Research

18 pages, 10407 KiB

Open AccessArticle

Multilayer Perceptron for the Future Urban Growth of the Kharj Region in 2040

by Abear Safar Alshahrane and Hamad Ahmed Altuwaijri

Sustainability 2023, 15(9), 7037; https://0-doi-org.brum.beds.ac.uk/10.3390/su15097037 - 22 Apr 2023

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Urban growth is described as an increase in the size and use of cities, which is frequently the consequence of an increase in the number of residents due to internal or external migration and an increase in economic activity rates. In recent decades, [...] Read more.

Urban growth is described as an increase in the size and use of cities, which is frequently the consequence of an increase in the number of residents due to internal or external migration and an increase in economic activity rates. In recent decades, modern technology and mathematical models have been used to determine future urban growth on a large scale and develop sustainable urban policies in the long term. The cities of the Kingdom of Saudi Arabia have witnessed economic growth in recent decades, which has resulted in urban expansion, as is evident in this case study of the Kharj region. Since most of the previous studies have not applied mathematical models to predict the urban growth of the Kharj region, this study aims at simulating urban growth over the next two decades, between 2020 and 2040, by monitoring the growth during the past thirty years, which is the period between 1990 and 2020. This study relies on the satellite visualizations of the Landsat satellites 5, 7, and 8 for classifying the land cover by applying the land change model (LCM) and comparing the land-use maps for the years 2000 and 2020. Then, the factors affecting urban growth, such as distance from the city center, the road network, valleys, and land slopes, are determined to monitor the prediction of urban growth. The results showed that the urban areas extended significantly toward the south, southeast, southwest, and northwest, with an area of 269 km². The results further revealed a significant decline in agricultural and vacant lands due to their transformation into residential areas, educational establishments, and industrial facilities. The model’s accuracy was tested to confirm the mathematical model’s validity. The Kappa index findings indicated a high percentage, ranging from 89% in 2010 to 90% in 2020. Full article

(This article belongs to the Special Issue Advances in Applications of Remote Sensing for Urban Sustainability)

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24 pages, 15132 KiB

Open AccessArticle

Study on the Spatial and Temporal Evolution of NDVI and Its Driving Mechanism Based on Geodetector and Hurst Indexes: A Case Study of the Tibet Autonomous Region

by Jian Wang, Junsan Zhao, Peng Zhou, Kangning Li, Zhaoxiang Cao, Haoran Zhang, Yang Han, Yuanyuan Luo and Xinru Yuan

Sustainability 2023, 15(7), 5981; https://0-doi-org.brum.beds.ac.uk/10.3390/su15075981 - 30 Mar 2023

Cited by 4 | Viewed by 2041

Abstract

The Tibet Autonomous Region (TAR) is located in the mid-latitude and high-cold regions, and the ecological environment in most areas is fragile. Studying its surface vegetation coverage can identify the ecosystem’s development trends and provide a specific contribution to global environmental change. The [...] Read more.

The Tibet Autonomous Region (TAR) is located in the mid-latitude and high-cold regions, and the ecological environment in most areas is fragile. Studying its surface vegetation coverage can identify the ecosystem’s development trends and provide a specific contribution to global environmental change. The normalized difference vegetation index (NDVI) can better reflect the coverage of surface vegetation. Therefore, based on remote sensing data with a resolution of 1 km², air temperature, precipitation, and other data in the same period in the study area from 1998 to 2019, this paper uses trend analysis, F-significance tests, the Hurst index, and the Geodetector model to obtain the spatial distribution, change characteristics, and evolution trends of the NDVI in the TAR in the past 22 years. At the same time, the quantitative relationship between natural and human factors and NDVI changes is also obtained. The study results show that the NDVI in the southern and southeastern parts of the TAR is higher, with mean values greater than 0.5 showing that vegetation cover is better. The NDVI in the western and northwestern parts of the TAR is lower, with mean values less than 0.3, indicating vegetation cover is worse. NDVI in the TAR showed an overall increasing trend from 1998 to 2019 but a decreasing trend in ridgelines, snow cover, and glacier-covered areas. The areas where NDVI values show a trend of increasing and then decreasing in the future account for 53.69% of the total area of the TAR. The most crucial factor affecting NDVI changes in the TAR is soil type, followed by influencing factors such as vegetation cover type, average annual air temperature, and average annual precipitation. The influence of natural elements is generally more significant than anthropogenic factors. The influencing factors have synergistic effects, and combining anthropogenic factors and other factors will show mutual enhancement and non-linear enhancement relationships. This study provides a theoretical basis for natural resource conservation, ecosystem restoration, and sustainable human development strategies in the TAR. Full article

