Characteristics of vegetation cover change and its relationship with climate factors in Ganzi Prefecture

doi:10.11755/j.issn.1006-7639-2024-06-0944

Abstract

Abstract:

It is of great significance to study the change of vegetation cover and the influence of climate factors in Ganzi Prefecture for ecological protection and development. Based on MODIS (Moderate-resolution Imaging Spectroradiometer) -NDVI (Normalized Difference Vegetation Index) data during 2003-2022, trend analysis, Hurst index were used to analyze the changes in vegetation cover in Ganzi Prefecture over the past twenty years. Temperature and precipitation data which are significantly correlated with vegetation index were used to analyze the relationship between vegetation cover change and climate factors. The results indicated that NDVI in Ganzi Prefecture showed an overall upward trend during the study period, and the distribution of vegetation cover was correlated with altitude and watershed. In the past twenty years, the area of vegetation cover increasing accounted for 67.83% of the total area of Ganzi Prefecture, and 25.37% of the area may maintain the same evolution trend as the present in the future. There are regional differences in the spatial distribution of precipitation and temperature, and the precipitation fluctuates significantly in summer, which is related to the influence of Qinghai-Tibet High Pressure in summer. Compared with precipitation, the correlation between temperature and NDVI is more significant. The influence of precipitation and temperature can explain 70% of the regional vegetation cover change during the study period, and the contribution of temperature to vegetation cover change is higher than that of precipitation.

Key words: vegetation cover change, NDVI, climate factors, correlation analysis, Ganzi Prefecture

摘要：

研究甘孜州地区多年植被覆盖变化与气候因子的关系，可以增进对该区域生态环境发展和生态结构变化的了解，对甘孜州生态保护有重要意义。基于2003—2022年MODIS（Moderate-resolution Imaging Spectroradiometer）-NDVI（Normalized Difference Vegetation Index）数据，使用趋势分析、Hurst指数等分析甘孜州近20 a植被覆盖变化，利用与植被指数存在明显关联性的温度和降水数据，分析植被覆盖变化与气候因子的关系。结果表明，植被覆盖分布与海拔和流域存在一定关联，研究时段内甘孜州NDVI整体呈上升趋势。过去20 a，甘孜州植被覆盖上升区域约占其总面积的67.83%，且有25.37%的区域或将保持与当下相同的演化趋势。降水和温度的空间分布存在区域差异，降水在夏季有明显起伏变化，这与当地夏季受青藏高压影响存在一定联系；降水和温度影响可解释研究时段内区域植被覆盖变化的70%，且温度对植被覆盖变化的贡献高于降水。

关键词: 植被覆盖度, NDVI, 气候因子, 相关性分析, 甘孜州

CLC Number:

P461+.7

WANG Min, SUN Shujun, ZHANG Jian, LI Tianfang, CHEN Rui, YANG Xing, XIAO Yawen. Characteristics of vegetation cover change and its relationship with climate factors in Ganzi Prefecture[J]. Journal of Arid Meteorology, 2024, 42(6): 944-952.

王敏, 孙树峻, 张健, 李天方, 陈锐, 杨星, 肖雅文. 甘孜州植被覆盖度演变特征及其与气候因子的相关性研究[J]. 干旱气象, 2024, 42(6): 944-952.

Figures/Tables 9

Fig.1 Distribution of topography （the color shaded， Unit： m） and meteorological stations in Ganzi Prefecture

Fig.2 The variations of monthly (a) and annual (b) average NDVI from 2003 to 2022 in Ganzi Prefecture

Fig. 3 The spatial distribution of multi-year mean NDVI (a) and change trend of NDVI (b) from 2003 to 2022 in Ganzi Prefecture

Fig.4 The spatial distribution of Hurst index of NDVI in Ganzi Prefecture

Fig.5 The spatial distribution of average monthly precipitation (Unit: mm) (a) and monthly precipitation series (b) from 2003 to 2022 in Ganzi Prefecture

Fig.6 Spatial distribution of average monthly temperature (a) (Unit: ℃) and monthly average temperature series (b) from 2003 to 2022 in Ganzi Prefecture

