金沙江流域植被时空演变特征及对气候因子的响应

doi:10.13287/j.1001-9332.202511.023

摘要/Abstract

摘要： 植被是生态系统的重要组成部分,是能量流动及物质循环的纽带。研究植被动态变化及其对气候因子的响应,对于陆地生态系统保护具有重要意义。本研究选取金沙江流域为研究区,基于2001—2022年归一化植被指数(NDVI)、降水、气温数据,利用Mann-Kendall趋势检验和Sen's斜率分析探究了流域内植被覆盖的变化趋势和空间分布特征,利用偏相关分析讨论了植被对气温和降水的滞后响应,以及不同土地类型响应的滞后差异。结果表明: 2001—2022年,金沙江流域植被覆盖呈现整体向好趋势,NDVI的增长速度为0.002·(10 a)^-1;植被变化的空间分异性特征明显,植被显著改善区域占流域面积的25.4%;研究区域NDVI均值与海拔之间呈显著负相关,相关系数为-0.76。金沙江流域气候呈干热发展趋势,流域平均NDVI对降水的响应约滞后1个月,对温度呈现出即时响应的特点。耕地和灌木区植被覆盖增加,草地和林地区则保持稳定。草地NDVI与降水和温度的相关性均最高,林地对于二者的相关性均最低。不同土地类型NDVI对降水和温度响应的滞后性有差异,耕地、草地和灌木对降水响应滞后时间为1个月,林地则表现为即时响应;耕地和林地对温度表现为即时响应,而草地和灌木的滞后时间差异较大。本研究结果可为该流域的生态保护和资源管理提供科学依据,研究方法对其他地区的植被演变研究具有参考价值。

关键词: 金沙江流域, 归一化植被指数, 土地类型, 气候因子

Abstract: Vegetation plays a crucial role in ecosystem functioning by linking energy flow and material cycling. Understanding vegetation dynamics and their responses to climate is essential for ecosystem conservation. Based on normalized difference vegetation index (NDVI), precipitation, and temperature data of the Jinsha River Basin from 2001 to 2022, we used Mann-Kendall trend test and Sen's slope analysis to analyze the temporal and spatial variations of vegetation cover, while applied partial correlation analysis to explore the lagged responses of vegetation to temperature and precipitation and the lag differences across the responses of different land types. Results showed that vegetation coverage in the basin improved overall from 2001 to 2022, with the increasing rate of NDVI being 0.002·(10 a)^-1. There were significant spatial variations of vegetation changes, with 25.4% of the area showing improvement. The mean NDVI negatively correlated with altitude (correlation coefficient was -0.76). The basin's climate condition exhibited drier and warmer trends. NDVI showed a one-month lagged response to precipitation and a no-lagged response to temperature. Vegetation coverage in cultivated land and shrubland increased, while that in grassland and forest remained stable. The changes in grassland coverage had the strongest correlation with both precipitation and temperature, while forest coverage had the lowest correlation. Land types exhibited varying lag times in their response to the variations of precipitation and temperature. The lag time of precipitation response for cultivated land, grassland, and shrubland was one month, while forest showed an immediate response. The cultivated land and forest showed immediate response to temperature, while grassland and shrublands had significant differences in lag time. These findings would offer scientific basis for ecological protection and resource management in the basin and provide methodological insights for examining vegetation dynamics in other regions.

Key words: Jinsha River Basin, normalized difference vegetation index, land type, climate factor

张文杰, 赵乾佐, 崔磊, 李冲, 张璇, 程红光. 金沙江流域植被时空演变特征及对气候因子的响应[J]. 应用生态学报, 2025, 36(11): 3419-3430.

ZHANG Wenjie, ZHAO Qianzuo, CUI Lei, LI Chong, ZHANG Xuan, CHENG Hongguang. Spatio-temporal variations of vegetation in Jinsha River Basin and their responses to climatic factors[J]. Chinese Journal of Applied Ecology, 2025, 36(11): 3419-3430.

