Responses of Vegetation in Mu Us Sandy Land to Climate Change from 1982 to 2020

doi:10.13466/j.cnki.lyzygl.2023.03.011

Abstract

Abstract:

Mu Us Sandy Land belongs to the agro-pastoral staggered area in northern China,the ecological environment is fragile,and the vegetation growth is greatly affected by the climatic environment.Based on GIMMS NDVI3g.v1 from 1982 to 1999 and MOD13Q1 from 2000 to 2020,combined with the temperature and precipitation data of relevant meteorological stations,this paper discussed the spatiotemporal characteristics of vegetation growth and its response to climate change in Mu Us Sandy Land from 1982 to 2020 by using trend analysis,coefficient of variation,partial correlation analysis and Hurst index.The research results showed:1) The vegetation growth of Mu Us Sandy Land showed a continuous upward trend,with an increase rate of 1.3% per 10 a,with 2005 being the inflection point,and the vegetation growth being slow before 2005 and rapidly thereafter.2) The NDVI_average range of Mu Us Sandy Land was between 0.10~<0.41,and spatial distribution characterized by "low west and high east ",and the change trend was mainly increased,accounting for about 94.07% of the total area,of which a significant increase accounted for 57.43% and a very significant increase accounted for 30.14%;The spatial variation and stability of vegetation varied significantly,and the proportion of high fluctuation change area was 35.95%,and the bands were distributed in areas with strong vegetation growth at the southeast edge of sandy land.3) The positive correlation coefficient between NDVI and temperature in April,June and August was high,and the positive correlation coefficient between NDVI and precipitation from June to September was strong,and precipitation had a greater impact on the growth and development of sandy vegetation,and the co-directional characteristics of vegetation change were stronger than the reverse characteristics.4) The future change trend of Mu Us Sandy Land would be mainly continuous improvement and continuous degradation,with continuous improvement accounting for 51.49% of the total area and continuous degradation accounting for 46.49%.

Key words: Mu Us Sandy Land, temporal and spatial change, climate response, trend analysis, coefficient of variation, partial correlation analysis, Hurst index

CLC Number:

Q948

WANG Cuiping, HAN Xiaohong, WANG Haochen, YOU Jiaqi. Responses of Vegetation in Mu Us Sandy Land to Climate Change from 1982 to 2020[J]. FOREST RESOURCES WANAGEMENT, 2023, (3): 80-89.

