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GeoPlanning 
Journal of Geomatics and Planning                                                                                                                Vol. 10, No. 1, 2023     

 

Original Research 

Temporal Analysis of Land Use and Land 

Cover Changes in Vizianagaram District, 

Andhra Pradesh, India using Remote Sensing 

and GIS Techniques 

Yenda Padmini1, Mallula Srinivasa Rao2, Gara Raja Rao2* 

1. Department of Geosciences, Dr. B.R. Ambedkar University, Srikakulam, India 

2. Department of Geology, Andhra University, Visakhapatnam, India  

DOI: 10.14710/geoplanning.10.1.1-10 

Abstract 

Land use and land cover change (LULCC) has become a significant global concern due to its wide-ranging environmental, 

social and economic impacts. This literature review aims to provide a comprehensive overview of the key ideas, drivers, 

consequences and approaches to studying LULCC. By synthesizing various research articles, this review offers insights into 

the causes and impacts of LULCC, as well as the methods used to analyze and monitor these changes. The review also 

highlights the importance of understanding LULCC dynamics for sustainable land management and policy making. 

Between 2017 and 2022, the LULC categories underwent several changes. Data acquisition process for satellite imagery 

combining Sentinel-2 digital remote sensing data digital remote sensing data through the Copernicus Open Access Hub. 

The spectral resolution is 10, 20, and 30 meters respectively, while the spatial resolution is 10 meters which was used for 

the LULC analysis of the study area. This analysis underscores the importance of LULCC monitoring to inform sustainable 

land management practices and conservation efforts. The trends identified provide a basis for further investigation into the 

underlying drivers of these changes and their potential impacts on ecosystems, water resources and human well-being. 

Continued monitoring and proactive measures are essential to mitigate adverse impacts and promote sustainable land use 

in the future. 

Copyright © 2023 GJGP-Undip 

This open access article is distributed under a  

Creative Commons Attribution (CC-BY-NC-SA) 4.0 International license 

1. Introduction  

Land use and land cover changes (LULCC) refer to the transformation of the Earth's surface due to human 

activities, which change the physical and biological appearances of the land. These changes encompass various 

processes such as deforestation, urbanization, agricultural expansion, industrialization, and infrastructure 

development (Aneesha Satya et al., 2020; Krishna & Reddy, 2017; Reddy et al., 2019; Singh et al., 2019). LULCC 

is a global phenomenon that has profound implications for the environment, society, and economy. The 

significance of studying LULCC lies in its far-reaching impacts on both natural and human systems. 

Understanding the drivers, consequences, and patterns of LULCC is crucial for effective land management, 

conservation, and sustainable development (Pattanaik et al., 2011). 

Environmental Implications: LULCC significantly affects ecosystems, biodiversity, and natural 

resources. Deforestation and habitat fragmentation, for example, lead to the loss of species and disruption of 

ecological processes. Changes in land cover also impact carbon sequestration, water resources, soil quality, and 

climate regulation, contributing to global environmental encounters for instance climate change and loss of 

ecosystem services (Kandrika & Roy, 2008). 

e-ISSN: 2355-6544 
 
Received: 06 May 2023; 
Accepted: 20 August 2023;  
Published: 25 September 2023. 
 
Keywords:  
Landuse, Landcover, Remote 
Sensing, GIS, Change Detection 
and Vegetation 
 
*Corresponding author(s)  
email: rajaraogeo@gmail.com  
  
 
 

 

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Social and Economic Impacts: LULCC directly influences human societies and economies. Urbanization 
and industrialization drive economic growth but often result in land degradation, pollution, and social 
inequalities. Changes in land use can displace communities, trigger land conflicts, and affect traditional 
livelihoods, thereby impacting social stability and human well-being (Kandrika & Roy, 2008; Sethi et al., 2014). 
Understanding these social and economic consequences is crucial for equitable and sustainable development. 

