🔍Time Series Analysis LST and UHT Project🔍
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1. The study analyzed NDVI trends from 2019-2022 in Islamabad, Pakistan using Landsat 8 imagery to assess changes in vegetation health and land cover over time. 2. The analysis revealed fluctuations in NDVI values indicating seasonal variations and impacts on vegetation from urbanization. Less vegetation was observed over the study period. 3. The results provide important information for urban planning and environmental management by highlighting the influence of urbanization on green spaces and the need to balance development with preserving vegetation.
🔍Time Series Analysis LST and UHT Project🔍
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- 2. 2 TITTLE NDVI Time Series Analysis on Islamabad City Using Landsat 8 Imagery (2019-2022) EXECUTIVE SUMMARY The NDVI (Normalized Difference Vegetation Index) Time Series Analysis project on Islamabad City utilizing Landsat 8 imagery spanning from 2019 to 2022 aimed to assess the temporal changes in vegetation health and land cover dynamics. The study employed advanced GIS techniques to derive meaningful insights into the city's environmental conditions over the specified period. INTRODUCTION Background: The project focused on Islamabad City, the capital of Pakistan, using Landsat 8 satellite imagery. Landsat 8 provides high-resolution multispectral data, allowing for accurate NDVI calculations, which serve as a proxy for vegetation health. Objectives: Analyze NDVI trends to identify changes in vegetation over the years. Assess the impact of urbanization on green spaces. Provide valuable information for urban planning and environmental management. STUDY AREA The latitudinal and longitudinal extent of the study area naming ISLAMABAD, Pakistan is defined as 33.6844°N and 73.0479°E. The total area of the selected study area is 906 km2 and Elevation is 507 m. According to provisional results of the 2022 World Population Prospects 2023 is 1,232,000.
- 3. 3 Figure 1 Study Area METHODOLOGY Data Acquisition: Landsat 8 imagery from the years 2019, 2020, 2021, and 2022 were obtained. The images were pre-processed to correct for atmospheric effects and enhance the quality of the data. Data and Data Sources: Datasets that are using in these analysis are admin Pakistan District boundary shapefile, and Landsat 8 images. District boundary has been collected from a website naming DIVA GIS, and Landsat data has been downloaded from website naming USG Earth Explorer. These data sources are freely available on these websites. All data and data sources names are showing in table 1. The Geographical Information System and Remote Sensing software and techniques are used in getting data and performing different analysis. Go to these websites and you can easily access these data sets. GIS software used in data analysis and data visualization. Data should be corrected. Preprocessing techniques are used in to analyze. Some techniques are listing here, error correction, atmospheric correction etc.
- 4. 4 Table 1 showing data and data sources Datasets Data Sources Pakistan District Boundary Shapefile DIVA GIS Landsat 8 Imagery USGS Creation of File Geodatabase: File geodatabase is a vital key factor for any GIS project. We can save project analysis in file geodatabase and can easily share these files in one folder to anyone without losing any data. For this go to arctoolbox and right click on any folder in which you want to add gdb so go to new and click on file geodatabase. Figure 2. GDB Creation
- 5. 5 NDVI Calculation: The NDVI values were calculated using the formula: NDVI=NIR-RED/NIR+RED Where NIR is the Near-Infrared band, and Red is the Red band from Landsat 8. Figure 3. NDVI Calculation RESULTS Temporal NDVI Trends: The analysis revealed fluctuations in NDVI values over the study period, indicating seasonal variations and potential impacts on vegetation health. Land Cover Changes: Land cover changes, particularly in urban areas, were identified through the analysis, shedding light on the encroachment on green spaces and potential environmental concerns.
- 6. 6 Figure 4. NDVI 2019 Figure 5. NDVI 2020
- 7. 7 Figure 7. NDVI 2021 Figure 8. NDVI 2022
- 8. 8 DISCUSSION Urbanization Impact: The project highlighted the influence of urbanization on vegetation cover, providing insights for sustainable urban development practices. In this time series project we can identify due to more urbanization in these four years vegetation health has been decrease with the passage of time. In 2019 the vegetation low and highest values are -0.13 to 0.50. In 2020 these values decrease from 0.08 to 0.43 due to more urbanization pattern. But in 2021 there is no urbanization has been made so value of vegetation has been increased. But in 2022 again value of vegetation has been decrease from -0.01 to 0.4. So, by this analysis we can better understand that variation from 2019 to 2022 in vegetation due to urbanization pattern. Seasonal Dynamics: Understanding seasonal NDVI variations contributes to better planning for agricultural activities and ecosystem management. We can see seasonal variation in NDVI values that indicate that the vegetation has been decrease from 2019 to 2022 seasonally. Years High Values Low Values 2019 0.506078 -0.135192 2020 0.437593 -0.0813397 2021 0.505116 -0.14829 2022 0.405116 -0.014829 NDVI Time Series Analysis -0.2 -0.1 0 0.1 0.2 0.3 0.4 0.5 0.6 2018.5 2019 2019.5 2020 2020.5 2021 2021.5 2022 2022.5 NDVI TIME SERIES ANALYSIS High Valuse Low Valuse
- 9. 9 Conclusion: The NDVI time series analysis on Islamabad City using Landsat 8 imagery has yielded vital insights into the dynamic relationship between vegetation health and urbanization from 2019 to 2022. The study highlights the sensitivity of vegetation to seasonal variations, providing essential information for agricultural planning and climate change adaptation. Urbanization's discernible impact on green spaces, identified through land cover change analysis, emphasizes the need for strategic urban planning to balance development with environmental conservation. The project's outcomes have significant implications for sustainable development, urging policymakers to prioritize the preservation and expansion of green areas for the well-being of residents and the creation of a resilient urban environment. The results also underscore the importance of regular NDVI monitoring for adaptive management, enabling timely interventions in areas requiring attention. While this project focused on NDVI analysis, future research should adopt holistic approaches, incorporating additional environmental parameters for a more comprehensive understanding of Islamabad's ecosystem dynamics. In conclusion, this study provides a foundational resource for informed decision-making, advocating for a harmonious coexistence between urban expansion and the preservation of Islamabad's vital green spaces.