.05% whereas barren land detected a growth of -9.75%.\\u003c/p\\u003e\\n \\u003cp\\u003eThe change detection analysis shows certain alteration in LULC classes. Agricultural area has been converted to dense vegetation by 7.17km\\u0026sup2; from 1991\\u0026ndash;2001, whereas it is converted to low vegetation by 11.75km\\u0026sup2;, water bodies by 6.43km\\u0026sup2;, built \\u0026ndash; up by 9.18km\\u0026sup2; during the same period. The area under dense vegetation cover is converted to agricultural area by 26.32km\\u0026sup2;, low vegetation by 114.42 km\\u0026sup2;, water bodies by 1.23km\\u0026sup2;, built \\u0026ndash; up by 4.8km\\u0026sup2; from 1991\\u0026ndash;2001. Whereas low vegetation cover has been converted to agricultural area by 10.12 km\\u0026sup2;, dense vegetation cover by 29.89 km\\u0026sup2;, water bodies by 1.09 km\\u0026sup2; and built up by 4.47 km\\u0026sup2;. Moreover built \\u0026ndash; up area is converted to agricultural land by 5.39 km\\u0026sup2;, dense vegetation by 0.77 km\\u0026sup2;, low vegetation by 5.36 km\\u0026sup2; and water bodies by 0.18 km\\u0026sup2;. Water bodies\\u0026rsquo; area is converted to agricultural land by 2.93 km\\u0026sup2;, dense vegetation cover by 3.16 km\\u0026sup2;, low vegetation by 1.67 km\\u0026sup2;, built \\u0026ndash; up area by 6.97 km\\u0026sup2; and barren land to .56 km\\u0026sup2;. during the same study period (1991\\u0026ndash;2001) barren land had converted to agricultural land by 3.2 km\\u0026sup2;, dense vegetation by 3.45 km\\u0026sup2;, low vegetation by 2.39 km\\u0026sup2;, water bodies by 0.41 km\\u0026sup2;, and built \\u0026ndash; up by 1.48 km\\u0026sup2;.\\u003c/p\\u003e\\n \\u003cp\\u003eThe LULC change analysis for the year 2001\\u0026ndash;2011 and 2011\\u0026ndash;2021 also shows a significant conversion of LULC classes. Agriculture area conversion to dense vegetation cover was 11.7 km\\u0026sup2; in 2001\\u0026ndash;2001 and by 18 km\\u0026sup2; in 2011\\u0026ndash;2021. Agriculture area conversion to low vegetation cover was 12.6 km\\u0026sup2; in 2001\\u0026ndash;2011 and by 6.1 km\\u0026sup2; in 2011\\u0026ndash;2021. Agricultural area conversion to built \\u0026ndash; up area by 8.1 km\\u0026sup2; in 2001\\u0026ndash;2011 and by 17 km\\u0026sup2; in 2011\\u0026ndash;2021. The area under dense vegetation has converted to agricultural by 14.84 km\\u0026sup2; and by 5.6 km\\u0026sup2; in the year 2001\\u0026ndash;2011 and 2011\\u0026ndash;2021 respectively. The conversion of dense forest to low vegetation was 34 km\\u0026sup2; in 2001\\u0026ndash;2011 and by 367 km\\u0026sup2; in 2011\\u0026ndash;2021. Dense vegetation area to water bodies was 0.56 km\\u0026sup2; in 2001\\u0026ndash;2011 and 2.1 km\\u0026sup2; in 2011\\u0026ndash;2021, furthermore from dense vegetation to built \\u0026ndash; up area was 10. 3 km\\u0026sup2; in 2001\\u0026ndash;2011 and 25 km\\u0026sup2; in 2011\\u0026ndash;2021. Low vegetation cover is shifted to agricultural land by 25 km\\u0026sup2; during 2001\\u0026ndash;2011 and by 7 km\\u0026sup2; in 2011\\u0026ndash;2021. Low vegetation area to dense vegetation cover was 197 km\\u0026sup2; in 2001\\u0026ndash;2011 and 12 km\\u0026sup2; in 2011\\u0026ndash;2021. Low vegetation cover to water bodies has converted by .51 km\\u0026sup2; in 2001\\u0026ndash;2011and by .12 km\\u0026sup2; in 2011\\u0026ndash;2021, whereas the conversion of low vegetation to built \\u0026ndash; up was 19 km\\u0026sup2; in 2001\\u0026ndash;2011 and 4.1 km\\u0026sup2; in 2011\\u0026ndash;2021. Water bodies to agricultural land conversion was 2.2 km\\u0026sup2; in 2001\\u0026ndash;2011 and 1.3 km\\u0026sup2; in 2011\\u0026ndash;2021. Water bodies are converted to dense vegetation by 1.26 km\\u0026sup2; in 2001\\u0026ndash;2011 and 1.06 km\\u0026sup2; in 2011\\u0026ndash;2021 and again the conversion from water bodies to low vegetation cover was 0.61 km\\u0026sup2; in 2001\\u0026ndash;2011 and 1.2 km\\u0026sup2; in 