Periodic Relations between Vegetation Cover and Climatic Factors in East Khasi Hills, Meghalaya, India

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Employing a combination of quantitative and qualitative methodologies, the research spans seven years, from 1993 to 2022, integrating primary data from satellite images and secondary climatologically data. The methodology involves a comparison approach utilizing the Normalized Difference Vegetation Index (NDVI) and rule-based classification for vegetation cover analysis at five-year intervals. Optical satellite images from Landsat, along with Terra MODIS LST data, provide critical insights into the land surface conditions. Additionally, ASTER data is employed for automatic stream network extraction and analysis. Elevation and slope classifications offer a spatial understanding of the geographical distribution across different elevation ranges and slope categories. The results indicate a declining trend in vegetation cover, with elevation-wise variations and a mix of gentle to moderately steep slopes in the region. Furthermore, the extraction of Land Surface Temperature (LST) from MODIS, Landsat-5, and Landsat-8 data reveals temperature variations that influence vegetation growth patterns. Monthly and annual climatological data from the India Meteorological Department contribute to the verification of temporal climatic factors, providing valuable context to the vegetation dynamics. The study's findings underscore the dynamic interplay between vegetation cover, topography, and climatic conditions in the East Khasi Hill district. These insights are crucial for informed decision-making in land management, conservation efforts, and understanding the region's environmental changes. The presented methodological flowchart visually summarizes the research approach, providing a roadmap for future studies in similar contexts. NDVI Climate factors Vegetation change LST Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 Figure 12 Figure 13 Figure 14 Figure 15 Figure 16 Figure 17 Figure 18 1. Introduction The importance of vegetation in Meghalaya is multi-faceted, encompassing ecological, economic, and cultural aspects (Chakma et.al., 2014)The diverse array of plant species contributes to ecosystem stability by regulating nutrient cycles, supporting biodiversity, and preventing soil erosion. Additionally, vegetation plays a pivotal role in carbon sequestration, influencing regional climate patterns and mitigating the impact of climate change (Berhe et al., 2018 ; Rodrigues et al., 2023 ).Preserving the diverse vegetation in Meghalaya is crucial for maintaining the ecological balance, sustaining local livelihoods, and ensuring the well-being of the unique flora and fauna in the region (Rawal et.al, 2013 ).Meghalaya features a diverse range of vegetation types, including tropical rainforests, grasslands, bamboo groves, medicinal plants, and orchids and ferns(Singh., 2010).This article introduces an improved method for analyzing satellite images through an enhanced Change Detection technique, utilizing the Normalized Difference Vegetation Index (NDVI) (Gandhi.,2015).The alteration of vegetation cover, whether through deforestation, afforestation, urbanization, or natural processes, has direct consequences on the thermal characteristics of the land surface (Wan. et. al., 2020). The relationship between vegetation cover change and LST is complex and can be influenced by various factors viz Cooling Effect of Vegetation, Urban Heat Island Effect, etc.(Mohan andKikegawa.,2013).Climatological factors like precipitation and temperature influence vegetation growth, which, in turn, affects LST. Integrating vegetation-related climatic data with LST analysis provides insights into the complex feedback mechanisms between land surface conditions and climate(Liu, et.al., 2023).Elevation and slope emerge as pivotal influencers in determining the distribution of vegetation across East Khasi Hills (Dikshit et al., 2014 ). The diverse elevations, ranging from lowlands to high plateaus, act as natural architects, shaping microclimates that dictate the types of plants that thrive and their growth patterns (Reid et.al,2019). 1.1. Study Area The East Khasi Hills District is a significant geographical region within the state of Meghalaya, constituting a central part with a total area of 2,748 square kilometers or 274,800 hectares. Its geographical coordinates lie approximately between 25°07” to 25°41” N latitude and 91°21” to 92°09” E longitude, as depicted in Fig. 1. This district is characterized by a subtropical highland climate, featuring distinct seasons.The region experiences a diverse temperature range, with winter temperatures around 2°C (October to March). The summer season, lasting from May to October, is relatively short, with an average temperature of 25.0°C.Most of the annual rainfall, approximately 90%, occurs during the monsoon season from June to September.Average Annual Rainfall: The district receives an average annual rainfall of about 2000 mm, emphasizing the significance of the monsoon season as a primary contributor to the region's water resources.Being a central part of Meghalaya, the district holds strategic importance in terms of its geography and topography. 2. Material and Methodology Quantitative and qualitative analysis were performed in which a combination of primary and secondary data was used to evaluate the vegetation cover change and climate factor for the East Khasi Hill district. First, a comparison approach using NDVI and rule-based classification was adopted and vegetation cover changes for every five-year interval like 1993, 1998, 2004, 2008, 2013, 2018 and 2022. 2.1. Data Sources 2.1.1. Satellite Images Used Optical Satellite images Landsat-TM (Thematic Mapper) and Landsat-OLI (Operational Land Imager) with Rows 136, 137 and Path 42, 43 were used to acquire information for the month of October to December for seven different years 1993, 1998, 2004, 2008, 2013, 2018 and 2022 with 30m spatial resolution. The Terra Moderate Resolution Imaging Spectroradiometer (MODIS) LST (Land Surface Temperature) with 8 days emissivity product (MOD112A) images were downloaded for the month of June and December for 2004, 2008, 2013, 2018 and 2022.This product provides 8 days per pixel LST temperature and emissivity with 1km spatial resolutions. The Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) was used for automatic steam network extraction and analysis. ASTER is a Japanese multispectral imaging RS instrument onboard the Terra sensor lunched by NASA in 1999 and has provide data from February 2000. These data downloaded from earth explorer USGS of the United State Geological Survey (USGS) (website https://earthexplorer.usgs.gov ). The data obtained from these sources have been processed to gain the reflectance value that involves DN, to Radiance conversion and Radiance to Reflectance conversion. 2.1.2. Secondary data Monthly and annual rainfall, minimum and maximum temperature data used for the verification of the temporal climatological factors analysis in East Khasi Hill district. The all-climatological data provided by India Meteorological Department, Ministry of Earth Science, Shillong (Fig. 2). 2.2. Methodology 2.2.1. Extraction of Vegetation Cover Normalized Difference Vegetation Index (NDVI) is a geospatial technique used to extraction of vegetation cover (Kumar et al, 2022 ). It is performed based on visible range RED and NEAR INFRA RED (NIR) reflectance’s and normalized that difference by the sum of reflectance. From the Eq. 1 is calculated as Where RED is visible red reflectance, and NIR is near infrared reflectance. The wavelength range of NIR band is (750–1300 nm) and red band is (600–700 nm). Rule based classification technique was used classification of vegetation cover and non-vegetation cover. values based on minimum value of vegetation cover (Singh et al, 2021 ). 2.2.2. Elevation Classification The spatial analyst tools from Arc toolbox were used for the classification of elevation. Firstly, classify the elevation based on value using reclass tool. Then, the reclassify tool was used for extraction of classified elevation image based on equal elevation values and calculated the elevation wise Total Geographical Area (TGA) of East Khasi Hill. Table 1Elevation wise TGA of EKH Elevation (m) Area (hac) Area (%) 0-200 25350 9.22 200–400 16779 6.11 400–600 18385 6.69 600–800 19169 6.98 800–1000 23258 8.46 1000–1200 29476 10.73 1200–1400 32616 11.87 1400–1600 37664 13.71 1600–1800 57584 20.95 > 1800 14520 5.28 This analysis provides an overview of how the total area is distributed across different elevation ranges, giving insights into the landscape composition in terms of elevation (Fig. 4). The areas seem to increase with higher elevations, peaking in the 1600–1800 meters range (57584 hectares), which constitutes the largest portion (20.95%) of the total area and this data is representing in the table 1 and graphical representation with Fig. 3. 2.2.3. Slope Classification The slope plays a major role in terms of vegetation sustainability and plantation roots can increase the strength properties of soil which stability of slope. Firstly, classify the slope (degree) using surface tool. Then, extraction of classified slope image based on degree values like Very Gentle (0–5), Gentle (5–10), Moderate (10–15), Moderately Steep (15–25), Steep (25–35) and Excessively Steep (> 35). The geographical area was calculated of East Khasi Hill. In the Table 2 , geographical area distributed among the various slope classes 23.51%, 21.23%, 17.59%, 15.93%, 13.08% and 8.66% respectively moderately steep, gentle, moderate Steep Very Gentle and Excessively Steep (Fig. 5). Table 2 Slope wise TGA of EKH Slope Class (Degree) Area (hac) Area (%) Very Gentle (0–5) 35941 13.08 Gentle (5–10) 58331 21.23 Moderate (10–15) 48326 17.59 Moderately Steep (15–25) 64610 23.51 Steep (25–35) 43784 15.93 Excessively Steep (> 35) 23808 8.66 The data suggests the terrain has a mix of gentle to moderately steep slopes, with a smaller portion being excessively steep. The percentages offer a proportional view of the land distribution across these slope categories. This information could be valuable for land management, urban planning, or environmental studies. 2.2.4. Land Surface Temperature (LST) Land Surface Temperature (LST) is one of the three major influences on the global pattern of vegetation growth. Along with sunlight and water, temperatures determine whether the land will support forest, grassland and fallow land. LST is a measurement of how hot the land is to the touch and differs from the air temperature because land heats and cools more quickly than air. In the study proceeds the extraction of LST using MODIS, Landsat-5and Landsat-8 data. Firstly, geometric correction was done for the MOD11A2 product. Secondly, approach to the estimation of LST from Landsat data. In the research band 10 was used for Landsat-8 and band 6 for the Landsat-5 or Landsat-7+. The first step of the performed to conversion of DN to radiance using following equation: After the conversion of DN to radiance, the thermal band should be converted to Brightness Temperature (BT) using the thermal constant data and equation: Table 3 constant value of K-1 and K-2 Sensor Constant K-1 W/(m 2 *sr*um) Kelvin K-2 Landsat-8 (OLI) Band 10 774.8853 1321.0789 Landsat-7 (ETM+) Band 6 666.09 1282.71 Landsat-5 (TM) Band 6 607.76 1260.56 Table 3 shows the values seem to represent the constants for different sensors used in remote sensing applications, specifically indicating their radiometric calibration coefficients. The brightness temperature data was converted to degree kelvin to degree Celsius, it is necessary to revise by adding absolute zero value which is proportional to -273.15. Finally, LST (Degree Celsius) has extracted using equation: 3. Result and Discussion 3.1 Vegetation Cover The figures given below depict the declining vegetation cover (green color) and increasing non-vegetation cover (yellow color) in the East-Khasi hill district of Meghalaya in every five year since 1993. Figure 7 and table 4 showing the depicts 85.58% of area of the East Khasi hill dominated by vegetation cover. In the course of the five year in 1998, the vegetation cover of East Khasi hills district decreased from 235177 hectares in 1993 to 226916 hectares in 1998. In 1998, 82.58 % area is covered by vegetation. Further, in five years, in 2004 the vegetation declined to 225118 hectares. In terms of percentage, the total area covered by vegetation reached to 81.92%. In 2008, the vegetation cover decreased to 222229 hectares in absolute terms and 80.87 in percentage terms.A major decline in the vegetation cover can be noticed in 2013, 2018 and 2022. From 2008 to 2013, the vegetation cover declined from 80.87 in 2008 to 77.47 in 2013, to 70.94 % in 2018 and to 67.75 % in 2023. Even the Graph shows that the significant declines in vegetation cover from 2013 to 2022 (figure 8). Table 4 Temporal vegetation cover scenario of East Khasi Hills district Year Area (hec) Area (%) 1993 235177 85.58 1998 226916 82.58 2004 225118 81.92 2008 222229 80.87 2013 212894 77.47 2018 194929 70.94 2022 186188 67.75 3.1.1. Vegetation Cover Change The below table shows the percentage of vegetation cover and the decrease in vegetation cover area percentage wise of East Khasi Hills of Meghalaya in every five years. As per table 5 the highest decline in vegetation cover is noticed between 2013-2018 is 6.54 %, followed by 2018-2022 is 3.18%. The lowest decline is experienced in the period between 1998 to 2004 is 0.65%. The bar graph plots the percentage decline in area of vegetation cover over five years’ time periods from 1993-2022 (figure 9). Table 5 vegetation cover change analysis of the five years interval Year 1993 1998 2004 2008 2013 2018 2022 Area (%) 85.58 82.58 81.92 80.87 77.47 70.94 67.75 3.1.2. Elevation wise Vegetation Cover Change The vegetation cover change analysis of the five years interval and Table 6 shows the elevation wise vegetation cover change in East Khasi hills of Meghalaya. Elevation less than 200 m the percentage of vegetation cover declined from 8.46 % in 1993 to 7.54 % in 2022. For 200-400 m elevation the major decline in vegetation cover noticed between 2013-2018, from 6.02% to 5.69%. Table 6 Elevation wise vegetation cover change Year Elevation (m) 1993 1998 2004 2008 2013 2018 2022 <200 8.46 8.30 8.20 8.25 8.05 7.84 7.54 200-400 6.04 5.99 6.12 6.05 6.02 5.69 5.75 400-600 6.66 6.64 6.78 6.69 6.69 6.55 6.50 600-800 6.94 6.94 7.10 6.99 6.99 6.88 6.70 800-1000 7.97 7.84 7.76 7.71 7.60 7.38 7.31 1000-1200 9.99 9.38 9.47 9.37 9.24 8.53 8.72 1200-1400 10.20 9.33 9.35 10.04 9.49 7.83 7.81 1400-1600 10.48 9.59 9.74 9.77 9.19 7.41 7.51 1600-1800 15.08 14.81 13.61 12.85 11.24 10.17 8.11 >1800 3.76 3.75 3.80 3.16 2.98 2.66 1.81 In figure 10 shows the drastic decrease of vegetation cover above 1400m elevation and below 1400m have been shows the minimum decreasing trend over EKH district of Meghalaya. Analyzing overall changes in vegetation cover during the period 1993 to 2022. The maximum area decreasing in above 1000m elevation 14.28% and below the 1000m elevation shows the change of 2.5%. This change represents the high human influence in this elevation and maximum geographical area covered (62.54%) by 1000m above elevation. 