Fig.7 Spatial distribution of partial correlation coefficients between average monthly precipitation and NDVI (a) and time series of partial correlation coefficients between them (b) from 2003 to 2022 in Ganzi Prefecture

Fig.8 Spatial distribution of partial correlation coefficients between average monthly temperature and NDVI (a) and time series of partial correlation coefficient between them (b) from 2003 to 2022 in Ganzi Prefecture

Fig.9 Monthly variation of correlation coefficients between precipitation (a), temperature (b) and NDVI from 2003 to 2022 in Ganzi Prefecture

References 37

[1]	白子怡, 薛亮, 张翀, 2019. 基于土壤湿度与植被覆盖变化的黄土高原生态恢复项目适宜性评价[J]. 水土保持研究, 26(4): 292-298.
[2]	蔡怡亨, 李帅, 张强, 等, 2023. 1997—2021年四川省干旱时空变化特征分析[J]. 干旱气象, 41(2): 241-250. DOI
[3]	陈月庆, 周诗恬, 朱明, 等, 2021. 河南省植被覆盖变化与驱动因子的相关性分析[J]. 信阳师范学院学报: 自然科学版, 34(3): 401-407.
[4]	邓彪, 孙蕊, 邢开瑜, 等, 2024. 1961—2022年四川省区域性干旱过程识别及时空演变特征[J]. 高原山地气象研究, 44(1): 85-93.
[5]	范倩倩, 赵安周, 王金杰, 等, 2020. 1982—2015年黄土高原NDVI时空演变及其对气候变化的季节响应[J]. 生态学杂志, 39(5): 1 664-1 675
[6]	冯妍, 何彬方, 唐怀瓯, 等, 2012. 安徽省2000—2009年不同类型植被的变化及其与气温、降水的关系[J]. 生态学杂志, 31(11): 2 926-2 934
[7]	李建飞, 李小兵, 周义, 2019. 2000—2015年乌兰察布市生长季NDVI时空变化及其影响因素[J]. 干旱区研究, 36(5): 1 238-1 249
[8]	李妍钰, 2021. 基于生态安全格局的城乡空间优化策略研究:以四川省甘孜州为例[D]. 重庆: 西南交通大学.
[9]	刘志伟, 李胜男, 韦玮, 等, 2019. 近三十年青藏高原湿地变化及其驱动力研究进展[J]. 生态学杂志, 38(3): 856-862.
[10]	罗晓玲, 丁思聪, 杨梅, 等, 2022. 气候变化对石羊河流域生态环境的影响分析[J]. 沙漠与绿洲气象, 16(4): 49-56.
[11]	马森, 张代青, 张惠, 等, 2023. 塔里木河流域NDVI时空变化特征及其影响因素分析[J]. 高原山地气象研究, 43(4): 76-83.
[12]	马小红, 林菲, 原黎明, 等, 2023. 2000—2020年汾河流域植被覆盖度变化及其对生态工程的响应[J]. 中国沙漠, 43(3): 86-95. DOI
[13]	庞家泰, 段金亮, 张瑞, 等, 2021. 2000—2019年渭河流域植被覆盖度时空演变特征及气候响应[J]. 水土保持研究, 28(5): 230-237.
[14]	宋怡, 马明国, 2008. 基于GIMMS AVHRR NDVI数据的中国寒旱区植被动态及其与气候因子的关系[J]. 遥感学报, 12(3): 499-505.
[15]	孙蕊, 邓彪, 王顺久, 等, 2023. 2022年夏季四川省区域性高温和干旱过程监测评估[J]. 高原山地气象研究, 43(2): 72-80.
[16]	王青霞, 吕世华, 鲍艳, 等, 2014. 青藏高原不同时间尺度植被变化特征及其与气候因子的关系分析[J]. 高原气象, 33(2): 301-312. DOI
[17]	王姝, 张亮, 朱红秀, 等, 2024. 2022年7—8月干旱对川西高原植被长势的影响[J]. 高原山地气象研究, 44(1): 94-103.
[18]	王永财, 孙艳玲, 王中良, 2014. 1998—2011年海河流域植被覆盖变化及气候因子驱动分析[J]. 资源科学, 36(3): 594-602.
[19]	武鹏飞, 玉奴斯·唐阿塔尔, 代鹏超, 等, 2024. 2001—2020年新疆伊犁河谷植被主要生长季NDVI时空变化与水热因子关系研究[J]. 沙漠与绿洲气象, 18(4): 143-150.
[20]	杨瑞瑞, 易桂花, 张廷斌, 等, 2018. 2000—2015年若尔盖地区植被覆盖度变化及气候因子驱动分析[J]. 草业科学, 35(12): 2 822-2 835
[21]	尹振良, 冯起, 王凌阁, 等, 2022. 2000—2019年中国西北地区植被覆盖变化及其影响因子[J]. 中国沙漠, 42(4): 11-21. DOI
[22]	张成才, 娄洋, 李颖, 等, 2020. 基于像元二分模型的伏牛山地区植被覆盖度变化[J]. 水土保持研究, 27(3): 301-307.