2011\\u0026ndash;2021. Water bodies to built \\u0026ndash; up area is 1.7 km\\u0026sup2; and 0.96 km\\u0026sup2; during 2001\\u0026ndash;2011 and 2011\\u0026ndash;2021. Water bodies\\u0026rsquo; conversion to barren land is 0.67 km\\u0026sup2; and 1.04 km\\u0026sup2; from 2001\\u0026ndash;2011 and 2011\\u0026ndash;2021. During the year 2001\\u0026ndash;2011 and 2011\\u0026ndash;2021 the area under built \\u0026ndash;up has converted to agricultural land by 8 km\\u0026sup2; (2001\\u0026ndash;2011) and 5 km\\u0026sup2; (2011\\u0026ndash;2021). Adair from built \\u0026ndash; up to dense vegetation cover is 5 km\\u0026sup2; in 2001\\u0026ndash;2011 and 1.29 km\\u0026sup2; in 2011\\u0026ndash;2021. Built \\u0026ndash; up area to low vegetation cover is 4 km\\u0026sup2; in 2001\\u0026ndash;2011 and 3.65 km\\u0026sup2; in 2011\\u0026ndash;2021. Furthermore the area under built \\u0026ndash; up shift to water bodies by 0.19 km\\u0026sup2; and 1.37 km\\u0026sup2; in 2001\\u0026ndash;2011 and 2011\\u0026ndash;2021 and from built \\u0026ndash; up to barren land was 2.76 km\\u0026sup2; in 2001\\u0026ndash;2011 and 3.1 km\\u0026sup2; in 2011\\u0026ndash;2021. Furthermore, barren land is converted to agricultural land by 1.1 km\\u0026sup2; and 2.5 km\\u0026sup2; in 2001\\u0026ndash;2011 and 2011\\u0026ndash;2021 and barren land to low vegetation cover is 2.69 km\\u0026sup2; and 3.2 km\\u0026sup2; in 2001\\u0026ndash;2011 and 2011\\u0026ndash;2021. The conversion of barren land to built \\u0026ndash; up area is 3.45 km\\u0026sup2; in 2001\\u0026ndash;2011 and 4.93 km\\u0026sup2; in 2011\\u0026ndash;2021, and further from barren land to water bodies was 0.59 km\\u0026sup2; in 2001\\u0026ndash;2011 and 1.23 km\\u0026sup2; in 2011\\u0026ndash;2021.\\u003c/p\\u003e\\n\\u003c/div\\u003e\\n\\u003cdiv id=\\\"Sec10\\\" class=\\\"Section2\\\"\\u003e\\n \\u003ch2\\u003e3.3 Population growth and changing LULC\\u003c/h2\\u003e\\n \\u003cp\\u003ePopulation growth and environmental degradation goes hand in hand. Population growth alter the pattern of land use and land cover as with the growing population need for more resources arise which has significant impact on the environment, economy and society.\\u003c/p\\u003e\\n \\u003cdiv class=\\\"gridtable\\\"\\u003e\\u0026nbsp;\\u003ctable id=\\\"Tab4\\\" border=\\\"1\\\"\\u003e\\n \\u003ccaption language=\\\"En\\\"\\u003e\\n \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 4\\u003c/div\\u003e\\n \\u003cdiv class=\\\"CaptionContent\\\"\\u003e\\n \\u003cp\\u003ePopulation data from 1991\\u0026ndash;2021\\u003c/p\\u003e\\n \\u003c/div\\u003e\\n \\u003c/caption\\u003e\\n \\u003ccolgroup cols=\\\"2\\\"\\u003e\\u003c/colgroup\\u003e\\n \\u003cthead\\u003e\\n \\u003ctr\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eYear\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003ePopulation\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/thead\\u003e\\n \\u003ctbody\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1991\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e158374\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e2001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e232085\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e2011\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e194622\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e2021*\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e227989\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/tbody\\u003e\\n \\u003ctfoot\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd colspan=\\\"2\\\"\\u003e\\u003cem\\u003eSource: Census of India\\u003c/em\\u003e\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd colspan=\\\"2\\\"\\u003e\\u003cem\\u003eEstimated Population*\\u003c/em\\u003e\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/tfoot\\u003e\\n \\u003c/table\\u003e\\n \\u003c/div\\u003e\\n \\u003cp\\u003eOver the past few decades Mokokchung district as a whole