Spatio-temporal variation of grassland above-ground biomass and its response to climate change in the Three-River Source region

doi:10.11686/cyxb2025087

Abstract

Abstract:

The aim of this research was to better understand the spatio-temporal dynamics of grassland above-ground biomass （AGB） and its response to climate change in the Three-River Source region. We integrated ground-based observations from 15 ecological and meteorological stations in Qinghai Province with remotely sensed AGB estimates from 2003 to 2022. We then systematically analyzed the spatial distribution， interannual trends， and the response of AGB to variations in growing-season temperature and precipitation. It was found that： 1） According to station observations， the multi-year average AGB ranged from 606.4 to 7545.8 kg·ha^-1， with higher values at eastern and southern sites and lower values in the northwest. From 2003 to 2022， most stations exhibited an increasing trend in AGB， with the most significant increase observed at Nangqên station. 2） The remotely sensed results revealed a distinct spatial pattern of AGB characterized by higher values in the southeast and lower values in the northwest. High-biomass areas were mainly concentrated in the eastern part of the study region， whereas low-biomass areas were distributed in the north and southwest， with the lowest levels in the western alpine steppe. Over the past two decades， the regional AGB showed a weak increasing trend， with no significant change in more than 80% of the area， and a significantly increasing trend in 6.84% of the area-mainly in the east and south. 3） Partial correlation analysis showed that grassland AGB in the study area exhibited positive correlations with growing-season temperature and precipitation. Specifically， the correlation coefficient between AGB and growing-season precipitation （0.24） was higher than that between AGB and temperature （0.10）， indicating that precipitation was the primary factor driving the increase in AGB in this region. Areas with significantly positive correlations between grassland AGB and temperature were mainly distributed in the eastern parts of the study area， whereas regions with significantly positive correlations between AGB and precipitation were concentrated in the western and northern areas. These findings provide a scientific basis and decision-making support for grassland conservation， resource management， and climate adaptation strategies in the Three-River Source region.

Key words: above-ground biomass of grassland, climate change, spatio-temporal variation, Three-River Source region

Zhe WANG, Jing WANG, Yao-wen XIE, Hui-fang ZHAO, Rui-xiang XIAO, Cai-wen-qiu-zang ZONGSA. Spatio-temporal variation of grassland above-ground biomass and its response to climate change in the Three-River Source region[J]. Acta Prataculturae Sinica, 2026, 35(1): 1-12.

Figures/Tables 10

Fig.1 Distribution of coverage types in the Three-River Source region

Table 1 Classification of grassland types at the ecological monitoring stations in the Three-River Source region

草地类型 Grassland type	生态监测站点 Ecological monitoring stations
高寒草甸Alpine meadow	称多Chindu
	甘德Gande
	曲麻莱Qumarleb
	玛沁Maqin
	河南Henan
	玛多Maduo
	泽库Zeku
	囊谦Nangqên
	杂多Zadoi
	班玛Banma
	久治Jiuzhi
	达日Dari
高寒草原Alpine steppe	同德Tongde
高寒草原Alpine steppe	沱沱河Tuotuo River
温性草原Temperate steppe	兴海Xinghai

Table 2 Remote sensing monitoring model for above-ground biomass （AGB） of various grassland types

