Quantitatively determine the dominant driving factors of the spatial—temporal changes of vegetation NPP in the Hengduan Mountain area during 2000–2015

Abstract

The Hengduan mountain area, located in the upper reaches of the Yangtze River of China, is an important ecological barrier that significantly impacts the climate and ecological environment of the surrounding region and western China as a whole. This paper introduces the gravity center model used to analyze the spatial-temporal variation patterns of vegetation Net Primary Productivity (NPP) from 2000 to 2015, which were determined by the use of MOD17A3 NPP products. Additionally, the dominant driving factors of the spatial—temporal changes of vegetation NPP of the Hengduan Mountain area were quantitatively determined with a geographical detector over 2000–2015. The results revealed that: (1) From 2000 to 2015, there was an increasing trend of vegetation NPP in the Hengduan mountain area. Throughout the whole study region, the vegetation NPP with a mean value of 611.37 gC·m−2·a−1 indicated a decreasing trend from southeast to northwest in terms of spatial distribution. (2) The gravity centers of vegetation NPP in 2000–2015 were mainly concentrated in Zhongdian County. During the study period, the gravity center of vegetation NPP moved northward, which indicated that the increment and increasing rate of vegetation NPP in the northern parts were greater than that of the southern areas. (3) The vegetation NPP showed a moderately positive correlation with temperature, accumulated temperature (>10°C), and sunshine, while there was an overall negative relationship between NPP and precipitation. (4) The dominant factors and interactive dominant factors changed in different sub-regions over different segments of the study period. The dominant factors of most sub-regions in Hengduan mountain were natural factors, and the climate change factors played an increasingly greater role over the 16 years of the study period.

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References

  1. Bonan GB (1995) Land-atmosphere CO2 exchange simulated by a land surface process model coupled to an atmospheric general circulation model. J Geophys Res-Earth 100(D2):2817–2813. https://doi.org/10.1029/94JD02961

    Article  Google Scholar 

  2. Buyantuyev A, Wu J (2009) Urbanization alters spatiotemporal patterns of ecosystem primary production: A case study of the Phoenix metropolitan region, USA. J Arid Environ 73(4–5): 512–520. https://doi.org/10.1016/j.jaridenv.2008.12.015

    Article  Google Scholar 

  3. Chen YY, Chen W, Wu W, et al. (2015) Temporal and spatial variation of vegetation NPP and its response to climate factors in southern China. J Yangzhou Univ 36(03): 104–110. (In Chinese) https://doi.org/10.16872/j.cnki.1671-4652.2015.03.021

    Google Scholar 

  4. Chi DK, Wang H, Li XB, et al. (2018) Assessing the effects of grazing on variations of vegetation NPP in the Xilingol Grassland, China, using a grazing pressure index. Ecol Indic 88(05): 372–383. https://doi.org/10.1016/j.ecolind.2018.01.051

    Article  Google Scholar 

  5. Gao Y, Zhou X, Wang Q, et al. (2013) Vegetation net primary productivity and its response to climate change during 2001–2008 in the Tibetan Plateau. Sci Total Environ 444: 356–362. https://doi.org/10.1016/j.scitotenv.2012.12.014

    Article  Google Scholar 

  6. Guo B, Wen Y (2020) An optimal monitoring model of desertification in Naiman banner based on feature space utilizing Landsat8 OLI image. IEEE Access 8: 4761–4768. https://doi.org/10.1109/ACCESS.2019.2962909

    Article  Google Scholar 

  7. Guo B, Yang F, Han BM, et al. (2020a) 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. J Arid Land 12(1): 1–17. https://doi.org/10.1007/s40333-019-0070-1

    Article  Google Scholar 

  8. Guo B, Yang G, Zhang F, et al. (2018) Dynamic monitoring of soil erosion in the upper Minjiang catchment using an improved soil loss equation based on remote sensing and geographic information system. Land Degrad Dev 29(3): 521–533. https://doi.org/10.1002/ldr.2882

    Article  Google Scholar 

  9. Guo B, Zang WQ, Han BM, et al.(2020b) Dynamic monitoring of desertification in Naiman Banner based on feature space models with typical surface parameters derived from Landsat images. Land Degrad Dev 31: 1573–1592. https://doi.org/10.1002/ldr.3533

    Article  Google Scholar 

  10. Guo B, Zang W, Luo W, et al. (2020c) Detection model of soil salinization information in the Yellow River Delta based on feature space models with typical surface parameters derived from Landsat8 OLI image. Geomat Nat Haz Risk 11(1): 288–300. https://doi.org/10.1080/2150704X.2019.1610981