[23]	张一然, 文小航, 罗斯琼, 等, 2022. 近20年若尔盖湿地植被覆盖变化与气候因子关系研究[J]. 高原气象, 41(2): 317-327. DOI
[24]	赵倩倩, 张京朋, 赵天保, 等, 2021. 2000年以来中国区域植被变化及其对气候变化的响应[J]. 高原气象, 40(2): 292-301. DOI
[25]	朱盛明, 1982. 相关系数稳定性分析方法及其应用[J]. 气象学报, 40(4): 113-117.
[26]	朱文会, 毛飞, 徐影, 等, 2019. 三江源区植被指数对气候变化的响应及预测分析[J]. 高原气象, 38(4): 693-704. DOI
[27]	CHEN Y, YANG J, XU Y, et al, 2022. Remote-sensing drought monitoring in Sichuan Province from 2001 to 2020 based on MODIS data[J]. Atmosphere, 13(12): 1-14.
[28]	GUO B, ZHOU Y, WANG S X, et al, 2014. The relationship between normalized difference vegetation index (NDVI) and climate factors in the semiarid region: A case study in Yalu Tsangpo River basin of Qinghai-Tibet Plateau[J]. Journal of Mountain Science, 11(4): 926-940.
[29]	HURST H E, 1951. Long-term storage capacity of reservoirs[J]. American Society of Civil Engineers Tans, 116(1): 770-799.
[30]	JING X, YAO W Q, WANG J H, et al, 2011. A study on the relationship between dynamic change of vegetation coverage and precipitation in Beijing's mountainous areas during the last 20 years[J]. Mathematical & Computer Modelling, 54(3/4): 1 079-1 085
[31]	KONG Z J, LING H B, DENG M J, et al, 2023. Past and projected future patterns of fractional vegetation coverage in China[J]. Science of the Total Environment, 902(1): 1-13.
[32]	NYANDWI E, VELDKAMP T, AMER S, 2016. Regional climate sensitivity of wetland environments in Rwanda: The need for a location-specific approach[J]. Regional Environmental Change, 16(6): 1 635-1 647
[33]	ROMSHOO S A, RASHID I, 2014. Assessing the impacts of changing land cover and climate on Hokersar wetland in Indian Himalayas[J]. Arabian Journal of Geosciences, 7(1): 143-160.
[34]	SHEN Z X, FU G, YU C Q, et al, 2014. Relationship between the growing season maximum enhanced vegetation index and climatic factors on the Tibetan Plateau[J]. Remote Sensing, 6(8): 6 765-6 789
[35]	STOW D A, HOPE A, MCGUIRE D, et al, 2004. Remote sensing of vegetation and landcover change in Arctic Tundra ecosystems[J]. Remote Sensing of Environment, 89(3): 281-308.
[36]	SUN J, QIN X J, 2016. Precipitation and temperature regulate the seasonal changes of NDVI across the Tibetan Plateau[J]. Environmental Geology and Water Sciences, 75(4): 1-9.
[37]	WOOKEY P A, AERTS R, BARDGETT R D, et al, 2010. Ecosystem feedbacks and cascade processes: Understanding their role in the responses of Arctic and alpine ecosystems to environmental change[J]. Global Change Biology, 15(5): 1 153-1 172