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 unavailability 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 observations 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 essential 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 decades. 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.

Monitoring Land-Use Efficiency in China’s Yangtze River Economic Belt from 2000 to 2018

“…There are several papers in the literature on LCRPGR monitoring at the global [4,5], regional [6,7], national [8], and local [9][10][11][12][13][14][15] levels. As highlighted by Li et al [6], the procedures used to assess the indicator SDG 11.3.1 differ from city to city.…”

Ha Long—Cam Pha Cities Evolution Analysis Utilizing Remote Sensing Data

“…Although in the coming years, together with Van Don Special Economic Zone, the two adjacent cities of Ha Long and Cam Pha will become the pole of development in the North-East region. However, there was not research on urban evolution in this dynamic region except at Ha Long city, where the relevant topic had been considered in the works of Brömme et al [52], Hens et al [6], Jalilov et al [53]. Therefore, the present study examines the spatio-temporal land cover change utilizing Landsat multitemporal and multispectral images for a period of 30 years (i.e., 1991, 2001, and 2019).…”

The Spatial Disparities of Land-Use Efficiency in Mainland China from 2000 to 2015

“…Although the metrics and methods of the SDG 11.3.1 are relatively simple [ 9 ], the monitoring of SDG 11.3.1 faces the problem of data inconsistency [ 10 ]. Relevant literature and reports have mainly used impervious surface or land cover data to quantify built-up areas (BUA).…”