Investigating the impacts of urbanization on different land cover classes and land surface temperature using GIS and RS techniques

Pokhariya, Hemant Singh; Singh, Devesh Pratap; Prakash, Rishi

doi:10.1007/s13198-021-01512-1

Cited by 7 publications

(5 citation statements)

References 24 publications

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“…Converting barren land to other LULC classes may also have trade-offs, such as loss of natural habitats, alteration of hydrological cycles, and emission of greenhouse gases. The statement above draws on some general knowledge about the characteristics and impacts of barren land, which can be found in various sources such as [46,47].…”

Section: Discussionmentioning

confidence: 99%

Predicting Future Land Use Utilizing Economic and Land Surface Parameters with ANN and Markov Chain Models

Rani,

Gupta,

Singh

et al. 2023

Earth

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The main aim of this study is to comprehensively analyze the dynamics of land use and land cover (LULC) changes in the Bathinda region of Punjab, India, encompassing historical, current, and future trends. To forecast future LULC, the Cellular Automaton–Markov Chain (CA) based on artificial neural network (ANN) concepts was used using cartographic variables such as environmental, economic, and cultural. For segmenting LULC, the study used a combination of ML models, such as support vector machine (SVM) and Maximum Likelihood Classifier (MLC). The study is empirical in nature, and it employs quantitative analyses to shed light on LULC variations through time. The result indicates that the barren land is expected to shrink from 55.2 km2 in 1990 to 5.6 km2 in 2050, signifying better land management or increasing human activity. Vegetative expanses, on the other hand, are expected to rise from 81.3 km2 in 1990 to 205.6 km2 in 2050, reflecting a balance between urbanization and ecological conservation. Agricultural fields are expected to increase from 2597.4 km2 in 1990 to 2859.6 km2 in 2020 before stabilizing at 2898.4 km2 in 2050. Water landscapes are expected to shrink from 13.4 km2 in 1990 to 5.6 km2 in 2050, providing possible issues for water resources. Wetland regions are expected to decrease, thus complicating irrigation and groundwater reservoir sustainability. These findings are confirmed by strong statistical indices, with this study’s high kappa coefficients of Kno (0.97), Kstandard (0.95), and Klocation (0.97) indicating a reasonable level of accuracy in CA prediction. From the result of the F1 score, a significant issue was found in MLC for segmenting vegetation, and the issue was resolved in SVM classification. The findings of this study can be used to inform land use policy and plans for sustainable development in the region and beyond.

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Section: Discussionmentioning

confidence: 99%

Predicting Future Land Use Utilizing Economic and Land Surface Parameters with ANN and Markov Chain Models

Rani,

Gupta,

Singh

et al. 2023

Earth

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“…The construction of more buildings and facilities has resulted from the establishment of new businesses and an influx of people migrating to the region for work (Fan et al, 2019;Pokhariya et al, 2021). To address the demand for housing, the government has invested in the development of new urban areas and housing projects.…”

Section: Land Use/land Cover Change From 1992 To 2022mentioning

confidence: 99%

Analyzing and modeling land use/land cover change in Phu Tho Province, Vietnam

Thien,

Phuong

2024

J. Degrade. Min. Land Manage.

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Planning, managing, and developing sustainably all depend on mapping and monitoring changes in land use and land cover (LULC) in Phu Tho Province, Vietnam. In this study, remote sensing and geographic information system (GIS) techniques were employed to track LULC changes in the study area from 1992 to 2022. The supervised classification method in ArcGIS 10.8 software was applied to Landsat satellite data (Landsat 5-TM for 1992 and 2010, and Landsat 9-OLI/TIRS for 2022) to detect and classify five main LULC classes: agricultural land, barren land, vegetation, built-up, and waterbodies. The accuracy of the classification was assessed using kappa coefficients, which were found to be 0.882, 0.891, and 0.915 for the years 1992, 2010, and 2022, respectively. During the period 1992-2022, the areas of agricultural land, built-up, and waterbodies increased by 148.84 km2, 304.15 km2, and 41.04 km2, respectively. However, the areas of barren land and vegetation decreased by 289.39 km2 and 204.64 km2, respectively. Furthermore, the Normalized Difference Vegetation Index (NDVI) and Normalized Difference Built-up Index (NDBI) were utilized to rapidly assess changes in LULC, and their trends were found to be consistent with the supervised classification results. These changes in LULC pose a significant threat to the environment, and the findings of this study can serve as a valuable resource for future land use planning and management in the area.

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“…The migration of people from rural to urban areas, known as urbanization, leads to various transformations of land, such as building construction, road development, deforestation, land reclamation, and industrial expansion. People migrate from villages to cities seeking better opportunities, driving the need for urbanization [2]. The rapid pace of urbanization has resulted in a wide range of problems-both social and ecological-as well as changes in land cover.…”

Section: Introductionmentioning

confidence: 99%

Examining the effect of urbanization on various land cover classes and environmental quality using remote sensing and GIS methods

Pokhariya,

Jain,

Jain

2024

Eng. Res. Express

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It is vital for public awareness and decision-makers to investigate the impact caused by changes in urban land use and land cover on ecosystem service values (ESVs) in order to promote sustainable urban development, protect the environment, protect public health, and ensure communities’ long-term well-being. Changes in ecosystem services brought on by changes in land use and cover (LULC) are a crucial early indicator and warning sign of ecological shifts. Using remote sensing and GIS tools, this study examined the shifting dynamics of ESVs in the Haridwar district in Uttarakhand state, India. The evaluation of land use land cover (LULC) changes was carried out by analyzing satellite images of the research region from 2001 to 2021. In addition, we examined changes in ecosystem services according to the study area’s LULC classifications. Using a random forest machine learning classifier, satellite images were classified into five categories of land cover: water bodies, forests, built-up areas, agricultural land, and bare land. The findings showed that from 2001 to 2021, forest cover and water bodies fell by 15.35% and 41.50%, respectively, while urban area and agricultural area rose by 69.24% and 12.31%, respectively. Due to increased urbanization and degradation of water bodies and forest areas, ESV fell from 103.19 USD ha −1 year −1 in 2001 to 70.53 USD ha −1 year −1 in 2021. The combination of LULC with the value of ecosystem services may improve our knowledge of several difficulties in an urban ecosystem. As a result, we advocate for the incorporation of LULC and ESVs as a tool that might be useful to urban planners and land managers in the future for sustainable development of natural resources.

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Investigating the impacts of urbanization on different land cover classes and land surface temperature using GIS and RS techniques

Cited by 7 publications

References 24 publications

Predicting Future Land Use Utilizing Economic and Land Surface Parameters with ANN and Markov Chain Models

Predicting Future Land Use Utilizing Economic and Land Surface Parameters with ANN and Markov Chain Models

Analyzing and modeling land use/land cover change in Phu Tho Province, Vietnam

Examining the effect of urbanization on various land cover classes and environmental quality using remote sensing and GIS methods

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