Deep learning models for monitoring landscape changes in a UNESCO Global Geopark.

Autor: Pham TT; Institute of Human Geography, Vietnam Academy of Social Sciences, No.176, Thai Ha, Dong Da, Hanoi, Viet Nam. Electronic address: trampham.iesd@gmail.com., Dang KB; VNU University of Science, Vietnam National University, 334 Nguyen Trai, Thanh Xuan, Hanoi, Viet Nam. Electronic address: dangkinhbac@hus.edu.vn., Giang TL; VNU University of Science, Vietnam National University, 334 Nguyen Trai, Thanh Xuan, Hanoi, Viet Nam; VNU Institute of Vietnamese Studies and Development Science (VNU-IVIDES), Vietnam National University, 336 Nguyen Trai, Thanh Xuan, Hanoi, Viet Nam. Electronic address: gianglinh1031@gmail.com., Hoang THN; Institute of Geography, Vietnam Academy of Science and Technology, 18, Hoang Quoc Viet, Cau Giay, Hanoi, Viet Nam. Electronic address: ngoc.hoanghuyen@gmail.com., Le VH; Institute of Human Geography, Vietnam Academy of Social Sciences, No.176, Thai Ha, Dong Da, Hanoi, Viet Nam. Electronic address: levanhakhxh@gmail.com., Ha HN; Vietnam Institute of Economics, Vietnam Academy of Social Sciences, No.1, Lieu Giai, Ba Dinh, Hanoi, Viet Nam. Electronic address: huyngoc47ql@yahoo.com.
Jazyk: angličtina
Zdroj: Journal of environmental management [J Environ Manage] 2024 Mar; Vol. 354, pp. 120497. Date of Electronic Publication: 2024 Feb 27.
DOI: 10.1016/j.jenvman.2024.120497
Abstrakt: By identifying Earth heritage sites, UNESCO Global Geoparks (UGGps) have promoted geo-tourism and regional economic prosperity. However, commercial and tourism development has altered the natural contexts of these geoparks, diminishing their initial value. Before implementing land use policies, spatial landscape parameters should be monitored in multiple dimensions and in real time. This study aims to develop Bilateral Segmentation Network (BiSeNet) models employing an upgraded U-structured neural network in order to monitor land use/cover changes and landscape indicators in a Vietnamese UGGp. This network has proven effective at preserving input image data and restricting the loss of spatial information in decoding data. To demonstrate the utility of deep learning, eight trained BiSeNet models were evaluated against Maximum Likelihood, Support Vector Machine, and Random Forest. The trained BSN-Nadam model (128x128), with a precision of 94% and an information loss of 0.1, can become a valuable instrument for analyzing and monitoring monthly changes in land uses/covers once tourism activities have been rapidly expanded. Three tourist routes and 41 locations in the Dak Nong UGGp were monitored for 30 years using three landscape indices: Disjunct Core Area Density (DCAD), Total Edge Contrast Index (TECI), Shannon's Diversity Index (SHDI), based on the results of the model. As a result, 18 identified geo-sites in the Daknong Geopark have been influenced significantly by agricultural and tourist activities since 2010, making these sites less uniform and unsustainable management. It promptly alerts UNESCO management to the deterioration of geological sites caused by urbanization and tourist development.
Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
(Copyright © 2024 Elsevier Ltd. All rights reserved.)
Databáze: MEDLINE
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