草地类型

Grassland type

草地地上生物量遥感监测模型

Remote sensing monitoring model for AGB

References 42

[1]	Adams J M， Faure H， Faure-Denard L， et al. Increases in terrestrial carbon storage from the last glacial maximum to the present. Nature， 1990， 348（6303）： 711-714.
[2]	Zhou W， Gang C C， Li J L， et al. Spatial-temporal dynamics of grassland coverage and its response to climate change in China during 1982-2010. Acta Geographica Sinica， 2014， 69（1）： 15-30.
	周伟，刚成诚，李建龙，等. 1982-2010年中国草地覆盖度的时空动态及其对气候变化的响应. 地理学报， 2014， 69（1）： 15-30.
[3]	Scurlock J M O， Asner G P， Gower S T. Worldwide historical estimates of leaf area index， 1932-2000. Office of Scientific & Technical Information Technical Reports， 2002， 77（4）： 554-556.
[4]	Yang S X， Feng Q S， Meng B P， et al. Temporal and spatial dynamics of alpine grassland biomass in the Three-River Headwater region. Pratacultural Science， 2018， 35（5）： 956-968.
	杨淑霞，冯琦胜，孟宝平，等. 三江源地区高寒草地地上生物量时空动态变化. 草业科学， 2018， 35（5）： 956-968.
[5]	Akiyama T， Kawamura K. Grassland degradation in China： Methods of monitoring， management and restoration. Grassland Science， 2007， 53（1）： 1-17.
[6]	Zhao F， Xu B， Yang X C， et al. Remote sensing estimates of grassland aboveground biomass based on MODIS net primary productivity （NPP）： A case study in the Xilingol grassland of Northern China. Remote Sensing， 2014， 6（6）： 5368-5386.
[7]	Xu D， Guo X L. Some insights on grassland health assessment based on remote sensing. Sensors， 2015， 15（2）： 3070-3089.
[8]	Xu B， Yang X C， Tao W G， et al. MODIS-based remote-sensing monitoring of the spatiotemporal patterns of China’s grassland vegetation growth. International Journal of Remote Sensing， 2013， 34（11）： 3867-3878.
[9]	Claverie M， Demarez V， Duchemin B， et al. Maize and sunflower biomass estimation in southwest France using high spatial and temporal resolution remote sensing data. Remote Sensing of Environment， 2012， 124： 844-857.
[10]	Lai Z M， Lai R W， Xue X， et al. Estimation of aboveground biomass of different degraded alpine grassland based on vegetation coverage and height. Journal of Desert Research， 2019， 39（5）： 127-134.
	赖炽敏，赖日文，薛娴，等. 基于植被盖度和高度的不同退化程度高寒草地地上生物量估算. 中国沙漠， 2019， 39（5）： 127-134.
[11]	Liu Y H， Cai Z L， Bao N S， et al. Research of grassland vegetation coverage and biomass estimation method based on major quadrat from UAV photogrammetry. Ecology and Environmental Sciences， 2018， 27（11）： 2023-2032.
	刘艳慧，蔡宗磊，包妮沙，等. 基于无人机大样方草地植被覆盖度及生物量估算方法研究. 生态环境学报， 2018， 27（11）： 2023-2032.
[12]	He H L， Ge R， Ren X L， et al. Reference carbon cycle dataset for typical Chinese forests via collocated observations and data assimilation. Scientific Data， 2021， 8： 42.
[13]	Zhao H F， Li X D， Zhang D， et al. Aboveground biomass in grasslands in Qinghai Province estimated from MODIS data and its influencing factors. Acta Prataculturae Sinica， 2020， 29（12）： 5-16.
	赵慧芳，李晓东，张东，等. 基于MODIS数据的青海省草地地上生物量估算及影响因素研究. 草业学报， 2020， 29（12）： 5-16.
[14]	Yao T D， Liu X D， Wang N L. The magnitude of climate change in the Tibetan Plateau region.Science Bulletin， 2000（1）： 98-106.
	姚檀栋，刘晓东，王宁练. 青藏高原地区的气候变化幅度问题. 科学通报， 2000（1）： 98-106.
[15]	Liu J Y， Xu X L， Shao Q Q. The spatial and temporal characteristics of grassland degradation in the Three-River Headwaters Region in Qinghai Province. Acta Geographica Sinica， 2008， 63（4）： 364-376.
	刘纪远，徐新良，邵全琴. 近30年来青海三江源地区草地退化的时空特征. 地理学报， 2008， 63（4）： 364-376.