(This article belongs to the Special Issue Advances in Applications of Remote Sensing for Urban Sustainability)

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16 pages, 2224 KiB

Open AccessArticle

Changes in Impervious Surfaces in Lhasa City, a Historical City on the Qinghai–Tibet Plateau

by Sishi Wang, Xin Tan and Fenglei Fan

Sustainability 2023, 15(6), 5510; https://doi.org/10.3390/su15065510 - 21 Mar 2023

Cited by 2 | Viewed by 1096

Abstract

Impervious surface cover reflects the urban environment and urban expansion. Lhasa City is a historical city and one of the most populous on the Qinghai–Tibetan Plateau, and has been experiencing rapid urbanization in recent years. Analyzing the impervious surface distribution can reveal urban [...] Read more.

Impervious surface cover reflects the urban environment and urban expansion. Lhasa City is a historical city and one of the most populous on the Qinghai–Tibetan Plateau, and has been experiencing rapid urbanization in recent years. Analyzing the impervious surface distribution can reveal urban development characteristics and provide data for sustainable urban planning to protect the heritage. This study explored the spatial and temporal changes and expansion patterns of impervious surfaces in different zones of Lhasa City. Impervious surface maps (2014 and 2021) were extracted from Gaofen-1 images with a high spatial resolution (2 m) using an object-based image analysis method. Next, a gravity center, standard deviational ellipses and landscape indices were used to characterize impervious surface expansions in different zones. The result indicated that the impervious surface in Lhasa expanded from 51.149 km² in 2014 to 63.299 km² in 2021. The growth rates of impervious surfaces inside the Environmental Coordination zone were lower than in the zones outside. From 2014 to 2021, the impervious surface of Lhasa expanded in the southeast direction. Infilling and consolidation were the primary impervious surface development patterns. The expansion of the impervious surface was related to topography, population, and economic and policy factors. Full article

(This article belongs to the Special Issue Advances in Applications of Remote Sensing for Urban Sustainability)

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16 pages, 3104 KiB

Open AccessArticle

Study on Rationality of Public Fitness Service Facilities in Beijing Based on GIS

by Xuefeng Tan, Chenggen Guo and Pu Sun

Sustainability 2023, 15(2), 1496; https://0-doi-org.brum.beds.ac.uk/10.3390/su15021496 - 12 Jan 2023

Viewed by 1194

Abstract

Public fitness service facilities are one of the most important factors affecting national fitness. Analyzing the distribution characteristics and influencing factors of public fitness service facilities is of great significance to optimizing public fitness services. In this paper, using the population concentration index [...] Read more.

Public fitness service facilities are one of the most important factors affecting national fitness. Analyzing the distribution characteristics and influencing factors of public fitness service facilities is of great significance to optimizing public fitness services. In this paper, using the population concentration index and consistency test, recent proximity index, coupling coordinated development model, buffer analysis, and correlation analysis, combined with ArcGis 10.7 software research, we found that: the site layout of public fitness service facilities in the main urban area of Beijing is relatively reasonable and has good coupling, and each area becomes a cluster trend; In the main urban area of Beijing, the public fitness service facilities and the transportation line are combined better; Public fitness service facilities in the main urban area of Beijing are well combined with schools (kindergartens, primary schools), with schools as the center; The public fitness service facilities in the main urban area of Beijing are well combined with medical sites, and some marginal areas are not covered; Public fitness service facilities in the main urban area in Beijing show uneven distribution when drawing with influential business districts; The layout of public fitness service facilities in the main urban area of Beijing is positively related to the housing price. Full article

(This article belongs to the Special Issue Advances in Applications of Remote Sensing for Urban Sustainability)

