Landsat-Based Land Cover Change in the Beijing-Tianjin-Tangshan Urban Agglomeration in 1990, 2000 and 2010

Yang, Aqiang; Sun, Guoqing

doi:10.3390/ijgi6030059

Cited by 9 publications

(3 citation statements)

References 78 publications

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“…Among them, the GWR model proposed by Brunsdon et al has been applied in geography and its related disciplines [35]. Wang et al investigated the relationship between land cover and surface temperature in the Beijing-Tianjin-Tanggu urban agglomeration by establishing a GWR model, and the results showed that the land cover ratio and LST were significantly correlated [36]. Kashki et al evaluated the relationship between LST and influencing factors in Shiraz City, Iran.…”

Section: Introductionmentioning

confidence: 99%

Spatiotemporal Characteristics and Influencing Factors of Urban Heat Island Based on Geographically Weighted Regression Model: A Case Study of Urumqi City

Ma,

Mamitimin,

Tiemuerbieke

et al. 2023

Land

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Rapid urbanization threatens the ecological environment and quality of life by significantly altering land use and land cover (LULC) and heat distribution. One of the most significant environmental consequences of urbanization is the urban heat island effect (UHI). This study investigated the spatiotemporal characteristics of the SUHI and its relationship with land use types from 2000 to 2020 in Urumqi City, located in an arid and semi-arid region of northwestern China. Additionally, the ordinary least squares (OLS) and geographically weighted regression (GWR) models were used to quantify the relationship between the land surface temperature (LST) and influencing factors. The results showed that the area of the lower surface temperature classes has decreased significantly. In comparison, the area of the higher surface temperature classes has experienced a steady rise over the last two decades. From 2000 to 2020, the share of the area occupied by the temperature range <30 °C decreased by 67.09%. In addition, the LST varied significantly from one category of land use to another. The average LST of built-up land and unused land was higher than the average LST of other land use types in all years, while the average LST of grassland, forest land, and water bodies was significantly lower. Finally, the results of the GWR model showed that R2 and adjusted R2 of the GWR were 0.75 and 0.73, obviously larger than the 0.58 of the OLS models. The GWR model’s higher R2 and adjusted R2 compared to the OLS model indicates that the relationship between LST and the influencing factors underlying the model may exhibit spatial non-stationarity, and the GWR model performs better than the OLS model. The results of both OLS and GWR models show that the normalized difference vegetation index (NDVI) and slope were negatively correlated with LST, while the urban index (UI) and normalized difference built-up index (NDBI) were positively correlated with LST. The findings of the study indicate that increasing green spaces and limiting the unplanned expansion of urban areas are effective measures to mitigate the UHIs in the study area. The results of the study may provide valuable insights into the spatiotemporal characteristics of the UHI and its drivers. Understanding the spatiotemporal characteristics of the UHI can help urban planners, policymakers, and scientists develop more effective urban cooling strategies and improve the urban thermal environment.

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Section: Introductionmentioning

confidence: 99%

Spatiotemporal Characteristics and Influencing Factors of Urban Heat Island Based on Geographically Weighted Regression Model: A Case Study of Urumqi City

Ma,

Mamitimin,

Tiemuerbieke

et al. 2023

Land

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“…Although it has been shown to be accurate for impervious surface mapping, it requires comprehensive reference data, including both land cover labels, and information on change type [2,[24][25][26]. The post-classification analysis classifies each image individually, and information regarding the change is derived based on per-pixel comparison [27,28], nevertheless, the limited availability of multi-temporal retrospective reference data has been a challenge [29].…”

Section: Introductionmentioning

confidence: 99%

Monitoring of Urban Sprawl and Densification Processes in Western Germany in the Light of SDG Indicator 11.3.1 Based on an Automated Retrospective Classification Approach

et al. 2021

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By 2050, two-third of the world’s population will live in cities. In this study, we develop a framework for analyzing urban growth-related imperviousness in North Rhine-Westphalia (NRW) from the 1980s to date using Landsat data. For the baseline 2017-time step, official geodata was extracted to generate labelled data for ten classes, including three classes representing low, middle, and high level of imperviousness. We used the output of the 2017 classification and information based on radiometric bi-temporal change detection for retrospective classification. Besides spectral bands, we calculated several indices and various temporal composites, which were used as an input for Random Forest classification. The results provide information on three imperviousness classes with accuracies exceeding 75%. According to our results, the imperviousness areas grew continuously from 1985 to 2017, with a high imperviousness area growth of more than 167,000 ha, comprising around 30% increase. The information on the expansion of urban areas was integrated with population dynamics data to estimate the progress towards SDG 11. With the intensity analysis and the integration of population data, the spatial heterogeneity of urban expansion and population growth was analysed, showing that the urban expansion rates considerably excelled population growth rates in some regions in NRW. The study highlights the applicability of earth observation data for accurately quantifying spatio-temporal urban dynamics for sustainable urbanization and targeted planning.

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“…Therefore, it is not separated in use and the term broadly used among remote sensing scientists. LULC changes represents result of human activity influence on surrounding area [3][4][5][6][7]. Multi-spectral data is widely used among RS researchers to detect and learn timely temporal changes of human-environment interaction as well as natural disaster on land use [8][9][10][11][12].…”

Section: Introductionmentioning

confidence: 99%

Land Use and Land Cover Change in Khorezm, Uzbekistan

et al. 2021

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This article depicts on discussions about land use and land cover change distribution in Khorezm province, Uzbekistan between 1987 and 2019. For the study Landsat 5 TM and Landsat 8 OLI respectively used to detect land use changes in the study area. Khorezm region affected by Aral Sea shrinkage having received salt wind from northeast of the region. Moreover, population increased within study period, making population density intense. Research is carried out to detect reflection of ecology and density in land use. RS techniques maximum likelihood employed to classify land use to generate land cover distribution map. In total seven class selected such as agricultural land, built up, bare land, lowland, saline land, sand and waterbody. The research of Khorezm region for 32 years has been thoroughly studied and found out that agricultural land, built up and saline land increased tremendously while lowland and bare soil are decreased accordingly. The result map can be used for decision makers and government bodies for future long term urban and regional planning.

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Landsat-Based Land Cover Change in the Beijing-Tianjin-Tangshan Urban Agglomeration in 1990, 2000 and 2010

Cited by 9 publications

References 78 publications

Spatiotemporal Characteristics and Influencing Factors of Urban Heat Island Based on Geographically Weighted Regression Model: A Case Study of Urumqi City

Spatiotemporal Characteristics and Influencing Factors of Urban Heat Island Based on Geographically Weighted Regression Model: A Case Study of Urumqi City

Monitoring of Urban Sprawl and Densification Processes in Western Germany in the Light of SDG Indicator 11.3.1 Based on an Automated Retrospective Classification Approach

Land Use and Land Cover Change in Khorezm, Uzbekistan

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