耦合SOFM与SVM的生态功能分区方法——以鄂尔多斯市为例
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摘要
辨析区域主要生态环境问题及其空间异质性,划定生态功能区,对保障区域生态安全和国土开发优化具有重要指导意义。以往自下而上的分区研究多基于行政区或流域开展,难以体现行政区或流域内部的生态功能分异。以鄂尔多斯市为例,基于生态系统服务与生态敏感性构建区域生态功能分区指标体系,耦合自组织特征映射(SOFM)网络与支持向量机(SVM)划定鄂尔多斯市生态功能分区。结果表明,区域内各生态功能分区指标呈现明显的空间分异特征,通过SOFM网络基于栅格进行指标聚类,构建分类效果指数筛选最佳聚类方案,将区域分为7种不同的生态功能类型。最终,利用SVM识别最优分区界线,将鄂尔多斯市分为11个生态功能区。本文构建分类效果指数实现多分类方案优选,使用机器学习算法解决自下而上的自然分区容易弱化要素空间位置属性的问题,完成了从分类到分区的定量转换,有助于提升分区的空间精度与客观性,为生态功能分区与分区边界划定提供了新的方法途径。
It is of great significance to analyze the main environmental issues, coupled with their spatial heterogeneity, and divide the ecological function zones to achieve ecological security and optimization of territory development in a certain region. In recent years,ecological function zoning, widely concerned by scholars, has played a vital role in regional ecosystem management and sustainable development. There arose a problem that the spatial characteristics of ecological functions were hard to be reflected in the previous studies based on spatial average data over basins or geopolitical regions such as counties, cities and provinces.This paper, using the approaches of coupling self-organizing feature map(SOFM) and support vector machine(SVM), attempts to develop an automatic demarcation and zoning approach,and explores the best possible division of ecological functions in the city of Ordos with the index system of ecological function zoning in mind involving the ecosystem services as well as ecological sensitivity. Ordos, which is located in the transitional zone between temperate grasslands and desserts, has become a key research area of global terrestrial ecosystem. The ecological functional zoning indexes indicate that there appears an obvious spatial heterogeneity of ecosystem functions in Ordos. Accordingly, land grids have been clustered into 7 ecological function types by SOFM with clustering quality index(CQI) in view. Hence,Ordos has been divided into 11 ecological function zones owing to the implementation of SVM to identify the optimal division borders, in which the border demarcation is treated as a classification system in spatial domain. The optimal combination of SVM hyperparameters is determined by grid search method. In this study, machine learning algorithm has been adopted to cope with the situation where the bottom-up physical regionalization might weaken the spatial position attribute of the partition features, and to realize the quantitative conversion from classification to partition. It has turned out that such SOFM-SVM-coupled zoning approach could effectively improve the spatial accuracy of the partition, which can be considered as a new way to realize the automatic ecological zoning.
It is of great significance to analyze the main environmental issues, coupled with their spatial heterogeneity, and divide the ecological function zones to achieve ecological security and optimization of territory development in a certain region. In recent years,ecological function zoning, widely concerned by scholars, has played a vital role in regional ecosystem management and sustainable development. There arose a problem that the spatial characteristics of ecological functions were hard to be reflected in the previous studies based on spatial average data over basins or geopolitical regions such as counties, cities and provinces.This paper, using the approaches of coupling self-organizing feature map(SOFM) and support vector machine(SVM), attempts to develop an automatic demarcation and zoning approach,and explores the best possible division of ecological functions in the city of Ordos with the index system of ecological function zoning in mind involving the ecosystem services as well as ecological sensitivity. Ordos, which is located in the transitional zone between temperate grasslands and desserts, has become a key research area of global terrestrial ecosystem. The ecological functional zoning indexes indicate that there appears an obvious spatial heterogeneity of ecosystem functions in Ordos. Accordingly, land grids have been clustered into 7 ecological function types by SOFM with clustering quality index(CQI) in view. Hence,Ordos has been divided into 11 ecological function zones owing to the implementation of SVM to identify the optimal division borders, in which the border demarcation is treated as a classification system in spatial domain. The optimal combination of SVM hyperparameters is determined by grid search method. In this study, machine learning algorithm has been adopted to cope with the situation where the bottom-up physical regionalization might weaken the spatial position attribute of the partition features, and to realize the quantitative conversion from classification to partition. It has turned out that such SOFM-SVM-coupled zoning approach could effectively improve the spatial accuracy of the partition, which can be considered as a new way to realize the automatic ecological zoning.
引文
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[2]Wu Shaohong,Gao Jiangbo,Dai Erfu,et al.Research on dynamic of terrestrial system of China:Academic logic and synthetic scheme.Advances in Earth science,2017,32(6):569-576.[吴绍洪,高江波,戴尔阜,等.中国陆地表层自然地域系统动态研究:思路与方案.地球科学进展,2017,32(6):569-576.]
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[7]Fu Bojie,Chen Liding,Liu Guohua.The objectives,tasks and characteristics of China ecological regionalization.Acta Ecologica Sinica,1999,19(5):3-7.[傅伯杰,陈利顶,刘国华.中国生态区划的目的、任务及特点.生态学报,1999,19(5):3-7.]
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[9]Gao Jiangbo,Huang Jiao,Li Shuangcheng,et al.The new progresses and development trends in the research of physiogeographical regionalization in China.Progress in Geography,2010,29(11):1400-1407.[高江波,黄姣,李双成,等.中国自然地理区划研究的新进展与发展趋势.地理科学进展,2010,29(11):1400-1407.]