Policy and Decision-Making: LULCC research provides valuable insights for policymakers, land 
managers, and urban planners. Comprehensive understanding of the drivers and consequences of LULCC can 
inform the formulation of land-use policies, zoning regulations, and resource management strategies. By 
integrating scientific knowledge and evidence-based approaches, decision-makers can address environmental and 
socio-economic challenges, promote sustainable land use practices, and mitigate the negative influences of 
LULCC (Buyadi et al., 2014; Kandrika & Roy, 2008; Langat et al., 2021; Sethi et al., 2014; Srivastava et al., 2020).  

Climate Change Mitigation and Adaptation: LULCC play a significant role in climate change 
mitigation and adaptation. Forest conservation and restoration efforts, for instance, contribute to carbon 
sequestration and the decrease of greenhouse gas releases (Sudhakar et al., 2006). Recognizing the potential of 
land-based solutions is essential for achieving climate goals and enhancing resilience to climate change impacts. 

Conservation and Biodiversity Protection: LULCC research contributes to the conservation and 

protection of biodiversity and ecosystems. Identifying areas of high ecological value, understanding habitat 

connectivity, and assessing the impacts of LULC decisions on biodiversity are critical for prioritizing 

conservation efforts and designing effective protected area networks (Cihlar & Jansen, 2001; Jaiswal et al., 1999; 

Yadav et al., 2012). The importance of studying the LULCC in India cannot be overstated due to the country's 

unique socio-economic and environmental context. Here are some key reasons why studying LULCC in India is 

crucial: 

Rapid Urbanization: India is undergoing significant urbanization, with a rapidly growing population and 

increasing migration from rural to urban areas. This urban expansion leads to the conversion of agricultural 

land and natural habitats into built-up areas, impacting ecosystems, water resources, and biodiversity (Jiang & 

Tian, 2010; Wolch et al., 2014). Understanding the patterns and consequences of urbanization is vital for 

sustainable urban planning, resource management, and mitigating the associated environmental and social 

challenges. 

Agricultural Expansion and Intensification: Agriculture is a vital sector for India's economy and 

sustenance of its large population. However, the expansion and intensification of agriculture, driven by factors 

such as population growth and changing consumption patterns, result in the conversion of forests, grasslands, 

and wetlands into agricultural lands (Areendran et al., 2013). Studying LULCC related to agriculture helps 

identify sustainable farming practices, balance food security with environmental conservation, and address issues 

such as soil erosion, water scarcity, and pesticide use. 

Forest Conservation and Biodiversity: India is home to diverse and ecologically significant forest 

ecosystems, including tropical rainforests, mangroves, and dry deciduous forests. LULCC studies play a crucial 

role in monitoring deforestation rates, identifying areas of high biodiversity value, and understanding the drivers 

of forest loss, such as logging, encroachment, and infrastructure development (Butt et al., 2015). This knowledge 

supports conservation efforts, restoration initiatives, and the protection of endangered species and their habitats. 

Water Resource Management: India faces significant challenges related to water scarcity, pollution, and 

unsustainable water management practices. LULCC studies provide insights into land cover changes affecting 

watersheds, rivers, and aquifers. Understanding the impacts of LULCC on water availability, quality, and 

hydrological processes helps inform water resource management strategies, groundwater recharge initiatives, 

and sustainable irrigation practices (Rajasekhar et al., 2019; Rajasekhar et al., 2020). 

Climate Change Adaptation and Mitigation: India is vulnerable to the impacts of climate change, 

including increased frequency and intensity of extreme weather events, rising temperatures, and changing 

rainfall patterns. LULCC research is essential for assessing the contribution of land-based activities to 

greenhouse gas emissions, identifying carbon sinks, and developing climate change adaptation strategies. 

Sustainable land management practices, such as afforestation, agroforestry, and sustainable land-use planning, 

can play a crucial role in climate change mitigation and adaptation (Kudnar & Rajasekhar, 2019; Pradesh, 2018; 

Rajasekhar et al., 2019; Rajasekhar, 2019; Siddi Raju et al., 2018). 