3.1.3. Slope wise Vegetation Cover Change Table 7 presents a time-series analysis of the vegetation cover percentage across different slope categories (measured in degrees) in East Khasi Hills for the year’s temporal years.The vegetation cover percentage in Very Gentle (0-5) category has decreased over the years, from 9.06% in 1993 to 5.81% in 2022. This suggests a notable decline in vegetation on very gentle slopes.A decreasing trend is observed in Gentle (5-10) category as well, with vegetation cover declining from 15.95% in 1993 to 10.89% in 2022. This indicates a reduction in vegetation on slopes with a moderate incline.The vegetation cover percentage on Moderate (10-15) slopes has decreased from 21.82% in 1993 to 17.95% in 2022. This indicates a reduction in vegetation on slopes with a relatively steeper incline.The vegetation cover on Steep (25-35) slopes has experienced a decline from 15.60% in 1993 to 14.21% in 2022. Table: 7 Slope wise vegetation cover change 1993 1998 2004 2008 2013 2018 2022 Very Gentle (0-5) 9.06 8.40 7.84 7.80 7.49 7.42 5.81 Gentle (5-10) 15.95 15.03 14.99 14.32 13.72 11.43 10.89 Moderate (10-15) 14.63 14.00 13.98 13.68 12.91 11.23 10.80 Moderately Steep (15-25) 21.82 21.28 21.23 21.20 19.86 18.38 17.95 Steep (25-35) 15.60 15.42 15.45 15.45 15.00 14.36 14.21 Excessively Steep (>35) 8.52 8.44 8.43 8.42 8.46 8.11 8.09 This suggests a modest reduction in vegetation on steeper terrain. The Excessively Steep (>35)slopes exhibit a varying trend, with a slight increase in vegetation cover from 8.52% in 1993 to 8.09% in 2022 after experiencing a decline in the intervening years (figure 11).The declining trends in most slope categories suggest potential environmental changes or human impacts affecting vegetation distribution on different terrains in the region. Understanding these variations is crucial for informed land management and conservation strategies. 3.2. Land Surface Temperature 3.2.1. Winter Season Table 8 provides a comparative analysis of Ambient Temperature (AT) and Land Surface Temperatures (LST) for the winter season. Additionally, it calculates the difference between AT and LST, indicating the variation between these two parameters.The minimum AT temperature ranges from 6.9°C in 2018 to 9.25°C in 2008. The maximum AT temperature varies from 8.45°C in 1993 to 9.25°C in 2008.The LST exhibits a minimum value of 16.65°C in 2004 and a maximum of 19.7°C in 2018. The temperature differences between AT and LST are calculated for each year. The minimum difference occurs in 2018 with 9.95°C, while the maximum difference is observed in 2013 with 15.3°C.The fluctuation in temperature differences over the years suggests dynamic interactions between atmospheric and land surface temperatures, potentially influenced by climatic variations or local environmental changes. Table 8 Temporal Land Surface Temperature (LST) map of East Khasi Hills Minimum Maximum Deference of AT and LST. Year AT LST AT LST Minimum Maximum 1993 8.45 10.60 17.70 33.00 2.15 15.3 1998 9.25 13.20 18.75 29.80 3.95 11.05 2004 8.50 16.65 17.30 31.73 8.15 14.43 2008 8.75 18.00 17.40 31.00 9.25 13.6 2013 7.30 17.65 17.30 30.45 10.35 13.15 2018 6.90 18.17 19.70 29.65 11.27 9.95 2022 8.60 19.00 19.42 31.43 10.4 12.01 Figure12 and 13 present a comprehensive view of AT and LST over several years, highlighting the variations and temperature differences between these two parameters. Understanding these differences is crucial for interpreting local climate dynamics and their potential implications for the environment (figure 14). 3.2.2. Summer Season The lowest AT temperature is recorded in 2022, measuring 16.80°C and the highest AT temperature is observed in 2013, reaching 18.05°C.The lowest LST occurs in 2018, registering 20°C and highest noted in 2022, reaching 35.4°C (table 9).The calculated differences between AT and LST provide insights into the thermal contrast between the atmospheric boundary and land surface (table 8). The minimum difference is 2.4°C in 2018, while the maximum difference is 13.24°C in 2022 (figure 15 and figure 16). Table 9 Temporal Land Surface Temperature (LST) map of East Khasi Hills Minimum Maximum Deference of AB and LS Temp. Year AB Temp. LS Temp. AB Temp. LS Temp. Minimum Maximum 2004 17.55 23.55 23.30 31.63 6 8.33 2008 17.75 23 23.15 32 5.25 8.85 2013 18.05 24.39 24.85 33.77 6.34 8.92 2018 17.60 20 23.90 32.21 2.4 8.31 2022 16.80 22.49 22.16 35.4 5.69 13.24 The fluctuation in temperature differences over the years indicates dynamic interactions between the atmospheric boundary and land surface temperatures, potentially influenced by changes in environmental factors.Monitoring AT and LST and their differences is essential for understanding the complex dynamics of the Earth's surface-atmosphere interactions.Variationsin these temperatures can have implications for local climate, ecology, and human activities, making this data valuable for scientific research and environmental monitoring.These variations are crucial for understanding the local climate dynamics and their potential impacts on the environment (figure 17). 3.3. Comparative analysis of vegetation cover change and Climate Factor The provided data presents a comparative analysis of Average Temperature (AT), Average Land Surface Temperature (LST), and Vegetation Cover Percentage over the years 1993, 1998, 2004, 2008, 2013, 2018, and 2022. The Average Temperature fluctuates over the years, ranging from 12.3°C in 2013 to 14.0°C in 1998 and 2022.The variation in Average Temperature suggests temporal changes in the region's climate, potentially influenced by atmospheric conditions, geographical factors, or climate cycles. The Average LST exhibits variability, with a noticeable increase from 21.8°C in 1993 to 25.2°C in 2022 (table 10). Rising LST values may indicate changes in land surface conditions, urbanization, or altered thermal characteristics, emphasizing the need for further investigation into local environmental factors.Vegetation Cover shows a declining trend from 85.6% in 1993 to 67.8% in 2022 (figure 18). As LST increases, vegetation cover tends to decrease. This pattern suggests a potential correlation between land surface temperature and vegetation dynamics.The interplay between Average Temperature, Average LST, and Vegetation Cover Percentage reveals potential environmental changes that warrant further investigation for sustainable land management and conservation efforts. Table 10 Comparative analysis of the VC and climate 1993 1998 2004 2008 2013 2018 2022 Average Temp (˚C) 13.1 14.0 12.9 13.1 12.3 13.3 14.0 Average LST (˚C) 21.8 21.5 24.2 24.5 24.1 23.9 25.2 Vegetation Cover (%) 85.6 82.6 81.9 80.9 77.5 70.9 67.8 Conclusion In summary, the data indicates an overall decline in vegetation cover with elevations throughout the years, accompanied by fluctuations in specific elevation ranges. Overall, there is a trend of decreasing vegetation cover with slope percentages across all categories from 2013 to 2022. The changes in slope percentages might have implications for land use, development, and environmental considerations. Conservation efforts may need to focus on areas with steeper slopes to prevent erosion and protect ecosystems. Based on the results, a comparison between average temperature and vegetation cover showed potential correlations, as changes in land surface temperature can affect the types of vegetation that can thrive in an area. A decrease in vegetation cover is associated with temperature changes. The decrease in vegetation cover might have ecological implications, including habitat loss, changes in biodiversity, or potential impacts on local climate conditions. Declarations Acknowledgement I cannot express enough thanks to my organization for their continued support and encouragement to Agromet Division giving the opportunities to work under the Gramin Krishi Mausam Seva (GKMS) project, India Meteorological Department, Ministry of Earth Science, New Delhi. I offer my sincere appreciation for the learning opportunities provided by organization. I would like to express my deep and sincere gratitude to Dr. M. Mahapatra, DGM, India Meteorological Department, Ministry of Earth Science, New Delhi and Sri K. N. Mohan, Scientist-G,Regional Meteorological Centre, Guwahati, for giving me the opportunity to do research. Author contribution Nimish Narayan Gautam: conceptualization, design methodology, formal analysis, data correction. Thangjalal Lhouvum: conceptualization, design methodology, supervision, validation, review. Niewkor Warbah: data correction writing original draft and editing. Funding No funding. Data availability All data generated or analyzed during this study are included in this published article. Should any raw data files be needed in another format, they are available from the corresponding author upon reasonable request. Competing interest The authors declare no competing interests. References Abrams, M. (2000). The Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER): data products for the high spatial resolution imager on NASA's Terra platform. international Journal of Remote sensing , 21 (5), 847-859. Berhe, A. A., Barnes, R. T., Six, J., & Marín-Spiotta, E. (2018). Role of soil erosion in biogeochemical cycling of essential elements: carbon, nitrogen, and phosphorus. Annual Review of Earth and Planetary Sciences , 46 , 521-548. Chakma, J. B. (2020). Tourism Marketing in Northeast India: A Comparative Study of Meghalaya and Mizoram (Doctoral dissertation, Mizoram University). Dikshit, K. R., Dikshit, J. K., Dikshit, K. R., & Dikshit, J. K. (2014). Natural vegetation: forests and grasslands of North-East India. North-East India: Land, People and Economy , 213-255. Gandhi, G. M., Parthiban, B. S., Thummalu, N., & Christy, A. (2015). Ndvi: Vegetation change detection using remote sensing and gis–A case study of Vellore District. Procedia computer science , 57 , 1199-1210. Kumar, S., Arya, S., & Jain, K. (2022). A SWIR-based vegetation index for change detection in land cover using multi-temporal Landsat satellite dataset. International Journal of Information Technology , 1-14. Mohan, M., Kikegawa, Y., Gurjar, B. R., Bhati, S., & Kolli, N. R. (2013). Assessment of urban heat island effect for different land use–land cover from micrometeorological measurements and remote sensing data for megacity Delhi. Theoretical and applied climatology , 112 , 647-658. Raducan, G. (2018). The impact of bushfires on water quality (Doctoral dissertation, RMIT University). Rawal, R. S., Bhatt, I. D., Sekar, K. C., & Nandi, S. K. (2013). The himalayan biodiversity: richness, representativeness, uniqueness and life-support values. GB Pant Institute of Himalayan Environment and Development (GBPIHED), Kosi-Katarmal, Almora, Uttarakhand, India, 84pp . Reid, A. J., Carlson, A. K., Creed, I. F., Eliason, E. J., Gell, P. A., Johnson, P. T., ... & Cooke, S. J. (2019). Emerging threats and persistent conservation challenges for freshwater biodiversity. Biological Reviews , 94 (3), 849-873. Rodrigues, C. I. D., Brito, L. M., & Nunes, L. J. (2023). Soil carbon sequestration in the context of climate change mitigation: A review. Soil Systems , 7 (3), 64. Singh, A. K. (2010). Probable agricultural biodiversity heritage sites in India: V. the Garo, Khasi, and Jaintia hills region. Asian Agri-History , 14 (2), 133-156. Singh, D., Gautam, N. N., & Gupta, S. (2021). Assessment of crop parameters and groundwater level in a policy-implemented micro-watershed environment using geospatial technology. Arabian Journal of Geosciences , 14 (22), 2439. Tenzin, G. (2014). Snowmelt runoff analysis and impact assessment of temperature change in the upper Punatsang Chu basin (Doctoral dissertation, School of Remote Sensing Institute of Science Suranaree University of Technology). Wan Mohd Jaafar, W. S., Abdul Maulud, K. N., Muhmad Kamarulzaman, A. M., Raihan, A., Md Sah, S., Ahmad, A., ... & Razzaq Khan, W. (2020). The influence of deforestation on land surface temperature—A case study of Perak and Kedah, Malaysia. Forests , 11 (6), 670. Yu, L., Liu, Y., Li, X., Yan, F., Lyne, V., & Liu, T. (2023). Vegetation-induced asymmetric diurnal land surface temperatures changes across global climate zones. Science of the Total Environment , 896 , 165255. Additional Declarations No competing interests reported. 