基于MGWR的长江流域植被演化及其影响因素

Abstract
Figure/Table
References
Related Citation (15)

Download: PDF (4636 KB) HTML (0 KB)
Export: BibTeX | EndNote (RIS)

Abstract The Yangtze River Basin is an important industrial and agricultural production area and ecological security barrier in China. In-depth research on the spatial-temporal evolution of vegetation in the basin and its influencing factors and scale effects is of great significance for understanding the regional vegetation growth under changing environments and grasping the ecological environment quality. However, previous studies have not fully considered the difference between/in scale effects of different influencing factors of vegetation spatial differentiation pattern. Therefore, this study took the vegetation coverage of the Yangtze River Basin from 2000 to 2022 as the dependent variable and used terrain, meteorology, and socio-economic factors as independent variables. With the help of multiscale geographic weighted regression (MGWR) which was good at handling scale differences, the spatial-temporal changes of vegetation and its influencing factors were explored. The results showed that:1) Vegetation coverage in the Yangtze River Basin fluctuated between 2000 and 2022, with an overall improvement trend and a growth rate of 0.245% per year. The spatial distribution pattern of vegetation in the basin showed a low-east and high-central pattern, indicating significant spatial differentiation. However, there was a risk of degradation in most areas of the basin in the future. 2) Different influencing factors had obvious spatial differences in their effects on vegetation in the Yangtze River, among which slope, elevation, temperature, and relative humidity were the main driving factors. It was noteworthy that anthropogenic factors had relatively small influence on vegetation. 3) The response scales of vegetation to various influencing factors were significantly different, with natural factors such as terrain and climate having smaller impact scales (only 43) than social factors (over 870).

Key words： Yangtze River Basin fractional vegetation cover spatial-temporal changes influencing factors multiscale geographically weighted regression scale effect

Received: 16 June 2023

PACS:

X171.1

	Service

	E-mail this article
	Add to my bookshelf
	Add to citation manager
	E-mail Alert
	RSS
	Articles by authors
	LI Yong-jun
	CHEN Qing-chang
	FANG He
	LI Jian

Cite this article:

LI Yong-jun,CHEN Qing-chang,FANG He等. Vegetation evolution and its influencing factors in the Yangtze River Basin based on multi-scale geographical weighted regression[J]. CHINA ENVIRONMENTAL SCIENCECE, 2024, 44(1): 352-362.

URL:

