Research article |
|
|
|
|
Quantitative distinction of the relative actions of climate change and human activities on vegetation evolution in the Yellow River Basin of China during 1981-2019 |
LIU Yifeng1, GUO Bing1,2,3,4,5,*(), LU Miao6,*(), ZANG Wenqian4,7, YU Tao4,7,8, CHEN Donghua8 |
1School of Civil Architectural Engineering, Shandong University of Technology, Zibo 255000, China 2Key Laboratory of Urban Land Resources Monitoring and Simulation, Ministry of Natural Resources, Shenzhen 518000, China 3Key Laboratory of National Geographic Census and Monitoring, Ministry of Natural Resources, Wuhan 430072, China 4Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China 5State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China 6Key Laboratory of Agricultural Remote Sensing, Ministry of Agriculture and Rural Affairs/Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, China 7Zhongke Langfang Institute of Spatial Information Applications, Langfang 065000, China 8College of Computer and Information Engineering, Chuzhou University, Chuzhou 239000, China |
|
|
Abstract Under the combined influence of climate change and human activities, vegetation ecosystem has undergone profound changes. It can be seen that there are obvious differences in the evolution patterns and driving mechanisms of vegetation ecosystem in different historical periods. Therefore, it is urgent to identify and reveal the dominant factors and their contribution rates in the vegetation change cycle. Based on the data of climate elements (sunshine hours, precipitation and temperature), human activities (population intensity and GDP intensity) and other natural factors (altitude, slope and aspect), this study explored the spatial and temporal evolution patterns of vegetation NDVI in the Yellow River Basin of China from 1989 to 2019 through a residual method, a trend analysis, and a gravity center model, and quantitatively distinguished the relative actions of climate change and human activities on vegetation evolution based on Geodetector model. The results showed that the spatial distribution of vegetation NDVI in the Yellow River Basin showed a decreasing trend from southeast to northwest. During 1981-2019, the temporal variation of vegetation NDVI showed an overall increasing trend. The gravity centers of average vegetation NDVI during the study period was distributed in Zhenyuan County, Gansu Province, and the center moved northeastwards from 1981 to 2019. During 1981-2000 and 2001-2019, the proportion of vegetation restoration areas promoted by the combined action of climate change and human activities was the largest. During the study period (1981-2019), the dominant factors influencing vegetation NDVI shifted from natural factors to human activities. These results could provide decision support for the protection and restoration of vegetation ecosystem in the Yellow River Basin.
|
Received: 05 July 2022
Published: 31 January 2023
|
Corresponding Authors:
*GUO Bing (E-mail: guobingjl@163.com);LU Miao (E-mail: lumiao@caas.cn)
|
|
|
[1] |
Agatova A R, Nepop R K, Carling P A, et al. 2020. Last ice-dammed lake in the Kuray basin, Russian Altai: New results from multidisciplinary research. Earth-Science Reviews, 205: 103183, doi: 10.1016/j.earscirev.2020.103183.
doi: 10.1016/j.earscirev.2020.103183
|
|
|
[2] |
Amundson R, Heimsath A, Owen J, et al. 2015. Hillslope soils and vegetation. Geomorphology, 234: 122-132.
doi: 10.1016/j.geomorph.2014.12.031
|
|
|
[3] |
Berzaghi F, Wright I J, Kramer K, et al. 2020. Towards a new generation of trait-flexible vegetation models. Trends in Ecology & Evolution, 35(3): 191-205.
doi: 10.1016/j.tree.2019.11.006
|
|
|
[4] |
Chen C, Wang Y M, Li Y Y, et al. 2022. Vegetation changes and influencing factors in different climatic regions of Yellow River basin from 1982 to 2015. Journal of Yangtze River Scientific Research, 39(2): 56-62, 81. (in Chinese)
|
|
|
[5] |
Chen S T, Guo B, Zhang R, et al. 2021. Quantitatively determine the dominant driving factors of the spatial-temporal changes of vegetation NPP in the Hengduan Mountain area during 2000-2015. Journal of Mountain Science, 18(2): 427-445.
doi: 10.1007/s11629-020-6404-9
|
|
|
[6] |
Dai L, Zhang L, Wang K, et al. 2014. Vegetation changing trend and its affecting factors in Mongolian Plateau. Bulletin of Soil and Water Conservation, 34(5): 218-225. (in Chinese)
|
|
|
[7] |
Deng C N, Liu L S, Li H S, et al. 2021. A data-driven framework for spatiotemporal characteristics, complexity dynamics, and environmental risk evaluation of river water quality. Science of The Total Environment, 785: 147134, doi: 10.1016/j.scitotenv.2021.147134.