has experience drastic population growth. In the year 1991 the population of Mokokchung district was 158,374, which increased to 232085 in 2001 which shows a decadal growth of 46.5%. A significant development in the population structure occurred between 2001\\u0026ndash;2011 census years. During this census period (2001\\u0026ndash;2011) the district experienced a negative growth in the population. In 2001 the population of Mokokchung was 232,085 which changed to 194622 in 2021 which shows a decadal growth of -16.14%. The estimated population for the year 2021 is 227989 which indicate a estimated growth of 17.14% (2011\\u0026ndash;2021).\\u003c/p\\u003e\\n \\u003cdiv class=\\\"gridtable\\\"\\u003e\\u0026nbsp;\\u003ctable id=\\\"Tab5\\\" border=\\\"1\\\"\\u003e\\n \\u003ccaption language=\\\"En\\\"\\u003e\\n \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 5\\u003c/div\\u003e\\n \\u003cdiv class=\\\"CaptionContent\\\"\\u003e\\n \\u003cp\\u003eDecadal growth Rate (1991\\u0026ndash;2021)\\u003c/p\\u003e\\n \\u003c/div\\u003e\\n \\u003c/caption\\u003e\\n \\u003ccolgroup cols=\\\"4\\\"\\u003e\\u003c/colgroup\\u003e\\n \\u003cthead\\u003e\\n \\u003ctr\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eYear\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1991\\u0026ndash;2001\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e2001\\u0026ndash;2011\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e2011\\u0026ndash;2021*\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/thead\\u003e\\n \\u003ctbody\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003ePopulation growth (in %)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e46.5\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e-16.14\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e17.14\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/tbody\\u003e\\n \\u003ctfoot\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd colspan=\\\"4\\\"\\u003e\\u003cem\\u003eSource: Census of India\\u003c/em\\u003e\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd colspan=\\\"4\\\"\\u003e\\u003cem\\u003eEstimated Population*\\u003c/em\\u003e\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/tfoot\\u003e\\n \\u003c/table\\u003e\\n \\u003c/div\\u003e\\n \\u003cdiv class=\\\"gridtable\\\"\\u003e\\n \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\"\\u003e\\u003cbr\\u003e\\u003c/div\\u003e\\u0026nbsp;\\u003ctable id=\\\"Tab6\\\" border=\\\"1\\\"\\u003e\\n \\u003ccaption language=\\\"En\\\"\\u003e\\n \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 6\\u003c/div\\u003e\\n \\u003cdiv class=\\\"CaptionContent\\\"\\u003e\\n \\u003cp\\u003ePearson correlation between population and LULC classes.\\u003c/p\\u003e\\n \\u003c/div\\u003e\\n \\u003c/caption\\u003e\\n \\u003ccolgroup cols=\\\"7\\\"\\u003e\\u003c/colgroup\\u003e\\n \\u003ctbody\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" rowspan=\\\"2\\\"\\u003e\\n \\u003cp\\u003ePopulation Rho(q) value\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eAgriculture\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eDense Vegetation\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eLow Vegetation\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eBuilt \\u0026ndash; Up\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eBarren Land\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eWater Bodies\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.056\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e-0.53\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.47\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.48\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.55\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e-0.48\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/tbody\\u003e\\n \\u003ctfoot\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd colspan=\\\"7\\\"\\u003e\\u003cem\\u003eSource: calculated by