http://www.zghjkx.com.cn/EN/ OR http://www.zghjkx.com.cn/EN/Y2024/V44/I1/352

[1]	Yang L, Guan Q, Lin J, et al.Evolution of NDVI secular trends and responses to climate change:A perspective from nonlinearity and nonstationarity characteristics [J].Remote Sensing of Environment, 2021,254:112247.
[2]	Chen Y, Feng X, Tian H, et al.Accelerated increase in vegetation carbon sequestration in China after 2010:A turning point resulting from climate and human interaction [J].Global Change Biology, 2021,27(22):5848-5864.
[3]	Ding Y, Zheng X, Zhao K, et al.Quantifying the impact of NDVIsoil determination methods and NDVIsoil variability on the estimation of fractional vegetation cover in northeast China [J].Remote Sensing, 2016,8,29.
[4]	罗赵慧,朱璐平,张晓君,等.粤港澳大湾区植被CUE变化及与气候变化的关系[J].中国环境科学, 2021,41(12):5793-5805.Luo Z H, Zhu L P, Zhang X J, et al.Spatiotemporal variation of CUE and its correlation with climate change in Guangdong-Hong Kong-Macao Greater Bay Area [J].China Environmental Science, 2021,41(12):5793-5805.
[5]	赵安周,刘宪锋,朱秀芳,等.2000~2014年黄土高原植被覆盖时空变化特征及其归因[J].中国环境科学, 2016,36(5):1568-1578.Zhao A Z, Liu X F, Zhu X F, et al.Spatiotemporal analyses and associated driving forces of vegetation coverage change in the Loess Plateau [J].China Environmental Science, 2016,36(5):1568-1578.
[6]	Huang M, Piao S, Janssens I A, et al.Velocity of change in vegetation productivity over northern high latitudes [J].Nature Ecology & Evolution, 2017,1(11):1649-1654.
[7]	Zhang D, Geng X, Chen W, et al.Inconsistency of global vegetation dynamics driven by climate change:evidences from spatial regression [J].Remote Sensing, 2021,13(17):3442.
[8]	Zuo D, Han Y, Xu Z, et al.Time-lag effects of climatic change and drought on vegetation dynamics in an alpine river basin of the Tibet Plateau, China [J].Journal of Hydrology 2021,600:126532.
[9]	Zhang W, Randall M, Jensen M B, et al.Socio-economic and climatic changes lead to contrasting global urban vegetation trends [J].Global Environmental Change, 2021,71:102385.
[10]	Smith T, Traxl D, Boer N.Empirical evidence for recent global shifts in vegetation resilience [J].Nature Climate Change, 2022,12(5):477-484.
[11]	徐勇,郑志威,郭振东,等.2000~2020年长江流域植被NDVI动态变化及影响因素探测[J].环境科学, 2022,43(7):3730-3740.Xu Y, Zheng Z W, Guo Z D, et al.Dynamic variation in vegetation cover and its influencing factor detection in the Yangtze River Basin from 2000to 2020[J].Environmental Science, 2022,43(7):3730-3740.
[12]	袁喆,喻志强,冯兆洋,等.长江流域陆地生态系统NDVI时空变化特征及其对水热条件的响应[J].长江科学院院报, 2019,36(11):7-15.Yuan Z, Yu Z Q, Feng Z Y, et al.Spatiotemporal variations of NDVI in terrestrial ecosystem in Yangtze River Basin and response to hydrothermal condition [J].Journal of Yangtze River Scientific Research Institute, 2019,36(11):7-15.
[13]	邓元杰,姚顺波,侯孟阳,等.长江流域中上游植被NDVI时空变化及其地形分异效应[J].长江流域资源与环境, 2020,29(1):66-78.Deng Y J, Yao S B, Hou M Y, et al.Temporal and spatial variation of vegetation NDVI and its topographic differentiation effectinthe middle and upper reaches of the Yangtze River basin [J].Resources and Environment in the Yangtze Basin, 2020,29(1):66-78.
[14]	韩继冲,喻舒琳,杨青林,等.1999~2015年长江流域上游植被覆盖特征及其对气候和地形的响应[J].长江科学院院报, 2019,36(9):51-57.Han J C, Yu S L, Yang Q L, et al.Responses of vegetation coverage in the upper reaches of the Yangtze River Basin to climate and topography from 1999 to 2015[J].Journal of Yangtze River Scientific Research Institute, 2019,36(9):51-57.
[15]	Brunsdon C, Fotheringham A, Charlton M.Geographically weighted regression:a method for exploring spatial nonstationarity [J].Geographical Analysis, 1996,28:281-298.