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Policy Formulation and Planning: Comprehensive studies on LULCC provide valuable inputs for policy 

formulation and planning processes. Evidence-based research helps policymakers understand the drivers and 

consequences of land-use changes, assess the effectiveness of existing policies, and design strategies to promote 

sustainable land use practices (Rajasekhar et al., 2018; Rajasekhar et al., 2019; Rajasekhar et al., 2019). This 

includes formulating land-use zoning regulations, protecting ecologically sensitive areas, and integrating socio-

environmental considerations into urban and regional planning. 

The LULCC research in India is essential for addressing urgent environmental, social, and economic 

issues. India can develop and implement effective policies and strategies that promote sustainable development, 

safeguard biodiversity, ensure water security, and contribute to climate change mitigation and adaptation by 

comprehending the drivers, consequences, and patterns of land-use changes. In conclusion, the primary goals of 

LULCCs include understanding the drivers, assessing environmental and socio-economic impacts, devising 

sustainable land use strategies, enhancing climate change resilience, and supporting informed policy and decision 

making. Researchers can contribute to the preservation of natural resources, the promotion of sustainable 

development, and the health of ecosystems and human societies by pursuing these goals.  

2. Data and Methods 

2.1. Study Area 

The study focuses on analysing land use and land cover changes in Vizianagaram District, located in the 

state of Andhra Pradesh, India. Vizianagaram District is situated in the north-eastern part of the state, between 

17° 49' 42" N and 18° 43' 21" N latitude and 82° 59' 51" E and 83° 50' 55" E longitude. It covers an area of 

approximately 3846 square kilometres (Fig 1). The district is characterized by a diverse landscape, encompassing 

various land use types such as agricultural fields, forests, urban areas, water bodies, and barren land. It is known 

for its agricultural productivity and is a key contributor to the state's economy. Vizianagaram District is subject 

to ongoing development and urbanization pressures, which have led to significant LULCC over time.  

 
Source: Analysis, 2022 

Figure 1. Location map of the Vizianagaram District, Andhra Pradesh, India 

The analysis aims to understand the extent and patterns of these changes, identifying areas of conversion, 

expansion, and degradation of different land cover types. The study utilizes spatial approaches to analyze multi-

temporal satellite imagery, including data from different sensors such as Landsat, Sentinel, or similar sources. 

Various spatial analysis tools and classification algorithms will be employed to delineate and quantify land use 

and land cover classes accurately. The findings of this study will provide valuable insights into the changing 

aspects of LULC in Vizianagaram District, facilitating better land management and planning decisions for 

sustainable development in the region. 

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2.2. Methodology 

We primarily employed two categories of data in the current study. These are remote sensing and 

topographic map data. Sentinel 2's georeferenced and combined digital remote sensing data are obtainable 

through the Copernicus Open Access Hub. The spectral resolutions are 10, 20, and 30 meters, respectively, while 

the spatial resolution is 10 meters used for the analysis of the LULC in the study area. 

2.2.1. Data Acquisition 

The data acquisition process for Sentinel-2 satellite imagery involves several steps. Here is a detailed 

overview of the data acquisition process for Sentinel-2 imagery. The Sentinel-2 mission is part of the European 

Space Agency's (ESA) Copernicus program, which aims to provide Earth observation data for various 

applications. Sentinel-2 satellites are equipped with multispectral sensors that capture high-resolution imagery 

of the Earth's surface. Sentinel-2 satellites have a global coverage and provide regular and systematic acquisition 

of data over specific regions (Buyadi et al., 2014; Perea-Ardila et al., 2022; Singh et al., 2019). The imagery is 

freely available to users worldwide through various data access portals, including the Copernicus Open Access 

Hub and commercial providers.  

