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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

View Article: PubMed Central - PubMed

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.

No MeSH data available.


Land-uses of the Paochiao watershed in (a) 1990; (b) 1993; (c) 1998; (d) 2000.
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f2-sensors-08-00658: Land-uses of the Paochiao watershed in (a) 1990; (b) 1993; (c) 1998; (d) 2000.

Mentions: Four SPOT images were purchased from the space and remote-sensing research center in Taiwan, and were selected for watershed land-use classification on March 27, 1990, December 25, 1993, July 16, 1998, and January 2, 2000. Supervised classification and fuzzy convolution are performed using the software ERDAS IMAGINE with 1/5000 black and white aerial photographs. The forest, built-up land, cultivated land, grassland, water, and, bare land classes were marked on the 1/5000 aerial photographs by the Aerial Survey Office, Forestry Bureau in Taiwan. Next, the classified images and geographical data (roads, buildings, slopes and band ranges) of the study watershed formed the knowledge base in the Knowledge Engineer of IMAGINE for final classifications of SPOT images. The IMAGINE user manual provided details of theorems of these effective classification methods. Moreover, kappa values were calculated for the final classification accuracy assessment. Land uses were classified into the following six categories: forest; built-up land; cultivated land; grassland; water; and, bare land (Figure 2). In total, 300 pixels were used in assessing classification accuracy, and each training class had 10–134 pixels. The total accuracy and kappa values were 90% and 0.86, 90% and 0.85, 90% and 0.86, and 89% and 0.84 for classification of images for 1990, 1993, 1998, and 2000, respectively. Land-use classes of forest, built-up land, cultivated land and Grassland had high classification accuracies (92%∼96%, 85%∼96%, 73%∼90% and 63%∼90%), while class bare land had low classification accuracies (42%∼60%) in the classifications.


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
Land-uses of the Paochiao watershed in (a) 1990; (b) 1993; (c) 1998; (d) 2000.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC3927508&req=5

f2-sensors-08-00658: Land-uses of the Paochiao watershed in (a) 1990; (b) 1993; (c) 1998; (d) 2000.
Mentions: Four SPOT images were purchased from the space and remote-sensing research center in Taiwan, and were selected for watershed land-use classification on March 27, 1990, December 25, 1993, July 16, 1998, and January 2, 2000. Supervised classification and fuzzy convolution are performed using the software ERDAS IMAGINE with 1/5000 black and white aerial photographs. The forest, built-up land, cultivated land, grassland, water, and, bare land classes were marked on the 1/5000 aerial photographs by the Aerial Survey Office, Forestry Bureau in Taiwan. Next, the classified images and geographical data (roads, buildings, slopes and band ranges) of the study watershed formed the knowledge base in the Knowledge Engineer of IMAGINE for final classifications of SPOT images. The IMAGINE user manual provided details of theorems of these effective classification methods. Moreover, kappa values were calculated for the final classification accuracy assessment. Land uses were classified into the following six categories: forest; built-up land; cultivated land; grassland; water; and, bare land (Figure 2). In total, 300 pixels were used in assessing classification accuracy, and each training class had 10–134 pixels. The total accuracy and kappa values were 90% and 0.86, 90% and 0.85, 90% and 0.86, and 89% and 0.84 for classification of images for 1990, 1993, 1998, and 2000, respectively. Land-use classes of forest, built-up land, cultivated land and Grassland had high classification accuracies (92%∼96%, 85%∼96%, 73%∼90% and 63%∼90%), while class bare land had low classification accuracies (42%∼60%) in the classifications.

View Article: PubMed Central - PubMed

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.

No MeSH data available.