[16]	Wang X, Shi S, Zhao X, et al.Detecting sspatially non-stationary between vegetation and related factors in the Yellow River Basin from 1986to 2021 using multiscale geographically weighted regression based on Landsat [J].Remote Sensing, 2022,14(24),6276.
[17]	牛陆,张正峰,彭中,等.中国地表城市热岛驱动因素及其空间异质性[J].中国环境科学, 2022,42(2):945-953.Niu L, Zhang Z F, Peng Z, et al.China's surface urban heat island drivers and its spatial heterogeneity [J].China Environmental Science, 2022,42(2):945-953.
[18]	Jiao K, Gao J, Liu Z.Precipitation drives the NDVI distribution on the Tibetan Plateau while high warming rates may intensify its ecological droughts [J].Remote Sensing, 2021,13(7):1305.
[19]	Birhanu A, Adgo E, Frankl A, et al.Modelling spatial relationships between land cover change and its drivers in the Afro-alpine belt of Mount Guna (Ethiopia) [J].Land Degradation & Development, 2021,32(14):3946-3961.
[20]	Niu L, Zhang Z, Peng Z, et al.Identifying surface urban heat island drivers and their spatial heterogeneity in China's 281cities:an empirical study based on multiscale geographically weighted regression [J].Remote Sensing, 2021,13(21):4428.
[21]	Fotheringham A S, Yang W, Kang W.Multiscale Geographically Weighted Regression (MGWR) [J].Annals of the American Association of Geographers, 2017,107(6):1247-1265.
[22]	周志凌,程先富.基于MGWR模型的中国城市PM2.5影响因素空间异质性[J].中国环境科学, 2021,41(6):2552-2561.Zhou Z L, Cheng X F.Spatial heterogeneity of influencing factors of PM2.5 in Chinese cities based on MGWR model [J].China Environmental Science, 2021,41(6):2552-2561.
[23]	李苗苗,吴炳方,颜长珍,等.密云水库上游植被覆盖度的遥感估算[J].资源科学, 2004,26(4):153-159.Li M M, Wu B F, Yan Z Z, et al.Estimation of vegetation fraction in the upper basin of Miyun Reservoir by remote sensing [J].Resources Science, 2004,26(4):153-159.
[24]	王晓蕾,石守海.基于GEE的黄河流域植被时空变化及其地形效应研究[J].地球信息科学学报, 2022,24(6):1087-1098.Wang X L, Shi S H.Study on temporal and spatial changes of vegetation and topographic effects in the Yellow River basin based on GEE [J].Journal of Geo-Information Science, 2022,24(6):1087-1098.
[25]	何宝忠,丁建丽,张喆,等.新疆植被覆盖度趋势演变实验性分析[J].地理学报, 2016,71(11):1948-1966.He B Z, Ding J L, Zhang Z, et al.Experimental analysis of spatial and temporal dynamics of fractional vegetation cover in Xinjiang [J].Acta Geographica Sinica, 2016,71(11):1948-1966.
[26]	Yue S, Pilon P, Cavadias G.Power of the Mann-Kendall and Spearman's Rho tests for detecting monotonic trends in hydrological series [J].Journal of Hydrology, 2002,259:254-271.
[27]	Jiang W, Yuan L, Wang W, et al.Spatio-temporal analysis of vegetation variation in the Yellow River Basin [J].Ecological Indicators, 2015,51:117-126.
[28]	秦格霞,芦倩,孟治元,等.1982~2015年中国北方草地NDVI时空动态及其对气候变化的响应[J].水土保持研究, 2021,28(1):101-108,17.Qin G X, Lu Q, Meng Z Y, et al.Spatial-temporal dynamics of grassland NDVI and its response to climate change in northern China from 1982 to 2015[J].Research of Soil and Water Conservation, 2021, 28(1):101-108,117
[29]	王晓蕾,石守海,陈江朝霞.黄河流域植被覆盖度变化及驱动因素[J].中国环境科学, 2022,42(11):5358-5368.Wang X L, Shi S H, Chen H Z X.Change and driving factors of vegetation coverage in the Yellow River Basin [J].China Environmental Science, 2022,42(11):5358-5368.
[30]	Hurst H E.Long-term storage capacity of reservoirs [J].Transactions of the American Society of Civil Engineers, 1951,116:770-799.
[31]	王建邦,赵军,李传华,等.2001~2015年中国植被覆盖人为影响的时空格局[J].地理学报, 2019,74(3):504-519.Wang J B, Zhao J, Li C H, et al.The spatial-temporal patterns of the impact of human activities on vegetation coverage in China from 2001 to 2015[J].Acta Geographica Sinica, 2019,74(3):504-519.