The hub provides access to the complete archive of Sentinel-2 data, allowing users to search, browse, and 

download imagery for their desired location and time period. Sentinel-2 data is available in different product 

types, including orthorectified top-of-atmosphere reflectance values, while Level-2A products include 

atmospherically corrected surface reflectance values. The choice of product type depends on the specific analysis 

requirements. Sentinel-2 imagery has a spatial resolution of 10 meters (for visible and near-infrared bands) and 

20 meters (for red-edge and shortwave infrared bands). The sensors onboard Sentinel-2 capture data in 13 

spectral bands, ranging from visible to shortwave infrared.  

Sentinel-2 imagery undergoes rigorous calibration and validation processes to ensure its quality and 

accuracy (Ayele et al., 2018; Buyadi et al., 2014; Falcucci & Maiorano, 2007; Hegazy & Kaloop, 2015; Perea-

Ardila et al., 2022; Singh et al., 2019; Yang & Lo, 2002). Calibration parameters and metadata accompany the 

imagery, allowing users to assess the quality and make any necessary adjustments during subsequent analysis. 

It is important to note that the availability and access to Sentinel-2 imagery may vary based on the specific user's 

location, data access agreements, and any limitations or restrictions imposed by the data providers. 

2.2.2. Image Pre-processing 

The pre-processing of Sentinel-2 data involves several steps to prepare the imagery for further analysis. 

Sentinel-2 imagery undergoes radiometric calibration to convert the raw digital numbers (DN) acquired by the 

satellite sensors into calibrated at-sensor radiance values. This step corrects for sensor-specific characteristics, 

such as detector variations and radiometric response. Atmospheric correction is performed to remove the effects 

of atmospheric scattering and absorption on the satellite imagery (Ayele et al., 2018; Buyadi et al., 2014; Cihlar 

& Jansen, 2001; Falcucci & Maiorano, 2007; Hegazy & Kaloop, 2015; Park & Lee, 2016; Perea-Ardila et al., 2022; 

Scroll & For, n.d.; Singh et al., 2019; Yang & Lo, 2002). 

This step is crucial for obtaining accurate and comparable surface reflectance values across different time 

periods and locations. Various algorithms, such as the Sen2Cor algorithm, can be applied for atmospheric 

correction. Geometric correction, also known as orthorectification, is carried out to remove geometric distortions 

caused by the sensor viewing geometry and Earth's terrain. It involves aligning the imagery to a geodetic 

reference system and correcting for distortions such as terrain relief, tilt, and rotation. Ground control points 

(GCPs) from accurate reference data sources, such as high-resolution orthophotos or digital elevation models 

(DEMs), are used for accurate georeferencing.   

Sentinel-2 data is acquired in tiles, and for larger areas of interest, it may be necessary to mosaic multiple 

tiles together to create a seamless composite image. Mosaicking involves aligning and blending adjacent tiles to 

create a continuous image. Additionally, if multiple acquisitions of the same area are available, they can be 

temporally assembled to create composite images representing a specific time period.  

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Sentinel-2 imagery has different spatial resolutions for different spectral bands. If needed, the imagery 

can be resampled to a common spatial resolution to ensure consistency in subsequent analysis steps. Each of 

these pre-processing steps is essential to ensure accurate and reliable analysis results using Sentinel-2 imagery. 

The specific pre-processing workflow may vary based on the analysis objectives, software tools, and user 

requirements. 

2.2.3. Image Classification 

ERDAS Imagine software provides a range of tools and capabilities for image classification of Sentinel 

data. Start by importing the Sentinel imagery into ERDAS Imagine. This can be done by accessing the "Data 

Manager" and selecting the appropriate data format for Sentinel data, such as GeoTIFF. The Sentinel imagery 

to enhance its quality and prepare it for accurate classification. This may involve radiometric calibration, 

atmospheric correction, geometric correction, and noise reduction techniques. 

