Estimation of urban land-use efficiency for sustainable development by integrating over 30-year landsat imagery with population data: A case study of ha long, Vietnam

Abstract

Humans are moving into urban areas at an accelerated pace. An increasing urban population fuels urban expansion and reduces nearby agricultural lands and natural environments such as forests, swamps, other water-pervious areas. Unsustainable development creates a disproportion between the growth of urban areas and the growth in urban population. The UN SDG indicator 11.3.1 specifically addresses the issue of the measurement of land-use efficiency. While the metric and methodology to estimate the indicator are straightforward, it faces problems of data unavaila-bility and inconsistency. Vietnam has a record of tremendous economic growth that has translated into more urban settlements of size. Consequently, rural population movement into urban areas has led to many urban sustainable planning and development challenges. In the absence of previous work on estimating land-use efficiency in Vietnamese cities, this study makes the first attempt to examine land-use efficiency in Ha Long, one of the country’s fast-growing cities in recent decades. We mapped land use from high-resolution Landsat imagery (30 m) spanning multi-decadal obser-vations from 1986 to 2020. An advanced machine learning approach, the Support Vector Machine algorithm, was applied to estimate the built-up area, which, by integration with census data, is es-sential for calculating SDG indicator 11.3.1. This study shows that the land-use efficiency metric was positive but small at the beginning of the considered period but increased in 2000–2020. These results suggest that before 2000, the urban land consumption rate in Ha Long was lower than the population growth rate, implying denser urban land use. The situation changed to the opposite when the urban land consumption rate exceeded the population growth rate in the past two dec-ades. The study’s approach is applicable to regional and district levels to provide comparative analyses between cities or parts of a region or districts of the city. These analyses are valuable tools for assessing the impact of local urban and municipal planning policies on urban development.