[32]	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.Liu X F, Pan Y Z, Zhu X F, et al.Spatiotemporal variation of vegetation coverage in Qinling-Daba Mountains in relation to environmental factors [J].Journal of Geographical Sciences, 2016, 26(1):45-58.
[33]	Fotheringham A S, Yu H, Wolf L J, et al.On the notion of ‘bandwidth’ in geographically weighted regression models of spatially varying processes [J].International Journal of Geographical Information Science, 2022,36:1-18.
[34]	Wu C, Ren F, Hu W, et al.Multiscale geographically and temporally weighted regression:exploring the spatiotemporal determinants of housing prices [J].International Journal of Geographical Information Science, 2018,33(3):489-511.
[35]	沈体雁,于瀚辰,周麟,等.北京市二手住宅价格影响机制——基于多尺度地理加权回归模型(MGWR)的研究[J].经济地理, 2020, 40(3):75-83.Shen T Y, Yu H C, Zhou L, et al.On hedonic price of second-hand houses in Beijing based on multi-scale geographically weighted regression:scale law of spatial heterogeneity [J].Economic Geography, 2020,40(3):75-83.
[36]	Li Z, Fotheringham A S.Computational improvements to multi-scale geographically weighted regression [J].International Journal of Geographical Information Science, 2020,34(7):1378-1397.
[37]	叶许春,杨晓霞,刘福红,等.长江流域陆地植被总初级生产力时空变化特征及其气候驱动因子[J].生态学报, 2021,41(17):6949-6959.Ye X C, Yang X X, Liu H F, et al.Spatio-temporal variations of land vegetation gross primary production in the Yangtze river basin and correlation with meteorological factors [J].Acta Ecologica Sinica, 2021,41(17):6949-6959.
[38]	Qu S, Wang L, Lin A, et al.Distinguishing the impacts of climate change and anthropogenic factors on vegetation dynamics in the Yangtze River Basin, China [J].Ecological Indicators, 2020,108:105724.
[39]	Wang Q, Ju Q, Wang Y, et al.Regional patterns of vegetation dynamics and their sensitivity to climate variability in the Yangtze River Basin [J].Remote Sensing, 2022,14(21):5623.
[40]	Chen L, Zhang H, Zhang X, et al.Vegetation changes in coal mining areas:naturally or anthropogenically driven? [J].Catena, 2022,208:105712.
[41]	Xiong Y, Li Y, Xiong S, et al.Multi-scale spatial correlation between vegetation index and terrain attributes in a small watershed of the upper Minjiang River [J].Ecological Indicators, 2021,126:107610.
[42]	Qu S, Wang L, Lin A, et al.What drives the vegetation restoration in Yangtze River basin, China:climate change or anthropogenic factors? [J].Ecological Indicators, 2018,90:438-450.
[43]	Jiang H, Xu X, Guan M, et al.Determining the contributions of climate change and human activities to vegetation dynamics in agro-pastural transitional zone of northern China from 2000 to 2015[J].Science of The Total Environment, 2020,718:134871.
[44]	Oliva M, Pereira P, Antoniades D.The environmental consequences of permafrost degradation in a changing climate [J].Science of The Total Environment, 2018,616-617:435-7.
[45]	Shi S, Yu J, Wang F, et al.Quantitative contributions of climate change and human activities to vegetation changes over multiple time scales on the Loess Plateau [J].Science of The Total Environment, 2021,755:142419.
[46]	Wu T, Zhou L, Jiang G, et al.Modelling spatial heterogeneity in the effects of natural and socioeconomic factors, and their interactions, on atmospheric PM2.5 concentrations in China from 2000~2015[J].Remote Sensing, 2021,13(11):2152.
[47]	Bi S, Chen M, Dai F.The impact of urban green space morphology on PM2.5pollution in Wuhan, China:a novel multiscale spatiotemporal analytical framework [J].Building and Environment, 2022,221:109340.