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. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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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-4315829","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":312177412,"identity":"f49c3d0b-0fd6-4209-861b-39f1f50020fe","order_by":0,"name":"Nimish Narayan 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Department","correspondingAuthor":false,"prefix":"","firstName":"Thangjalal","middleName":"","lastName":"Lhouvum","suffix":""},{"id":312177414,"identity":"c3303d3f-84bf-48bc-8583-dd198f94b24f","order_by":2,"name":"Niewkor Warbah","email":"","orcid":"","institution":"India Meteorological Department","correspondingAuthor":false,"prefix":"","firstName":"Niewkor","middleName":"","lastName":"Warbah","suffix":""}],"badges":[],"createdAt":"2024-04-24 06:14:56","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4315829/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4315829/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":58387493,"identity":"61776fec-2a99-435f-84e0-b339abde20e4","added_by":"auto","created_at":"2024-06-14 18:53:09","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":235615,"visible":true,"origin":"","legend":"\u003cp\u003eGeographical setting of study area\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-4315829/v1/a16e694a8f6b68ca428593b1.png"},{"id":58386643,"identity":"51590ae0-0210-4c35-88f0-a7be4168ba86","added_by":"auto","created_at":"2024-06-14 18:45:09","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":109332,"visible":true,"origin":"","legend":"\u003cp\u003eMethodological flowchart for the research analysis\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-4315829/v1/6e62625eebfb9a8e87ad4b2f.png"},{"id":58387492,"identity":"fa46b329-12a2-4147-a18c-b2c9b73d68cd","added_by":"auto","created_at":"2024-06-14 18:53:09","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":48958,"visible":true,"origin":"","legend":"\u003cp\u003eElevation wise TGA of EKH\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-4315829/v1/6486d23f9afdf046bc8ed297.png"},{"id":58386660,"identity":"acd00cc9-ff5c-4d85-bd62-fbb30c05ccc8","added_by":"auto","created_at":"2024-06-14 18:45:10","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":182882,"visible":true,"origin":"","legend":"\u003cp\u003eElevation classification map of EKH.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-4315829/v1/3abd0329f99a4268c505987f.png"},{"id":58386646,"identity":"b4b12a26-af56-46a8-b5ca-5fadb1e8b289","added_by":"auto","created_at":"2024-06-14 18:45:09","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":30060,"visible":true,"origin":"","legend":"\u003cp\u003eSlope wise TGA of EKH\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-4315829/v1/7c3b5f6ae8468a34411dadde.png"},{"id":58386656,"identity":"f27ecd70-5b20-4665-88dc-e96b3205fb66","added_by":"auto","created_at":"2024-06-14 18:45:10","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":146008,"visible":true,"origin":"","legend":"\u003cp\u003eSlope classification map of EKH.\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-4315829/v1/c22947d1219a78cba09d65de.png"},{"id":58386654,"identity":"798817c3-2771-4b04-8a99-e76090b1722d","added_by":"auto","created_at":"2024-06-14 18:45:10","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":543105,"visible":true,"origin":"","legend":"\u003cp\u003eTemporal vegetation cover map of East Khasi Hills district\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-4315829/v1/59232360751344cb9f520177.png"},{"id":58387494,"identity":"f6d04f98-b7ed-4a11-a949-02351ec44230","added_by":"auto","created_at":"2024-06-14 18:53:09","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":27656,"visible":true,"origin":"","legend":"\u003cp\u003eVegetation cover scenario of East Khasi Hills district\u003c/p\u003e","description":"","filename":"8.png","url":"https://assets-eu.researchsquare.com/files/rs-4315829/v1/eb8eaf650d5aeeb69060a7f3.png"},{"id":58388032,"identity":"642495ab-84f6-4f99-8571-255bb325239e","added_by":"auto","created_at":"2024-06-14 19:01:09","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":19401,"visible":true,"origin":"","legend":"\u003cp\u003eTemporal vegetation cover change analysis of EKH\u003c/p\u003e","description":"","filename":"9.png","url":"https://assets-eu.researchsquare.com/files/rs-4315829/v1/b45d0e0fe01fef837d420799.png"},{"id":58388033,"identity":"8aa17b0c-ff8d-4bc4-b0d6-52d24592d197","added_by":"auto","created_at":"2024-06-14 19:01:09","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":120568,"visible":true,"origin":"","legend":"\u003cp\u003eElevation wise Vegetation Cover Change\u003c/p\u003e","description":"","filename":"10.png","url":"https://assets-eu.researchsquare.com/files/rs-4315829/v1/e33fb032372315c6495e249f.png"},{"id":58386648,"identity":"cc9c18f9-edc6-4dca-aaac-912531a21d42","added_by":"auto","created_at":"2024-06-14 18:45:09","extension":"png","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":22893,"visible":true,"origin":"","legend":"\u003cp\u003eElevation wise Vegetation Cover Change\u003c/p\u003e","description":"","filename":"11.png","url":"https://assets-eu.researchsquare.com/files/rs-4315829/v1/a48836e87b0ce8f2c2d98310.png"},{"id":58386652,"identity":"99754844-c673-4a31-b947-e6b8ac8f99b2","added_by":"auto","created_at":"2024-06-14 18:45:09","extension":"png","order_by":12,"title":"Figure 12","display":"","copyAsset":false,"role":"figure","size":31593,"visible":true,"origin":"","legend":"\u003cp\u003eMinimum temperature of AT and LST in winter season\u003c/p\u003e","description":"","filename":"12.png","url":"https://assets-eu.researchsquare.com/files/rs-4315829/v1/a32835f74fac311d1967308e.png"},{"id":58386651,"identity":"b5dc721a-9b60-4bbf-8e29-69b9b3beb5b4","added_by":"auto","created_at":"2024-06-14 18:45:09","extension":"png","order_by":13,"title":"Figure 13","display":"","copyAsset":false,"role":"figure","size":25668,"visible":true,"origin":"","legend":"\u003cp\u003eMinimum temperature of AT and LST in winter season\u003c/p\u003e","description":"","filename":"13.png","url":"https://assets-eu.researchsquare.com/files/rs-4315829/v1/afa6298f58b2e568efa0c3f9.png"},{"id":58386658,"identity":"eddd127e-5115-46ef-a1af-863f6c67c177","added_by":"auto","created_at":"2024-06-14 18:45:10","extension":"png","order_by":14,"title":"Figure 14","display":"","copyAsset":false,"role":"figure","size":586755,"visible":true,"origin":"","legend":"\u003cp\u003eTemporal Land Surface Temperature (LST) in winter season of East Khasi Hills\u003c/p\u003e","description":"","filename":"14.png","url":"https://assets-eu.researchsquare.com/files/rs-4315829/v1/09a28cbd8fe76cad23be557b.png"},{"id":58386653,"identity":"fa47033f-a72d-468a-84ba-0d904eb46a96","added_by":"auto","created_at":"2024-06-14 18:45:09","extension":"png","order_by":15,"title":"Figure 15","display":"","copyAsset":false,"role":"figure","size":23223,"visible":true,"origin":"","legend":"\u003cp\u003eMinimum temperature of AT and LST in summer season\u003c/p\u003e","description":"","filename":"15.png","url":"https://assets-eu.researchsquare.com/files/rs-4315829/v1/391ad0df0e711f31de620480.png"},{"id":58386655,"identity":"acb854e2-b30f-4f51-94d5-9c3c39f02fe2","added_by":"auto","created_at":"2024-06-14 18:45:10","extension":"png","order_by":16,"title":"Figure 16","display":"","copyAsset":false,"role":"figure","size":23936,"visible":true,"origin":"","legend":"\u003cp\u003eMaximum temperature of AT and LST in summer season\u003c/p\u003e","description":"","filename":"16.png","url":"https://assets-eu.researchsquare.com/files/rs-4315829/v1/eac1405bc42545d7b8e3d5bd.png"},{"id":58386657,"identity":"65254daf-2cba-4303-8009-311bb1a7fa63","added_by":"auto","created_at":"2024-06-14 18:45:10","extension":"png","order_by":17,"title":"Figure 17","display":"","copyAsset":false,"role":"figure","size":420679,"visible":true,"origin":"","legend":"\u003cp\u003eTemporal Land Surface Temperature (LST) in winter season of East Khasi Hills\u003c/p\u003e","description":"","filename":"17.png","url":"https://assets-eu.researchsquare.com/files/rs-4315829/v1/45e377ee74fad650e57c5474.png"},{"id":58387497,"identity":"5ae855fd-6aa4-4fcd-9b94-913c9a76ccdf","added_by":"auto","created_at":"2024-06-14 18:53:10","extension":"png","order_by":18,"title":"Figure 18","display":"","copyAsset":false,"role":"figure","size":56451,"visible":true,"origin":"","legend":"\u003cp\u003eComparative analysis of the VC and climate\u003c/p\u003e","description":"","filename":"18.png","url":"https://assets-eu.researchsquare.com/files/rs-4315829/v1/63c71e121047af3f5cf99060.png"},{"id":60914847,"identity":"53b9e9ec-d486-4323-9d9d-0d5fd3db9606","added_by":"auto","created_at":"2024-07-23 13:29:48","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3223040,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4315829/v1/5a4c2bb7-321d-4919-91e6-f32326ffa4d5.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Periodic Relations between Vegetation Cover and Climatic Factors in East Khasi Hills, Meghalaya, India","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eThe importance of vegetation in Meghalaya is multi-faceted, encompassing ecological, economic, and cultural aspects (Chakma et.al., 2014)The diverse array of plant species contributes to ecosystem stability by regulating nutrient cycles, supporting biodiversity, and preventing soil erosion. Additionally, vegetation plays a pivotal role in carbon sequestration, influencing regional climate patterns and mitigating the impact of climate change (Berhe et al., \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e; Rodrigues et al., \u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e).Preserving the diverse vegetation in Meghalaya is crucial for maintaining the ecological balance, sustaining local livelihoods, and ensuring the well-being of the unique flora and fauna in the region (Rawal et.al, \u003cspan class=\"CitationRef\"\u003e2013\u003c/span\u003e).Meghalaya features a diverse range of vegetation types, including tropical rainforests, grasslands, bamboo groves, medicinal plants, and orchids and ferns(Singh., 2010).This article introduces an improved method for analyzing satellite images through an enhanced Change Detection technique, utilizing the Normalized Difference Vegetation Index (NDVI) (Gandhi.,2015).The alteration of vegetation cover, whether through deforestation, afforestation, urbanization, or natural processes, has direct consequences on the thermal characteristics of the land surface (Wan. et. al., 2020). The relationship between vegetation cover change and LST is complex and can be influenced by various factors viz Cooling Effect of Vegetation, Urban Heat Island Effect, etc.(Mohan andKikegawa.,2013).Climatological factors like precipitation and temperature influence vegetation growth, which, in turn, affects LST. Integrating vegetation-related climatic data with LST analysis provides insights into the complex feedback mechanisms between land surface conditions and climate(Liu, et.al., 2023).Elevation and slope emerge as pivotal influencers in determining the distribution of vegetation across East Khasi Hills (Dikshit et al., \u003cspan class=\"CitationRef\"\u003e2014\u003c/span\u003e). The diverse elevations, ranging from lowlands to high plateaus, act as natural architects, shaping microclimates that dictate the types of plants that thrive and their growth patterns (Reid et.al,2019).\u003c/p\u003e\n\u003cdiv id=\"Sec2\" class=\"Section2\"\u003e\n \u003ch2\u003e1.1. Study Area\u003c/h2\u003e\n \u003cp\u003eThe East Khasi Hills District is a significant geographical region within the state of Meghalaya, constituting a central part with a total area of 2,748 square kilometers or 274,800 hectares. Its geographical coordinates lie approximately between 25\u0026deg;07\u0026rdquo; to 25\u0026deg;41\u0026rdquo; N latitude and 91\u0026deg;21\u0026rdquo; to 92\u0026deg;09\u0026rdquo; E longitude, as depicted in Fig.\u0026nbsp;1. This district is characterized by a subtropical highland climate, featuring distinct seasons.The region experiences a diverse temperature range, with winter temperatures around 2\u0026deg;C (October to March). The summer season, lasting from May to October, is relatively short, with an average temperature of 25.0\u0026deg;C.Most of the annual rainfall, approximately 90%, occurs during the monsoon season from June to September.Average Annual Rainfall: The district receives an average annual rainfall of about 2000 mm, emphasizing the significance of the monsoon season as a primary contributor to the region\u0026apos;s water resources.Being a central part of Meghalaya, the district holds strategic importance in terms of its geography and topography.