院报 ›› 2022, Vol. 39 ›› Issue (8): 41-49.DOI: 10.11988/ckyyb.20210470

• 水土保持与生态修复 • 上一篇    下一篇

青藏高原土地利用/覆被时空变化特征

彭海月1, 任燕1,2,3, 李琼1,2,3, 魏加华1,2,3,4   

  1. 1.青海大学 水利电力学院,西宁 810016;
    2.青海大学 三江源生态与高原农牧国家重点实验室,西宁 810016;
    3.青海大学 黄河上游生态保护与高质量发展实验室,西宁 810016;
    4.清华大学 水沙科学与水利水电工程国家重点实验室,北京 100084
  • 收稿日期:2021-05-12 修回日期:2022-01-05 出版日期:2022-08-01 发布日期:2022-08-26
  • 通讯作者: 李 琼(1986-),女,甘肃渭源人,副教授,博士,研究方向为水文生态。E-mail: liqiong1118@126.com
  • 作者简介:彭海月(1995-),女,青海西宁人,硕士研究生,研究方向为水文与水资源。E-mail: 1502989456@qq.com
  • 基金资助:
    青海省科技厅基础研究计划项目青年基金(2019-ZJ-968Q);青海省重点研发与转化计划-科技成果转化专项(2019-SF-146);清华大学水沙科学与水利水电工程国家重点实验室开放基金(sklhse-2020-Iowol)

Spatial and Temporal Land Use/Cover Change Characteristics of Qinghai-Tibet Plateau

PENG Hai-yue1, REN yan1,2,3, LI Qiong1,2,3, WEI Jia-hua1,2,3,4   

  1. 1. School of Water Resources and Electric Power,Qinghai University,Xining 810016,China;
    2. State Key Laboratory of Plateau Ecology and Agriculture in the Headwaters of Three Rivers,Qinghai University,Xining 810016,China;
    3. Laboratory of Ecological Protection and High Quality Development in the Upper Yellow River,Qinghai University,Xining 810016,China;
    4. State Key Laboratory of Hydroscience and Engineering,Tsinghua University,Beijing 100084,China
  • Received:2021-05-12 Revised:2022-01-05 Online:2022-08-01 Published:2022-08-26

摘要: 在对公开的土地利用数据集精度评价的基础上,选用较高精度的青藏高原科学研究所300 m土地利用数据(简称TPDC_LUCC),采用土地利用时空分布和变化率等指标,分析青藏高原1992—2015年土地利用变化特征,并结合气候数据集及社会经济数据对土地利用变化归因进行了分析。结果表明:①公开数据集中,TPDC_LUCC数据集序列最长,精度最高,尤其是对耕地、城镇用地及水体分类精度较好;欧洲航天局300 m 土地利用数据(ESA_LUCC)对草地的分类精度最高,耕地精度最差;中国科学院空天信息创新研究院30 m土地利用数据(CASearth_LUCC)对冰川和城镇用地的分类较准确,未利用土地误差较大;②青藏高原草地分布最广,多年平均占比约70.02%,其次是沙地、裸地等未利用土地,约占15.81%;城镇用地扩张较快,1992—2005年、2005—2015年两期增长率分别是2.34%、4.69%;1992—2015年,青藏高原未利用土地的9.14%转为草地,灌丛和耕地的3.27%转为林地,冰川的5.5%转为水体;③1992—2018年,青藏高原平均气温上升了1.17 ℃,降水和气温增加,是东部区域草地、灌丛、林地增加的主要驱动因素,气温明显上升致使西北部冰川融化,城市化促使城镇用地增加、耕地减少,实施生态保护政策对青藏高原草地、林地的恢复有显著推动作用。

关键词: 土地利用/覆被, 时空变化特征, 青藏高原, 数据集评价, 归因分析

Abstract: The characteristics of land use change from 1992 to 2015 in the Qinghai-Tibet Plateau are analyzed by examining the spatial-temporal distribution and change rate of land use using the 300 m high-precision land use data (abbreviated as TPDC_LUCC) of Qinghai-Tibet Plateau Research Institute on the basis of evaluating the accuracy of public land use data set. The attribution of land use change is also analyzed based on climate data and social economic data. Results reveal that:1) Among the public datasets,the TPDC_LUCC dataset has the longest sequence and the highest accuracy,especially the classification accuracy of cultivated land,urban land and water bodies;the 300 m land use data (abbreviated as ESA_LUCC) of the European Space Agency has the highest classification accuracy for grassland and the worst for cultivated land;the 30 m land use data (abbreviated as CASearth_LUCC) of the Institute of Space Information Innovation,Chinese Academy of Sciences has a relatively accurate classification of glacier and urban land,yet with a large error of unused land. 2) Grassland is the most widely distributed in Qinghai-Tibet Plateau,with a multi-year average proportion reaching 70.02%,followed by unused land such as sandy land and bare land,accounting for 15.81%. Urban land expanded rapidly,with a growth rate of 2.34% from 1992 to 2005 and 4.69% from 2005 to 2015. From 1992 to 2015,9.14% of unused land on the Qinghai-Tibet Plateau was turned into grassland,3.27% of shrub and arable land into forest land,and 5.5% of glaciers into water. 3) From 1992 to 2018,the average temperature of the Qinghai-Tibet Plateau increased by 1.17 ℃,and the increase of precipitation and temperature was the main driving factor for the increase of grassland,shrub and forest land in the eastern region. The significant increase of temperature led to the melting of glaciers in the northwest,and urbanization resulted in the increase of urban land and the decrease of cultivated land. Implementing ecological protection policies has significantly promoted the restoration of grassland and forest land on the Qinghai-Tibet Plateau.

Key words: land use/cover change, spatiotemporal characteristics, Qinghai-Tibet Plateau, data set evaluation, factor analysis

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