ERDAS Imagine provides a range of pre-processing tools to perform these tasks (Division & Road, 2013; 

Munahar et al., 2022). To perform supervised classification, you need to collect training data representing 

different land cover classes within the Sentinel image. This involves selecting representative sample areas on the 

imagery and assigning them to specific classes. ERDAS Imagine provides tools for interactive digitizing, 

polygon creation, or importing training data from external sources (Boori & Voženílek, 2014; Fahad et al., 2020; 

Keerthi & Nehru, n.d.). Extract relevant features from the Sentinel imagery that can discriminate between 

different land cover classes.  

ERDAS Imagine offers various spectral, textural, and contextual feature extraction methods. These 

features can include band values, vegetation indices (e.g., NDVI), texture measures (e.g., GLCM), or spatial 

attributes. ERDAS Imagine provides a range of classification algorithms that can be applied to the extracted 

features and training data. These include Maximum Likelihood, Support Vector Machines (SVM), Random 

Trees, Neural Networks, and Decision Trees. Each algorithm has its own advantages and limitations, and the 

choice depends on the specific analysis requirements. Once the model is trained, it can be applied to classify the 

entire Sentinel image.  

ERDAS Imagine provides tools for applying the classification algorithm to the image and generating a 

classified image or a thematic map (Civco et al., 2002; Malaviya et al., 2010; Schmid, 2017; Shalaby & Tateishi, 

2007). The classified image assigns each pixel to a specific land cover class based on the model's classification 

results. ERDAS Imagine offers a comprehensive set of tools and functionalities for image classification of Sentinel 

data, allowing users to extract meaningful information about land cover and land use patterns from the imagery. 

2.2.4. Accuracy Assessment  

Assess the accuracy of the classification results by comparing them with reference data or ground truth 

information. ERDAS Imagine provides tools for accuracy assessment, such as error matrices, kappa coefficient 

calculation, and class-level or pixel-level accuracy metrics. After classification, you can perform post-

classification processing to refine the results and generate thematic maps. This may include techniques like 

majority filtering, sieve filtering, or object-based classification to remove small or isolated classification errors 

and improve map accuracy. It is important to note that the specific methodology adopted for land use/land cover 

analysis and change detection analysis may vary depending on the study objectives, available data, and the chosen 

software or tools. 

3. Result and Discussion 

Change detection analysis examined land use/land cover variability. LULC photos for 2017–2022. These 
LULC pictures show land use land cover variations in Vizianagaram district, Andhra Pradesh, India, over the 
research period. This range matches the 2017–2022 natural vegetation range across Puliyeru river basin (Gandhi 
et al., 2015). The employed spectral data offers land use landcover Vizianagaram at low spatial resolution (10 
m). 

 

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Source: Analysis, 2022 

Figure 2.  Land Use Land Cover Analysis 2017, 2018 and 2019 years of The Study Area 

 
Source: Analysis, 2022 

Figure 3. Land Use Land Cover Analysis 2020, 2021 and 2022 years of The Study Area 

The LULC changed from 2017 to 2022 in the research region (Fig 2 and Fig 3).  In the Dry zone, October 
to February is moist and May to September is dry. LULC decreased in the dry season and increased in the rain 
season in both Dry and Intermediate zones. The late dry season (July–October) has the lowest vegetation values 
in the dry zone. Thus, LULC readings may track Dry zone dry and wet episodes throughout the year.  

Table 1. Percentage of Area under Different LULC 2017 to 2022 of Vizianagaram, Andhra Pradesh, India 

LULC Categories 
Area of Percentage 

2017 2018 2019 2020 2021 2022 

Waterbodies 2.05 1.59 1.23 2.19 2.00 3.02 

Dense Vegetation 18.40 15.39 13.05 15.37 15.53 16.66 

Flooded Vegetation 0.30 0.29 0.18 0.32 0.15 0.23 

Agriculture, Crop Lands 67.42 69.99 70.69 68.42 67.76 65.05 

Built up Lands 5.18 5.17 5.57 5.90 5.76 6.29 

Scrub/Vegetation 0.09 0.06 0.05 0.07 0.06 0.07 

Degraded Lands 6.56 7.51 9.22 7.71 8.74 8.67 

Source: Analysis, 2022 

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 Source: Analysis, 2022 

Figure 4. Percentage based Land Use/Land Cover Trends (2017-2022) of Vizianagaram,  
Andhra Pradesh, India 