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"2. Material and Methodology","content":"\u003cp\u003eQuantitative and qualitative analysis were performed in which a combination of primary and secondary data was used to evaluate the vegetation cover change and climate factor for the East Khasi Hill district. First, a comparison approach using NDVI and rule-based classification was adopted and vegetation cover changes for every five-year interval like 1993, 1998, 2004, 2008, 2013, 2018 and 2022.\u003c/p\u003e\n\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\n \u003ch2\u003e2.1. Data Sources\u003c/h2\u003e\n \u003cdiv id=\"Sec5\" class=\"Section3\"\u003e\n \u003ch2\u003e2.1.1. Satellite Images Used\u003c/h2\u003e\n \u003cp\u003eOptical Satellite images Landsat-TM (Thematic Mapper) and Landsat-OLI (Operational Land Imager) with Rows 136, 137 and Path 42, 43 were used to acquire information for the month of October to December for seven different years 1993, 1998, 2004, 2008, 2013, 2018 and 2022 with 30m spatial resolution. The Terra Moderate Resolution Imaging Spectroradiometer (MODIS) LST (Land Surface Temperature) with 8 days emissivity product (MOD112A) images were downloaded for the month of June and December for 2004, 2008, 2013, 2018 and 2022.This product provides 8 days per pixel LST temperature and emissivity with 1km spatial resolutions. The Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) was used for automatic steam network extraction and analysis. ASTER is a Japanese multispectral imaging RS instrument onboard the Terra sensor lunched by NASA in 1999 and has provide data from February 2000. These data downloaded from earth explorer USGS of the United State Geological Survey (USGS) (website \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://earthexplorer.usgs.gov\u003c/span\u003e\u003c/span\u003e). The data obtained from these sources have been processed to gain the reflectance value that involves DN, to Radiance conversion and Radiance to Reflectance conversion.\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e\n \u003ch2\u003e2.1.2. Secondary data\u003c/h2\u003e\n \u003cp\u003eMonthly and annual rainfall, minimum and maximum temperature data used for the verification of the temporal climatological factors analysis in East Khasi Hill district. The all-climatological data provided by India Meteorological Department, Ministry of Earth Science, Shillong (Fig.\u0026nbsp;2).\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\n \u003ch2\u003e2.2. Methodology\u003c/h2\u003e\n \u003cdiv id=\"Sec8\" class=\"Section3\"\u003e\n \u003ch2\u003e2.2.1. Extraction of Vegetation Cover\u003c/h2\u003e\n \u003cp\u003eNormalized Difference Vegetation Index (NDVI) is a geospatial technique used to extraction of vegetation cover (Kumar et al, \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e). It is performed based on visible range RED and NEAR INFRA RED (NIR) reflectance\u0026rsquo;s and normalized that difference by the sum of reflectance. From the Eq.\u0026nbsp;1 is calculated as\u003c/p\u003e\n \u003cdiv id=\"Equa\" class=\"Equation\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\u003cimg src=\"data:image/png;base64,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\"\u003e\u003c/div\u003e\n \u003c/div\u003e\n \u003cp\u003eWhere RED is visible red reflectance, and NIR is near infrared reflectance. The wavelength range of NIR band is (750\u0026ndash;1300 nm) and red band is (600\u0026ndash;700 nm). Rule based classification technique was used classification of vegetation cover and non-vegetation cover. values based on minimum value of vegetation cover (Singh et al, \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e\n \u003ch2\u003e2.2.2. Elevation Classification\u003c/h2\u003e\n \u003cp\u003eThe spatial analyst tools from Arc toolbox were used for the classification of elevation. Firstly, classify the elevation based on value using reclass tool. Then, the reclassify tool was used for extraction of classified elevation image based on equal elevation values and calculated the elevation wise Total Geographical Area (TGA) of East Khasi Hill.\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eTable 1Elevation wise TGA of EKH\u003c/em\u003e\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tabc\" border=\"1\"\u003e\n \u003ccolgroup cols=\"3\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eElevation (m)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eArea (hac)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eArea (%)\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\u003e0-200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e25350\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9.22\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e200\u0026ndash;400\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e16779\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.11\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e400\u0026ndash;600\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e18385\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.69\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e600\u0026ndash;800\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e19169\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.98\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e800\u0026ndash;1000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e23258\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8.46\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1000\u0026ndash;1200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e29476\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10.73\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1200\u0026ndash;1400\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e32616\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11.87\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1400\u0026ndash;1600\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e37664\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e13.71\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1600\u0026ndash;1800\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e57584\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e20.95\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026gt;\u0026thinsp;1800\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e14520\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.28\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cdiv class=\"BlockQuote\"\u003e\n \u003cp\u003eThis analysis provides an overview of how the total area is distributed across different elevation ranges, giving insights into the landscape composition in terms of elevation (Fig. 4). The areas seem to increase with higher elevations, peaking in the 1600\u0026ndash;1800 meters range (57584 hectares), which constitutes the largest portion (20.95%) of the total area and this data is representing in the table 1 and graphical representation with Fig. 3.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec10\" class=\"Section3\"\u003e\n \u003ch2\u003e2.2.3. Slope Classification\u003c/h2\u003e\n \u003cp\u003eThe slope plays a major role in terms of vegetation sustainability and plantation roots can increase the strength properties of soil which stability of slope. Firstly, classify the slope (degree) using surface tool. Then, extraction of classified slope image based on degree values like Very Gentle (0\u0026ndash;5), Gentle (5\u0026ndash;10), Moderate (10\u0026ndash;15), Moderately Steep (15\u0026ndash;25), Steep (25\u0026ndash;35) and Excessively Steep (\u0026gt;\u0026thinsp;35). The geographical area was calculated of East Khasi Hill. In the Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e, geographical area distributed among the various slope classes 23.51%, 21.23%, 17.59%, 15.93%, 13.08% and 8.66% respectively moderately steep, gentle, moderate Steep Very Gentle and Excessively Steep (Fig. 5).\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 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eSlope wise TGA of EKH\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"3\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSlope Class (Degree)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eArea (hac)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eArea (%)\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\u003eVery Gentle (0\u0026ndash;5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e35941\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e13.08\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGentle (5\u0026ndash;10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e58331\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e21.23\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eModerate (10\u0026ndash;15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e48326\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e17.59\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eModerately Steep (15\u0026ndash;25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e64610\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e23.51\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSteep (25\u0026ndash;35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e43784\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e15.93\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eExcessively Steep (\u0026gt;\u0026thinsp;35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e23808\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8.66\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eThe data suggests the terrain has a mix of gentle to moderately steep slopes, with a smaller portion being excessively steep. The percentages offer a proportional view of the land distribution across these slope categories. This information could be valuable for land management, urban planning, or environmental studies.\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec11\" class=\"Section3\"\u003e\n \u003ch2\u003e2.2.4. Land Surface Temperature (LST)\u003c/h2\u003e\n \u003cp\u003eLand Surface Temperature (LST) is one of the three major influences on the global pattern of vegetation growth. Along with sunlight and water, temperatures determine whether the land will support forest, grassland and fallow land. LST is a measurement of how hot the land is to the touch and differs from the air temperature because land heats and cools more quickly than air. In the study proceeds the extraction of LST using MODIS, Landsat-5and Landsat-8 data. Firstly, geometric correction was done for the MOD11A2 product. Secondly, approach to the estimation of LST from Landsat data. In the research band 10 was used for Landsat-8 and band 6 for the Landsat-5 or Landsat-7+. The first step of the performed to conversion of DN to radiance using following equation:\u003c/p\u003e\n \u003cdiv id=\"Equb\" class=\"Equation\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e\u003cimg 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\"\u003e\u003c/div\u003e\n \u003c/div\u003e\n \u003cp\u003eAfter the conversion of DN to radiance, the thermal band should be converted to Brightness Temperature (BT) using the thermal constant data and equation:\u003c/p\u003e\n \u003cdiv id=\"Equh\" class=\"Equation\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equh\" name=\"EquationSource\"\u003e\u003cimg src=\"data:image/png;base64,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\"\u003e\u003c/div\u003e\n \u003c/div\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv align=\"char\" class=\"colspec\"\u003e\u003cbr\u003e\u003c/div\u003e\u0026nbsp;\u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003econstant value of K-1 and K-2\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"3\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSensor\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eConstant K-1\u003c/p\u003e\n \u003cp\u003eW/(m\u003csup\u003e2\u003c/sup\u003e*sr*um)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eKelvin K-2\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\u003eLandsat-8 (OLI) Band 10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e774.8853\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1321.0789\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLandsat-7 (ETM+) Band 6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e666.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1282.71\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLandsat-5 (TM) Band 6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e607.