Figures/Tables 10

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References 37

[1]	朱震达. 中国土地荒漠化的概念、成因与防治[J]. 第四纪研究, 1998(2):145-155.
[2]	李元. 关于荒漠化问题的浅略思考[J]. 环境与发展, 2016, 28(3):97-100.
[3]	张梦嫚. 毛乌素沙地植被恢复以及其对生态系统服务的影响[D]. 北京: 北京林业大学, 2020.
[4]	杨达. 三江源不同生态地理区NDVI时空变化特征与驱动因素分析[D]. 成都: 成都理工大学, 2021.
[5]	Liu Yanlan, Lei Huimin. Responses of natural vegetation dynamics to climate drivers in China from 1982 to 2011[J]. Remote Sensing, 2015, 7(8):10243-10268. doi: 10.3390/rs70810243
[6]	Qu Bo, Zhu Wenbin, Jia Saofeng, et al. Spatiotemporal changes in vegetation activity and its driving factors during the growing season in China from 1982 to 2011[J]. Remote Sensing, 2015, 7(10):13729-13752. doi: 10.3390/rs71013729
[7]	李姣, 张春来, 李庆, 等. 近15年来呼伦贝尔沙地土地沙漠化发展及其驱动力[J]. 北京师范大学学报:自然科学版, 2017, 53(3):323-328.
[8]	吕家欣, 李秀芬, 郑晓, 等. 近40年科尔沁沙地植被时空变化及其驱动力[J]. 生态学杂志, 2020, 39(5):1399-1408.
[9]	丁文广, 陈利珍, 徐浩, 等. 气候变化对甘肃河西走廊地区沙漠化影响的风险评价[J]. 兰州大学学报:自然科学版, 2016, 52(6):746-755.
[10]	李娜, 颜长珍. 毛乌素沙地现代人为活动的生态环境效应研究进展[J]. 中国沙漠, 2015, 35(2):487-492. doi: 10.7522/j.issn.1000-694X.2014.00022
[11]	冯颖. 毛乌素沙地植被盖度变化及其对气候变化的响应[D]. 北京: 北京林业大学, 2015.
[12]	杨梅焕, 靳小燕, 王涛. 毛乌素沙地植被物候变化及其对气候变化的响应[J]. 水土保持通报, 2022, 42(2):242-249.
[13]	王立新, 刘华民, 杨劼, 等. 毛乌素沙地气候变化及其对植被覆盖的影响[J]. 自然资源学报, 2010, 25(12):2030-2039. doi: 10.11849/zrzyxb.2010.12.004
[14]	朱娅坤, 秦树高, 张宇清, 等. 毛乌素沙地植被物候动态及其对气象因子变化的响应[J]. 北京林业大学学报, 2018, 40(9):98-106.
[15]	曹艳萍, 庞营军, 贾晓红. 2001—2016年毛乌素沙地植被的生长状况[J]. 水土保持通报, 2019, 39(2):29-37.
[16]	刘婷. 毛乌素沙地湖泊沉积记录的冰消期以来气候变化[D]. 太原: 山西大学, 2021.
[17]	宋云民. 毛乌素沙地主要树种水分特征及植被恢复模式研究[D]. 长沙: 中南林业科技大学, 2007.
[18]	郑颖. 基于数值模拟的毛乌素沙地植被变化对区域气候和水分平衡影响研究[D]. 呼和浩特: 内蒙古大学, 2021.
[19]	Goetz S J, Fiske G J, Bunn A G. Using satellite time-series data sets to analyze fire disturbance and forest recovery across Canada[J]. Remote Sensing of Environment, 2006, 101(3):352-365. doi: 10.1016/j.rse.2006.01.011
[20]	赵桔超, 朱彦辉, 段国辉, 等. 基于MOD13Q1数据分析2001—2015年西双版纳植被变化特征[J]. 生态学杂志, 2019, 38(4):1083-1092.
[21]	刘宪锋, 潘耀忠, 朱秀芳, 等. 2000-2014年秦巴山区植被覆盖时空变化特征及其归因[J]. 地理学报, 2015, 70(5):705-716. doi: 10.11821/dlxb201505003
[22]	徐根生, 何应法, 付炜, 等. 基于MOD13Q1的永州近15年植被变化研究[J]. 甘肃农业科技, 2015(3):27-29.
[23]	崔珍珍, 马超, 陈登魁. 1982—2015年科尔沁沙地植被时空变化及气候响应[J]. 干旱区研究, 2021, 38(2):536-544.
[24]	吕洋, 董国涛, 杨胜天, 等. 雅鲁藏布江流域NDVI时空变化及其与降水和高程的关系[J]. 资源科学, 2014, 36(3):603-611.
[25]	刘宪锋, 潘耀忠, 朱秀芳, 等. 2000-2014年秦巴山区植被覆盖时空变化特征及其归因[J]. 地理学报, 2015, 70(5):705-716. doi: 10.11821/dlxb201505003
[26]	任荣仪. 贵州省植被指数/NDVI时空分布特征及对气候变化的响应[D]. 贵阳: 贵州师范大学, 2021.
[27]	Liu Xianfeng, Zhang Jinshui, Zhu Xiufang, et al. Spatiotemporal changes in vegetation coverage and its driving factors in the Three-River Headwaters Region during 2000-2011[J]. Journal of Geographical Sciences, 2014, 24(2):288-302. doi: 10.1007/s11442-014-1088-0
[28]	刘玮琦. 东北亚植被NDVI变化特征及其对气候因子的响应[D]. 延吉: 延边大学, 2021.
[29]	赵安周, 张安兵, 刘海新, 等. 退耕还林(草)工程实施前后黄土高原植被覆盖时空变化分析[J]. 自然资源学报, 2017, 32(3):449-460. doi: 10.11849/zrzyxb.20160411
[30]	阿多, 赵文吉, 宫兆宁, 等. 1981—2013华北平原气候时空变化及其对植被覆盖度的影响[J]. 生态学报, 2017, 37(2):576-592.
[31]	Peng Liang, Yang Xiaoping. Landscape spatial patterns in the Maowusu(Mu Us) Sandy Land,northern China and their impact factors[J]. Catena, 2016, 145:321-333. doi: 10.1016/j.catena.2016.06.023
[32]	原媛. 毛乌素沙地植被覆盖和物候变化对总初级生产力的影响[D]. 北京: 北京林业大学, 2021.
[33]	苏日罕, 郭恩亮, 王永芳, 等. 1982—2020年内蒙古地区极端气候变化及其对植被的影响[J]. 生态学报, 2023, 43(1):419-431.
[34]	王相麾, 张颖. 我国毛乌素沙地荒漠化逆转因素分析[J]. 安徽农业科学, 2020, 48(10):1-6.
[35]	杨梅焕, 靳小燕, 王涛. 毛乌素沙地植被物候变化及其对气候变化的响应[J]. 水土保持通报, 2022, 42(2):242-249.
[36]	袁玮. 气候变化对中国东北部及四大沙地植被覆盖的影响(1982-2013)[D]. 南京: 南京大学, 2018.
[37]	黄绪梅, 张翼, 李建平. 毛乌素荒漠草原植被特征对降水变化的响应[J]. 草地学报, 2022, 30(1):178-187. doi: 10.11733/j.issn.1007-0435.2022.01.022

变化等级	变异系数范围	面积占比/%
高波动	0.13~1.27	35.95
较高波动	0.10~<0.13	21.10
中等波动	0.08~<0.10	17.58
较低波动	0.05~<0.08	23.06
低波动	0.02~<0.05	2.30