[16]	Zhou W， Yang H， Huang L， et al. Grassland degradation remote sensing monitoring and driving factors quantitative assessment in China from 1982 to 2010. Ecological Indicators， 2017， 83： 303-313.
[17]	Rao P Z， Wang Y C， Wang F. Analysis on the NDVI change and influence factors of vegetation cover in the Three-River Headwaters region. Acta Agrestia Sinica， 2021， 29（3）： 572-582.
	饶品增，王义成，王芳. 三江源植被覆盖区NDVI变化及影响因素分析. 草地学报， 2021， 29（3）： 572-582.
[18]	Ning X C， Yang M X， Cao W Q， et al. Spatio-temporal pattern of vegetation coverage and its climate driving mechanism in the Three Rivers Headwaters region from 2000-2022. Bulletin of Surveying and Mapping， 2024（12）： 70-76.
	宁晓春，杨明新，曹文强，等. 2000-2022年三江源植被覆盖度时空变化格局及其气候驱动机制. 测绘通报， 2024（12）： 70-76.
[19]	Zuo C， Wang J B， Zhang X J， et al. Changes and influencing factors of vegetation net primary productivity in the Sanjiangyuan National Park. Acta Ecologica Sinica， 2022， 42（14）： 5559-5573.
	左婵，王军邦，张秀娟，等. 三江源国家公园植被净初级生产力变化趋势及影响因素. 生态学报， 2022， 42（14）： 5559-5573.
[20]	Zhao H F， Cao X Y. Vegetation cover changes and its climate driving in Three-River-Source National Park. Plateau Meteorology， 2022， 41（2）： 328-337.
	赵慧芳，曹晓云. 三江源国家公园植被覆盖时空变化及其气候驱动因素. 高原气象， 2022， 41（2）： 328-337.
[21]	Han B H， Zhou B R， Zhao H H， et al. Analysis of spatiotemporal variation of grassland vegetation and its impact factors over Three-River Resource region. Chinese Journal of Grassland， 2020， 42（3）： 77-85.
	韩炳宏，周秉荣，赵恒和，等. 三江源区草地植被时空变化及其影响因子分析. 中国草地学报， 2020， 42（3）： 77-85.
[22]	Liang T G， Yang S X， Feng Q S， et al. Multi-factor modeling of above-ground biomass in alpine grassland： A case study in the Three-River Headwaters region， China. Remote Sensing of Environment， 2016， 186： 164-172.
[23]	Tucker C J， Slayback D A， Pinzon J E， et al. Higher northern latitude normalized difference vegetation index and growing season trends from 1982 to 1999. International Journal of Biometeorology， 2001， 45（4）： 184-190.
[24]	Wang J B， Wang J W， Ye H， et al. An interpolated temperature and precipitation dataset at 1-km grid resolution in China （2000-2012）. China Scientific Data， 2017， 2（1）： 73-80， 205-212.
	王军邦，王居午，叶辉，等. 2000-2012年全国气温和降水1 km网格空间插值数据集. 中国科学数据， 2017， 2（1）： 73-80， 205-212.
[25]	Dong T F， Liu J G， Shang J L， et al. Assessing the impact of climate variability on cropland productivity in the Canadian Prairies using time series MODIS FAPAR. Remote Sensing， 2016， 8（4）： 281.
[26]	Li X， Liu X M， Sun B， et al. Machine learning-based assessment of grassland aboveground biomass in Gansu Province. Pratacultural Science， 2024， 41（2）： 297-307.
	李霞，刘兴明，孙斌，等. 基于机器学习算法的甘肃省草原地上生物量. 草业科学， 2024， 41（2）： 297-307.
[27]	Cao X Y， Zhu C X， Chen G Q， et al. Surface greenness change and topographic differentiation over Qaidam Basin from 2000 to 2021. Ecology and Environmental Sciences， 2022， 31（6）： 1080-1090.
	曹晓云，祝存兄，陈国茜，等. 2000-2021年柴达木盆地地表绿度变化及地形分异研究. 生态环境学报， 2022， 31（6）： 1080-1090.
[28]	Xu J H. Mathematical methods in contemporary geography （third edition）. Beijing： Higher Education Press， 2017： 133-138.
	徐建华. 现代地理学中的数学方法（第3版）. 北京：高等教育出版社， 2017： 133-138.
[29]	Liu J， Ji Y H， Zhou G S， et al. Temporal and spatial variations of net primary productivity （NPP） and its climate driving effect in the Qinghai-Tibet Plateau， China from 2000 to 2020. Chinese Journal of Applied Ecology， 2022， 33（6）： 1533-1538.
	刘杰，汲玉河，周广胜，等. 2000-2020年青藏高原植被净初级生产力时空变化及其气候驱动作用. 应用生态学报， 2022， 33（6）： 1533-1538.