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1260.56\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eTable \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e shows the values seem to represent the constants for different sensors used in remote sensing applications, specifically indicating their radiometric calibration coefficients. The brightness temperature data was converted to degree kelvin to degree Celsius, it is necessary to revise by adding absolute zero value which is proportional to -273.15. Finally, LST (Degree Celsius) has extracted using equation:\u003c/p\u003e\n \u003cdiv id=\"Equi\" class=\"Equation\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equi\" name=\"EquationSource\"\u003e\u003cimg src=\"data:image/png;base64,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\"\u003e\u003c/div\u003e\n \u003c/div\u003e\n \u003c/div\u003e\n\u003c/div\u003e"},{"header":"3. Result and Discussion","content":"\u003cp\u003e\u003cem\u003e3.1 Vegetation Cover\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe figures given below depict the declining vegetation cover (green color) and increasing non-vegetation cover (yellow color) in the East-Khasi hill district of Meghalaya in every five year since 1993. Figure 7 and table 4 showing the depicts 85.58% of area of the East Khasi hill dominated by vegetation cover. In the course of the five year in 1998, the vegetation cover of East Khasi hills district decreased from 235177 hectares in 1993 to 226916 hectares in 1998. In 1998, 82.58 % area is covered by vegetation. Further, in five years, in 2004 the vegetation declined to 225118 hectares. In terms of percentage, the total area covered by vegetation reached to 81.92%. In 2008, the vegetation cover decreased to 222229 hectares in absolute terms and 80.87 in percentage terms.A major decline in the vegetation cover can be noticed in 2013, 2018 and 2022. From 2008 to 2013, the vegetation cover declined from 80.87 in 2008 to 77.47 in 2013, to 70.94 % in 2018 and to 67.75 % in 2023. Even the Graph shows that the significant declines in vegetation cover from 2013 to 2022 (figure 8).\u003c/p\u003e\n\u003cp\u003eTable 4 Temporal vegetation cover scenario of East Khasi Hills district\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.292929292929294%\"\u003e\n \u003cp\u003e\u003cstrong\u003eYear\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"36.36363636363637%\"\u003e\n \u003cp\u003e\u003cstrong\u003eArea (hec)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"34.343434343434346%\"\u003e\n \u003cp\u003e\u003cstrong\u003eArea (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.292929292929294%\"\u003e\n \u003cp\u003e1993\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"36.36363636363637%\"\u003e\n \u003cp\u003e235177\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"34.343434343434346%\"\u003e\n \u003cp\u003e85.58\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.292929292929294%\"\u003e\n \u003cp\u003e1998\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"36.36363636363637%\"\u003e\n \u003cp\u003e226916\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"34.343434343434346%\"\u003e\n \u003cp\u003e82.58\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.292929292929294%\"\u003e\n \u003cp\u003e2004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"36.36363636363637%\"\u003e\n \u003cp\u003e225118\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"34.343434343434346%\"\u003e\n \u003cp\u003e81.92\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.292929292929294%\"\u003e\n \u003cp\u003e2008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"36.36363636363637%\"\u003e\n \u003cp\u003e222229\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"34.343434343434346%\"\u003e\n \u003cp\u003e80.87\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.292929292929294%\"\u003e\n \u003cp\u003e2013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"36.36363636363637%\"\u003e\n \u003cp\u003e212894\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"34.343434343434346%\"\u003e\n \u003cp\u003e77.47\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.292929292929294%\"\u003e\n \u003cp\u003e2018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"36.36363636363637%\"\u003e\n \u003cp\u003e194929\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"34.343434343434346%\"\u003e\n \u003cp\u003e70.94\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.292929292929294%\"\u003e\n \u003cp\u003e2022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"36.36363636363637%\"\u003e\n \u003cp\u003e186188\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"34.343434343434346%\"\u003e\n \u003cp\u003e67.75\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003e3.1.1.\u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Vegetation Cover Change\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe below table shows the percentage of vegetation cover and the decrease in vegetation cover area percentage wise of East Khasi Hills of Meghalaya in every five years. As per table 5 the highest decline in vegetation cover is noticed between 2013-2018 is 6.54 %, followed by 2018-2022 is 3.18%. The lowest decline is experienced in the period between 1998 to 2004 is 0.65%. The bar graph plots the percentage decline in area of vegetation cover over five years\u0026rsquo; time periods from 1993-2022 (figure 9).\u003c/p\u003e\n\u003cp\u003eTable \u003cem\u003e5\u003c/em\u003e vegetation cover change analysis of the five years interval\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.75%\"\u003e\n \u003cp\u003e\u003cstrong\u003eYear\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.458333333333334%\"\u003e\n \u003cp\u003e\u003cstrong\u003e1993\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.458333333333334%\"\u003e\n \u003cp\u003e\u003cstrong\u003e1998\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\"\u003e\n \u003cp\u003e\u003cstrong\u003e2004\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.458333333333334%\"\u003e\n \u003cp\u003e\u003cstrong\u003e2008\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.458333333333334%\"\u003e\n \u003cp\u003e\u003cstrong\u003e2013\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.458333333333334%\"\u003e\n \u003cp\u003e\u003cstrong\u003e2018\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.458333333333334%\"\u003e\n \u003cp\u003e\u003cstrong\u003e2022\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.75%\"\u003e\n \u003cp\u003e\u003cstrong\u003eArea (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.458333333333334%\"\u003e\n \u003cp\u003e85.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.458333333333334%\"\u003e\n \u003cp\u003e82.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\"\u003e\n \u003cp\u003e81.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.458333333333334%\"\u003e\n \u003cp\u003e80.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.458333333333334%\"\u003e\n \u003cp\u003e77.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.458333333333334%\"\u003e\n \u003cp\u003e70.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.458333333333334%\"\u003e\n \u003cp\u003e67.75\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003e3.1.2.\u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Elevation wise Vegetation Cover Change\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe vegetation cover change analysis of the five years interval and Table 6 shows the elevation wise vegetation cover change in East Khasi hills of Meghalaya. Elevation less than 200 m the percentage of vegetation cover declined from 8.46 % in 1993 to 7.54 % in 2022. For 200-400 m elevation the major decline in vegetation cover noticed between 2013-2018, from 6.02% to 5.69%.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 6 Elevation wise vegetation cover change\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.94736842105263%\"\u003e\n \u003cp\u003e\u003cstrong\u003eYear\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eElevation (m)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.578947368421053%\"\u003e\n \u003cp\u003e\u003cstrong\u003e1993\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.578947368421053%\"\u003e\n \u003cp\u003e\u003cstrong\u003e1998\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.578947368421053%\"\u003e\n \u003cp\u003e\u003cstrong\u003e2004\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.578947368421053%\"\u003e\n \u003cp\u003e\u003cstrong\u003e2008\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.578947368421053%\"\u003e\n \u003cp\u003e\u003cstrong\u003e2013\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.578947368421053%\"\u003e\n \u003cp\u003e\u003cstrong\u003e2018\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.578947368421053%\"\u003e\n \u003cp\u003e\u003cstrong\u003e2022\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.94736842105263%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;200\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.578947368421053%\"\u003e\n \u003cp\u003e8.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.578947368421053%\"\u003e\n \u003cp\u003e8.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.578947368421053%\"\u003e\n \u003cp\u003e8.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.578947368421053%\"\u003e\n \u003cp\u003e8.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.578947368421053%\"\u003e\n \u003cp\u003e8.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.578947368421053%\"\u003e\n \u003cp\u003e7.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.578947368421053%\"\u003e\n \u003cp\u003e7.54\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.94736842105263%\"\u003e\n \u003cp\u003e\u003cstrong\u003e200-400\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.578947368421053%\"\u003e\n \u003cp\u003e6.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.578947368421053%\"\u003e\n \u003cp\u003e5.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.578947368421053%\"\u003e\n \u003cp\u003e6.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.578947368421053%\"\u003e\n \u003cp\u003e6.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.578947368421053%\"\u003e\n \u003cp\u003e6.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.578947368421053%\"\u003e\n \u003cp\u003e5.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.578947368421053%\"\u003e\n \u003cp\u003e5.75\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.94736842105263%\"\u003e\n \u003cp\u003e\u003cstrong\u003e400-600\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.578947368421053%\"\u003e\n \u003cp\u003e6.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.578947368421053%\"\u003e\n \u003cp\u003e6.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.578947368421053%\"\u003e\n \u003cp\u003e6.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.578947368421053%\"\u003e\n \u003cp\u003e6.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.578947368421053%\"\u003e\n \u003cp\u003e6.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.578947368421053%\"\u003e\n \u003cp\u003e6.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.578947368421053%\"\u003e\n \u003cp\u003e6.50\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.94736842105263%\"\u003e\n \u003cp\u003e\u003cstrong\u003e600-800\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.578947368421053%\"\u003e\n \u003cp\u003e6.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.578947368421053%\"\u003e\n \u003cp\u003e6.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.578947368421053%\"\u003e\n \u003cp\u003e7.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.578947368421053%\"\u003e\n \u003cp\u003e6.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.578947368421053%\"\u003e\n \u003cp\u003e6.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.578947368421053%\"\u003e\n \u003cp\u003e6.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.578947368421053%\"\u003e\n \u003cp\u003e6.70\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.94736842105263%\"\u003e\n \u003cp\u003e\u003cstrong\u003e800-1000\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.578947368421053%\"\u003e\n \u003cp\u003e7.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.578947368421053%\"\u003e\n \u003cp\u003e7.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.578947368421053%\"\u003e\n \u003cp\u003e7.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.578947368421053%\"\u003e\n \u003cp\u003e7.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.578947368421053%\"\u003e\n \u003cp\u003e7.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.578947368421053%\"\u003e\n \u003cp\u003e7.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.578947368421053%\"\u003e\n \u003cp\u003e7.31\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.94736842105263%\"\u003e\n \u003cp\u003e\u003cstrong\u003e1000-1200\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.578947368421053%\"\u003e\n \u003cp\u003e9.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.578947368421053%\"\u003e\n \u003cp\u003e9.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.578947368421053%\"\u003e\n \u003cp\u003e9.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.578947368421053%\"\u003e\n \u003cp\u003e9.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.578947368421053%\"\u003e\n \u003cp\u003e9.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.578947368421053%\"\u003e\n \u003cp\u003e8.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.578947368421053%\"\u003e\n \u003cp\u003e8.72\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.94736842105263%\"\u003e\n \u003cp\u003e\u003cstrong\u003e1200-1400\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.578947368421053%\"\u003e\n \u003cp\u003e10.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.578947368421053%\"\u003e\n \u003cp\u003e9.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.578947368421053%\"\u003e\n \u003cp\u003e9.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.578947368421053%\"\u003e\n \u003cp\u003e10.