Based on the results of the land use/land cover change detection analysis (Table. 1 and Fig 4), the 
following results were obtained: 
1. Waterbodies encompass natural and artificial bodies of water such as lakes, rivers, ponds, and reservoirs. In 

2017, waterbodies covered 2.05% of the total land area, which decreased slightly to 1.59% in 2018. However, 
there was a subsequent increase to 1.23% in 2019 and a significant jump to 2.19% in 2020. In 2021, it slightly 
decreased to 2.00%, but then rose again to 3.02% in 2022; 

2. Dense vegetation category represents areas with dense vegetation, including forests, woodlands, and regions 
with a high concentration of trees and plants. In 2017, dense vegetation covered 18.40% of the land area, 
which decreased to 15.39% in 2018. The percentage continued to decline to 13.05% in 2019 but showed a 
slight increase to 15.37% in 2020. In 2021 and 2022, there was a further rise to 15.53% and 16.66%, 
respectively; 

3. Flooded vegetation refers to areas that are temporarily flooded or inundated with water. This category 
includes marshes, swamps, and locations prone to seasonal flooding. The percentage of flooded vegetation 
was relatively low throughout the years, ranging from 0.18% in 2019 to 0.32% in 2020 (Fig 4). In 2022, it 
reached its lowest point at 0.15% but slightly increased to 0.23% in the same year; 

4. Agriculture, crop lands comprise areas utilized for cultivating crops such as farmlands, plantations, and 
fields. In 2017, agriculture occupied 67.42% of the land area, which increased to 69.99% in 2018 (Table 1). 
The percentage continued to rise, reaching its highest point at 70.69% in 2019. However, there was a decline 
in subsequent years, with values of 68.42% in 2020, 67.76% in 2021, and 65.05% in 2022; 

5. Built-up lands pertain to areas transformed by human activities, encompassing structures like buildings, 
roads, and urban developments. The percentage of built-up lands remained relatively stable over the years, 
ranging from 5.17% to 6.29%. The highest value was observed in 2022 (Table 1), indicating a slight increase 
in urbanization and infrastructure development; 

6. Scrub vegetation refers to low-lying vegetation characterized by shrubs, bushes, and sparse plant cover. The 
percentage of scrub/vegetation was consistently minimal, ranging from 0.05% to 0.09% throughout the 
years; 

7. Degraded lands represent areas that have undergone ecological deterioration, often due to human activities 
or natural factors. The percentage of degraded lands varied from 6.56% in 2017 to 9.22% in 2019. Although 
there were fluctuations, the values remained relatively consistent in subsequent years, with a range of 7.51% 
to 8.74% from 2018 to 2021. In 2022 (Table 1), the percentage of degraded lands decreased slightly to 8.67%. 
These percentages provide insights into the spatial distribution and changes in land cover categories over 
time. 

The fluctuations in each category reflect the dynamics of land use and can be indicative of environmental changes, 
urbanization, and shifts in agricultural practices (El-Kawy et al., 2011; Mishra et al., 2020; Poyatos et al., 2003).  