    Article  Google Scholar 

  11. Guo B, Zang W, Luo W (2020d) Spatial-temporal shifts of ecological vulnerability of Karst Mountain ecosystem-impacts of climate change and anthropogenic interference. Sci Total Environ 741: 140256. https://doi.org/1016/j.scitotenv.2020.140256

    Article  Google Scholar 

  12. Guo B, Zang WQ, Yang X, et al.(2020e) Improved evaluation method of the soil wind erosion intensity based on the cloud-AHP model under the stress of global climate change. Sci Total Environ 746: 141271.https://doi.org/10.1016/j.scitotenv.2020.141271

    Article  Google Scholar 

  13. Guo B, Zang W, Zhang R (2020f) Soil salizanation information in the Yellow River Delta based on feature surface models using Landsat 8 OLI Data. IEEE Acess 8(1): 94394–94403. https://doi.org/10.1080/19475705.2020.1721573

    Article  Google Scholar 

  14. Guo LF, Lai Q, Yi BL, et al. (2017) Spatiotemporal changes of net primary productivity of River Wetland and its driving factors in Hulun Buir Sandy land in 2000–2014. Res Soil Water Conserv 24(6): 267–272. (In Chinese) https://doi.org/10.13869/j.cnki.rswc.20170912.001

    Google Scholar 

  15. Han WY, Zhang C, Zeng Y, et al. (2018) Spatio-temporal changes and driving factors in the net primary productivity of Lhasa River Basin from 2000 to 2015. Acta Ecol Sinica 38(24): 8787–8798. (In Chinese) https://doi.org/10.5846/stxb201806021233

    Google Scholar 

  16. IGBP Terrestrial Carbon Working Group (1998) The terrestrial carbon cycle: implications for the Kyoto Protocol. Science 280(5368): 1393–1394. https://doi.org/10.1126/science.280.5368.1393

    Article  Google Scholar 

  17. Jiang YL, Guo J, Peng Q, et al. (2020) The effects of climate factors and human activities on net primary productivity in Xinjiang. Int J Biometeorol 115: 1–13.https://doi.org/10.1016/j.ecolind.2020.106392

    Google Scholar 

  18. Kaenchan P, Guinée J, Gheewala SH (2018) Assessment of ecosyste m productivity damage due to land use. Sci Total Environ 621: 1320–1329. https://doi.org/10.1016/j.scitotenv.2017.10.096

    Article  Google Scholar 

  19. Li DK, Fan JZ, Wang J (2011) Variation characteristics of vegetation net primary productivity in Shaanxi Province based on MO17A3. J Ecol 30(12): 2776–2782. (In Chinese) https://doi.org/10.13292/j.1000-4890.2011.0433

    Google Scholar 

  20. Li J, Wang ZL, Lai CG (2018) Response of net primary production to land use and land cover change in mainland China since the late 1980s. Sci Total Environ 639: 237–247. https://doi.org/10.1016/j.scitotenv.2018.05.155

    Article  Google Scholar 

  21. Liu L, Li YC, Zhu CX, et al. (2013) The spatio-temporal variation characteristics of vegetation NPP in Chongqing and its relation with climatic factors from 2001 to 2010. Remote Sens Inform 28(5): 99–108. (In Chinese) https://doi.org/10.3969/j.issn.1000-3177.2013.05.019

    Google Scholar 

  22. Li CH, Zhao J (2013) Spatiotemporal variations of vegetation NPP and related driving factors in Shiyang River basin of Northwest China in 2000–2010. J Ecol 32(3): 712–718. (In Chinese) https://doi.org/10.13292/j.1000-4890.2013.0111

    Google Scholar 

  23. Li XR, Gao H, Han LP, et al. (2017) Spatio-temporal variations in vegetation NPP and the driving factors in Taihang Mountain Area. Chin J Eco-Agric 25(4): 498–508. https://doi.org/10.13930/j.cnki.cjea.160780

    Google Scholar 

  24. Liu G, Sun R, Xiao ZQ, et al. (2017) Analysis of spatial and temporal variation of net primary productivity and climate controls in China from 2001 to 2014. Acta Ecol Sinica 37(15): 4936–4945. (In Chinese) https://doi.org/10.5846/stxb201604290822

    Google Scholar 

  25. Li X, Yuan JG, Meng D (2018) Spatiotemporal distribution of vegetation net primary productivity and its relationship with climate factors in Hebei Province from 2005 to 2014. Res Soil Water Conserv 25(6): 109–114, 120. (In Chinese) https://doi.org/10.13869/j.cnki.rswc.2018.06.016

    Google Scholar 

  26. Liu D, Zhang J, Li HY, et al. (2018) Impact factors of soil phosphorus loss in watershed based on geographic detectors. Acta Sci Circumstantiae 38(12): 4814–4822. https://doi.org/10.13671/j.hjkxxb.2018.0313.