doi: 10.1016/j.scitotenv.2021.147134
|
|
|
[8] |
Duo A, Zhao W J, Qu X Y, et al. 2016. Spatio-temporal variation of vegetation coverage and its response to climate change in North China plain in the last 33 years. International Journal of Applied Earth Observation and Geoinformation, 53: 103-117.
doi: 10.1016/j.jag.2016.08.008
|
|
|
[9] |
Fang L L, Wang L C, Chen W X, et al. 2021. Identifying the impacts of natural and human factors on ecosystem service in the Yangtze and Yellow River Basins. Journal of Cleaner Production, 314: 127995, doi: 10.1016/j.jclepro.2021.127995.
doi: 10.1016/j.jclepro.2021.127995
|
|
|
[10] |
Ferreira V G, Yong B, Tourian M J, et al. 2020. Characterization of the hydro-geological regime of Yangtze River basin using remotely-sensed and modeled products. Science of The Total Environment, 718: 137354, doi: 10.1016/j.scitotenv.2020.137354.
doi: 10.1016/j.scitotenv.2020.137354
|
|
|
[11] |
Garioud A, Valero S, Giordano S, et al. 2021. Recurrent-based regression of Sentinel time series for continuous vegetation monitoring. Remote Sensing of Environment, 263: 112419, doi: 10.1016/j.rse.2021.112419.
doi: 10.1016/j.rse.2021.112419
|
|
|
[12] |
Garzanti E, Capaldi T, Tripaldi A, et al. 2022. Andean retroarc-basin dune fields and Pampean Sand Sea (Argentina): Provenance and drainage changes driven by tectonics and climate. Earth-Science Reviews, 231: 104077, doi: 10.1016/j.earscirev.2022.104077.
doi: 10.1016/j.earscirev.2022.104077
|
|
|
[13] |
Guo B, Zang W Q, Luo W. 2020a. Spatial-temporal shifts of ecological vulnerability of Karst Mountain ecosystem-impacts of global change and anthropogenic interference. Science of The Total Environment, 741: 140256, doi: 10.1016/j.scitotenv.2020.140256.
doi: 10.1016/j.scitotenv.2020.140256
|
|
|
[14] |
Guo B, Zang W Q, Yang F, et al. 2020b. Spatial and temporal change patterns of net primary productivity and its response to climate change in the Qinghai-Tibet Plateau of China from 2000 to 2015. Journal of Arid Land, 12(1): 1-17.
doi: 10.1007/s40333-019-0070-1
|
|
|
[15] |
Guo B, Wei C X, Yu Y, et al. 2022a. The dominant influencing factors of desertification changes in the source region of Yellow River: climate change or human activity? Science of The Total Environment, 813: 152512, doi: 10.1016/j.scitotenv.2021.152512.
doi: 10.1016/j.scitotenv.2021.152512
|
|
|
[16] |
Guo B, Yang F, Fan J F, et al. 2022b. The changes of spatiotemporal pattern of rocky desertification and its dominant driving factors in typical karst mountainous areas under the background of global change. Remote Sensing, 14(10): 2351, doi: 10.3390/rs14102351.
doi: 10.3390/rs14102351
|
|
|
[17] |
Han H H, Yang T B, Wang Y L. 2009. Land use and landscape pattern change in Guinan County of Qinghai Province in recent 30 years. Progress in Geography, 28(2): 207-215. (in Chinese)
|
|
|
[18] |
Hou Q Q, Pei T T, Yu X J, et al. 2022. The seasonal response of vegetation water use efficiency to temperature and precipitation in the Loess Plateau, China. Global Ecology and Conservation, 33: e01984, doi: 10.1016/j.gecco.2021.e01984.
doi: 10.1016/j.gecco.2021.e01984
|
|
|
[19] |
Jacques O, Pienitz R. 2022. Asbestos mining waste impacts on the sedimentological evolution of the Bécancour chain of lakes, southern Quebec (Canada). Science of The Total Environment, 807(3): 151079, doi: 10.1016/j.scitotenv.2021.151079.
doi: 10.1016/j.scitotenv.2021.151079
|
|
|
[20] |
Jiang H L, Sun X H, Yao Z Y, et al. 2021. Formation of lake dunes in an intramontane basin: A case study from Cuona Lake, on the Qinghai-Tibetan Plateau. Aeolian Research, 52: 100715, doi: 10.1016/j.aeolia.2021.100715.
doi: 10.1016/j.aeolia.2021.100715
|
|
|
[21] |
Kumar D, Scheiter S. 2019. Biome diversity in South Asia-How can we improve vegetation models to understand global change impact at regional level? Science of The Total Environment, 671: 1001-1016.