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.578947368421053%\"\u003e\n \u003cp\u003e9.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.578947368421053%\"\u003e\n \u003cp\u003e7.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.578947368421053%\"\u003e\n \u003cp\u003e7.81\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.94736842105263%\"\u003e\n \u003cp\u003e\u003cstrong\u003e1400-1600\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.578947368421053%\"\u003e\n \u003cp\u003e10.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.578947368421053%\"\u003e\n \u003cp\u003e9.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.578947368421053%\"\u003e\n \u003cp\u003e9.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.578947368421053%\"\u003e\n \u003cp\u003e9.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.578947368421053%\"\u003e\n \u003cp\u003e9.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.578947368421053%\"\u003e\n \u003cp\u003e7.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.578947368421053%\"\u003e\n \u003cp\u003e7.51\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.94736842105263%\"\u003e\n \u003cp\u003e\u003cstrong\u003e1600-1800\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.578947368421053%\"\u003e\n \u003cp\u003e15.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.578947368421053%\"\u003e\n \u003cp\u003e14.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.578947368421053%\"\u003e\n \u003cp\u003e13.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.578947368421053%\"\u003e\n \u003cp\u003e12.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.578947368421053%\"\u003e\n \u003cp\u003e11.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.578947368421053%\"\u003e\n \u003cp\u003e10.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.578947368421053%\"\u003e\n \u003cp\u003e8.11\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.94736842105263%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026gt;1800\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.578947368421053%\"\u003e\n \u003cp\u003e3.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.578947368421053%\"\u003e\n \u003cp\u003e3.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.578947368421053%\"\u003e\n \u003cp\u003e3.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.578947368421053%\"\u003e\n \u003cp\u003e3.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.578947368421053%\"\u003e\n \u003cp\u003e2.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.578947368421053%\"\u003e\n \u003cp\u003e2.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.578947368421053%\"\u003e\n \u003cp\u003e1.81\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eIn figure 10 shows the drastic decrease of vegetation cover above 1400m elevation and below 1400m have been shows the minimum decreasing trend over EKH district of Meghalaya. Analyzing overall changes in vegetation cover during the period 1993 to 2022. The maximum area decreasing in above 1000m elevation 14.28% and below the 1000m elevation shows the change of 2.5%. This change represents the high human influence in this elevation and maximum geographical area covered (62.54%) by 1000m above elevation.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e3.1.3.\u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Slope wise Vegetation Cover Change\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eTable 7 presents a time-series analysis of the vegetation cover percentage across different slope categories (measured in degrees) in East Khasi Hills for the year\u0026rsquo;s temporal years.The vegetation cover percentage in Very Gentle (0-5) category has decreased over the years, from 9.06% in 1993 to 5.81% in 2022. This suggests a notable decline in vegetation on very gentle slopes.A decreasing trend is observed in Gentle (5-10) category as well, with vegetation cover declining from 15.95% in 1993 to 10.89% in 2022. This indicates a reduction in vegetation on slopes with a moderate incline.The vegetation cover percentage on Moderate (10-15) slopes has decreased from 21.82% in 1993 to 17.95% in 2022. This indicates a reduction in vegetation on slopes with a relatively steeper incline.The vegetation cover on Steep (25-35) slopes has experienced a decline from 15.60% in 1993 to 14.21% in 2022.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable: 7 Slope wise vegetation cover change\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.68421052631579%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e1993\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e1998\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e2004\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e2008\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e2013\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e2018\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e2022\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.68421052631579%\" valign=\"bottom\"\u003e\n \u003cp\u003eVery Gentle (0-5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\" valign=\"bottom\"\u003e\n \u003cp\u003e9.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\" valign=\"bottom\"\u003e\n \u003cp\u003e8.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\" valign=\"bottom\"\u003e\n \u003cp\u003e7.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\" valign=\"bottom\"\u003e\n \u003cp\u003e7.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\" valign=\"bottom\"\u003e\n \u003cp\u003e7.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\" valign=\"bottom\"\u003e\n \u003cp\u003e7.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\" valign=\"bottom\"\u003e\n \u003cp\u003e5.81\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.68421052631579%\" valign=\"bottom\"\u003e\n \u003cp\u003eGentle (5-10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\" valign=\"bottom\"\u003e\n \u003cp\u003e15.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\" valign=\"bottom\"\u003e\n \u003cp\u003e15.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\" valign=\"bottom\"\u003e\n \u003cp\u003e14.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\" valign=\"bottom\"\u003e\n \u003cp\u003e14.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\" valign=\"bottom\"\u003e\n \u003cp\u003e13.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\" valign=\"bottom\"\u003e\n \u003cp\u003e11.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\" valign=\"bottom\"\u003e\n \u003cp\u003e10.89\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.68421052631579%\" valign=\"bottom\"\u003e\n \u003cp\u003eModerate (10-15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\" valign=\"bottom\"\u003e\n \u003cp\u003e14.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\" valign=\"bottom\"\u003e\n \u003cp\u003e14.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\" valign=\"bottom\"\u003e\n \u003cp\u003e13.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\" valign=\"bottom\"\u003e\n \u003cp\u003e13.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\" valign=\"bottom\"\u003e\n \u003cp\u003e12.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\" valign=\"bottom\"\u003e\n \u003cp\u003e11.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\" valign=\"bottom\"\u003e\n \u003cp\u003e10.80\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.68421052631579%\" valign=\"bottom\"\u003e\n \u003cp\u003eModerately Steep (15-25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\" valign=\"bottom\"\u003e\n \u003cp\u003e21.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\" valign=\"bottom\"\u003e\n \u003cp\u003e21.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\" valign=\"bottom\"\u003e\n \u003cp\u003e21.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\" valign=\"bottom\"\u003e\n \u003cp\u003e21.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\" valign=\"bottom\"\u003e\n \u003cp\u003e19.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\" valign=\"bottom\"\u003e\n \u003cp\u003e18.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\" valign=\"bottom\"\u003e\n \u003cp\u003e17.95\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.68421052631579%\" valign=\"bottom\"\u003e\n \u003cp\u003eSteep (25-35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\" valign=\"bottom\"\u003e\n \u003cp\u003e15.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\" valign=\"bottom\"\u003e\n \u003cp\u003e15.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\" valign=\"bottom\"\u003e\n \u003cp\u003e15.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\" valign=\"bottom\"\u003e\n \u003cp\u003e15.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\" valign=\"bottom\"\u003e\n \u003cp\u003e15.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\" valign=\"bottom\"\u003e\n \u003cp\u003e14.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\" valign=\"bottom\"\u003e\n \u003cp\u003e14.21\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.68421052631579%\" valign=\"bottom\"\u003e\n \u003cp\u003eExcessively Steep (\u0026gt;35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\" valign=\"bottom\"\u003e\n \u003cp\u003e8.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\" valign=\"bottom\"\u003e\n \u003cp\u003e8.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\" valign=\"bottom\"\u003e\n \u003cp\u003e8.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\" valign=\"bottom\"\u003e\n \u003cp\u003e8.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\" valign=\"bottom\"\u003e\n \u003cp\u003e8.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\" valign=\"bottom\"\u003e\n \u003cp\u003e8.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.473684210526315%\" valign=\"bottom\"\u003e\n \u003cp\u003e8.09\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eThis suggests a modest reduction in vegetation on steeper terrain. The Excessively Steep (\u0026gt;35)slopes exhibit a varying trend, with a slight increase in vegetation cover from 8.52% in 1993 to 8.09% in 2022 after experiencing a decline in the intervening years (figure 11).The declining trends in most slope categories suggest potential environmental changes or human impacts affecting vegetation distribution on different terrains in the region. Understanding these variations is crucial for informed land management and conservation strategies.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.2.\u0026nbsp;Land Surface Temperature\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e3.2.1. \u0026nbsp; \u0026nbsp; \u0026nbsp;Winter Season\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eTable 8 provides a comparative analysis of Ambient Temperature (AT) and Land Surface Temperatures (LST) for the winter season. Additionally, it calculates the difference between AT and LST, indicating the variation between these two parameters.The minimum AT temperature ranges from 6.9\u0026deg;C in 2018 to 9.25\u0026deg;C in 2008. The maximum AT temperature varies from 8.45\u0026deg;C in 1993 to 9.25\u0026deg;C in 2008.The LST exhibits a minimum value of 16.65\u0026deg;C in 2004 and a maximum of 19.7\u0026deg;C in 2018. The temperature differences between AT and LST are calculated for each year. The minimum difference occurs in 2018 with 9.95\u0026deg;C, while the maximum difference is observed in 2013 with 15.3\u0026deg;C.The fluctuation in temperature differences over the years suggests dynamic interactions between atmospheric and land surface temperatures, potentially influenced by climatic variations or local environmental changes.\u003c/p\u003e\n\u003cp\u003eTable 8 Temporal Land Surface Temperature (LST) map of East Khasi Hills\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"104%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"7.142857142857143%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"27.551020408163264%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eMinimum\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.551020408163264%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eMaximum\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"37.755102040816325%\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eDeference of AT and LST.\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"7.291666666666667%\"\u003e\n \u003cp\u003e\u003cstrong\u003eYear\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.541666666666666%\"\u003e\n \u003cp\u003e\u003cstrong\u003eAT\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.541666666666666%\"\u003e\n \u003cp\u003e\u003cstrong\u003eLST\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.541666666666666%\"\u003e\n \u003cp\u003e\u003cstrong\u003eAT\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.541666666666666%\"\u003e\n \u003cp\u003e\u003cstrong\u003eLST\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.75%\"\u003e\n \u003cp\u003e\u003cstrong\u003eMinimum\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.791666666666668%\"\u003e\n \u003cp\u003e\u003cstrong\u003eMaximum\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"7.291666666666667%\"\u003e\n \u003cp\u003e1993\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.541666666666666%\"\u003e\n \u003cp\u003e8.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.541666666666666%\"\u003e\n \u003cp\u003e10.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.541666666666666%\"\u003e\n \u003cp\u003e17.