Temporal-Spatial Analysis of the Warming Effect of Different Cultivated Land Urbanization of Metropolitan Area in China

Li, Jiayang; Zhang, Chunxiao; Zheng, Xinqi; Chen, Youmin

doi:10.1038/s41598-020-59593-0

Cited by 12 publications

(17 citation statements)

References 67 publications

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“…Generally, satellite remote-sensing missions are originally designed to monitor the physical environment of the Earth, for which the mapping of LUCC information is one of the most important applications 39 , 40 . LUCC information is usually associated with and mainly driven by socioeconomic factors and is also a direct reflection of economic activities 41 – 43 . It is well documented that the relationship between economic growth and LUCC information is not a one-way effect, but rather a complex relationship of interactions 44 , 45 .…”

Section: Introductionmentioning

confidence: 99%

Analysis of regional economic development based on land use and land cover change information derived from Landsat imagery

Chen

Liu

et al. 2020

Sci Rep

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consistent means for monitoring economic activities 22-25. The initial purpose of the DMSP/OLS, however, was to observe the clouds illuminated by moonlight, and the night-time light imagery was a by-product of the data under cloud-free conditions 26-30. Consequently, there remain some limitations when using NTL data: (1) Underestimation of economic activities that emit less or no additional night-time light, and in the potentially serious measurement errors of gross domestic product (GDP) growth, particularly in developing and emerging economies, the growth is more likely to be underestimated 31-33. (2) As the most important NTL data, DMSP-OLS data are provided by multiple DMSP satellites, and the fact that NTL data from different satellites in different years cannot be directly compared due to lack of onboard calibration is likely to be the main obstacle to a time-series analysis 34-37. (3) The DMSP-OLS NTL data provided by the NGDC have a geographic grid resolution of 30 arc seconds and a grid cell size that is approximately 0.86 km 2 at the equator 16. Therefore, the probability of multiple ground objects belonging to the same pixel is large, which affects the accuracy of economic activity evaluation 38. Generally, satellite remote-sensing missions are originally designed to monitor the physical environment of the Earth, for which the mapping of LUCC information is one of the most important applications 39,40. LUCC information is usually associated with and mainly driven by socioeconomic factors and is also a direct reflection of economic activities 41-43. It is well documented that the relationship between economic growth and LUCC information is not a one-way effect, but rather a complex relationship of interactions 44,45. On the one hand, economic activities have profoundly changed the surface morphology of the Earth. Moreover, with economic development and population increase, land use changes have accelerated sharply, and the land cover pattern changes have become more and more significant. On the other hand, the variation process of land use and land cover has significant impacts on economy. As the foundation for economic activities, land is an indispensable production factor for economic development, and the input of land resources plays an important role in promoting economic growth 34,46,47. Thus, in the coordinated process of LUCC information and economic development, changes in economic activity intensity can be reflected through LUCC information. Remotely-sensed image can intuitively and comprehensively reflect the dynamics of land use and land cover, and the types, quantities and locations of LUCC information can be obtained via classification technology from remotely-sensed image. Normal multispectral optical satellite data with various spatial, temporal and spectral resolutions have been extensively applied in investigations of LUCC information and its driving mechanism of socioeconomic factors 44,48. However, economic indicators for assessing economic development have rarely been connected to LU...

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Section: Introductionmentioning

confidence: 99%

Analysis of regional economic development based on land use and land cover change information derived from Landsat imagery

Chen

Liu

et al. 2020

Sci Rep

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“…The prediction of LUCC and LST can be improved using the CA-Markov model if the previous LUCC and LST patterns are consistent. As a result, CA-Markov models do not provide accurate spatial predictions for raster datasets [59]. Since influential factors can be directly determined between CA-Markov and other factors, CA-Markov is based on a probability matrix [60].…”

Section: Limitations Of the Ca-markov Modelmentioning

confidence: 99%

The Impact of Urbanization on Urban Heat Island: Predictive Approach Using Google Earth Engine and CA-Markov Modelling (2005–2050) of Tianjin City, China

Ullah

Siddique

Ding

et al. 2023

IJERPH

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Urbanization has adverse environmental effects, such as rising surface temperatures. This study analyzes the relationship between the urban heat island (UHI) intensity and Tianjin city’s land cover characteristics. The land use cover change (LUCC) effects on the green areas and the land surface temperature (LST) were also studied. The land cover characteristics were divided into five categories: a built-up area, an agricultural area, a bare area, a forest, and water. The LST was calculated using the thermal bands of spatial images taken from 2005 to 2020. The increase in the built-up area was mainly caused by the agricultural area decreasing by 11.90%. The average land surface temperature of the study area increased from 23.50 to 36.51 °C, and the region moved to a high temperature that the built-up area’s temperature increased by 1.5%. Still, the increase in vegetation cover was negative. From 2020 to 2050, the land surface temperature is expected to increase by 9.5 °C. The high-temperature areas moved into an aerial distribution, and the direction of urbanization determined their path. Urban heat island mitigation is best achieved through forests and water, and managers of urban areas should avoid developing bare land since they may suffer from degradation. The increase in the land surface temperature caused by the land cover change proves that the site is becoming more urbanized. The findings of this study provide valuable information on the various aspects of urbanization in Tianjin and other regions. In addition, future research should look into the public health issues associated with rapid urbanization.

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“…Overall, the research can be divided into two main aspects. First, the research about interactions between meteorological and environmental factors, such as underlying surface properties, terrain, urban landscape pattern, and energy structure [14][15][16][17][18], has been widely conducted. The other aspect is the impact of the meteorological environment on air pollution, extreme weather and catastrophe, and vegetation growth [19][20][21][22][23].…”

Section: Meteorological Simulationmentioning

confidence: 99%

Construction of Meteorological Simulation Knowledge Graph Based on Deep Learning Method

Xiao

Zhang

2021

Sustainability

Self Cite

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With the maturity of meteorological simulation technology, the research literature in this field is undergoing a rapid increase. The published literature can provide useful guidance for current research to get scientific results; however, it tends to be rather time consuming to obtain exact knowledge from massive literature, and it is necessary to transform the literature into structured knowledge to meet the efficient management, sharing, and reuse of meteorological simulation knowledge. In this paper, methods of meteorological simulation knowledge extraction and knowledge graph construction are proposed. A deep learning model based on bilateral long short-term memory-conditional random field (BiLSTM-CRF) is used to extract the meteorological simulation knowledge from the massive literature. Then, the Neo4j graph database is used to construct the meteorological simulation knowledge graph. Based on the meteorological simulation knowledge graph, it can realize the structured storage and integration of meteorological simulation knowledge, which can bridge the gap in the transformation of massive literature to sharable and reusable knowledge. Furthermore, the meteorological simulation knowledge graph can be used as an expert resource and contribute to sustainable guidance and optimization for meteorological simulation research.

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Temporal-Spatial Analysis of the Warming Effect of Different Cultivated Land Urbanization of Metropolitan Area in China

Cited by 12 publications

References 67 publications

Analysis of regional economic development based on land use and land cover change information derived from Landsat imagery

Analysis of regional economic development based on land use and land cover change information derived from Landsat imagery

The Impact of Urbanization on Urban Heat Island: Predictive Approach Using Google Earth Engine and CA-Markov Modelling (2005–2050) of Tianjin City, China

Construction of Meteorological Simulation Knowledge Graph Based on Deep Learning Method

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