Transition of blue space in the urban area of khulna city corporation using geospatial and machine learning techniques

Urban blue-space loss and grey-space expansion have significantly altered the surface water systems of the Khulna City Corporation, increasing the environmental degradation and hydrological vulnerability of this climate-sensitive coastal city. This study used multi-temporal Landsat imagery, including Landsat 5 TM data from 1994 to 2004 and Landsat 8 OLI data from 2014 to 2024. The analysis integrated the Normalized Difference Vegetation Index, Normalized Difference Built-up Index, and Normalized Difference Water Index with Random Forest classification and post-classification change detection to quantify long-term land-use/land-cover transitions and urban blue-space transformation. Post-classification comparison generated directional transition matrices documenting conversion pathways among land-cover categories. The results show that surface water bodies have decreased from 4.20 km2 in 1994 to 2.27 km2 in 2024, while the built-up area has increased from 25.9 to 53.3% of the area studied. The accuracy assessment resulted in an overall accuracy of 84.5 to 89.3%, with Kappa coefficients ranging from 0.78 to 0.86. Water body classifications have remained reliable throughout all periods, which has supported the use of classifiable maps for long-term migration analysis. Temporal analysis revealed two main phases: an early phase characterized by mixed land reclamation and later phases dominated by conversion of water bodies into built-up areas. Spatial analysis identified critical hotspots of canal infill, wetland loss, and riverbank modification, mainly in central and southern municipal zones. These findings highlight the need to treat urban blue spaces as critical infrastructure and to promote integrated management, natural drainage solutions and regular remote sensing monitoring. The study frames the loss of blue space as a process of urban transformation and advocates better monitoring of urban water systems in coastal secondary coastal cities.

Authors

Institutions

Publication Details

Journal
Scientific Reports
Published
2026-05-29
DOI
https://doi.org/10.1038/s41598-026-52656-8
Primary Topic
Urban Green Space and Health
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Transition of blue space in the urban area of khulna city corporation using geospatial and machine learning techniques

Md. Wahidul Islam, Md Refath Hossan, Zahid Hassan Shaon, Mohammad Ismail Hossain
Scientific Reports
Urban Green Space and Health
article

Transition of blue space in the urban area of khulna city corporation using geospatial and machine learning techniques

Md. Wahidul Islam, Md Refath Hossan, Zahid Hassan Shaon, Mohammad Ismail Hossain
article en

Abstract

Urban blue-space loss and grey-space expansion have significantly altered the surface water systems of the Khulna City Corporation, increasing the environmental degradation and hydrological vulnerability of this climate-sensitive coastal city. This study used multi-temporal Landsat imagery, including Landsat 5 TM data from 1994 to 2004 and Landsat 8 OLI data from 2014 to 2024. The analysis integrated the Normalized Difference Vegetation Index, Normalized Difference Built-up Index, and Normalized Difference Water Index with Random Forest classification and post-classification change detection to quantify long-term land-use/land-cover transitions and urban blue-space transformation. Post-classification comparison generated directional transition matrices documenting conversion pathways among land-cover categories. The results show that surface water bodies have decreased from 4.20 km2 in 1994 to 2.27 km2 in 2024, while the built-up area has increased from 25.9 to 53.3% of the area studied. The accuracy assessment resulted in an overall accuracy of 84.5 to 89.3%, with Kappa coefficients ranging from 0.78 to 0.86. Water body classifications have remained reliable throughout all periods, which has supported the use of classifiable maps for long-term migration analysis. Temporal analysis revealed two main phases: an early phase characterized by mixed land reclamation and later phases dominated by conversion of water bodies into built-up areas. Spatial analysis identified critical hotspots of canal infill, wetland loss, and riverbank modification, mainly in central and southern municipal zones. These findings highlight the need to treat urban blue spaces as critical infrastructure and to promote integrated management, natural drainage solutions and regular remote sensing monitoring. The study frames the loss of blue space as a process of urban transformation and advocates better monitoring of urban water systems in coastal secondary coastal cities.

Scientific Reports
Khulna University of Engineering and Technology (BD), Gopalganj Science and Technology University (BD)
Sustainable cities and communities
Openalex Percentile: Top 7%
Urban Green Space and Health
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.