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.541666666666666%\"\u003e\n \u003cp\u003e33.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.75%\"\u003e\n \u003cp\u003e2.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.791666666666668%\"\u003e\n \u003cp\u003e15.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"7.291666666666667%\"\u003e\n \u003cp\u003e1998\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.541666666666666%\"\u003e\n \u003cp\u003e9.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.541666666666666%\"\u003e\n \u003cp\u003e13.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.541666666666666%\"\u003e\n \u003cp\u003e18.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.541666666666666%\"\u003e\n \u003cp\u003e29.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.75%\"\u003e\n \u003cp\u003e3.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.791666666666668%\"\u003e\n \u003cp\u003e11.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"7.291666666666667%\"\u003e\n \u003cp\u003e2004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.541666666666666%\"\u003e\n \u003cp\u003e8.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.541666666666666%\"\u003e\n \u003cp\u003e16.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.541666666666666%\"\u003e\n \u003cp\u003e17.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.541666666666666%\"\u003e\n \u003cp\u003e31.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.75%\"\u003e\n \u003cp\u003e8.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.791666666666668%\"\u003e\n \u003cp\u003e14.43\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"7.291666666666667%\"\u003e\n \u003cp\u003e2008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.541666666666666%\"\u003e\n \u003cp\u003e8.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.541666666666666%\"\u003e\n \u003cp\u003e18.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.541666666666666%\"\u003e\n \u003cp\u003e17.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.541666666666666%\"\u003e\n \u003cp\u003e31.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.75%\"\u003e\n \u003cp\u003e9.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.791666666666668%\"\u003e\n \u003cp\u003e13.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"7.291666666666667%\"\u003e\n \u003cp\u003e2013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.541666666666666%\"\u003e\n \u003cp\u003e7.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.541666666666666%\"\u003e\n \u003cp\u003e17.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.541666666666666%\"\u003e\n \u003cp\u003e17.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.541666666666666%\"\u003e\n \u003cp\u003e30.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.75%\"\u003e\n \u003cp\u003e10.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.791666666666668%\"\u003e\n \u003cp\u003e13.15\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"7.291666666666667%\"\u003e\n \u003cp\u003e2018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.541666666666666%\"\u003e\n \u003cp\u003e6.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.541666666666666%\"\u003e\n \u003cp\u003e18.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.541666666666666%\"\u003e\n \u003cp\u003e19.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.541666666666666%\"\u003e\n \u003cp\u003e29.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.75%\"\u003e\n \u003cp\u003e11.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.791666666666668%\"\u003e\n \u003cp\u003e9.95\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"7.291666666666667%\"\u003e\n \u003cp\u003e2022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.541666666666666%\"\u003e\n \u003cp\u003e8.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.541666666666666%\"\u003e\n \u003cp\u003e19.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.541666666666666%\"\u003e\n \u003cp\u003e19.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.541666666666666%\"\u003e\n \u003cp\u003e31.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.75%\"\u003e\n \u003cp\u003e10.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.791666666666668%\"\u003e\n \u003cp\u003e12.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eFigure12 and 13 present a comprehensive view of AT and LST over several years, highlighting the variations and temperature differences between these two parameters. Understanding these differences is crucial for interpreting local climate dynamics and their potential implications for the environment (figure 14).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e3.2.2.\u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Summer Season\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe lowest AT temperature is recorded in 2022, measuring 16.80\u0026deg;C and the highest AT temperature is observed in 2013, reaching 18.05\u0026deg;C.The lowest LST occurs in 2018, registering 20\u0026deg;C and highest noted in 2022, reaching 35.4\u0026deg;C (table 9).The calculated differences between AT and LST provide insights into the thermal contrast between the atmospheric boundary and land surface (table 8). The minimum difference is 2.4\u0026deg;C in 2018, while the maximum difference is 13.24\u0026deg;C in 2022 (figure 15 and figure 16).\u003c/p\u003e\n\u003cp\u003eTable 9 Temporal Land Surface Temperature (LST) map of East Khasi Hills\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"101%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.16326530612245%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"27.551020408163264%\" colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003eMinimum\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.551020408163264%\" colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003eMaximum\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"36.734693877551024%\" colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003eDeference of AB and LS Temp.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.333333333333334%\" valign=\"bottom\"\u003e\n \u003cp\u003eYear\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.541666666666666%\" valign=\"bottom\"\u003e\n \u003cp\u003eAB Temp.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.541666666666666%\" valign=\"bottom\"\u003e\n \u003cp\u003eLS Temp.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.541666666666666%\" valign=\"bottom\"\u003e\n \u003cp\u003eAB Temp.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.541666666666666%\" valign=\"bottom\"\u003e\n \u003cp\u003eLS Temp.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.75%\" valign=\"bottom\"\u003e\n \u003cp\u003eMinimum\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.75%\" valign=\"bottom\"\u003e\n \u003cp\u003eMaximum\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.333333333333334%\" valign=\"bottom\"\u003e\n \u003cp\u003e2004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.541666666666666%\" valign=\"bottom\"\u003e\n \u003cp\u003e17.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.541666666666666%\" valign=\"bottom\"\u003e\n \u003cp\u003e23.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.541666666666666%\" valign=\"bottom\"\u003e\n \u003cp\u003e23.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.541666666666666%\" valign=\"bottom\"\u003e\n \u003cp\u003e31.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.75%\" valign=\"bottom\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.75%\" valign=\"bottom\"\u003e\n \u003cp\u003e8.33\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.333333333333334%\" valign=\"bottom\"\u003e\n \u003cp\u003e2008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.541666666666666%\" valign=\"bottom\"\u003e\n \u003cp\u003e17.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.541666666666666%\" valign=\"bottom\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.541666666666666%\" valign=\"bottom\"\u003e\n \u003cp\u003e23.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.541666666666666%\" valign=\"bottom\"\u003e\n \u003cp\u003e32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.75%\" valign=\"bottom\"\u003e\n \u003cp\u003e5.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.75%\" valign=\"bottom\"\u003e\n \u003cp\u003e8.85\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.333333333333334%\" valign=\"bottom\"\u003e\n \u003cp\u003e2013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.541666666666666%\" valign=\"bottom\"\u003e\n \u003cp\u003e18.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.541666666666666%\" valign=\"bottom\"\u003e\n \u003cp\u003e24.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.541666666666666%\" valign=\"bottom\"\u003e\n \u003cp\u003e24.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.541666666666666%\" valign=\"bottom\"\u003e\n \u003cp\u003e33.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.75%\" valign=\"bottom\"\u003e\n \u003cp\u003e6.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.75%\" valign=\"bottom\"\u003e\n \u003cp\u003e8.92\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.333333333333334%\" valign=\"bottom\"\u003e\n \u003cp\u003e2018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.541666666666666%\" valign=\"bottom\"\u003e\n \u003cp\u003e17.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.541666666666666%\" valign=\"bottom\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.541666666666666%\" valign=\"bottom\"\u003e\n \u003cp\u003e23.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.541666666666666%\" valign=\"bottom\"\u003e\n \u003cp\u003e32.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.75%\" valign=\"bottom\"\u003e\n \u003cp\u003e2.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.75%\" valign=\"bottom\"\u003e\n \u003cp\u003e8.31\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"8.333333333333334%\" valign=\"bottom\"\u003e\n \u003cp\u003e2022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.541666666666666%\" valign=\"bottom\"\u003e\n \u003cp\u003e16.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.541666666666666%\" valign=\"bottom\"\u003e\n \u003cp\u003e22.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.541666666666666%\" valign=\"bottom\"\u003e\n \u003cp\u003e22.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.541666666666666%\" valign=\"bottom\"\u003e\n \u003cp\u003e35.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.75%\" valign=\"bottom\"\u003e\n \u003cp\u003e5.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.75%\" valign=\"bottom\"\u003e\n \u003cp\u003e13.24\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eThe fluctuation in temperature differences over the years indicates dynamic interactions between the atmospheric boundary and land surface temperatures, potentially influenced by changes in environmental factors.Monitoring AT and LST and their differences is essential for understanding the complex dynamics of the Earth\u0026apos;s surface-atmosphere interactions.Variationsin these temperatures can have implications for local climate, ecology, and human activities, making this data valuable for scientific research and environmental monitoring.These variations are crucial for understanding the local climate dynamics and their potential impacts on the environment (figure 17).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.3.\u0026nbsp; Comparative analysis of vegetation cover change and Climate Factor\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe provided data presents a comparative analysis of Average Temperature (AT), Average Land Surface Temperature (LST), and Vegetation Cover Percentage over the years 1993, 1998, 2004, 2008, 2013, 2018, and 2022. The Average Temperature fluctuates over the years, ranging from 12.3\u0026deg;C in 2013 to 14.0\u0026deg;C in 1998 and 2022.The variation in Average Temperature suggests temporal changes in the region\u0026apos;s climate, potentially influenced by atmospheric conditions, geographical factors, or climate cycles. The Average LST exhibits variability, with a noticeable increase from 21.8\u0026deg;C in 1993 to 25.2\u0026deg;C in 2022 (table 10). Rising LST values may indicate changes in land surface conditions, urbanization, or altered thermal characteristics, emphasizing the need for further investigation into local environmental factors.Vegetation Cover shows a declining trend from 85.6% in 1993 to 67.8% in 2022 (figure 18). As LST increases, vegetation cover tends to decrease. This pattern suggests a potential correlation between land surface temperature and vegetation dynamics.The interplay between Average Temperature, Average LST, and Vegetation Cover Percentage reveals potential environmental changes that warrant further investigation for sustainable land management and conservation efforts.