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中国农学通报 ›› 2026, Vol. 42 ›› Issue (12): 94-102.doi: 10.11924/j.issn.1000-6850.casb2025-0950

• 资源·环境·生态·土壤·气象 • 上一篇    下一篇

2000—2022年疏勒河流域植被覆盖时空变化及其驱动机制

安韵如1,2(), 贾鸿儒1(), 张美美3, 靳惠安1,4   

  1. 1 甘肃林业职业技术大学, 甘肃天水 741020
    2 西北农林科技大学草业与草原学院, 陕西杨凌 712100
    3 新疆大学地理与遥感科学学院, 乌鲁木齐 830000
    4 西北师范大学地理与环境科学学院, 兰州 730070
  • 收稿日期:2025-11-26 修回日期:2026-04-29 出版日期:2026-06-25 发布日期:2026-06-23
  • 通讯作者:
    贾鸿儒,男,1980年出生,甘肃天水人,副教授,硕士,研究方向:生态遥感与水土保持。通信地址:741020 甘肃省天水市麦积区麦积大道200号 甘肃林业职业技术大学,Tel:0938-2111068,E-mail:
  • 作者简介:

    安韵如,女,1994年出生,甘肃会宁人,副教授,博士,研究方向:遥感与草地农业生态。通信地址:741020 甘肃省天水市麦积区麦积大道200号 甘肃林业职业技术大学,Tel:0938-2111068,E-mail:

  • 基金资助:
    甘肃省高校教师创新基金项目“渭河流域甘肃段水土保持与饲草双功能优质草本植物的筛选研究”(2024A-247); 中国(北方)现代林业职业教育集团林业科学研究项目“甘肃省草地主要有毒杂草入侵区分布评估与预测研究”(LZJB2024KY007); 甘肃省自然科学基金项目“甘肃省小陇山林区野生木本观赏植物评价与筛选”(25JRRE014); 中国科学院“西部之光”人才培养引进计划项目

Spatio-temporal Variation of Vegetation Coverage and Driving Mechanism During 2000-2022 in Shule River Basin

AN Yunru1,2(), JIA Hongru1(), ZHANG Meimei3, JIN Hui’an1,4   

  1. 1 Gansu Forestry Voctech University, Tianshui, Gansu 741020
    2 College of Grassland Agriculture, Northwest A & F University, Yangling, Shaanxi 712100
    3 College of Geography and Remote Sensing Science, Xinjiang University, Urumqi 830000
    4 College of Geography and Environmental Science, Northwest Normal University, Lanzhou 730070
  • Received:2025-11-26 Revised:2026-04-29 Published:2026-06-25 Online:2026-06-23

摘要:

本研究旨在评估疏勒河流域植被覆盖的时空变化及其驱动机制。基于GEE平台,选取2000—2022年MODIS-NDVI数据源,采取Theil-Sen斜率、Mann-Kendall显著性检验、Hurst指数和地理探测器等方法,分析河流域植被覆盖的时空演变特征及影响因素。结果表明:(1)研究期间疏勒河流域植被覆盖度(NDVI)平均值为0.18,植被覆盖面积占流域总面积的35.7%,低度、中度覆盖分别为20.6%和8.2%,较高和高度覆盖共占流域面积4.3%。植被覆盖总体呈现南高北低的格局,祁连—阿尔金山区为主要的植被覆盖区,占流域植被面积的74.4%。(2)时序变化上,2000—2022年间疏勒河流域植被覆盖总体呈波动上升趋势,增长速率为3.5%/10 a,前11年增速较缓(1.2%/10 a),后12年增速较快(4.7%/10 a)。空间变化上,祁连—阿尔金山区、中部河西走廊绿洲区大部分地区集中呈植被改善趋势,改善区域面积占流域面积32.7%,退化区域面积仅占2.3%,主要分布于马鬃山北部和中部河西走廊城镇区域。(3)地理探测器分析显示,疏勒河流域植被改善主要受气候和人类活动的影响,其中气温和降水是植被变化主要驱动力,且交互作用解释力最强。Hurst指数分析预测,未来疏勒河流域植被覆盖将以正向持续性为主,但部分区域存在退化风险。本研究结果可为疏勒河流域生态保护和管理提供科学依据。

关键词: NDVI, 趋势分析, M-K检验, Hurst指数, 地理探测器, 疏勒河流域

Abstract:

The purpose of this study is to evaluate the spatial and temporal changes of vegetation coverage and its driving mechanism in the Shule River Basin. Based on the GEE platform and MODIS-NDVI data, optimal NDVI values were determined. The methods such as the Theil-Sen slope, the Mann-Kendall test, the Hurst index and geographical detector were employed to study the spatio-temporal changes of vegetation coverage and their driving factors in Shule River Basin from 2000 to 2022. The results showed that: (1) the mean NDVI in Shule River Basin from 2000 to 2022 was 0.18, with vegetation coverage accounted for 35.7% of the basin’s total area; low and moderate coverage accounted for 20.6% and 8.2%, respectively, the higher and highest coverage together accounted for 4.3%. The vegetation coverage pattern generally showed higher in the south and lower in the north, with the Qilian-Altun Mountains regions constituted the main area of vegetation coverage, accounted for 74.4% of the basin’s vegetation area; (2) in terms of temporal change, the vegetation coverage of the Shule River Basin generally showed a fluctuating upward trend during 2000-2022, with a growth rate of 3.5%/10 a. The growth rate was slower in the first 11 years (1.2%/10 a) and faster in the last 12 years (4.7%/10 a).; in terms of spatial changes, the vegetation in most parts of the Qilian-Altun Mountains and Hexi Corridor oasis zones showed an improving trend, with the improved area accounted for 32.7% of the basin, only 2.3% of the degraded area was located mainly in the northern part of the Mazong Mountains and in the towns and cities of the Hexi Corridor; (3) vegetation coverage improvement in Shule River Basin was primarily influenced by climate and human activities, temperature and precipitation were the main drivers of vegetation change, and their interaction provided a strong explanatory power. In the future, vegetation coverage will continue to be dominated by positive persistence, with local areas showing inverse persistence.

Key words: NDVI, trend analysis, Mann-Kendall test, Hurst index, geographical detector, Shule River Basin

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