Article: Dynamic monitoring and evolution of urban green space landscape sustainability based on spatiotemporal analysis algorithm Journal: International Journal of Environment and Sustainable Development (IJESD) 2026 Vol.25 No.5 pp.3 - 23 Abstract: To address the issue of disconnect between the spatial-temporal characteristics of urban green space landscape dynamics and sustainability assessment, this study utilises the spatial-temporal DBSCAN (ST-DBSCAN) algorithm to identify hotspot areas of green space coverage changes and the spatial-temporal variations in green space fragmentation, and combines this with a geographically weighted regression (GTWR) model to quantitatively analyse the impact of driving factors such as population growth and land development intensity on the ecological service functions of green spaces. The experimental results show that the LSTM+ST-ConvNet model predicts the green space area for 2020-2024 with an error of only 50 square kilometres, outperforming other prediction models such as ARIMA, XGBoost, and Random Forest (the ARIMA model had an error of 70 square kilometres). This model can more accurately predict trends in green space area changes, providing theoretical support for urban green space. Inderscience Publishers - linking academia, business and industry through research

Title: Dynamic monitoring and evolution of urban green space landscape sustainability based on spatiotemporal analysis algorithm

Authors: Liping Ouyang; Yichen He; Zufan Chen; Ke He

Addresses: School of Digital Media and Interaction Design, Guangzhou Maritime University, Guangzhou, 510000, Guangdong, China ' China Faculty of Arts and Humanities, University of Macau, Macau, 100084, Macau, China ' School of Art and Design, Guangzhou Institute of Science and Technology, Guangzhou, 510000, Guangdong, China ' School of Architecture and Art, Guangdong Lingnan Institute of Technology, Guangzhou, 510000, Guangdong, China

Abstract: To address the issue of disconnect between the spatial-temporal characteristics of urban green space landscape dynamics and sustainability assessment, this study utilises the spatial-temporal DBSCAN (ST-DBSCAN) algorithm to identify hotspot areas of green space coverage changes and the spatial-temporal variations in green space fragmentation, and combines this with a geographically weighted regression (GTWR) model to quantitatively analyse the impact of driving factors such as population growth and land development intensity on the ecological service functions of green spaces. The experimental results show that the LSTM+ST-ConvNet model predicts the green space area for 2020-2024 with an error of only 50 square kilometres, outperforming other prediction models such as ARIMA, XGBoost, and Random Forest (the ARIMA model had an error of 70 square kilometres). This model can more accurately predict trends in green space area changes, providing theoretical support for urban green space.

Keywords: urban green landscape; geographically and temporally weighted regression; spatial-temporal DBSCAN; dynamic monitoring; ecological function; urban green space; UGS; change vector analysis; CVA.

DOI: 10.1504/IJESD.2026.151846

International Journal of Environment and Sustainable Development, 2026 Vol.25 No.5, pp.3 - 23

Received: 31 Mar 2025
Accepted: 21 Aug 2025
Published online: 23 Feb 2026 *

Title: Dynamic monitoring and evolution of urban green space landscape sustainability based on spatiotemporal analysis algorithm

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