New remote sensing image fusion for exploring spatiotemporal evolution of urban land use and land cover,Journal of Applied Remote Sensing

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New remote sensing image fusion for exploring spatiotemporal evolution of urban land use and land cover
Journal of Applied Remote Sensing ( IF 1.4 ) Pub Date : 2022-08-01 , DOI: 10.1117/1.jrs.16.034527
Linfeng Liu ₁ , Chengcai Zhang ₁ , Weiran Luo ₁ , Shaodan Chen ₁ , Feng Yang ₂ , Jisheng Liu ₂

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An evaluation of land use and cover change is a vital component of any study into climate change, ecological evolution, and human civilization’s long-term growth. Remote sensing image data-based land use and cover change (LUCC) research has become an essential and frequently utilized approach. Given the scarcity of high spatial resolution imagery in urban remote sensing, as well as the low accuracy and efficiency of urban land use classification, a new satellite image fusion methodology defined as nonshear wave transformation, a pulse linked neural network, and intensity–hue–saturation theory are suggested. From 2000 to 2020, the upgraded convolutional neural network approach is used to classify fused pictures and perform an in-depth investigation of the spatiotemporal evolution features of urban LUCC in Zhengzhou, Henan, China. According to the findings, the extent of urbanized land in Zhengzhou has expanded dramatically during the last 20 years. The share of urbanized land has risen from 9% in 2000 to 22% by 2020. The comprehensive dynamic degree and single dynamic grade of land use display varied features in different areas and counties; the comprehensive index of the extent of land use demonstrates more evident regional disparities. The research findings can expose the man-land system’s inherent conflicting interaction mechanism and give data to promote urban-related research.

更新日期：2022-08-01

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