摘要
为探索各种空间因素作用下耕地未来空间布局的发展方向及其管控策略,该研究以位于长三角地区的常州市为例,采用机器学习算法构建了土地资源可耕性评价模型,并分别用PLUS模型和InVEST模型计算城市发展潜力和生境保护潜力,最后使用蚁群优化算法实现权衡多目标的耕地空间配置模拟并划定优化分区。结果表明:1)通过土地资源可耕性评价发现,常州市不可耕作区中有168.25 km^(2)现状为耕地,高度可耕作区中有254.11 km^(2)为非耕地,耕地资源分布存在明显的不均衡性。2)对比不同情景下的耕地空间配置结果,确定城市发展-生境保护-集中耕作情景取得了最理想的效果,平均可耕作潜力为0.9387,有助于优化耕地资源合理布局。3)统筹耕地空间配置结果与土地利用现状将常州市划分为核心保护区、质量提升区、潜力储备区、建设缓冲区、生态保育区共5区并提出差异化的优化管控策略。该研究的技术路径和结果对于重新认识区域耕地资源本底、有效调节土地资源错配具有参考意义。
Here future spatial layout of cultivated land was investigated under the influence of various spatial factors in the city of Changzhou in the Yangtze River Delta.The cultivability of land resource was evaluated to integrate the urban development potential,habitat conservation potential,and the degree of agglomeration.A machine learning-based model was constructed to assess the land cultivability using multiple algorithms.CatBoost also demonstrated the highest accuracy of classification.The PLUS and InVEST model were then used to calculate the urban development and habitat conservation potential,respectively.Finally,the resulting sub-objectives were input into an Ant Colony Optimization(ACO).As such,the multi-objective spatial allocation of cultivated land was simulated to delineate the optimization zones.The results show that:1)Training labels were set using NPP(Net Primary Productivity),elevation,and slope in the land cultivability assessment,considering the influencing factors,such as topography,soil,climate,location,and irrigation.CatBoost was outperformed the rest in the accuracy of classification.The evaluation of cultivability revealed that Changzhou shared a potential cultivated land area exceeding the current cultivated land by 543.61 km^(2),indicating the potential for further cultivation.However,168.25 km²of the current cultivated land was located in uncultivatable zones,while 254.11 km^(2) of highly cultivatable land remained unused,indicating the significantly spatial imbalances.2)Changzhou's urban development and habitat conservation potential were calculated using the PLUS and InVEST models,respectively,in order to manage land use conflicts during the optimization of cultivated land layout.The urban development potential exhibited both central outward expansion and road-oriented diffusion.The habitat conservation potential showed the high potential in water bodies and forests,while the low potential in built-up areas.Different weight values were set for the sub-objectives in the utility function.The optimal spatial allocation of cultivated land was simulated under various scenarios.Neglecting degree of agglomeration resulted in the distribution of fragmented land,while the lacking on habitat conservation led to the unreasonable occupation of ecological areas,such as mountains and lakes.The urban development potential was lacking on the substantial cultivated land within core urban development zones.The most optimal layout was achieved to balance the scenario,such as the urban development,habitat conservation,and intensive farming yielded.An average cultivability potential of 0.9387 was obtained for a rational and efficient distribution of cultivated land resources.3)The spatial allocation was integrated with the current status of land use.A zoning strategy was proposed to maximize the natural endowments of cultivated land,in order to effectively coordinate with urban economic development and ecological conservation.Five zones were divided into the area of core conservation,quality improvement,potential reserve,construction buffer,and ecological conservation.Management strategies were tailored for each zone to enhance the efficient use for the optimal management of cultivated land resources.This finding can provide the new pathway and insights to optimize the cultivated land layout at the patch scale.Regional cultivated land resources were reassessed to effectively mitigate the mismatches of land resource.A comprehensive approach was offered to future spatial layout and management strategies for the cultivated land under multiple spatial factors.The technical insights can be expected to re-evaluate the regional cultivated land resources for the sustainable development.
作者
黄炳元
黄秋昊
阳艳
郑锦浩
陈逸航
HUANG Bingyuan;HUANG Qiuhao;YANG Yan;ZHENG Jinhao;CHEN Yihang(School of Geography and Ocean Science,Nanjing University,Nanjing 210023,China;Key Laboratory of the Coastal Zone Exploitation and Protection,Ministry of Natural Resources,Nanjing 210023,China;Key Laboratory for Land Satellite Remote Sensing Applications,Ministry of Natural Resources,Nanjing 210023,China;Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology,Nanjing 210023,China;Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application,Nanjing 210023,China)
出处
《农业工程学报》
EI
CAS
CSCD
北大核心
2024年第19期240-249,共10页
Transactions of the Chinese Society of Agricultural Engineering
基金
国家自然科学基金面上项目(41571082)。
关键词
耕地布局优化
多目标权衡
机器学习
蚁群优化算法
常州市
cultivated land optimization
multi-objective trade-off
machine learning
ant colony optimization
Changzhou City
