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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.
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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.
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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
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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.
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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
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2018.5 2019 2019.5 2020 2020.5 2021 2021.5 2022 2022.5
NDVI TIME SERIES ANALYSIS
High Valuse Low Valuse
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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.