researcher\\u003c/em\\u003e\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/tfoot\\u003e\\n \\u003c/table\\u003e\\n \\u003c/div\\u003e\\n \\u003cp\\u003eIn order to determine the correlation between population growth and land use and land cover change Person\\u0026rsquo;s correlation coefficient rho (q) has been used. The calculation matrix shows significant relationship between the two variables. Population growth and agricultural area shows a positive connection with rho (q) 0.056. population growth and built \\u0026ndash; up show a rho (q) 0.48 which shows a strong interconnection between population growth and built \\u0026ndash; up area. This strongly indicates that with the growing population more human induced activities are stimulated. In addition, a strong relation is found between population growth and barren land with Rho (q) 0.55. the reason might be due to deforestation (especially inclined with jhum cultivation, road construction and for the purpose of firewood) and urbanization. Low vegetation cover and population growth shows a correlation of Rho (q) 0.47, this positive correlation is associated with the jhum cycle, deforestation and the increase in built \\u0026ndash; up area. Other LULC classes such as dense vegetation cover and water bodies show a Rho (q) -0.53 and \\u0026minus;\\u0026thinsp;0.48. The negative correlation between population and this two LULC class is due to human encroachment. The decline in dense forest cover is related with the clearing of unexplored dense forest cover for infrastructural development and for agricultural land use. on the other hand, the negative correlation between population growth and water bodies is related with the filling up of wetland and ponds for construction of new infrastructure and in addition climate change has played a significant role which led to drying up of many streams. The cumulative result reflects the impact of growing anthropogenic activities on the LULC of the study area.\\u003c/p\\u003e\\n\\u003c/div\\u003e\"},{\"header\":\"4. Conclusion\",\"content\":\"\\u003cp\\u003eThe research was conducted in Mokokchung district of Nagaland which is one of the most rapidly growing districts in the state of Nagaland. To assess the impact of growing pressure of human population on LULC, geospatial techniques were use for the study. The study focus to study the relationship between human population growth and its implication on LULC in Mokokchung district. The study demonstrates that there was detectable change in the LULC during the study period (1991\\u0026ndash;2021). The study shows that with the growing population the LULC classes under built \\u0026ndash; up, agriculture, barren land and low vegetation cover shows a positive correlation in the land use pattern and dense vegetation cover and water bodies shows a negative correlation. Barren land and built \\u0026ndash; up shows a strong correlation with the growing population with Rho (q) 0.56 and 0.48 which depict that with the growing human population demand for more, housing and other infrastructural development increases which led to environmental destruction which eventually leave the area as barren land. Population growth is dynamic in nature. Though there are lots of efforts made to control the human population growth it is impossible to put an end to its growth. In order to bring a balance between population growth and LULC change a proper LULC change detection analysis should be done timely and precise information about the same should be updated which will help the policy makers and resource planners to act accordingly which will help in a more sustainable management of land resources.\\u003c/p\\u003e\"},{\"header\":\"Declarations\",\"content\":\"\\u003ch2\\u003eAuthor Contribution\\u003c/h2\\u003e\\u003cp\\u003eAuthors Contribution All the authors significantly contributed in preparing the study design. Data collection and analysis was done by Sentiakum Jamir. The first draft of the paper was devise by Sentiakum Jamir. Both author has revised the manuscript and commented on the previous draft. Sangyu Yaden supervises the study. Both author has read and approved the final manuscript.