Simulation and control of the evolution of construction land in Changzhutan metropolitan area under the constraints of ecological security pattern
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摘要:
为探索生态安全格局下区域可持续发展模式,选取长株潭都市圈为研究对象,通过遥感数据识别2000—2020年建设用地变化情况,采用综合生态重要性评估模型和MCR(minimal cumulative resistance)模型构建都市圈生态安全格局,基于FLUS模型(future land use change scenario simulation model)设置自然发展(ND)、核心生态块保护(CEP)、生态安全格局约束(ESPR) 3种建设用地模拟情景,在模拟结果的基础上划定长株潭都市圈城镇开发边界。结果表明:2000—2020年长株潭都市圈建设用地扩张迅速,且由急速无序扩张转为缓速集中扩张;利用综合生态重要性评价得到极重要性生态用地共计2 649.54 km2,筛选得到生态源地共计1 204.38 km2,占研究区总面积的13.97%,并构建出长株潭都市圈综合生态安全格局;在ND、CEP、ESPR 3种情景模式下,2030年长株潭都市圈建设用地规模分别达到1 345.88、1 345.79和1 284.94 km2。基于ESPR情景划定城镇开发边界范围,可有效实现土地经济和生态效益的最大化,并为该地区的生态保护和土地利用规划提供参考。
Abstract:With the rapid development of urbanization, the contradiction between construction land and ecological land has become increasingly prominent. Controlling the expansion of urban construction land from the perspective of ecological security is the embodiment of ecological priority and green development in land and space planning in the new era. Changzhutan metropolitan area was selected as the research object, and the changes in construction land from 2000 to 2020 were identified through remote sensing data. The comprehensive ecological importance assessment model and the minimal cumulative resistance (MCR) model were used to construct the ecological security pattern of the metropolitan area. Based on the future land use change scenario simulation (FLUS) model, three construction land simulation scenarios of natural development (ND), core ecological block protection (CEP), and ecological security pattern restriction (ESPR) were set up. And the urban development boundary of Changzhutan metropolitan area was delineated on the basis of the simulation results. The results showed that from 2000 to 2020, the construction land in Changzhutan metropolitan area expanded very rapidly, and gradually changed from rapid and disorderly expansion to slow and concentrated expansion. By using the comprehensive ecological importance evaluation, it was concluded that the extremely important ecological land was 2 649.54 km², the ecological source area was 1 204.38 km², accounting for 13.97% of the total area of the study area, and the comprehensive ecological security pattern of Changzhutan metropolitan area was constructed. Under the three scenarios of ND, CEP and ESPR, the scale of construction land reached 1 345.88 km2, 1 345.79 km2 and 1 284.94 km2, respectively. The delineation of urban development boundaries based on ESPR scenarios could effectively maximize land economy and ecological benefits, and provide references for ecological protection and land use planning in the region.
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表 1 生物多样性保护功能评价分级标准
Table 1. Classification standards of biodiversity conservation function evaluation
评价因子 分级指标 分值 权重 用地类别 林地、灌木林、疏林地、其他林地 1 0.8 河渠、湖泊、水库坑塘、滩地、沼泽地 2 高覆盖草地、中覆盖草地、低覆盖草地 3 水田、旱地 4 城镇用地、农村居民点、其他建设用地、
裸土地、裸岩石质地5 坡度/(°) 0~2 1 0.2 2~8 2 8~15 3 15~25 4 25~72 5 表 2 地质灾害的影响因子及其敏感性划分标准
Table 2. The influencing factors of geological disasters and the classification standard of susceptibility
敏感性分值 植被覆盖度/% 高程/m 坡度/(°) 用地分类 1 <30 <25 <2 林地、灌木林、疏林地、其他林地 2 30~45 25~50 2~8 河渠、湖泊、水库坑塘、滩地、沼泽地 3 45~60 50~100 8~15 高覆盖草地、中覆盖草地、低覆盖草地 4 60~75 100~200 15~25 水田、旱地 5 >75 >200 >25 城镇用地、农村居民点、其他建设用地、
裸土地、裸岩石质地权重 0.25 0.2 0.3 0.25 表 3 长株潭都市圈基本生态阻力系数
Table 3. Basic ecological resistance coefficient of Changzhutan metropolitan area
有林地、河渠、
湖泊水田、灌木林、水库
坑塘、沼泽地疏林地、其他
林地、滩地旱地、高覆盖
度草地中覆盖度草地、
低覆盖度草地裸土地、裸
岩石质地农村居
民点其他建设
用地城镇
用地1 10 20 30 50 200 300 400 500 表 4 FLUS模型模拟试验相关参数及具体设置情况
Table 4. Relevant parameters and specific settings of FLUS model simulation experiment
模型运行模块 主要参数 说明及要求 具体设置 基于神经网
络的出现概
率计算模块Land Use Data 初始年份用地类型栅格数据,用地类型编号从1开始,属于区域内的用地类型设置为“Valid Data”,
区域外设置为“NoData Value”初始年份为2015年 ANN Training 设置神经网络获取训练样本的采样模式、
采样比例及隐藏层数采样模式为均匀采样模式;采样比例为20,即采样点占研究区有效像元的2%;隐藏层数为12(默认值),层数增高可降低误差,提高概率数据精度 Save Path 设置存放路径及输出概率数据的精度类型 数据精度类型为Single Accuracy Driving Data 加载驱动因子栅格数据,各因子数据的行列数要与用地类型栅格数据保持一致 研究区驱动因子共12个,用于神经网络
适宜性概率计算基于自适应惯性机制的元胞自动机 Land Use Data 初始年份用地类型栅格数据,用地类型编号从1开始,属于区域内的用地类型设置为“Valid Data”,
区域外设置为“NoData Value”初始年份为2015年和2020年 Probability Data 加载适宜性概率数据 基于神经网络的适宜性概率计算模块得到的各用地类型的分布概率数据 Restricted Data 输入约束用地变化的限制数据。该数据为二值数据,即0和1。数值0表示不允许用地类型发生转化,1表示允许发生转化 研究区内的现状河流、核心源地、高安全格局
作为限制区域Simulation Setting 设置模拟参数:Maximum Number of Iteration,300;Neighborhood,3;Accelerate(0-1),0.1;Thread,1 初始年份的用地类型像元数为2015年各用地类型数量;模拟的目标像元数为2020年各用地类型数量;成本转换矩阵根据研究区2015—2020年建设用地面积转移矩阵设置;邻域因子参数为0~1,越接近1表示该用地类型的扩张能力越强,该参数设置情况经多次对比调整后确定 Save Simulation 设置模拟结果保存路径 表 5 2000—2020年长株潭都市圈建设用地增长速率与强度
Table 5. The growth rate and intensity of construction land in the Changzhutan metropolitan area from 2000 to 2020
时间段 增长速率/% 增长强度 2000—2005年 1 159.4 5.28 2005—2010年 3 921.91 14.13 2010—2015年 446.65 0.94 2015—2020年 183.33 0.37 -
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