doi: 10.1016/j.scitotenv.2019.03.251
|
|
|
[22] |
Li J G, Vandenberghe J, Mountney N P, et al. 2020. Grain-size variability of point-bar deposits from a fine-grained dryland river terminus, Southern Altiplano, Bolivia. Sediment Geology, 403: 105663, doi: 10.1016/j.sedgeo.2020.105663.
doi: 10.1016/j.sedgeo.2020.105663
|
|
|
[23] |
Li Q Q, Cao Y P, Miao S L. 2022. Spatial and temporal changes of vegetation in the Yellow River Basin and its response to climatic factors. Acta Ecologica Sinica, 42(10): 4041-4054. (in Chinese)
|
|
|
[24] |
Liu H, Liu F, Zheng L. 2021. Effects of climate change and human activities on vegetation cover change in the Yellow River Basin. Journal of Soil and Water Conservation, 35(4): 143-151. (in Chinese)
|
|
|
[25] |
Lu J, Wang X F, Cao Y Q. 2022. Effects of climate and human activities on vegetation variation in Liaoning Province. Advances in Science and Technology of Water Resources, 42(4): 7-14, 38. (in Chinese)
|
|
|
[26] |
Lu Q, Liu G L, Yan B, et al. 2021. Variation of extreme precipitation events and their impacts on vegetation coverage in central Asia under climate warming. Research of Soil and Water Conservation, 28(4): 226-235. (in Chinese)
|
|
|
[27] |
Lu Q Q, Jiang T, Liu D L, et al. 2020. The response characteristics of NDVI with different vegetation cover types to temperature and precipitation in China. Ecology and Environment Sciences, 29(1): 23-34. (in Chinese)
|
|
|
[28] |
Meng X, Kooijman A M, Temme A J A M, et al. 2022. The current and future role of biota in soil-landscape evolution models. Earth-Science Reviews, 226: 103945, doi: 10.1016/j.earscirev.
doi: 10.1016/j.earscirev
|
|
|
[29] |
Morgan B E, Chipman J W, Bolger D T. 2020. Spatiotemporal analysis of vegetation cover change in a large Ephemeral River: multi-sensor fusion of Unmanned Aerial Vehicle (UAV) and Landsat imagery. Remote Sensing, 13(1): 12457-12468.
|
|
|
[30] |
Oakley N S, Cannon F, Boldt E, et al. 2018. Origins and variability of extreme precipitation in the Santa Ynez River Basin of Southern California. Journal of Hydrology: Regional Studies, 19: 164-176.
doi: 10.1016/j.ejrh.2018.09.001
|
|
|
[31] |
Qu S, Wang L C, Lin A W, et al. 2020. Distinguishing the impacts of climate change and anthropogenic factors on vegetation dynamics in the Yangtze River Basin, China. Ecological Indicators, 108: 105724, doi: 10.1016/j.ecolind.2019.105724.
doi: 10.1016/j.ecolind.2019.105724
|
|
|
[32] |
Sidi A M A, Wu Y P, Kumar A. 2021. Spatiotemporal analysis of vegetation cover changes around surface water based on NDVI: a case study in Korama basin, Southern Zinder, Niger. Applied Water Science, 11(1): 568-579.
|
|
|
[33] |
Stecca G, Fedrizzi D, Measures R, et al. 2022. Development of a numerical model for braided river morphology and vegetation evolution with application to the Lower Waitaki River (Aotearoa - New Zealand). Advances in Water Resources, 166: 104236, doi: 10.1016/j.advwatres.2022.104236.
doi: 10.1016/j.advwatres.2022.104236
|
|
|
[34] |
Tardy Y, Bustillo V, Roquin C, et al. 2005. The Amazon. Bio-geochemistry applied to river basin management Part I. Hydro-climatology, hydrograph separation, mass transfer balances, stable isotopes, and modelling. Applied Geochemistry, 20(9): 1746-1829.
doi: 10.1016/j.apgeochem.2005.06.001
|
|
|
[35] |
Twilley R R, Day J W, Bevington A E, et al. 2019. Ecogeomorphology of coastal deltaic floodplains and estuaries in an active delta: Insights from the Atchafalaya Coastal Basin. Eatuarine Coastal and Shelf Science, 227: 106341, doi: 10.1016/j.ecss.2019.106341.
doi: 10.1016/j.ecss.2019.106341
|
|
|
[36] |
Wang X L, Shi S H, Chen J Z X. 2022. Study on vegetation coverage change and driving factors in the Yellow River Basin. China Environmental Science, doi: 10.19674/j.cnki.issn1000-6923.20220712.002. (in Chinese)
doi: 10.19674/j.cnki.issn1000-6923.20220712.002
|
|
|
[37] |
Wang Z Y, Wang G Q, Huang G H. 2008. Modeling of state of vegetation and soil erosion over large areas. International Journal of Sediment Research, 23(3): 181-196.