\\u003c/p\\u003e\\u003ch2\\u003eAcknowledgement\\u003c/h2\\u003e\\u003cp\\u003eThe authors would like to thank Nagaland University, Lumami and the Ministry of Tribal Affairs, Government Of India for providing assistance to conduct the present study. The authors also thank the Hon\\u0026rsquo;ble Vice Chancellor of Nagaland University Prof. Jagadish K. Patnaik for his constant encouragement and guidance throughout the completion of the study. Supports from family, friends and fellow scholar are also highly acknowledged.\\u003c/p\\u003e\"},{\"header\":\"References\",\"content\":\"\\u003col\\u003e\\n \\u003cli\\u003eBarana Babiso, Senbetie Toma, Aklilu Bajigo. Population Growth and Environmental Changes: Conclusions Drawn from the Contradictory Experiences of Developing Countries. International Journal of Environmental Monitoring and Analysis. Vol. 8, No. 5, 2020, pp. 161-169. doi: 10.11648/j.ijema.20200805.15\\u003c/li\\u003e\\n \\u003cli\\u003eBora R, Bora AK (2024) Population Pressure and Changing Land Use Land Cover in Morigaon District, Assam, India - A Geospatial Analysis. Indian Journal of Science and Technology 17(24): 2547-2556. https://doi.org/ 10.17485/IJST/v17i24.899\\u003c/li\\u003e\\n \\u003cli\\u003eBrown DG, Walker R, Manson S and Seto K (2012) Modeling land use and land cover change. In\\u0026nbsp;Land change science (pp. 395\\u0026ndash;409). Springer, Dordrecht.\\u003c/li\\u003e\\n \\u003cli\\u003eGogoi PP, Vinoj V, Swain D, Roberts G, Dash J, Tripathy S (2019) Land use and land cover change efect on surface temperature over Eastern India. Sci Rep 9(1):1\\u0026ndash;10\\u003c/li\\u003e\\n \\u003cli\\u003eGreen. K, Kempka. D, and Lackey. L, 1994, Using RS to detect and monitor land \\u0026ndash; cover and land \\u0026ndash; use change. (Photogrammetric Engineering and Remote Sensing), 60, 331 \\u0026ndash; 337.\\u003c/li\\u003e\\n \\u003cli\\u003eIslam K, Jashimuddin M, Nath B, Nath TK (2018) Land use classifcation and change detection by using multi-temporal remotely sensed imagery: The case of Chunati wildlife sanctuary, Bangladesh. Egyptian J Remote Sens Space Sci 21(1):37\\u0026ndash;47\\u003c/li\\u003e\\n \\u003cli\\u003eKhan, N., Raza, M., Shakoor, M.S.A. \\u003cem\\u003eet al.\\u003c/em\\u003e Dynamic of population growth and its effect on land use/land cover of bahraich district in Uttar Pradesh. \\u003cem\\u003eJ Environ Stud Sci\\u003c/em\\u003e \\u003cstrong\\u003e13\\u003c/strong\\u003e, 124\\u0026ndash;140 (2023). https://doi.org/10.1007/s13412-022-00805-6\\u003c/li\\u003e\\n \\u003cli\\u003eLillesand, T.M. \\u0026amp; Keifer, R.W. 1994, Remote Sensing and Image Interpretation, .\\u003c/li\\u003e\\n \\u003cli\\u003eLong, H., Tang, G., Li, X., \\u0026amp; Heilig, G. K. (2007). Socio-economic driving forces of land-use change in Kunshan, the Yangtze River Delta economic area of China. Journal of Environmental management, 83(3), 351-364.\\u003c/li\\u003e\\n\\u003c/ol\\u003e\"}],\"fulltextSource\":\"\",\"fullText\":\"\",\"funders\":[],\"hasAdminPriorityOnWorkflow\":false,\"hasManuscriptDocX\":true,\"hasOptedInToPreprint\":true,\"hasPassedJournalQc\":\"\",\"hasAnyPriority\":true,\"hideJournal\":true,\"highlight\":\"\",\"institution\":\"\",\"isAcceptedByJournal\":false,\"isAuthorSuppliedPdf\":false,\"isDeskRejected\":\"\",\"isHiddenFromSearch\":false,\"isInQc\":false,\"isInWorkflow\":false,\"isPdf\":false,\"isPdfUpToDate\":true,\"isWithdrawnOrRetracted\":false,\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"researchsquare\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":true,\"externalIdentity\":\"\",\"sideBox\":\"\",\"snPcode\":\"\",\"submissionUrl\":\"/submission\",\"title\":\"Research