STAPLE: A land use/-cover change model concerning spatiotemporal dependency and properties related to landscape evolution

Cellular automata (CA) based models are practical tools to simulate the spatiotemporal landscape evolution induced by the land use/-cover change (LUCC). Existing models are struggling to comprehensively handle the spatiotemporal driving relationships amid the nonlinear LUCC process. Besides, the lan...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Environmental modelling & software : with environment data news 2024-07, Vol.178, p.106059, Article 106059
Hauptverfasser: Geng, Jiachen, Cheng, Changxiu, Shen, Shi, Dai, Kaixuan, Zhang, Tianyuan
Format: Artikel
Sprache:eng
Schlagworte:
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung:Cellular automata (CA) based models are practical tools to simulate the spatiotemporal landscape evolution induced by the land use/-cover change (LUCC). Existing models are struggling to comprehensively handle the spatiotemporal driving relationships amid the nonlinear LUCC process. Besides, the landscape patterns are not considered in most models, making them struggled to support the development strategies. Aiming at overcoming these obstacles, a novel land use/-cover change model concerning spatiotemporal dependency and properties related to landscape evolution (STAPLE) is proposed in this paper. A potential generating module establishing the nonlinear spatiotemporal driving relationship and a spatial allocating module employing a landscape-based CA are integrated for realistic LUCC simulations. As a case study, the proposed model is applied in Zhengzhou, China to assess its performance. It is indicated that the STAPLE model achieves a higher simulation accuracy, and the landscape properties are effectively manipulated. It provides a reproducible tool for policy-makers to explore a low-ecological-risk landscape under different future scenarios and achieve sustainable developments. •Propose an LUCC model concerning spatiotemporal dependency and landscape evolution.•Control the spatial evolution of landscape using a novel CA algorithm.•Promote accuracy by employing ST-CNN to assimilate the latent spatiotemporal dependency.•Provide a reproducible tool for policy-makers to achieve sustainable development.
ISSN:1364-8152
1873-6726
DOI:10.1016/j.envsoft.2024.106059