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草业学报 ›› 2023, Vol. 32 ›› Issue (9): 1-16.DOI: 10.11686/cyxb2022405

• 研究论文 •    

内蒙古温性草原草地类型近20年时空动态变化研究

杨志贵1(), 张建国1, 李锦荣2, 于红妍3, 常丽4, 宜树华1, 吕燕燕1, 张玉琢1, 孟宝平1()   

  1. 1.南通大学脆弱生态研究所,地理科学学院,江苏 南通 226007
    2.内蒙古阴山北麓荒漠草原生态水文野外科学观测研究站,中国水利水电科学研究院,北京 100038
    3.祁连山国家公园青海服务保障中心,青海 西宁 810001
    4.兰州城市学院城市环境学院,甘肃 兰州 730070
  • 收稿日期:2022-10-10 修回日期:2022-11-28 出版日期:2023-09-20 发布日期:2023-07-12
  • 通讯作者: 孟宝平
  • 作者简介:E-mail: mengbp09@lzu.edu.cn
    杨志贵(2000-),男,甘肃庆阳人,在读本科。E-mail: 1822021025@stmail.ntu.edu.cn
  • 基金资助:
    内蒙古自治区科技计划项目(2021GG0050);中国水科院基本科研业务费项目(MK0199A122021);甘肃省自然科学基金(20JR5RA 094)

Spatiotemporal dynamic variation of temperate grassland classes in Inner Mongolia in the last 20 years

Zhi-gui YANG1(), Jian-guo ZHANG1, Jin-rong LI2, Hong-yan YU3, Li CHANG4, Shu-hua YI1, Yan-yan LYU1, Yu-zhuo ZHANG1, Bao-ping MENG1()   

  1. 1.Institute of Fragile Eco-environment,School of Geographic Science,Nantong University,Nantong 226007,China
    2.Yinshanbeilu National Field Research Station of Desert Steppe Eco-hydrological System,China Institute of Water Resources and Hydropower Research,Beijing 100038,China
    3.Qinghai Service and Guarantee Center of Qilian Mountain National Park,Xining 810001,China
    4.College of Urban Environment,Lanzhou City University,Lanzhou 730070,China
  • Received:2022-10-10 Revised:2022-11-28 Online:2023-09-20 Published:2023-07-12
  • Contact: Bao-ping MENG

摘要:

草地类型是人类科学开发、合理利用和有效保护草地资源的重要依据,同时还是维持草地生态系统可持续发展的重要依据。目前,国内外有关土地利用类型时空变化的研究已有丰硕成果,但区域尺度草地类型时空动态变化的研究鲜有报道。因此,以内蒙古温性草原为研究对象,基于遥感植被指数、气象、土壤、地形和无人机航拍资料,结合机器学习算法构建了内蒙古草地类型分类算法,并以此为依据,分析草地类型时空动态变化特征。结果表明:1)所有遥感分类特征指标中,降水、归一化植被指数(NDVI)对研究区草地类型分类的重要性值高于其他指标,前18个分类指标(按重要性值排)的累计贡献率达85%以上;2)随机森林(RF)模型对内蒙古温性草原草地类型的分类精度最高,总体分类精度(OA)为82.16%,卡帕系数(Kappa)为0.76;3)过去20年来内蒙古地区草地类型之间的转换比较剧烈,多发生在典型草原、荒漠化草原和荒漠之间。相较于20世纪80年代草地类型,2000-2009年草地类型多由湿润类型向干旱类型转变,然而2010-2019年的草地类型则由干旱类型向湿润类型转变。本研究结果可为全球气候变化和人类活动下内蒙古草地类型的变化研究提供科学依据,同时也可为内蒙古地区草地可持续发展提供理论依据和技术支撑。

关键词: 草地类型, 特征指数, 机器学习, 时空变化

Abstract:

Grassland classification is essential for rational utilization and effective protection of grassland resources and also crucial for maintaining sustainable development of grassland ecosystems. There has been considerable achievement in documenting spatio-temporal changes in land use types, but few reports on the spatio-temporal dynamic variation of grassland classes at the regional scale. Hence, this study explored the spatio-temporal variation of temperate grassland classes in Inner Mongolia. Several grassland class identification methods were constructed based on remote sensing vegetation index, meteorology, soil, topography, unmanned aerial vehicle (UAV) data and machine learning algorithms. Then the spatio-temporal variation was analyzed based on the optimal classification method. It was found that: 1) Among all the remote sensing classification characteristics and indexes evaluated, the importance values of precipitation and normalized difference vegetation index (NDVI) for grassland classification were higher than those of other indexes in the study area, and the cumulative contribution of the importance values of first 18 classification characteristics and indexes was more than 85%; 2) The random forest (RF) model gave the highest classification accuracy and was superior to other methods in grassland class identification in Inner Mongolia, with an overall accuracy of 82.16% and Kappa coefficient of 0.76; 3) In the past 20 years, the transition between grassland classes in Inner Mongolia has been intense, and has mainly occurred through transition among the classes typical steppe, desert steppe and desert. Compared with the grassland types in the 1980s, the grassland types in 2000-2009 changed from wet to dry, while the grassland types in 2010-2019 changed from dry to wet. The results of this study provide a scientific evaluation of the changes in grassland type in Inner Mongolia under global climate change and as a result of human activities, and provide a theoretical basis and technical support for planning sustainable grassland development in Inner Mongolia.

Key words: grassland classes, characteristic index, machine learning, spatio-temporal variation