[10]Zheng Du,Ou Yang,Zhou Chenghu.Understanding of and thinking over geographical regionalization methodology.Acta Geographica Sinica,2008,63(6):563-573.[郑度,欧阳,周成虎.对自然地理区划方法的认识与思考.地理学报,2008,63(6):563-573.]
[11]Peng Jian,Wu Wenhuan,Liu Yanxu,et al.Soil conservation service zoning in the Inner Mongolia Autonomous Region based on PSR framework.Acta Ecologica Sinica,2017,37(11):3849-3861.[彭建,武文欢,刘焱序,等.基于PSR框架的内蒙古自治区土壤保持服务分区.生态学报,2017,37(11):3849-3861.]
[12]Huang Jiao,Gao Yang,Zhao Zhiqiang,et al.Comprehensive physiographic regionalization of China using GIS and SOFM neural network.Geographical Research,2011,30(9):1648-1659.[黄姣,高阳,赵志强,等.基于GIS与SOFM网络的中国综合自然区划.地理研究,2011,30(9):1648-1659.]
[13]Zhang Xueru,Zhang Yili,Liu Linshan,et al.Zoning by land types based on SOFM network:A case study on transect of eastern Tibetan Plateau.Geographical Research,2013,32(5):839-847.[张学儒,张镱锂,刘林山,等.基于SOFM神经网络模型的土地类型分区尝试:以青藏高原东部样带为例.地理研究,2013,32(5):839-847.]
[14]Ma Cheng,Li Shuangcheng,Liu Jinlong,et al.Regionalization of ecosystem services of Beijing-Tianjin-Hebei Area based on SOFM neural network.Progress in Geography,2013,32(9):1383-1393.[马程,李双成,刘金龙,等.基于SOFM网络的京津冀地区生态系统服务分区.地理科学进展,2013,32(9):1383-1393.]
[15]Li Shuangcheng,Zhao Zhiqiang,Gao Jiangbo.Identifying eco-geographical boundary using spatial wavelet transform.Acta Ecologica Sinica,2008,28(9):4313-4322.[李双成,赵志强,高江波.基于空间小波变换的生态地理界线识别与定位.生态学报,2008,28(9):4313-4322.]
[16]Chang C C,Lin C J.LIBSVM:A Library for Support Vector Machines.ACM Transactions on Intelligent Systems and Technology,2011,2(3):1-27.
[17]Meng Jijun,Zhu Likai,Yang Qian,et al.Building ecological security pattern based on land use:A case study of Ordos,northern China.Acta Ecologica Sinica,2012,32(21):6755-6766.[蒙吉军,朱利凯,杨倩,等.鄂尔多斯市土地利用生态安全格局构建.生态学报,2012,32(21):6755-6766.]
[18]Dai Erfu,Zhai Ruixue,Ge Quansheng,et al.Topsoil organic carbon storage and its changes in Inner Mongolia grassland from the 1980s to 2010s.Acta Geographica Sinica,2014,69(11):1651-1660.[戴尔阜,翟瑞雪,葛全胜,等.1980s-2010s内蒙古草地表层土壤有机碳储量及其变化.地理学报,2014,69(11):1651-1660.]
[19]Song Naiping,Zhang Fengrong.The changing process and mechanism of the farming-grazing transitional land use pattern in Ordos.Acta Geographica Sinica,2007,62(12):1299-1308.[宋乃平,张凤荣.鄂尔多斯农牧交错土地利用格局的演变与机理.地理学报,2007,62(12):1299-1308.]
[20]Yin Jie,Dai Erfu,Wu Shaohong.Integrated risk assessment and zoning of typhoon disasters in China.Scientia Geographica Sinica,2013,33(11):1370-1376.[殷洁,戴尔阜,吴绍洪.中国台风灾害综合风险评估与区划.地理科学,2013,33(11):1370-1376.]
[21]Ouyang Zhiyun.China's ecological function zoning.China Investigation&Design,2007,1(3):70.[欧阳志云.中国生态功能区划.中国勘察设计,2007,1(3):70.]
[22]Pan Feng,Tian Changyan,Shao Feng,et al.Evaluation of ecological sensitivity in Karamay,Xinjiang,Northwest China.Acta Geographica Sinica,2011,66(11):1497-1507.[潘峰,田长彦,邵峰,等.新疆克拉玛依市生态敏感性研究.地理学报,2011,66(11):1497-1507.]
[23]Meng Jijun,Yan Qun,Xiang Yunyun.The optimization of ecological security pattern based on land use and assessment of schemes in Ordos,Inner Mongolia,China.Journal of Desert Reserch,2014,34(2):590-596.[蒙吉军,燕群,向芸芸.鄂尔多斯土地利用生态安全格局优化及方案评价.中国沙漠,2014,34(2):590-596.]
[24]Ouyang Zhiyun,Wang Xiaoke,Miao Hong.China's eco-environmental sensitivity and its spatial heterogeneity.Acta Ecologica Sinica,2000,20(1):10-13.[欧阳志云,王效科,苗鸿.中国生态环境敏感性及其区域差异规律研究.生态学报,2000,20(1):10-13.]
[25]Zhang Qingyu,Zhao Dongsheng,Wu Shaohong,et al.Research on vegetation changes and influence factors based on eco-geographical regions of Inner Mongolia.Scientia Geographica Sinica,2013,33(5):594-601.[张清雨,赵东升,吴绍洪,等.基于生态分区的内蒙古地区植被覆盖变化及其影响因素研究.地理科学,2013,33(5):594-601.]
[26]Groten S.NDVI:Crop monitoring and early yield assessment of Burkina Faso.International Journal of Remote Sensing,1993,14(8):1495-1515.
[27]Sharp R,Tallis H T,Ricketts T,et al.InVEST User's Guide.Stanford:The Natural Capital Project,2014.
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