Exploring the impact of land use/land cover changes on the dynamics of Deepor wetland (a Ramsar site) in Assam, India using geospatial techniques and machine learning models

Saha, Tamal Kanti; Sajjad, Haroon; Roshani,; Rahaman, Md Hibjur; Sharma, Yatendra

doi:10.1007/s40808-024-01999-0

Model. Earth Syst. Environ.

2024

DOI: 10.1007/s40808-024-01999-0

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Exploring the impact of land use/land cover changes on the dynamics of Deepor wetland (a Ramsar site) in Assam, India using geospatial techniques and machine learning models

Tamal Kanti Saha,

Haroon Sajjad,

Roshani

et al.

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Cited by 9 publications

References 99 publications

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Urban flooding risk assessment based on the impact of land cover spatiotemporal characteristics with hydrodynamic simulation

Geng,

Huang,

et al. 2024

Stoch Environ Res Risk Assess

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Urban flooding risk assessment based on the impact of land cover spatiotemporal characteristics with hydrodynamic simulation

Geng,

Huang,

et al. 2024

Stoch Environ Res Risk Assess

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Assessing climate change threats and urbanization impacts on surface runoff in Gdańsk (Poland): insights from remote sensing, machine learning and hydrological modeling

Gulshad,

Szydłowski,

Mustafa

2024

Stoch Environ Res Risk Assess

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This study investigates the impacts of Land Use/Land Cover (LULC) changes and climate change on surface runoff in Gdańsk, Poland, which is crucial for local LULC planning and urban flood risk management. The analysis employs two primary methodologies: remote sensing and hydrological modeling. Remote sensing was conducted using Google Earth Engine and Land Change Modeler in IDRISI Terrset software to analyze historical (1985–2022) and future (2050–2100) LULC. Hydrological modeling was performed using the Natural Resources Conservation Service curve number method to assess the overall impact of LULC changes on Gdańsk’s hydrology at the local scale. The Orunia basin, a critical area due to intensive LULC development, was selected for detailed hydrological analysis using the Hydrologic Modeling System (HEC-HMS). The analysis encompassed three scenarios: LULC changes, climate change, and combined LULC and climate change effects. The LULC analysis revealed a marked increase in urban area, a shift in forest and vegetation cover, and a reduction in agricultural land. HEC-HMS simulations showed an increase in the runoff coefficient across selected decades, which was attributed to the combined effect of LULC and climate change. The projected increases under the Representative Concentration Pathway (RCP) 4.5 and RCP 8.5 scenarios for 2050 and 2100 are projected to surpass those observed during the baseline period. The findings highlight that the synergistic effects of LULC and climate change have a more significant impact on Gdańsk’s hydrology at both local and basin scales than their separate effects. These insights into LULC shifts and urban hydrological responses hold implications for sustainable urban planning and effective flood risk management in Gdańsk and similar urban settings.

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Data mining techniques for LULC analysis using sparse labels and multisource data integration for the hilly terrain of Nilgiris district, Tamil Nadu, India

Kumaraperumal,

Raj,

Pazhanivelan

et al. 2024

Earth Sci Inform

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Exploring the impact of land use/land cover changes on the dynamics of Deepor wetland (a Ramsar site) in Assam, India using geospatial techniques and machine learning models

Cited by 9 publications

References 99 publications

Urban flooding risk assessment based on the impact of land cover spatiotemporal characteristics with hydrodynamic simulation

Urban flooding risk assessment based on the impact of land cover spatiotemporal characteristics with hydrodynamic simulation

Assessing climate change threats and urbanization impacts on surface runoff in Gdańsk (Poland): insights from remote sensing, machine learning and hydrological modeling

Data mining techniques for LULC analysis using sparse labels and multisource data integration for the hilly terrain of Nilgiris district, Tamil Nadu, India

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