    Google Scholar 

  27. Luo Y, Zhang SL (2019) Characteristics and spatial-temporal distribution of vegetation NPP in Shandong Province Analysis of driving factors. Guangxi Botanicals 39(5): 690–700. (In Chinese)https://doi.org/10.11931/guihaia.gxzw201812018

    Google Scholar 

  28. Liu SJ, Li WG, Chen XM, et al. (2019) Study on the spatial-temporal variation characteristics of vegatation primary productivity in Hainan province. Ecol Science 38(05): 52–57. (In Chinese) https://doi.org/10.14108/j.cnki.1008-8873.2019.05.008

    Google Scholar 

  29. Nemani RR, Keeling CD, Hashimoto H (2003) Climate-driven increases in global terrestrial net primary production from 1982 to 1999. Science 5625: 1560–1563.https://doi.org/10.1126/science.1082750

    Article  Google Scholar 

  30. Potter CS, Randerson JT, Field CB, et al. (1993) Terrestrial ecosystem production: a process model based on global satellite and surface data. Global Biogeochem Cy 7(4): 811–841. https://doi.org/1029/93GB02725.

    Article  Google Scholar 

  31. Qiao K, Guo W (2016) Estimation net primary productivity of alpine grassland in Qinghai Lake Basin. Bull Soil Water Conserv 36(6): 204. (In Chinese) https://doi.org/10.13961/j.cnki.stbctb.2016.06.035

    Google Scholar 

  32. Qi, JG, Tao SQ, Steven G (2019) Changes in land use/land cover and net primary productivity in the transboundary Ili-Balkhash basin of Central Asia, 1995–2015. Environ Research Commun 1:011006. https://doi.org/10.1088/2515-7620/ab5e1f

    Article  Google Scholar 

  33. Raich JW, Rastetter EB, Melillo JM, et al.(1991) Potential net primary productivity in south America: application of a global model. Ecol Appl 1:399–429. https://doi.org/10.2307/1941899

    Article  Google Scholar 

  34. Running F, Inge S, Michael SR (2006) Evaluation of satellite based primary production modeling in the semi-arid Sahel. Romote Sens Environ 105: 173–188. https://doi.org/10.1016/j.rse.2006.06.011

    Article  Google Scholar 

  35. Teng MJ, Zeng LX, Hu WJ, et al. (2020) The impacts of climate changes and human activities on net primary productivity vary across an ecotone zone in Northwest China. Sci Total Environ 714: 136691.https://doi.org/10.1016/j.scitotenv.2020.136691

    Article  Google Scholar 

  36. Tian ZH, Zhang DD, He XH (2019) Spatiotemporal variations in vegetation net primary productivity and their driving factors in Yellow River Basin from 2000 to 2015.Research Soil Water Conserv 26(2): 255–262. (In Chinese) https://doi.org/10.13869/j.cnki.rswc.2019.02.037

    Google Scholar 

  37. Wang J, Shi RH, Zhang L (2016) Estimation of net primary productivity in regions of northeast China. Remote Sens Inform 31(05): 47–52. (In Chinese) https://doi.org/10.3969/j.issn.1000-3177.2016.05.008

    Google Scholar 

  38. Wang J, Wang KL, Zhang MY, et al. (2015) Tempo-spatial variations of net primary productivity in hilly terrain of southern China. Acta Ecol Sinica 35(11): 3722–3732. (In Chinese) https://doi.org/10.5846/stxb201308162091

    Google Scholar 

  39. Wang JF, Xu CD (2017) Geodetector: principles and prospects. Acta Geogr Sinica 72(1): 116–134. (In Chinese) https://doi.org/10.11821/dlxb201701010

    Google Scholar 

  40. Wang JW, Zhang D, Nan Y, et al. (2019) Spatial patterns of net primary productivity and its driving forces: a multiscale analysis in the transnational area of the Tumen River. Front Earth Sci-Prc 14(1): 124–139. https://doi.org/10.1007/s11707-019-0759-7

    Article  Google Scholar 

  41. Wang Q, Zhang TB, Yi GH, et al.(2017) Tempo-spatial variations and driving factors analysis of net primary productivity in the Hengduan mountain area from 2004 to 2014. Acta Ecol Sinica 37(9): 3084–3095. (In Chinese) https://doi.org/10.5846/stxb201602030248

    Google Scholar 

  42. Wang YB, Zhao YH, Han L, et al.(2018) Spatiotemporal variation of vegetation net primary productivity and its driving factors from 2000 to 2015 in Qinling-Daba Mountains, China. J Appl Ecol 29(7): 2373–2381. (In Chinese) https://doi.org/10.13287/j.1001-9332.201807.010

