Monitoring of land use change in Zhengzhou City based on Google Earth Engine

Xinzhao Li; Jiandong Shang; Qing Zhang; Xiaolei Xiong

doi:10.1117/12.2667693

10 February 2023 Monitoring of land use change in Zhengzhou City based on Google Earth Engine

Xinzhao Li, Jiandong Shang, Qing Zhang, Xiaolei Xiong

Proceedings Volume 12552, International Conference on Geographic Information and Remote Sensing Technology (GIRST 2022); 125521G (2023) https://doi.org/10.1117/12.2667693
Event: International Conference on Geographic Information and Remote Sensing Technology (GIRST 2022), 2022, Kunming, China

Abstract

Acquiring the land use change information in Zhengzhou quickly and accurately can provide a scientific basis for the overall planning of land resources, ecological environmental protection and sustainable social and economic development in Zhengzhou. This paper takes Zhengzhou City as the research area, is supported by the Google Earth Engine (GEE) cloud platform, and uses the 2000-2020 Landsat-5 and Landsat-8 images as the remote sensing data source. In this paper, the random forest algorithm that combines spectral, textured, and topographic features is used for rapid monitoring of land use types. The research results show that: 1) The random forest algorithm based on GEE has good classification accuracy, the overall accuracy of the classification results in each target year is above 0.90, and the Kappa coefficient is above 0.86. 2) In the past 20 years, the land use in Zhengzhou has undergone profound changes. The most important feature is that the construction land has been continuously expanded from the city center to the outside, and has gradually evolved into a closely connected integration.

Citation Download Citation

Xinzhao Li, Jiandong Shang, Qing Zhang, and Xiaolei Xiong "Monitoring of land use change in Zhengzhou City based on Google Earth Engine", Proc. SPIE 12552, International Conference on Geographic Information and Remote Sensing Technology (GIRST 2022), 125521G (10 February 2023); https://doi.org/10.1117/12.2667693

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KEYWORDS

Random forests

Landsat

Remote sensing

Decision trees

Image classification

Near infrared

Analytical research

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