{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,13]],"date-time":"2026-04-13T15:16:04Z","timestamp":1776093364472,"version":"3.50.1"},"reference-count":98,"publisher":"MDPI AG","issue":"15","license":[{"start":{"date-parts":[[2024,7,27]],"date-time":"2024-07-27T00:00:00Z","timestamp":1722038400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Science Fund for Distinguished Young Scholars of China","award":["42225107"],"award-info":[{"award-number":["42225107"]}]},{"name":"National Science Fund for Distinguished Young Scholars of China","award":["2023RC66"],"award-info":[{"award-number":["2023RC66"]}]},{"name":"Scientific Research Foundation for the Talents of Jiaying University","award":["42225107"],"award-info":[{"award-number":["42225107"]}]},{"name":"Scientific Research Foundation for the Talents of Jiaying University","award":["2023RC66"],"award-info":[{"award-number":["2023RC66"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Cellular automata (CA) models have been extensively employed to predict and understand the spatiotemporal dynamics of land use. Driving factors play a significant role in shaping and driving land-use changes. Mining land-use transition rules from driving factors and quantifying the contribution of driving factors to land-use dynamics are fundamental aspects of CA simulation. However, existing CA models have limitations in obtaining accurate transition rules and reliable interpretations simultaneously for multiple land-use simulations. In this study, we constructed a CA model based on a tree-based deep learning algorithm, deep cascade forest (DCF), to improve multiple land-use simulations and driving factors analysis. The DCF algorithm was utilized to mine accurate multiple land-use transition rules without overfitting to improve CA simulation accuracy. Additionally, a novel ensemble mean decrease of impurity (MDI) factor importance analysis method (DCF-MDI), which leverages the cascade structure of the DCF model, was proposed to qualify the contribution of each driving factor to land-use dynamics stably and efficiently. To evaluate the effectiveness of the proposed DCF-CA, we applied the model to simulate land-use distributions and explore the driving mechanisms of land-use dynamics in the Pearl River Delta (PRD), China, from 2000 to 2010. Compared to existing models, the proposed DCF-CA model exhibits the highest accuracy (FoM = 23.79%, PA = 39.77%, UA = 36.35%, OA = 91.50%), which demonstrates its superiority in mining accurate transition rules for capturing multiple land-use dynamics. Factor importance analysis reveals that the proposed DCF-MDI method yields more stable ranking orders and lower standard deviation of contribution weights (&lt;0.10%) compared to the traditional method, indicating its robustness to random disturbances and effectiveness in elucidating the driving mechanisms of land-use dynamics. The DCF-CA model proposed in this study, demonstrating high simulation accuracy and reliable interpretability simultaneously, can provide substantial support for sustainable land use management.<\/jats:p>","DOI":"10.3390\/rs16152750","type":"journal-article","created":{"date-parts":[[2024,7,29]],"date-time":"2024-07-29T09:50:05Z","timestamp":1722246605000},"page":"2750","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Multiple Land-Use Simulations and Driving Factor Analysis by Integrating a Deep Cascade Forest Model and Cellular Automata: A Case Study in the Pearl River Delta, China"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7835-0578","authenticated-orcid":false,"given":"Haoming","family":"Zhuang","sequence":"first","affiliation":[{"name":"School of Geography and Tourism, Jiaying University, Meizhou 514015, China"},{"name":"Guangdong Key Laboratory for Urbanization and Geo-Simulation, School of Geography and Planning, Sun Yat-sen University, Guangzhou 510275, China"}]},{"given":"Xiaoping","family":"Liu","sequence":"additional","affiliation":[{"name":"Guangdong Key Laboratory for Urbanization and Geo-Simulation, School of Geography and Planning, Sun Yat-sen University, Guangzhou 510275, China"},{"name":"Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai 519082, China"}]},{"given":"Yuchao","family":"Yan","sequence":"additional","affiliation":[{"name":"Guangdong Key Laboratory for Urbanization and Geo-Simulation, School of Geography and Planning, Sun Yat-sen University, Guangzhou 510275, China"},{"name":"Sino-French Institute for Earth System Science, College of Urban and Environmental Sciences, Peking University, Beijing 100091, China"}]},{"given":"Bingjie","family":"Li","sequence":"additional","affiliation":[{"name":"Guangdong Key Laboratory for Urbanization and Geo-Simulation, School of Geography and Planning, Sun Yat-sen University, Guangzhou 510275, China"}]},{"given":"Changjiang","family":"Wu","sequence":"additional","affiliation":[{"name":"Guangdong Key Laboratory for Urbanization and Geo-Simulation, School of Geography and Planning, Sun Yat-sen University, Guangzhou 510275, China"}]},{"given":"Wenkai","family":"Liu","sequence":"additional","affiliation":[{"name":"Beidou Research Institute, Faculty of Engineering, South China Normal University, Foshan 528225, China"}]}],"member":"1968","published-online":{"date-parts":[[2024,7,27]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"024011","DOI":"10.1088\/1748-9326\/9\/2\/024011","article-title":"Will the World Run out of Land? 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