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

• Published in: Journal of environmental management 2024-03, Vol.354, p.120497-120497, Article 120497
• Main Authors: Pham, Thi Tram, Dang, Kinh Bac, Giang, Tuan Linh, Hoang, Thi Huyen Ngoc, Le, Van Ha, Ha, Huy Ngoc
• Format: Article
• Language: English
• Subjects: Agriculture; Artificial intelligence; BiSeNet; Conservation of Natural Resources; Deep Learning; Environmental Monitoring - methods; Land cover; Landscape index; UNESCO; Urbanization; Vietnam
• ISSN: 0301-4797; 1095-8630
• DOI: 10.1016/j.jenvman.2024.120497

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. •Eight BiSeNet model to classify basalt land uses/covers and landscape assessment were developed.•Performance of BiSeNet using input size of 128x128 with Nadam optimizer is highest.•Three different indices including DCAD, TECI, and SHDI were chosen to assess landscape quality.•Outcomes of BiSeNet models are used effectively to monitor different landscape indices.•Landscape quality in 41 geosites of Dak Nong UGGp was monitored over 30 years.