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15 pages, 3688 KiB

Open AccessArticle

Spatiotemporal Variations in Summertime Ground-Level Ozone around Gasoline Stations in Shenzhen between 2014 and 2020

by Yingying Mei, Xueqi Xiang and Deping Xiang

Sustainability 2022, 14(12), 7289; https://0-doi-org.brum.beds.ac.uk/10.3390/su14127289 - 14 Jun 2022

Cited by 1 | Viewed by 1233

Abstract

Ground-level ozone has become the primary air pollutant in many urban areas of China. Oil vapor pollution from gasoline stations accelerates the generation of ground-level ozone, especially in densely populated urban areas with high demands for transportation. An accurate spatiotemporal distribution of ground-level [...] Read more.

Ground-level ozone has become the primary air pollutant in many urban areas of China. Oil vapor pollution from gasoline stations accelerates the generation of ground-level ozone, especially in densely populated urban areas with high demands for transportation. An accurate spatiotemporal distribution of ground-level ozone concentrations (GOCs) around gasoline stations is urgently needed. However, urban GOCs vary sharply over short distances, increasing the need for GOCs at a high-spatial resolution. Thus, a high-spatial resolution (i.e., 1 km) concentration retrieval model based on the GLM and BME method was developed to obtain the daily spatiotemporal characteristics of GOCs. The hourly ozone records provided by the national air quality monitoring stations and multiple geospatial datasets were used as input data. The model exhibited satisfactory performance (R² = 0.75, RMSE = 10.86 µg/m³). The derived GOCs show that the ozone levels at gasoline stations and their adjacent areas (1~3 km away from the gasoline stations) were significantly higher than the citywide average level, and this phenomenon gradually eased with the increasing distance from the gasoline stations. The findings indicate that special attention should be given to the prevention and control of ground-level ozone exposure risks in human settlements and activity areas near gasoline stations. Full article

(This article belongs to the Special Issue Advances in Applications of Remote Sensing for Urban Sustainability)

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17 pages, 6437 KiB

Open AccessArticle

Analysis and Prediction of Expansion of Central Cities Based on Nighttime Light Data in Hunan Province, China

by Yuxin Liu, Tian He, Yi Wang, Changhui Peng, Hui Du, Shuai Yuan and Peng Li

Sustainability 2021, 13(21), 11982; https://0-doi-org.brum.beds.ac.uk/10.3390/su132111982 - 29 Oct 2021

Cited by 3 | Viewed by 1652

Abstract

Quantifying the characteristics of urban expansion as well as influencing factors is essential for the simulation and prediction of urban expansion. In this study, we extracted the built-up regions of 14 central cities in the Hunan province using the DMSP-OLS night light remote [...] Read more.

Quantifying the characteristics of urban expansion as well as influencing factors is essential for the simulation and prediction of urban expansion. In this study, we extracted the built-up regions of 14 central cities in the Hunan province using the DMSP-OLS night light remote sensing datasets from 1992 to 2018, and evaluated the spatial and temporal characteristics of the built-up regions in terms of the area, expansion speed, and main expansion direction. The backpropagation (BP) neural network and autoregressive integrated moving average (ARIMA) model were used to predict the area of the built-up regions from 2019 to 2026. The model predictions were based on the GDP, ratio of the secondary industry output to the GDP, ratio of the tertiary industry output to the GDP, year-end urban population, and urban road area. The results demonstrated that the built-up area and expansion speed of the central cities in the eastern part of the Hunan province were significantly higher than those in the western part. The main expansion directions of the 14 central cities were east and south. The urban road area, year-end urban population, and GDP were the main driving factors of the expansion. The urban expansion model based on the BP neural network provided a high prediction accuracy (R = 0.966). It was estimated that the total area of urban built-up regions in the Hunan province will reach 2463.80 km² by 2026. These findings provide a new perspective for predicting urban areas rapidly and simply, and it also provides a useful reference for studying the spatial expansion characteristics of central cities and formulating a sustainable urban development strategy during the 14th Five-Year Plan of China. Full article

(This article belongs to the Special Issue Advances in Applications of Remote Sensing for Urban Sustainability)