The land use and land cover (LULC) categories underwent several changes between 2017 and 2022. The 
waterbodies category experienced fluctuations throughout the period, with a decrease from 2.05% in 2017 to 
1.59% in 2018 and a further decline to 1.23% in 2019. However, there was a significant increase in 2020, reaching 

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2.19%, followed by a slight decrease to 2.00% in 2021 (Table 2). The most notable change occurred in 2022, with 
the waterbodies category expanding to cover 3.02% of the area. Dense vegetation showed a general decreasing 
trend, starting at 18.40% in 2017 and gradually declining to 13.05% in 2019. However, there was a recovery in 
2020, with coverage increasing to 15.37%. In 2022, dense vegetation covered 16.66% of the area, resulting in an 
overall decrease of 1.73% (Table 2). The flooded vegetation category remained relatively small, ranging from 
0.18% in 2019 to 0.32% in 2020, with a decrease to 0.23% in 2022. Agriculture, crop lands represented a 
significant portion of the land area, but there was a decrease from 67.42% in 2017 to 65.05% in 2022. Built-up 
lands showed a slight increase, from 5.18% in 2017 to 6.29% in 2022 (Table 2). Scrub/vegetation and degraded 
lands experienced minimal changes, with slight fluctuations over the years. Overall, these changes reflect the 
dynamic nature of LULC patterns over the specified period (Arévalo et al., 2020; Erener et al., 2012; Singh et al., 
2019; Suneela & Mamatha, 2016; Treitz & Rogan, 2004). 

Table 2. Percentage of Area Changes Under Different LULC 2017 to 2022 of Vizianagaram,  
Andhra Pradesh, India 

LULC Categories 2017 2018 2019 2020 2021 2022 Changes 

Waterbodies 2.05 1.59 1.23 2.19 2.00 3.02 0.97 

Dense Vegetation 18.40 15.39 13.05 15.37 15.53 16.66 -1.73 

Flooded Vegetation 0.30 0.29 0.18 0.32 0.15 0.23 -0.07 

Agriculture, Crop Lands 67.42 69.99 70.69 68.42 67.76 65.05 -2.37 

Built-up Lands 5.18 5.17 5.57 5.90 5.76 6.29 1.11 

Scrub/Vegetation 0.09 0.06 0.05 0.07 0.06 0.07 -0.01 

Degraded Lands 6.56 7.51 9.22 7.71 8.74 8.67 2.11 

Source: Analysis, 2023 

4. Conclusion 

In conclusion, the analysis of land use and land cover changes across different categories from 2017 to 

2022 provides valuable insights into the dynamic nature of the landscape. Waterbodies experienced a fluctuating 

pattern, with a decrease in 2018 and 2019, followed by an increase in 2020 and a slight decrease in 2021, before 

experiencing a significant rise in 2022. This suggests potential shifts in water resources and the need for further 

investigation into the factors driving these changes.  

Dense vegetation exhibited a gradual decline over the years, indicating potential deforestation or land 

clearing activities. While the decline was relatively small, it raises concerns about the preservation of biodiversity 

and ecosystem health in the area. Flooded vegetation remained relatively stable throughout the period, with only 

minor fluctuations. This category may be influenced by seasonal variations or specific hydrological patterns in 

the region. Agriculture, crop lands maintained a dominant presence, although a gradual decline was observed. 

This decrease may indicate a shift in land use practices, potentially driven by factors such as urbanization, 

changing agricultural practices, or the conversion of agricultural lands to other uses.  

Built-up lands experienced a steady increase over the years, indicating urban expansion and infrastructure 

development in the area. This trend highlights the need for sustainable urban planning and land management 

strategies to ensure efficient use of resources and minimize environmental impacts. Scrub/vegetation and 

degraded lands showed relatively minor changes during the analyzed period. However, these categories are still 

important in terms of ecological balance and the restoration of degraded ecosystems. Efforts to preserve and 

rehabilitate these lands should be considered to maintain biodiversity and ecosystem services.  

Overall, the analysis underscores the importance of monitoring LULCCs to inform sustainable land 

management practices and conservation efforts. The identified trends provide a basis for further investigation 

into the underlying drivers of these changes and the potential impacts on ecosystems, water resources, and 

human well-being. Continued monitoring and proactive measures are crucial to mitigate adverse effects and 

promote sustainable land use in the future. 

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