Monitoring and projecting sustainable transitions in urban land use using remote sensing and scenario-based modelling in a coastal megacity

Abstract

Under the framework of United Nations Sustainable Development Goals, an urban land use efficiency indicator (SDG11.3.1) was put forward to support formulating informed policies through the analysis of land consumption rate against population growth rate. The assessment of SDG11.3.1 indicator played an import role in understanding sustainable transitions in urban land use from local to regional and global scales. Based on remote sensing and scenario modeling, this study attempts to develop an approach for monitoring and making projections of the urban land use efficiency indicator to inform urban management and planning. Taking the coastal megacity of Tianjin, China as a case study, the spatial patterns of urban land use change were first mapped using multi-temporal satellite datasets and an urban sprawl matrix method. Then, the urban land use changes for the periods up to 2025 and 2030 under an environmental protection scenario were predicted by integrating local policy constraints into a cellular automata–Markov (CA–Markov) model using analytic hierarchy process and multi-criteria evaluation methods. Finally, values of the urban land use efficiency indicator SDG11.3.1 were derived for the period 2000 to 2030. The results showed that built-up area in Tianjin in 2020 doubled compared with 1990 and that 63.95% of the newly increased built-up land was converted from agricultural lands. The model prediction results indicate that the expansion of built-up land is more concentrated and land consumption and population growth tend to be more closely correlated when constrained by environmental protection measures. Extensive growth of built-up areas predicted in coastal areas might increase ecological and disaster risks, and should be strictly planned. The land use change modeling and analysis framework can be applied to growing coastal cities in other regions to inform sustainable land use planning.