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20 pages, 9677 KiB

Open AccessArticle

Assessing Potential Bioenergy Production on Urban Marginal Land in 20 Major Cities of China by the Use of Multi-View High-Resolution Remote Sensing Data

by Ben Zhang, Jie Yang and Yinxia Cao

Sustainability 2021, 13(13), 7291; https://0-doi-org.brum.beds.ac.uk/10.3390/su13137291 - 29 Jun 2021

Cited by 4 | Viewed by 2065

Abstract

For the purpose of bioenergy production, biomass cropping on marginal land is an appropriate method. Less consideration has been given to estimating the marginal land in cities at a fine spatial resolution, especially in China. Marginal land within cities has great potential for [...] Read more.

For the purpose of bioenergy production, biomass cropping on marginal land is an appropriate method. Less consideration has been given to estimating the marginal land in cities at a fine spatial resolution, especially in China. Marginal land within cities has great potential for bioenergy production. Therefore, in this research, the urban marginal land of 20 representative cities of China was estimated by using detailed land-cover and 3D building morphology information derived from Ziyuan-3 high-resolution remote sensing imagery, and ancillary geographical data, including land use, soil type, and digital elevation model data. We then classified the urban marginal land into “vacant land” and “land between buildings”, and further revealed its landscape patterns. Our results showed that: (1) the suitable marginal land area ranged from 17.78 ± 1.66 km² to 353.48 ± 54.19 km² among the 20 cities; (2) it was estimated that bioethanol production on marginal land could amount to 0.005–0.13 mT, corresponding to bioenergy of 2.1 × 10¹³–4.0 × 10¹⁴ J for one city; (3) from the landscape viewpoint, the marginal landscape pattern tended to be more fragmented in more developed cities. Our results will help urban planners to reclaim unused urban land and develop distributed bioenergy projects at the city scale. Full article

(This article belongs to the Special Issue Advances in Applications of Remote Sensing for Urban Sustainability)

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20 pages, 6963 KiB

Open AccessArticle

Dynamic Changes of Local Climate Zones in the Guangdong–Hong Kong–Macao Greater Bay Area and Their Spatio-Temporal Impacts on the Surface Urban Heat Island Effect between 2005 and 2015

by Yang Lu, Jiansi Yang and Song Ma

Sustainability 2021, 13(11), 6374; https://0-doi-org.brum.beds.ac.uk/10.3390/su13116374 - 03 Jun 2021

Cited by 15 | Viewed by 2379

Abstract

Local climate zones (LCZs) emphasize the influence of representative geometric properties and surface cover characteristics on the local climate. In this paper, we propose a multi-temporal LCZ mapping method, which was used to obtain LCZ maps for 2005 and 2015 in the Guangdong–Hong [...] Read more.

Local climate zones (LCZs) emphasize the influence of representative geometric properties and surface cover characteristics on the local climate. In this paper, we propose a multi-temporal LCZ mapping method, which was used to obtain LCZ maps for 2005 and 2015 in the Guangdong–Hong Kong–Macao Greater Bay Area (GBA), and we analyze the effects of LCZ changes in the GBA on land surface temperature (LST) changes. The results reveal that: (1) The accuracy of the LCZ mapping of the GBA for 2005 and 2015 is 85.03% and 85.28%, respectively. (2) The built type category showing the largest increase in area from 2005 to 2015 is LCZ8 (large low-rise), with a 1.01% increase. The changes of the LCZs also vary among the cities due to the different factors, such as the economic development level and local policies. (3) The area showing a warming trend is larger than the area showing a cooling trend in all the cities in the GBA study area. The main reasons for the warming are the increase of built types, the enhancement of human activities, and the heat radiation from surrounding high-temperature areas. (4) The spatial morphology changes of the built type categories are positively correlated with the LST changes, and the morphological changes of the LCZ4 (open high-rise) and LCZ5 (open midrise) built types exert the most significant influence. These findings will provide important insights for urban heat mitigation via rational landscape design in urban planning management. Full article

(This article belongs to the Special Issue Advances in Applications of Remote Sensing for Urban Sustainability)

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