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RISS 인기검색어
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- 저자 : Yonezawa Go (검색결과 2 건)
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Exploration of expansion patterns and prediction of urban growth for Colombo City, Sri Lanka
Jayasinghe Pavithra,Raghavan Venkatesh,Yonezawa Go 대한공간정보학회 2021 Spatial Information Research Vol.29 No.4
This study attempts to analyze and simulate urban growth pattern of Colombo city in Sri Lanka which is a dynamic and rapid urbanizing region. The spatiotemporal urban growth patterns during 1997–2019 were first analyzed by comparing Land Cover (LC) maps for time intervals between 1997–2008 and 2008–2019 using intensity and growth pattern analysis. Urban lands in Colombo have grown in a faster rate during 1997–2008 as compared to 2008–2019 period. The prominent spatial expansion pattern during 1997–2008 is outlying, as opposed to edge expansion which is predominant during 2008–2019. These major urban expansion patterns were modeled to predict the future urban structure of Colombo in 2030 using FUTURES (FUTure Urban-Regional Environment Simulation) model. FUTURES is a patch-based, multilevel modeling framework for simulating the emergence of landscape spatial structure in urbanizing regions. Simulated result generated from the model reveals substantial agreement with real ground urban changes showing a kappa value of 0.78. The model allows to predict three different scenarios, namely Business as Usual, Infill Growth and Sprawl showing over 100 km2 increase in urban lands by 2030. Predicted urban structure was then compared with proposed development plan. With certain limitations arising from available data, the model is effective in predicting possible urban scenarios and providing valuable inputs to support better decision making for sustainable development of Colombo city. The results demonstrated in this study would be useful in modelling urban growth in other cities and further validate the efficacy of the proposed workflow.
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Multi-scale object-based fuzzy classification for LULC mapping from optical satellite images
Hang T. Do,Venkatesh Raghavan,Luan Xuan Truong,Go Yonezawa 대한공간정보학회 2019 Spatial Information Research Vol.27 No.2
In this paper, a multi-scale object-based fuzzy approach is demonstrated for land use/land cover (LULC) classification using high-resolution multi-spectral optical RapidEye and IKONOS images of Lao Cai and Can Tho areas in Vietnam respectively. Optimal threshold for segmentation procedure is selected from rate of change-local variance graph. Object-based fuzzy approach is implemented to identify LULC classes and LULC initial sets, and then the initial sets are classified to final LULC classes. In case of Lao Cai area, normalized difference vegetation index (NDVI), normalized difference water index (NDWI), water index (WI) in object-based are used to generated water, terrace field classes, and built-up and vegetation sets. NDVI, soil index (SI) and red band are used to distinguish built-up set to building, bare land and road classes. NDVI and RedEgde band are inputs to classify rice field and forest classes from vegetation set. In case of Can Tho area, NDWI and WI are generated to water, vegetation, paddy field classes and built-up set, and then built-up set is classified to building, bare land, road, and paddy field classes. The technique is able to create LULC maps of Lao Cai and Can Tho areas with (90.8%, 0.84), and (92.3%, 0.90) classification accuracy and kappa coefficient, correspondingly.
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