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利用元胞自动机和交叉熵优化规则对生态用地的城市扩张进行建模。

Modeling urban encroachment on ecological land using cellular automata and cross-entropy optimization rules.

机构信息

College of Surveying & Geo-Informatics, Tongji University, Shanghai 200092, China; The Shanghai Key Laboratory of Space Mapping and Remote Sensing for Planetary Exploration, Tongji University, Shanghai 200092, China.

College of Surveying & Geo-Informatics, Tongji University, Shanghai 200092, China; College of Architecture & Urban Planning, Tongji University, Shanghai 200092, China.

出版信息

Sci Total Environ. 2020 Nov 20;744:140996. doi: 10.1016/j.scitotenv.2020.140996. Epub 2020 Jul 18.

Abstract

Rapid urban expansion often leads to substantial encroachment on ecological lands and destruction of natural environments. We developed a new cellular automata model (named CA) that uses cross-entropy optimization (CEO) to reproduce and project urban expansion into coastal areas and to assess urban encroachment on ecological lands. The CEO algorithm automatically searches for the near-optimal CA parameters and is capable of objectively parameterizing CA models to predict multi-objective scenarios. We calibrated CA by simulating urban expansion at Wenzhou from 1995 to 2005, validated the model from 2005 to 2015 using real data, and then predicted urban expansion for 2025 and 2035. End-state overall accuracies were 93.8% for 2005 and 94.4% for 2015, while figure-of-merit metrics were 27.9% for 2005 and 19.1% for 2015. We predicted four different scenarios to year 2025 and 2035: (1) a business-as-usual (BAU)-scenario using benchmark settings; (2) a District-scenario based on a district-oriented urban development strategy; (3) a Road-scenario based on a road network-oriented urban development strategy; and (4) a Coast-scenario based on a coast-oriented urban development strategy. Each scenario predicts a substantially different pattern of urban encroachment on ecological land and significant loss of farmland, forest, wetland and grassland. These scenarios should be useful in adjusting urban development strategies at Wenzhou and elsewhere.

摘要

快速的城市扩张往往导致大量侵占生态用地和破坏自然环境。我们开发了一种新的元胞自动机模型(CA),该模型使用交叉熵优化(CEO)来再现和预测沿海地区的城市扩张,并评估城市对生态用地的侵占。CEO 算法可以自动搜索 CA 参数的近优解,并且能够客观地对 CA 模型进行参数化以预测多目标情景。我们通过模拟温州 1995 年至 2005 年的城市扩张来校准 CA,使用实际数据验证了 2005 年至 2015 年的模型,然后预测了 2025 年和 2035 年的城市扩张。2005 年和 2015 年的最终整体精度分别为 93.8%和 94.4%,而衡量标准分别为 2005 年的 27.9%和 2015 年的 19.1%。我们预测了 2025 年和 2035 年的四个不同情景:(1)基准情景(BAU),使用基准设置;(2)基于区域导向型城市发展战略的区域情景;(3)基于路网导向型城市发展战略的道路情景;(4)基于沿海导向型城市发展战略的海岸情景。每个情景都预测了生态用地和大量农田、森林、湿地和草原的城市侵占的截然不同的模式。这些情景应该有助于调整温州及其他地区的城市发展战略。

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