Square\",\"twitterHandle\":\"researchsquare\",\"acdcEnabled\":true,\"dfaEnabled\":false,\"editorialSystem\":\"\",\"reportingPortfolio\":\"\",\"inReviewEnabled\":false,\"inReviewRevisionsEnabled\":true},\"keywords\":\"Land use, population growth, landsat image, agriculture\",\"lastPublishedDoi\":\"10.21203/rs.3.rs-6635027/v1\",\"lastPublishedDoiUrl\":\"https://doi.org/10.21203/rs.3.rs-6635027/v1\",\"license\":{\"name\":\"CC BY 4.0\",\"url\":\"https://creativecommons.org/licenses/by/4.0/\"},\"manuscriptAbstract\":\"\\u003cp\\u003e21\\u003csup\\u003est\\u003c/sup\\u003e century has seen unprecedented growth of population. The growing population has created lots of chaos as with the growing population expanding needs for resources also arise. The study of land use and land cover change is consequential in the current context as it helps in understanding as to what extend the resources are being utilized. Mokokchung district of Nagaland has vouch phenomenal growth of population over the past few decades. The unprecedented growth of population has appreciably impact on the land use and land cover pattern of the district. The districts population increases by approximately 86.52% over the past 4 decades (1991 – 2021). The total area under built – up areas has seen a major raise where as areas under agricultural land use has decline in area as per the detection analysis. For the purpose of the study Landsat image for the year 1991 and 2021 has been mapped. Data from Census of India and other statistical report were used for the study purpose. The study discloses a great interconnection between population growths on land use land cover change pattern in the study area.\\u003c/p\\u003e\",\"manuscriptTitle\":\"Population growth and its impact on land use land cover: Mokokchung district, Nagaland\",\"msid\":\"\",\"msnumber\":\"\",\"nonDraftVersions\":[{\"code\":1,\"date\":\"2025-05-29 09:21:17\",\"doi\":\"10.21203/rs.3.rs-6635027/v1\",\"editorialEvents\":[{\"type\":\"communityComments\",\"content\":0}],\"status\":\"published\",\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"researchsquare\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":true,\"externalIdentity\":\"\",\"sideBox\":\"\",\"snPcode\":\"\",\"submissionUrl\":\"/submission\",\"title\":\"Research Square\",\"twitterHandle\":\"researchsquare\",\"acdcEnabled\":true,\"dfaEnabled\":false,\"editorialSystem\":\"\",\"reportingPortfolio\":\"\",\"inReviewEnabled\":false,\"inReviewRevisionsEnabled\":true}}],\"origin\":\"\",\"ownerIdentity\":\"01f72132-7021-4c52-8832-f1168ee7e57d\",\"owner\":[],\"postedDate\":\"May 29th, 2025\",\"published\":true,\"recentEditorialEvents\":[],\"rejectedJournal\":{\"display\":false,\"email\":\"info@researchsquare.com\",\"identity\":\"journal-of-population-research\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":false,\"externalIdentity\":\"jpor\",\"sideBox\":\"Learn more about [Journal of Population Research](http://etrr.springeropen.com/)\",\"snPcode\":\"\",\"submissionUrl\":\"https://www.editorialmanager.com/jpor/default.aspx\",\"title\":\"Journal of Population Research\",\"twitterHandle\":\"\",\"acdcEnabled\":true,\"dfaEnabled\":true,\"editorialSystem\":\"em\",\"reportingPortfolio\":\"Springer Hybrid\",\"inReviewEnabled\":false,\"inReviewRevisionsEnabled\":false},\"revision\":\"\",\"amendment\":\"\",\"status\":\"posted\",\"subjectAreas\":[],\"tags\":[],\"updatedAt\":\"2025-05-29T09:21:17+00:00\",\"versionOfRecord\":[],\"versionCreatedAt\":\"2025-05-29 09:21:17\",\"video\":\"\",\"vorDoi\":\"\",\"vorDoiUrl\":\"\",\"workflowStages\":[]},\"version\":\"v1\",\"identity\":\"rs-6635027\",\"journalConfig\":\"researchsquare\"},\"__N_SSP\":true},\"page\":\"/article/[identity]/[[...version]]\",\"query\":{\"redirect\":\"/article/rs-6635027\",\"identity\":\"rs-6635027\",\"version\":[\"v1\"]},\"buildId\":\"8U1c8b4HqxoKbykW_rLl7\",\"isFallback\":false,\"isExperimentalCompile\":false,\"dynamicIds\":[84888],\"gssp\":true,\"scriptLoader\":[]}","source_license":"CC-BY-4.0","license_restricted":false}