Spatial Dynamics Model for Sustainability Analysis of Urban Settlements, Case Study of Surabaya Urban Wetlands, Indonesia

4th International Conference of Indonesian Society for Remote Sensing

物理学;能源学

Subagio, Habib^1^3 ; Koestoer, Raldi H.^2 ; Zagloel, Yuri M.^3 ; Darmajanti, Linda^4

Geospatial Information Agency, Jl. Raya Jakarta Bogor Km. 46, Cibinong, Indonesia^1

School of Enviroment Science, University of Indonesia, Jl. Salemba Raya 4, Jakarta Pusat

10430, Indonesia^2

Departement of Industrial Engineering, Faculty of Engineering, University of Indonesia, Depok

16424, Indonesia^3

Departement of Sociology, Faculty of Social and Political Scince, University of Indonesia, Depok

16424, Indonesia^4

关键词: Ecological functions;    Environmental balance;    fragmentation;    Integration method;    Spatial data mining;    Spatial dynamics;    Sustainability analysis;    Urban wetlands;   

Others  :  https://iopscience.iop.org/article/10.1088/1755-1315/280/1/012016/pdf
DOI  :  10.1088/1755-1315/280/1/012016

来源: IOP

【 摘 要 】

The city of Surabaya is the second largest city in Indonesia which still has active wetlands in the form of mangrove and ponds cultivation areas. This wetland has an important contribution to the environmental balance, protection of biodiversity, as well as maintaining the ecological function of the urban area to continue properly. Specifically this region contributes to surface water control which is an absolute prerequisite for the sustainability of settlements in urban zones. Urbanization and economic dynamics continue to drive increasingly highly demands of land, so that wetland areas continue to experience pressure and turn into built-up land use. The spatial dynamics model of land use change is one of the important instruments that can be used to analysis of urban dynamics complexes. This model using 2 methods in stages. First, the spatial data mining (SDM) method used to determine indicators of agents that contribute on the fragmentation of wetlands. Second, the integration method of Markov Change and Cellular Automata (CA) models for spatial land use projection. The results showed that the integration of SDM methods and Markov-CA contributed significantly to improving the accuracy of the results of land use projection. This model is then referred to as guided spatial projection where the process of urban sprawl can be shown to be more rational and logical. The results of the research are projections of the dynamics of land use that are used as input in calculating the surface runoff as one indicator of sustainability for urban settlements.