Remote Sensing and GIS for LULC Change Detection: A Review of Techniques and Findings

Abstract

This research focuses land use land cover changes (LULC) caused by Agricultural growth, urbanization, and green changes. These changes significantly affect local ecosystems and agricultural methods, affecting the region\'s sustainability. The research uses modern Geographic Information Systems (GIS), Remote Sensing (RS) technologies, and machine learning models to examine LULC dynamics. It aims to visualize current patterns, detect historical changes, and estimate future trends, providing a holistic picture of land-use shifts. The study also aims to uncover the underlying causes of these changes, such as population growth, infrastructural development, and climate variations, and assess their effects on environmental health and agricultural output. The use of machine learning techniques to improve LULC analysis is an important part of the study. These methods improve the accuracy of change detection and future trend prediction, allowing for a data-driven comprehension of land-use patterns. This research gives useful information about the consequences of unplanned development and unsustainable practices. This research highlights the importance of sustainable land management practices in reducing negative outcomes. It recommends techniques to balance development and environmental conservation, addressing issues like habitat loss, soil degradation, and reduced agricultural yields. Finally, this research aims to contribute to regional planning and policymaking by giving evidence-based suggestions. Its conclusions will help to balance economic development and environmental preservation, providing the region\'s long-term survival. The dataset was used by experimental for LULC changes, Landsat satellite Data, Cartosat, Quick bird satellite, Sentinel-2A MSI, HyRANK dataset, Multispectral Landsat ETM+ and Hyper spectral DAIS Data.The The study aims to enhance ecological improvement and informed policymaking by addressing both present challenges and future threats.

Introduction

Land Use and Land Cover (LULC) changes reflect the interaction between human activities and natural processes. Understanding these changes is essential for assessing their environmental, economic, and social impacts, especially in rapidly transforming areas.

Paithan Taluka, located in Maharashtra’s Chhatrapati Sambhajinagar district, serves as a case study due to its:

• Agricultural and cultural significance

• Rapid land transformation caused by urbanization, population growth, industrialization, and shifting farming practices

These changes have resulted in:

• Agricultural land loss

• Urban and industrial expansion

• Altered forest cover and water bodies

• Environmental challenges such as biodiversity loss, soil degradation, and water depletion

The study uses Remote Sensing (RS) and Geographical Information Systems (GIS) to analyze LULC changes over time, identify patterns, and inform sustainable land use planning.

2. Objectives

• Classify land cover types

• Identify key drivers of change (e.g., socio-economic growth, government policies, climate factors)

• Provide insights for regional planning, agricultural sustainability, and urban development

• Bridge the gap between scientific research and real-world application

3. Literature Review Highlights

Several studies support the methodological approach of using RS and GIS for LULC analysis. Key insights from related research:

Remote Sensing & GIS Methodologies

• Landsat imagery (Landsat 2, 5, 7, 8), IRS-LISS III, and satellite data from USGS

• Techniques: Unsupervised (ISODATA) and Supervised Classification (MLC); some used Machine Learning Algorithms (MLA), NDVI, SAR, and Google Earth Engine (GEE)

Findings from Prior Studies

• Maina J, Wandiga S (2020): Over 30 years, water bodies increased by 314.86%, farmlands by 160.45%, and bare lands by 73.18%, while forests declined by 45.94%. Demonstrates significant land transformation due to natural and human influences.

• Gidado et al. (2018): Used hybrid RS/GIS methods with accuracy of 75–95%. Effective for tracking LULC changes and their societal impacts.

• Kamarudin et al. (2018): Achieved 100% classification accuracy using MLC on ArcGIS 10.3. Classified LULC into vegetation, water bodies, and built-up areas.

• Akumu et al. (2018): Found a 97% decline in wetlands and bare land using Landsat images and post-classification comparison.

• Strode et al. (2017): Developed bivariate mapping and Sankey diagrams to visualize LULC interaction, aiding interpretation of human impacts.

• Priyanka (2017): Found urbanization in Ambala district led to loss of wetlands and rivers since 2001, calling for urgent conservation.

• Boori & Voženílek (2014): Demonstrated only 30% of land cover remained unchanged over three decades, revealing high LULC dynamics using NASA Landsat data and MLC.

• Zhigang Li et al. (2024): In Chengdu, land development rose while cultivated land decreased; ecosystem service value (ESV) fluctuated, with forests providing the highest services.

• Ibrahim & Nasir (2024): In Nowshera, urban expansion increased land surface temperature (LST) by 5.4°C between 2008–2018, indicating LULC’s role in urban heat island effects.

Conclusion

The research is likely to reveal major modifications in LU patterns in a Paithan Taluka, particularly loss of farming land due to urbanization. These findings highlight the environmental implications of such changes, which include risks to biodiversity, soil health, and water resources. The research also highlights agricultural production concerns, such as arable land loss and decrease of critical natural resources. This study establishes the groundwork for establishing strategies to promote sustainable land use in Paithan Taluka by giving vital insights into the ecological implications of LULC alterations. It helps to better understand the essential balance between development and sustainability., ensuring that future growth complies with sustainability standards. This study paper highlights the accuracy of current machine learning methods in Land Use Land Cover classification, with SS-JDL achieving the maximum accuracy and efficiency. RF beat SVM and SAM, with principal components enhancing accuracy while cutting fitting time by 28%. ANN-ACCA outperformed ANN-CA in LUCC modeling, obtaining higher accuracy in more areas. Woodland classification achieved the highest accuracy at 98%, with an overall accuracy of 91.40% in detecting changes in land cover. These findings highlight the importance of high-quality data, validation, and better assessment methods for dependable LULC simulations and sustainable land management.

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Copyright

Copyright © 2025 Reema Lahane, Dr. Ramesh R. Manza, Sujata Ambhore, Shital Katkade. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.