[30]	Shao Q Q， Fan J W， Liu J Y， et al. Assessment on the effects of the first-stage ecological conservation and restoration project in Sanjiangyuan region. Acta Geographica Sinica， 2016， 71（1）： 3-20.
	邵全琴，樊江文，刘纪远，等. 三江源生态保护和建设一期工程生态成效评估. 地理学报， 2016， 71（1）： 3-20.
[31]	Zhang Y X， Fan J W， Cao W， et al. Spatial and temporal dynamics of grassland yield and its response to precipitation in the Three River Headwater region from 2006 to 2013. Acta Prataculturae Sinica， 2017， 26（10）： 10-19.
	张雅娴，樊江文，曹巍，等. 2006-2013年三江源草地产草量的时空动态变化及其对降水的响应. 草业学报， 2017， 26（10）： 10-19.
[32]	Liang D L， Tang H P. Analysis of vegetation changes and water temperature driving factors in two alpine grasslands on the Qinghai-Tibet Plateau. Acta Ecologica Sinica， 2022， 42（1）： 287-300.
	梁大林，唐海萍. 青藏高原两种高寒草地植被变化及其水温驱动因素分析. 生态学报， 2022， 42（1）： 287-300.
[33]	Gao X X， Dong S K， Li S， et al. Using the random forest model and validated MODIS with the field spectrometer measurement promote the accuracy of estimating aboveground biomass and coverage of alpine grasslands on the Qinghai-Tibetan Plateau. Ecological Indicators， 2020， 112： 106114.
[34]	Zeng N， Ren X L， He H L， et al. Estimating grassland aboveground biomass on the Tibetan Plateau using a random forest algorithm. Ecological Indicators， 2019， 102： 479-487.
[35]	Zhang X C， Jin X M. Vegetation dynamics and responses to climate change and anthropogenic activities in the Three-River Headwaters Region， China. Ecological Indicators， 2021， 131： 108223.
[36]	Chen F H， Xie T T， Yang Y J， et al. Discussion of the “warming and wetting” trend and its future variation in the drylands of Northwest China under global warming. Scientia Sinica Terrae， 2023， 53（6）： 1246-1262.
	陈发虎，谢亭亭，杨钰杰，等. 我国西北干旱区“暖湿化”问题及其未来趋势讨论. 中国科学：地球科学， 2023， 53（6）： 1246-1262.
[37]	Xu X L， Wang L， Li J， et al. Analysis of the grassland restoration trend and degradation situation in the “Three-River Headwaters” region since the implementation of the ecological project. Journal of Geo-information Science， 2017， 19（1）： 50-58.
	徐新良，王靓，李静，等. 三江源生态工程实施以来草地恢复态势及现状分析. 地球信息科学学报， 2017， 19（1）： 50-58.
[38]	Xie Q L， Yang X， Hao L N. Spatio-temporal variation of vegetation cover and its driving factors in Three-River Headwaters region during 2001-2020. Bulletin of Soil and Water Conservation， 2022， 42（5）： 202-212.
	谢绮丽，杨鑫，郝利娜. 2001-2020年三江源区植被覆盖时空变化特征及其影响因素. 水土保持通报， 2022， 42（5）： 202-212.
[39]	Gao S Q， Dong G T， Jiang X H， et al. Analysis of vegetation coverage changes and natural driving factors in the Three-River Headwaters region based on geographical detector. Research of Soil and Water Conservation， 2022， 29（4）： 336-343.
	高思琦，董国涛，蒋晓辉，等. 基于地理探测器的三江源植被变化及自然驱动因子分析. 水土保持研究， 2022， 29（4）： 336-343.
[40]	Dong G Y， Fan L， Fensholt R， et al. Asymmetric response of primary productivity to precipitation anomalies in Southwest China. Agricultural and Forest Meteorology， 2023， 331： 109350.
[41]	Zeng N， Ren X L， He H L， et al. Spatial-temporal dynamics of the grassland aboveground biomass and its association with climate changes in the Three River Source National Park. Acta Ecologica Sinica， 2023， 43（3）： 1175-1184.
	曾纳，任小丽，何洪林，等. 三江源国家公园草地地上生物量时空动态及其气候影响. 生态学报， 2023， 43（3）： 1175-1184.
[42]	Cong N， Shen M G， Yang W， et al. Varying responses of vegetation activity to climate changes on the Tibetan Plateau grassland. International Journal of Biometeorology， 2017， 61（8）： 1433-1444.