The Massive Expansion and Spatial Transformation of Potentially Contaminated Land Across China in 1990–2020 Observed from Remote Sensing and Big-data

Dou, Ying; Guo, Changqing; Kuang, Wenhui; Chi, Wenfeng; Lei, Mei

doi:10.1007/s11769-022-1300-6

Cited by 3 publications

(1 citation statement)

References 49 publications

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“…Currently, the primary reliance is on manpower-intensive tactics, utilizing a combination of visual interpretation through human-machine interaction and on-site verification through detailed field checks. With the rapid development of artificial intelligence, adopting machine learning methods to achieve automatic, fast, and accurate interpretation of high-resolution remote sensing imagery has become the mainstream research direction [16][17][18][19]. For the long period and high data accuracy, CLUDs formed by the use of human-machine interaction interpretation technology are widely applied in hydrological studies and national land surveys [20][21][22][23].…”

Section: Introductionmentioning

confidence: 99%

A New Land Use Dataset Fusion Algorithm for the Runoff Simulation Accuracy Improvement: A Case Study of the Yangtze River Basin, China

Zhang,

Sang,

Liu

et al. 2024

Sustainability

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Monitoring and understanding the development of agricultural management requires fine information on multiple agricultural land use classes. According to the main data bulletin of China’s third national land survey released in 2021, the proportion of arable land in China accounts for 16% of the total land area. After excluding areas that are not suitable for development (slope greater than 25 degrees), the remaining area represents arable land affected by human activities, constituting 96.69% of the total arable land area in the country. Artificially irrigated areas are products of the intertwined interactions between the natural environment and human society. However, the original remote sensing image (China’s land use/cover datasets, CLUDs), which is designed to depict the land use and cover patterns in mainland China, categorizes arable land into two main types: paddy fields and dryland. It lacks a subdivision of artificially irrigated areas. We also found a data discrepancy of more than 10% between the statistical data of rice and the paddy field data in CLUDs. To refine land use data, and then improve the simulation accuracy of the hydrological model, this article proposes a dual-source datasets fusion algorithm, learning based on big data, namely the LUCC statistical data fusion (LUSF) algorithm for integrating the remote sensing-based cropland area dataset and statistics dataset. The runoff simulation results show that, in the Yangtze River Basin, using LUSF datasets, the mean absolute percentage error value of monthly simulated runoff decreased by 0.74%, and the root mean square error value decreased by 0.22 million m3. At the basin scale, the absolute error of the simulated runoff is reduced by an average of 433 million m3 per year, and 36 million m3 every month. The LUSF datasets corrected the canopy interception coefficient effectively; the runoff simulation error was reduced by 2.96 billion m3/a. In the past 40 years, the runoff variation in Dongting Lake and Hanjiang River has been most strongly impacted by changes in the underlying surface. These results reveal that the new data fusion method has some significant improvement over the original method, applicable to the runoff simulation disturbed by strong human activities.

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Section: Introductionmentioning

confidence: 99%

A New Land Use Dataset Fusion Algorithm for the Runoff Simulation Accuracy Improvement: A Case Study of the Yangtze River Basin, China

Zhang,

Sang,

Liu

et al. 2024

Sustainability

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Evaluation of eco-environmental quality and analysis of driving forces in the yellow river delta based on improved remote sensing ecological indices

Ma,

Huang,

Zhang

et al. 2024

Stoch Environ Res Risk Assess

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Urban Remote Sensing with Spatial Big Data: A Review and Renewed Perspective of Urban Studies in Recent Decades

Fang

2023

Remote Sensing

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During the past decades, multiple remote sensing data sources, including nighttime light images, high spatial resolution multispectral satellite images, unmanned drone images, and hyperspectral images, among many others, have provided fresh opportunities to examine the dynamics of urban landscapes. In the meantime, the rapid development of telecommunications and mobile technology, alongside the emergence of online search engines and social media platforms with geotagging technology, has fundamentally changed how human activities and the urban landscape are recorded and depicted. The combination of these two types of data sources results in explosive and mind-blowing discoveries in contemporary urban studies, especially for the purposes of sustainable urban planning and development. Urban scholars are now equipped with abundant data to examine many theoretical arguments that often result from limited and indirect observations and less-than-ideal controlled experiments. For the first time, urban scholars can model, simulate, and predict changes in the urban landscape using real-time data to produce the most realistic results, providing invaluable information for urban planners and governments to aim for a sustainable and healthy urban future. This current study reviews the development, current status, and future trajectory of urban studies facilitated by the advancement of remote sensing and spatial big data analytical technologies. The review attempts to serve as a bridge between the growing “big data” and modern urban study communities.

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The Massive Expansion and Spatial Transformation of Potentially Contaminated Land Across China in 1990–2020 Observed from Remote Sensing and Big-data

Cited by 3 publications

References 49 publications

A New Land Use Dataset Fusion Algorithm for the Runoff Simulation Accuracy Improvement: A Case Study of the Yangtze River Basin, China

A New Land Use Dataset Fusion Algorithm for the Runoff Simulation Accuracy Improvement: A Case Study of the Yangtze River Basin, China

Evaluation of eco-environmental quality and analysis of driving forces in the yellow river delta based on improved remote sensing ecological indices

Urban Remote Sensing with Spatial Big Data: A Review and Renewed Perspective of Urban Studies in Recent Decades

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