Monitoring and Predicting Land-use Changes and the Hydrology of the Urbanized Paochiao Watershed in Taiwan Using Remote Sensing Data, Urban Growth Models and a Hydrological Model

Abstract

Monitoring and simulating urban sprawl and its effects on land-use patterns and hydrological processes in urbanized watersheds are essential in land-use and water-resource planning and management. This study applies a novel framework to the urban growth model Slope, Land use, Excluded land, Urban extent, Transportation, and Hillshading (SLEUTH) and land-use change with the Conversion of Land use and its Effects (CLUE-s) model using historical SPOT images to predict urban sprawl in the Paochiao watershed in Taipei County, Taiwan. The historical and predicted land-use data was input into Patch Analyst to obtain landscape metrics. This data was also input to the Generalized Watershed Loading Function (GWLF) model to analyze the effects of future urban sprawl on the land-use patterns and watershed hydrology. The landscape metrics of the historical SPOT images show that land-use patterns changed between 1990–2000. The SLEUTH model accurately simulated historical land-use patterns and urban sprawl in the Paochiao watershed, and simulated future clustered land-use patterns (2001–2025). The CLUE-s model also simulated land-use patterns for the same period and yielded historical trends in the metrics of land-use patterns. The land-use patterns predicted by the SLEUTH and CLUE-s models show the significant impact urban sprawl will have on land-use patterns in the Paochiao watershed. The historical and predicted land-use patterns in the watershed tended to fragment, had regular shapes and interspersion patterns, but were relatively less isolated in 2001–2025 and less interspersed from 2005–2025 compared with land-use pattern in 1990. During the study, the variability and magnitude of hydrological components based on the historical and predicted land-use patterns were cumulatively affected by urban sprawl in the watershed; specifically, surface runoff increased significantly by 22.0% and baseflow decreased by 18.0% during 1990–2025. The proposed approach is an effective means of enhancing land-use monitoring and management of urbanized watersheds.

Correlation between land-use change and greenhouse gas emissions in urban areas

“…Land-use change patterns result from complex interactions between humans and the physical environment and are continually changing; therefore, the logistic regression method is widely used to determine the driving forces of land-use change from potential impact factors (Lin et al 2008;Styers et al 2010;Verburg et al 2004;Wyman and Stein 2010). Wyman and Stein (2010) used the binomial logistic model to assess deforestation drivers from social survey and land-cover change data within an protected area in Belize.…”

Supporting Global Environmental Change Research: A Review of Trends and Knowledge Gaps in Urban Remote Sensing

“…Research has shown that land use type alone is not an effective proxy for impervious surface quantity and that that finer resolution data are more appropriate for mapping impervious surfaces for runoff models [142,143]. [139] found the best accuracy in mapping impervious surfaces occurred when statistical tools (e.g., Impervious Surface Analysis Tool) combined remotely sensed data sources with population (e.g., US census data). Runoff models using impervious surface mapping have led to improved understanding of groundwater quality and recharge [144][145][146], floodplain management, and coastal flooding [147][148][149].…”

“…Most models of urbanization affects on runoff show that urbanization increases runoff amounts and rates because of reduced percolation into the ground [137][138][139]. Remotely sensed data including SPOT, Quickbird, and Landsat have been used to quantify runoff under normal and storm precipitation events [140,141].…”

Forecasting urban sprawl in Dhaka city of Bangladesh

“…Working almost in parallel, Leão et al, (2004) coupled SLEUTH outputs with a multi-criteria evaluation of landfill suitability (Siddiqui et al, 1996) (Landis, 1994) to assess stresses on biodiversity. Lin et al (2008) applied a framework to the SLEUTH with the Conversion of Land Use and its Effects (CLUE-s) model using historical SPOT images to predict urban sprawl in the Paochiao watershed in Taipei County, Taiwan. These researches demonstrate that SLEUTH can be successfully coupled with other models displaying the potential of the model to be incorporated into a wide array of applications ranging from urban development to environmental assessment and beyond.…”