Remote sensing dynamic monitoring and driving factor analysis for the Beijing section of Ming Great Wall

doi:10.6046/zrzyyg.2022354

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Abstract

The coordinated economic and ecological development and the cultural landscape preservation of the Great Wall cultural zone are crucial for regional social sustainability. To meet the need for integrated monitoring and evaluation of large-scale linear cultural heritage, this study proposed a remote sensing dynamic monitoring method that integrates object-oriented change vector analysis and U-net deep learning. Based on the suppression of classified scattered noise and the accurate dynamic description of key regional environmental components, this study achieved the interpretation and information mining of the factors driving cultural landscape changes by combining socio-economic data and remote sensing change detection. Building on the 2-m-resolution GF-2 fused images from 2015 to 2020, the Beijing section of Ming Great Wall was examined through remote sensing change detection of surface elements and quantitative analysis of the land cover change matrix for its landscape corridor using methods including multiresolution segmentation, change vector analysis and extraction, and U-net image classification. The study reveals that the land cover along the Beijing section of Ming Great Wall cultural zone yielded a change rate of 0.098%, primarily manifested in the shift from bare land and farmland to forests and the growth of artificial land. Meanwhile, the ecological environment of the cultural zone exhibited positive development and an overall favorable protection state. The research results will provide technical support for the coordinated economic and ecological development and the sustainable preservation of the cultural landscape along the Beijing section of Ming Great Wall.

Keywords Ming Great Wall deep learning change detection land cover change rate spatio-temporal analysis

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Issue Date: 21 December 2023

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	Hanwei LIU
	Fulong CHEN
	Yaao LIAO

Cite this article:

Hanwei LIU,Fulong CHEN,Yaao LIAO. Remote sensing dynamic monitoring and driving factor analysis for the Beijing section of Ming Great Wall[J]. Remote Sensing for Natural Resources, 2023, 35(4): 255-263.

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https://www.gtzyyg.com/EN/10.6046/zrzyyg.2022354 OR https://www.gtzyyg.com/EN/Y2023/V35/I4/255

Fig.1 Schematic diagram of research area of the cultural belt of Beijing section of Ming Great Wall

Fig.2 Technical flow chart of remote sensing dynamic monitoring and impact driven analysis of Ming Great Wall cultural belt

Fig.3 Image block diagram of cultural belt corridor along Beijing section of Ming Great Wall

Tab.1 Influence of different sample sizes on classification accuracy of U-net model

Fig.4 Change of classification accuracy of U-net model under different sample sizes

Fig.5 Training accuracy curves and loss curves of deep learning U-net model (2015 and 2020)

Tab.2 Confusion matrix of U-net classification results along the Beijing section of Ming Great Wall (2015)

Tab.3 Confusion matrix of U-net classification results along the Beijing section of Ming Great Wall (2020)

Fig.6 Land cover changes along the 2 km cultural belt of the Beijing section of Ming Great Wall

Tab.4 Comparison of the classification accuracy of DTC, SVM, RF and U-net

Fig.7 Classification results of test image and U-net model

Fig.8 Classification results of RF,DTC and SVM

Tab.5 Change matrix of land cover in the cultural belt of Beijing section of the Ming Great Wall from 2015 to 2020

Fig.9 Proportion of different change categories

Fig.10 Details of land cover changes

Fig.11 Distribution of output value and growth trend ratio in recent years

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