Multi-Scenario Simulation of Land System Change in the Guangdong–Hong Kong–Macao Greater Bay Area Based on a Cellular Automata–Markov Model

Abstract

As one of the four major bay areas in the world, the Guangdong–Hong Kong–Macao Greater Bay Area (GBA) is a highly integrated mega urban agglomeration and its unparalleled urbanization has induced prominent land contradictions between humans and nature, which hinders its sustainability and has become the primary concern in this region. In this paper, we probed the historical characteristics of land use and land cover change (LUCC) in the GBA from 2005 to 2015, and forecasted its future land use pattern for 2030, 2050, and 2070, using a cellular automata–Markov (CA–Markov) model, under three typical tailored scenarios, i.e., urban development (UD), cropland protection (CP), and ecology security (ES), for land use optimization. The major findings are as follows: (1) The encroachments of build-up land on the other land uses under rapid urbanization accounted for the leading forces of LUCCs in the past decade. Accordingly, the urban sprawl was up to 1441.73 km2 (23.47%), with cropland, forest land, and water areas reduced by 570.77 km2 (4.38%), 526.05 km2 (1.76%), and 429.89 km2 (10.88%), respectively. (2) Based on the validated CA–Markov model, significant differences are found in future land use patterns under multiple scenarios, with the discrepancy magnified over time and driven by different orientations. (3) Through comprehensive comparisons and tradeoffs, the ES scenario mode seems optimal for the GBA in the next decades, which optimizes the balance between socio-economic development and ecological protection. These results serve as an early warning for future land problems and can be applied to land use management and policy formulation to promote the sustainable development of the GBA.

Study on Tianjin Land-Cover Dynamic Changes, Driving Factor Analysis, and Forecasting

“…Han et al combined CLUES and Markov models to simulate development and protection scenarios for Beijing from 2010 to 2020 and identified a major feature of conversion from arable land to built-up land [22]. Yang et al utilized the CA-Markov model to predict land-cover changes to support development planning and formulate land-use policies in the Guangdong-Hong Kong-Macao Greater Bay Area [23]. Zhang et al integrated the CA-Markov model into the random forest algorithm to comprehend dynamics in LUCC under specific scenarios, assessing the impacts of multiple variables on regional-scale land-use evaluation, and applied this coupled model to Southeast China's largest watershed: Minjiang River Watershed [24].…”