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eTable 10 Comparative analysis of the VC and climate\u003c/em\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.292929292929294%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"10.1010101010101%\" valign=\"bottom\"\u003e\n \u003cp\u003e1993\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.1010101010101%\" valign=\"bottom\"\u003e\n \u003cp\u003e1998\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.1010101010101%\" valign=\"bottom\"\u003e\n \u003cp\u003e2004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.1010101010101%\" valign=\"bottom\"\u003e\n \u003cp\u003e2008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.1010101010101%\" valign=\"bottom\"\u003e\n \u003cp\u003e2013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.1010101010101%\" valign=\"bottom\"\u003e\n \u003cp\u003e2018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.1010101010101%\" valign=\"bottom\"\u003e\n \u003cp\u003e2022\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.292929292929294%\" valign=\"bottom\"\u003e\n \u003cp\u003eAverage Temp (˚C)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.1010101010101%\" valign=\"bottom\"\u003e\n \u003cp\u003e13.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.1010101010101%\" valign=\"bottom\"\u003e\n \u003cp\u003e14.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.1010101010101%\" valign=\"bottom\"\u003e\n \u003cp\u003e12.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.1010101010101%\" valign=\"bottom\"\u003e\n \u003cp\u003e13.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.1010101010101%\" valign=\"bottom\"\u003e\n \u003cp\u003e12.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.1010101010101%\" valign=\"bottom\"\u003e\n \u003cp\u003e13.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.1010101010101%\" valign=\"bottom\"\u003e\n \u003cp\u003e14.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.292929292929294%\" valign=\"bottom\"\u003e\n \u003cp\u003eAverage LST (˚C)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.1010101010101%\" valign=\"bottom\"\u003e\n \u003cp\u003e21.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.1010101010101%\" valign=\"bottom\"\u003e\n \u003cp\u003e21.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.1010101010101%\" valign=\"bottom\"\u003e\n \u003cp\u003e24.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.1010101010101%\" valign=\"bottom\"\u003e\n \u003cp\u003e24.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.1010101010101%\" valign=\"bottom\"\u003e\n \u003cp\u003e24.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.1010101010101%\" valign=\"bottom\"\u003e\n \u003cp\u003e23.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.1010101010101%\" valign=\"bottom\"\u003e\n \u003cp\u003e25.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.292929292929294%\" valign=\"bottom\"\u003e\n \u003cp\u003eVegetation Cover (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.1010101010101%\" valign=\"bottom\"\u003e\n \u003cp\u003e85.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.1010101010101%\" valign=\"bottom\"\u003e\n \u003cp\u003e82.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.1010101010101%\" valign=\"bottom\"\u003e\n \u003cp\u003e81.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.1010101010101%\" valign=\"bottom\"\u003e\n \u003cp\u003e80.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.1010101010101%\" valign=\"bottom\"\u003e\n \u003cp\u003e77.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.1010101010101%\" valign=\"bottom\"\u003e\n \u003cp\u003e70.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.1010101010101%\" valign=\"bottom\"\u003e\n \u003cp\u003e67.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn summary, the data indicates an overall decline in vegetation cover with elevations throughout the years, accompanied by fluctuations in specific elevation ranges. Overall, there is a trend of decreasing vegetation cover with slope percentages across all categories from 2013 to 2022. The changes in slope percentages might have implications for land use, development, and environmental considerations. Conservation efforts may need to focus on areas with steeper slopes to prevent erosion and protect ecosystems. Based on the results, a comparison between average temperature and vegetation cover showed potential correlations, as changes in land surface temperature can affect the types of vegetation that can thrive in an area. A decrease in vegetation cover is associated with temperature changes. The decrease in vegetation cover might have ecological implications, including habitat loss, changes in biodiversity, or potential impacts on local climate conditions.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgement\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eI cannot express enough thanks to my organization for their continued support and encouragement to Agromet Division giving the opportunities to work under the Gramin Krishi Mausam Seva (GKMS) project, India Meteorological Department, Ministry of Earth Science, New Delhi. I offer my sincere appreciation for the learning opportunities provided by organization.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eI would like to express my deep and sincere gratitude to Dr. M. Mahapatra, DGM, India Meteorological Department, Ministry of Earth Science, New Delhi and Sri K. N. Mohan, Scientist-G,Regional Meteorological Centre, Guwahati, for giving me the opportunity to do research.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contribution\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNimish Narayan Gautam: conceptualization, design methodology, formal analysis, data correction. Thangjalal Lhouvum: conceptualization, design methodology, supervision, validation, review. Niewkor Warbah: data correction writing original draft and editing.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo funding.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data generated or analyzed during this study are included in this published article. Should any raw data files be needed in another format, they are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interest\u003c/strong\u003e \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eAbrams, M. (2000). The Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER): data products for the high spatial resolution imager on NASA\u0026apos;s Terra platform. \u003cem\u003einternational Journal of Remote sensing\u003c/em\u003e, \u003cem\u003e21\u003c/em\u003e(5), 847-859.\u003c/li\u003e\n \u003cli\u003eBerhe, A. A., Barnes, R. T., Six, J., \u0026amp; Mar\u0026iacute;n-Spiotta, E. (2018). Role of soil erosion in biogeochemical cycling of essential elements: carbon, nitrogen, and phosphorus. \u003cem\u003eAnnual Review of Earth and Planetary Sciences\u003c/em\u003e, \u003cem\u003e46\u003c/em\u003e, 521-548.\u003c/li\u003e\n \u003cli\u003eChakma, J. B. (2020). \u003cem\u003eTourism Marketing in Northeast India: A Comparative Study of Meghalaya and Mizoram\u003c/em\u003e (Doctoral dissertation, Mizoram University).\u003c/li\u003e\n \u003cli\u003eDikshit, K. R., Dikshit, J. K., Dikshit, K. R., \u0026amp; Dikshit, J. K. (2014). Natural vegetation: forests and grasslands of North-East India. \u003cem\u003eNorth-East India: Land, People and Economy\u003c/em\u003e, 213-255.\u003c/li\u003e\n \u003cli\u003eGandhi, G. M., Parthiban, B. S., Thummalu, N., \u0026amp; Christy, A. (2015). Ndvi: Vegetation change detection using remote sensing and gis\u0026ndash;A case study of Vellore District. \u003cem\u003eProcedia computer science\u003c/em\u003e, \u003cem\u003e57\u003c/em\u003e, 1199-1210.\u003c/li\u003e\n \u003cli\u003eKumar, S., Arya, S., \u0026amp; Jain, K. (2022). A SWIR-based vegetation index for change detection in land cover using multi-temporal Landsat satellite dataset. \u003cem\u003eInternational Journal of Information Technology\u003c/em\u003e, 1-14.\u003c/li\u003e\n \u003cli\u003eMohan, M., Kikegawa, Y., Gurjar, B. R., Bhati, S., \u0026amp; Kolli, N. R. (2013). Assessment of urban heat island effect for different land use\u0026ndash;land cover from micrometeorological measurements and remote sensing data for megacity Delhi. \u003cem\u003eTheoretical and applied climatology\u003c/em\u003e, \u003cem\u003e112\u003c/em\u003e, 647-658.\u003c/li\u003e\n \u003cli\u003eRaducan, G. (2018). \u003cem\u003eThe impact of bushfires on water quality\u003c/em\u003e (Doctoral dissertation, RMIT University).\u003c/li\u003e\n \u003cli\u003eRawal, R. S., Bhatt, I. D., Sekar, K. C., \u0026amp; Nandi, S. K. (2013). The himalayan biodiversity: richness, representativeness, uniqueness and life-support values. \u003cem\u003eGB Pant Institute of Himalayan Environment and Development (GBPIHED), Kosi-Katarmal, Almora, Uttarakhand, India, 84pp\u003c/em\u003e.\u003c/li\u003e\n \u003cli\u003eReid, A. J., Carlson, A. K., Creed, I. F., Eliason, E. J., Gell, P. A., Johnson, P. T., ... \u0026amp; Cooke, S. J. (2019). Emerging threats and persistent conservation challenges for freshwater biodiversity.\u0026nbsp;\u003cem\u003eBiological Reviews\u003c/em\u003e, \u003cem\u003e94\u003c/em\u003e(3), 849-873.\u003c/li\u003e\n \u003cli\u003eRodrigues, C. I. D., Brito, L. M., \u0026amp; Nunes, L. J. (2023).\u0026nbsp;Soil carbon sequestration in the context of climate change mitigation: A review. \u003cem\u003eSoil Systems\u003c/em\u003e, \u003cem\u003e7\u003c/em\u003e(3), 64.\u003c/li\u003e\n \u003cli\u003eSingh, A. K. (2010). Probable agricultural biodiversity heritage sites in India: V. the Garo, Khasi, and Jaintia hills region. \u003cem\u003eAsian Agri-History\u003c/em\u003e, \u003cem\u003e14\u003c/em\u003e(2), 133-156.\u003c/li\u003e\n \u003cli\u003eSingh, D., Gautam, N. N., \u0026amp; Gupta, S. (2021). Assessment of crop parameters and groundwater level in a policy-implemented micro-watershed environment using geospatial technology. \u003cem\u003eArabian Journal of Geosciences\u003c/em\u003e, \u003cem\u003e14\u003c/em\u003e(22), 2439.\u003c/li\u003e\n \u003cli\u003eTenzin, G. (2014). \u003cem\u003eSnowmelt runoff analysis and impact assessment of temperature change in the upper Punatsang Chu basin\u003c/em\u003e (Doctoral dissertation, School of Remote Sensing Institute of Science Suranaree University of Technology).\u003c/li\u003e\n \u003cli\u003eWan Mohd Jaafar, W. S., Abdul Maulud, K. N., Muhmad Kamarulzaman, A. M., Raihan, A., Md Sah, S., Ahmad, A., ... \u0026amp; Razzaq Khan, W. (2020). The influence of deforestation on land surface temperature\u0026mdash;A case study of Perak and Kedah, Malaysia. \u003cem\u003eForests\u003c/em\u003e, \u003cem\u003e11\u003c/em\u003e(6), 670.\u003c/li\u003e\n \u003cli\u003eYu, L., Liu, Y., Li, X., Yan, F., Lyne, V., \u0026amp; Liu, T. (2023). Vegetation-induced asymmetric diurnal land surface temperatures changes across global climate zones. \u003cem\u003eScience of the Total Environment\u003c/em\u003e, \u003cem\u003e896\u003c/em\u003e, 165255.\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":"[email protected]","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":"NDVI, Climate factors, Vegetation change, LST","lastPublishedDoi":"10.21203/rs.3.rs-4315829/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4315829/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study conducts a comprehensive investigation into the vegetation cover dynamics and climatic factors influencing the East Khasi Hill district. Employing a combination of quantitative and qualitative methodologies, the research spans seven years, from 1993 to 2022, integrating primary data from satellite images and secondary climatologically data. The methodology involves a comparison approach utilizing the Normalized Difference Vegetation Index (NDVI) and rule-based classification for vegetation cover analysis at five-year intervals. Optical satellite images from Landsat, along with Terra MODIS LST data, provide critical insights into the land surface conditions. Additionally, ASTER data is employed for automatic stream network extraction and analysis. Elevation and slope classifications offer a spatial understanding of the geographical distribution across different elevation ranges and slope categories. The results indicate a declining trend in vegetation cover, with elevation-wise variations and a mix of gentle to moderately steep slopes in the region. Furthermore, the extraction of Land Surface Temperature (LST) from MODIS, Landsat-5, and Landsat-8 data reveals temperature variations that influence vegetation growth patterns. Monthly and annual climatological data from the India Meteorological Department contribute to the verification of temporal climatic factors, providing valuable context to the vegetation dynamics. The study's findings underscore the dynamic interplay between vegetation cover, topography, and climatic conditions in the East Khasi Hill district. These insights are crucial for informed decision-making in land management, conservation efforts, and understanding the region's environmental changes. The presented methodological flowchart visually summarizes the research approach, providing a roadmap for future studies in similar contexts.\u003c/p\u003e","manuscriptTitle":"Periodic Relations between Vegetation Cover and Climatic Factors in East Khasi Hills, Meghalaya, India","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-06-14 18:45:04","doi":"10.21203/rs.3.rs-4315829/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","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":"993e294a-6525-479f-a74a-8cf2feb64359","owner":[],"postedDate":"June 14th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-07-23T13:21:40+00:00","versionOfRecord":[],"versionCreatedAt":"2024-06-14 18:45:04","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4315829","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4315829","identity":"rs-4315829","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}