    Google Scholar 

  43. Wang YW, Yue HB, Peng Q (2020) Recent responses of grassland net primary productivity to climatic and anthropogenic factors in Kyrgyzstan. Land Degrad Dev 31(16): 1–17. https://doi.org/10.1002/ldr.3623

    Google Scholar 

  44. Wang F, Wang Z, Zhang Y (2018) Spatio-temporal variations in vegetation Net Primary Productivity and their driving factors in Anhui Province from 2000 to 2015. Acta Ecol Sinica 38(8): 2754–2767. (In Chinese) https://doi.org/10.5846/stxb201705160902

    Google Scholar 

  45. Wu LH, Wang SJ, Bai XY, et al. (2020) Climate change weakens the positive effect of human activities on karst vegetation productivity restoration in southern China. Ecol Indic 115: 106392. https://doi.org/10.1016/j.ecolind.2020.106392

    Article  Google Scholar 

  46. Yin SG, Li ZJ, Song WX, et al. (2018) Spatial differentiation and influence factors or residential rent in Nanjing based on geographical detector. J Geo-Inf Sci 01808: 1139–1149. (In Chinese) https://doi.org/10.10282/dqxxkx.2018.180072

    Google Scholar 

  47. Yin L, Dai EF, Zheng D, et al. (2020) What drives the vegetation dynamics in the Hengduan Mountain region, southwest China: Climate change or human activity? Ecol Indic 112: 106013. https://doi.org/10.1016/j.ecolind.2019.106013

    Article  Google Scholar 

  48. Yang X, Guo B, Han BM, et al. (2019) Analysis of the spatial-temporalevolution patterns of NPP and its driving mechanisms in the Qinghai-Tibet Plateau. Resour Environ in the Yangtze River Basin 28(12): 3038–3050. (In Chinese) https://doi.org/10.11870/cjlyzyyhj201912023

    Google Scholar 

  49. Zhong XC, Chen W, Liu T, et al (2016) Spatiotemporal changes of vegetation NPP in China from 2001 to 2010 and its relationship with climate. China Agric Resour Regionalization 37(09): 16–22. (In Chinese) https://doi.org/10.7621/cjarrp.1005-9121.20160904

    Google Scholar 

  50. Zhang Y, Zhang XL, Cai HS (2018) Temporal and spatial evolutions and its driving factors or ecological vulnerability in Wan’an county of Jiangxi province based on geogdetector. Bull Soil Water Conserv 38(4): 207–214. (In Chinese) https://doi.org/10.13961/j.cnki.stbctb.2018.04.034

    Google Scholar 

  51. Zhao XF, Li YY, Zhao YT (2018) Spatiotemporal differences and driving factors of land development degree in China based on geographical detector. Resour Environ in the Yangtze River Basin 27(11): 2425–2433. (In Chinese) https://doi.org/10.11870/cjlyzyyhj201811004

    Google Scholar 

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Acknowledgments

This work was supported by the Open fund of Key Laboratory of National Geographic Census and Monitoring, MNR (grant no.2020NGCM02);Open Research Fund of the Key Laboratory of Digital Earth Science, Chinese Academy of Sciences (grant no. 2019LDE006); the Open Fund of Key Laboratory of Urban Land Resources Monitoring and Simulation, Ministry of Natural Resources (grant no. KF-2020-05-001);Open fund of Key Laboratory of Land use, Ministry of Natural Resources (grant no.20201511835); Open Fund of Key Laboratory for Digital Land and Resources of Jiangxi Province, East China University of Technology (grant no. DLLJ202002); Open foundation of MOE Key Laboratory of Western China’ Environmental Systems, Lanzhou University and the fundamental Research funds for the Central Universities (grant no. lzujbky-2020-kb01); University-Industry Collaborative Education Program (grant no.201902208005); Open Fund of Key Laboratory of Meteorology and Ecological Environment of Hebei Province(grant no.Z202001H); Open Fund of Key Laboratory of Geomatics and Digital Technology of Shandong Province; Open Fund of Key Laboratory of Geomatics Technology and Application Key Laboratory of Qinghai Province (grant no. QHDX-2019-04); Natural Science Foundation of Shandong Province (grant no. ZR2018BD001).

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Correspondence to Bing Guo or Rui Zhang or Wen-qian Zang.

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Chen, St., Guo, B., Zhang, R. et al. Quantitatively determine the dominant driving factors of the spatial—temporal changes of vegetation NPP in the Hengduan Mountain area during 2000–2015. J. Mt. Sci. 18, 427–445 (2021). https://doi.org/10.1007/s11629-020-6404-9

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Keywords

  • Vegetation NPP
  • Spatial-temporal distribution
  • Driving factors
  • Geographic detector
  • Land use change