doi: 10.1016/S1001-6279(08)60018-9
|
|
|
[38] |
Wu D H, Wu H, Zhao X. 2014. Evaluation of spatiotemporal variations of global fractional vegetation cover based on GIMMS NDVI data from 1982 to 2011. Remote Sensing, 6(5): 4217-4239, doi: 10.3390/rs6054217.
doi: 10.3390/rs6054217
|
|
|
[39] |
Wu H W, Guo B, Fan J F, et al. 2021. A novel remote sensing ecological vulnerability index on large scale: A case study of the China-Pakistan Economic Corridor region. Ecological Indicators, 129: 107955, doi: 10.1016/j.ecolind.2021.107955.
doi: 10.1016/j.ecolind.2021.107955
|
|
|
[40] |
Wu Q S, Zuo Q T, Han C H, et al. 2022. Integrated assessment of variation characteristics and driving forces in precipitation and temperature under climate change: A case study of Upper Yellow River basin, China. Atmospheric Research, 272: 106156, doi: 10.1016/j.atmosres.2022.106156.
doi: 10.1016/j.atmosres.2022.106156
|
|
|
[41] |
Xing W Q, Wang W G, Zou S, et al. 2018. Projection of future runoff change using climate elasticity method derived from Budyko framework in major basins across China. Global Planet Change, 162: 120-135.
doi: 10.1016/j.gloplacha.2018.01.006
|
|
|
[42] |
Xu S Y, Fu P, Quincey D C, et al. 2022. UAV-based geomorphological evolution of the Terminus Area of the Hailuogou Glacier, Southeastern Tibetan Plateau between 2017 and 2020. Geomorphology, 411: 108293, doi: 10.1016/j.geomorph.2022.108293.
doi: 10.1016/j.geomorph.2022.108293
|
|
|
[43] |
Yi L, Ren Z Y, Zhang C, et al. 2014. Relationship between vegetation cover change and climate and human activity in the Loess Plateau. Resource Science, 36(1): 166-174. (in Chinese)
|
|
|
[44] |
Yuan L Y, Gao Y C, Cheng F Y, et al. 2022. The influence of oil exploitation on the degradation of vegetation: A case study in the Yellow River Delta Nature Reserve, China. Environment Technology & Innovation, 28: 102579, doi: 10.1016/j.eti.2022.102579.
doi: 10.1016/j.eti.2022.102579
|
|
|
[45] |
Zhang C Y. 2022. Study on monitoring the spatio-temporal dynamics of wetland vegetation in the Yellow River delta by remote sensing. Journal of East China Normal University, doi: 10.27149/d.cnki.ghdsu.2022.001705. (in Chinese)
doi: 10.27149/d.cnki.ghdsu.2022.001705
|
|
|
[46] |
Zhang H Y, Zhan C S, Xia J, et al. 2022. The role of groundwater in the spatio-temporal variations of vegetation water use efficiency in the Ordos Plateau, China. Journal of Hydrology, 605: 127332, doi: 10.1016/j.jhydrol.2021.127332.
doi: 10.1016/j.jhydrol.2021.127332
|
|
|
[47] |
Zhang Y H, Wang L, Jiang J, et al. 2022. Application of soil quality index to determine the effects of different vegetation types on soil quality in the Yellow River Delta wetland. Ecological Indicators, 141: 109116, doi: 10.1016/j.ecolind.2022.109116.
doi: 10.1016/j.ecolind.2022.109116
|
|
|
[48] |
Zhao Y, Wang M. 2021. Empirical Analysis of Green GDP Accounting in China in the New Era. Tropical Agricultural Engineering, 45(5): 39-44. (in Chinese)
|
|
|
[49] |
Zhong L, Ma Y M, Salama M S, et al. 2010. Assessment of vegetation dynamics and their response to variations in precipitation and temperature in the Tibetan Plateau. Climatic Change, 103: 519-535.
doi: 10.1007/s10584-009-9787-8
|
|
|
[50] |
Zhu Q, Zhang H. 2022. Groundwater drought characteristics and its influencing factors with corresponding quantitative contribution over the two largest catchments in China. Journal of Hydrology, 609: 127759, doi: 10.1016/j.jhydrol.2022.127759.
doi: 10.1016/j.jhydrol.2022.127759
|
|
|
|
Viewed |
|
|
|
Full text
|
|
|
|
|
Abstract
|
|
|
|
|
Cited |
|
|
|
|
|
Shared |
|
|
|
|
|
Discussed |
|
|
|
|