参考文献

[1] 韩宝龙, 束承继, 蔡文博, 等. 植被群落特征对城市生态系统服务的影响研究进展. 生态学报, 2021, 41(24): 9978-9989
[2] Augusto L, Boca A. Tree functional traits, forest biomass, and tree species diversity interact with site pro-perties to drive forest soil carbon. Nature Communications, 2022, 13: 12
[3] 邹桃红, 徐艳艳, 陈鹏, 等. 洞庭湖流域2000—2021年植被覆盖时空动态特征. 中国环境科学, 2024, 44(2): 961-971
[4] de la Iglesia Martinez A, Labib SM. Demystifying normalized difference vegetation index (NDVI) for greenness exposure assessments and policy interventions in urban greening. Environmental Research, 2023, 220: 12
[5] Liu JY, Zhao J, He JH, et al. Impact of natural and human factors on dryland vegetation in Eurasia from 2003 to 2022. Plants, 2024, 13: 18
[6] 张晶, 郝芳华, 吴兆飞, 等. 植被物候对极端气候响应及机制. 地理学报, 2023, 78(9): 2241-2255
[7] 刘静, 温仲明, 刚成诚. 黄土高原不同植被覆被类型NDVI对气候变化的响应. 生态学报, 2020, 40(2): 678-691
[8] 谢佩君, 宋小燕, 孙文义, 等. 基于GEE的黄土高原植被绿度线推移变化研究. 中国环境科学, 2023, 43(12): 6518-6529
[9] 马晓倩, 钟瑞森, 吴彬, 等. 塔里木河干流植被NDVI时空动态变化及影响因素探测[EB/OL]. (2024-11-28) [2025-03-01]. 环境科学. https://doi.org/10.13227/j.hjkx.202409115
[10] 侯婉, 侯西勇, 董婷. 1999—2020年中国沿海植被覆盖多时空尺度变化特征. 生态学报, 2024, 44(16): 7249-7263
[11] 徐士博, 张美玲, 宿茂鑫. 未来气候情景下青藏高原草地净初级生产力时空演变特征. 水土保持研究, 2024, 31(2): 190-201
[12] Xu SX, Wang YH, Liu Y, et al. Evaluating the cumulative and time-lag effects of vegetation response to drought in Central Asia under changing environments. Journal of Hydrology, 2023, 627: 13
[13] 张秀, 张璇, 崔磊, 等. 金沙江中游水电开发区生态系统服务综合功能时空变化与归因. 环境科学, 2025, 46(3): 1585-1596
[14] 赵冬林, 朱仕荣. 2010—2021年金沙江干热河谷植被覆盖度时空变化及其影响因素. 生态学杂志, 2024, 43(8): 2373-2381
[15] 黄瑾依, 孙倩, 黄永刚, 等. 干旱区典型县植被覆盖度的动态变化和景观格局分析. 环境科学, 2025, 46(3): 1645-1656
[16] 鞠艳, 张珂, 李炳锋, 等. 金沙江流域实际蒸散发时空分布特征及其影响因子. 水资源保护, 2022, 38(6): 104-110
[17] 程清平, 任钇潼, 金韩宇. 金沙江流域1961—2020年干湿日时空演变特征分析. 中国水土保持科学, 2024, 22(1): 42-51
[18] Yang Y, Chen RS, Ding YJ, et al. Variation in the hydrological cycle in the three-river headwaters region based on multi-source data. Frontiers in Environmental Science, 2022, 10: 19
[19] Zhao QZ, Zhang X, Li C, et al. Diverse vegetation response to meteorological drought from propagation perspective using event matching method. Journal of Hydrology, 2025, 653: 15
[20] 田智慧, 任祖光, 魏海涛. 2000—2020年黄河流域植被时空演化驱动机制. 环境科学, 2022, 43(2): 743-751
[21] 郝鑫怡, 张喆, 郑浩, 等. 天山北坡经济带植被覆盖度动态变化研究. 中国环境科学, 2024, 44(2): 1020-1031
[22] 王旭洋, 李玉霖, 连杰, 等. 半干旱典型风沙区植被覆盖度演变与气候变化的关系及其对生态建设的意义. 中国沙漠, 2021, 41(1): 183-194
[23] 王林, 陈文, 黄刚, 等. 中国西南超级干旱的变化特征和多尺度异常叠加效应分析. 中国科学: 地球科学, 2024, 54(7): 2114-2132
[24] 杨建和, 严冬春, 文安邦. 金沙江流域近十年地表变化与土壤侵蚀时空变化特征. 水土保持研究, 2023, 30(5): 13-20
[25] 过怡婷, 张思盈, 刘颖, 等. 蒋家沟干热河谷区灌草层植被与土壤的养分分布及生态化学计量特征. 山地学报, 2023, 41(6): 824-835
[26] 李永华, 徐海明, 刘德. 2006年夏季西南地区东部特大干旱及其大气环流异常. 气象学报, 2009, 67(1): 122-132
[27] 孙丞虎, 任福民, 周兵, 等. 2011/2012年冬季我国异常低温特征及可能成因分析. 气象, 2012, 38(7): 884-889
[28] Lin XH, Han PF, Zhang W, et al. Sensitivity of alpine grassland carbon balance to interannual variability in climate and atmospheric CO₂ on the Tibetan Plateau during the last century. Global and Planetary Change, 2017, 154: 23-32
[29] Chen Y, Zhang TB, Zhu X, et al. Quantitatively analyzing the driving factors of vegetation change in China: Climate change and human activities. Ecological Informatics, 2024, 82: 19
[30] 李晓婷, 郭伟, 倪向南, 等. 高寒草甸植物物候对温度变化的响应. 生态学报, 2019, 39(18): 6670-6680
[31] Sun XL, Li YH, Hu YM, et al. Human and natural factors affect habitat quality in ecologically fragile areas: Evidence from Songnen Plain, China. Frontiers in Plant Science, 2024, 15: 16
[32] 赵乾佐, 张璇, 费俊源, 等. 长江流域不同植被对2022年极端气象干旱事件的响应. 中国环境科学, 2024, 44(9): 5134-5144
[33] 李松阳, 刘康妮, 余杭, 等. 云南省蒋家沟不同植被类型土壤物理性质对水分入渗特征的影响. 山地学报, 2021, 39(6): 867-878
[34] 靖娟利, 孙佳荟, 赵婷, 等. 西南地区植被NPP对多尺度气象干旱的响应. 水土保持学报, 2024, 38(3): 335-344
[35] 宋有金, 吴超, 李子煜, 等. 水稻产量对生殖生长阶段不同时期高温的响应差异. 中国水稻科学, 2021, 35(2): 177-186
[36] 李君, 刘泽, 王牌, 等. 西藏珠峰地区乔松径向生长对气候变化的响应. 应用生态学报, 2024, 35(5): 1205-1213
[37] Zhang JR, Xiao JF, Tong XJ, et al. NIRv and SIF better estimate phenology than NDVI and EVI: Effects of spring and autumn phenology on ecosystem production of planted forests. Agricultural and Forest Meteorology, 2022, 315: 13