Vegetation Dynamics and Climate from A Perspective of Lag-Effect: A Study Case in Loess Plateau, China
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Area
2.2. Datasets and Pre-Processing
2.3. Methods
2.3.1. Weighted Time-Lag Method
2.3.2. Linear Regression Analysis of the NDVI Data and Climate Variables
3. Results
3.1. Spatial Patterns of Relationship between NDVI and Precipitation/Temperature in Different Months
3.2. Relationship between Different Vegetation Types and Precipitation/Temperature
3.3. The Time-Lag Effect of Different Climatic Factors on Different Vegetation
4. Discussion
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
- Walther, G.-R.; Post, E.; Convey, P.; Menzel, A.; Parmesan, C.; Beebee, T.J.; Fromentin, J.-M.; Hoegh-Guldberg, O.; Bairlein, F. Ecological responses to recent climate change. Nature 2002, 416, 389. [Google Scholar] [CrossRef] [PubMed]
- Usman, M.; Nichol, J.E. A spatio-temporal analysis of rainfall and drought monitoring in the Tharparkar region of Pakistan. Remote Sens. 2020, 12, 580. [Google Scholar] [CrossRef]
- Li, Z.; Li, H.; Xu, C.; Jia, Y.; Wang, F.; Wang, P.; Yue, X. 60-year changes and mechanisms of Urumqi Glacier No. 1 in the eastern Tianshan of China, Central Asia. Sci. Cold Arid Reg. 2021, 12, 380–388. [Google Scholar]
- Bhushan, B.; Sharma, A. Sea-Level Rise Due to Climate Change. In Flood Handbook; CRC Press: Boca Raton, FL, USA, 2022; pp. 265–284. [Google Scholar]
- Mohammad, A.G.; Adam, M.A. The impact of vegetative cover type on runoff and soil erosion under different land uses. Catena 2010, 81, 97–103. [Google Scholar] [CrossRef]
- Chuai, X.; Huang, X.; Wang, W.; Bao, G. NDVI, temperature and precipitation changes and their relationships with different vegetation types during 1998–2007 in Inner Mongolia, China. Int. J. Climatol. 2013, 33, 1696–1706. [Google Scholar] [CrossRef]
- Ge, W.; Deng, L.; Wang, F.; Han, J. Quantifying the contributions of human activities and climate change to vegetation net primary productivity dynamics in China from 2001 to 2016. Sci. Total Environ. 2021, 773, 145648. [Google Scholar] [CrossRef] [PubMed]
- Li, C.; Wang, J.; Hu, R.; Yin, S.; Bao, Y.; Ayal, D.Y. Relationship between vegetation change and extreme climate indices on the Inner Mongolia Plateau, China, from 1982 to 2013. Ecol. Indic. 2018, 89, 101–109. [Google Scholar] [CrossRef]
- Songqi, P.; Caineng, Z.; Yong, L.; Zhenhua, J.; Entao, L.; Ming, Y.; ZHANG, G.; Zhi, Y.; Songtao, W.; Zhen, Q. Major biological events and fossil energy formation: On the development of energy science under the earth system framework. Pet. Explor. Dev. 2021, 48, 581–594. [Google Scholar]
- Fu, B. Soil erosion and its control in the Loess Plateau of China. Soil Use Manag. 1989, 5, 76–82. [Google Scholar] [CrossRef]
- Fu, B.; Chen, L. Agricultural landscape spatial pattern analysis in the semi-arid hill area of the Loess Plateau, China. J. Arid Environ. 2000, 44, 291–303. [Google Scholar] [CrossRef]
- Shi, S.; Yu, J.; Wang, F.; Wang, P.; Zhang, Y.; Jin, K. Quantitative contributions of climate change and human activities to vegetation changes over multiple time scales on the Loess Plateau. Sci. Total Environ. 2021, 755, 142419. [Google Scholar] [CrossRef] [PubMed]
- Wang, C.; Liang, W.; Yan, J.; Jin, Z.; Zhang, W.; Li, X. Effects of vegetation restoration on local microclimate on the Loess Plateau. J. Geogr. Sci. 2022, 32, 291–316. [Google Scholar] [CrossRef]
- Davis, M.B. Lags in vegetation response to greenhouse warming. Clim. Chang. 1989, 15, 75–82. [Google Scholar] [CrossRef]
- Suttle, K.; Thomsen, M.A.; Power, M.E. Species interactions reverse grassland responses to changing climate. Science 2007, 315, 640–642. [Google Scholar] [CrossRef]
- Zhang, C.; Liu, G.; Xue, S.; Zhang, C. Rhizosphere soil microbial properties on abandoned croplands in the Loess Plateau, China during vegetation succession. Eur. J. Soil Biol. 2012, 50, 127–136. [Google Scholar] [CrossRef]
- Feng, X.; Li, J.; Cheng, W.; Fu, B.; Wang, Y.; Lü, Y. Evaluation of AMSR-E retrieval by detecting soil moisture decrease following massive dryland re-vegetation in the Loess Plateau, China. Remote Sens. Environ. 2017, 196, 253–264. [Google Scholar] [CrossRef]
- Liu, Z.; Liu, Y. Does anthropogenic land use change play a role in changes of precipitation frequency and intensity over the Loess Plateau of China? Remote Sens. 2018, 10, 1818. [Google Scholar] [CrossRef]
- Zheng, K.; Wei, J.-Z.; Pei, J.-Y.; Cheng, H.; Zhang, X.-L.; Huang, F.-Q.; Li, F.-M.; Ye, J.-S. Impacts of climate change and human activities on grassland vegetation variation in the Chinese Loess Plateau. Sci. Total Environ. 2019, 660, 236–244. [Google Scholar] [CrossRef]
- AbdelRahman, M.A.; Afifi, A.A.; Scopa, A. A time series investigation to assess climate change and anthropogenic impacts on quantitative land degradation in the North Delta, Egypt. ISPRS Int. J. Geo-Inf. 2022, 11, 30. [Google Scholar] [CrossRef]
- Xin, Z.; Xu, J.; Zheng, W. Spatiotemporal variations of vegetation cover on the Chinese Loess Plateau (1981–2006): Impacts of climate changes and human activities. Sci. China Ser. D Earth Sci. 2008, 51, 67–78. [Google Scholar] [CrossRef]
- Sun, Q.; Liu, C.; Chen, T.; Zhang, A. A weighted-time-lag method to detect lag vegetation response to climate variation: A case study in Loess Plateau, China, 1982–2013. Remote Sens. 2021, 13, 923. [Google Scholar] [CrossRef]
- Lim, C.; Kafatos, M. Frequency analysis of natural vegetation distribution using NDVI/AVHRR data from 1981 to 2000 for North America: Correlations with SOI. Int. J. Remote Sens. 2002, 23, 3347–3383. [Google Scholar] [CrossRef]
- Peng, D.-I.; Huang, J.-F.; Huete, A.R.; Yang, T.-M.; Gao, P.; Chen, Y.-C.; Chen, H.; Li, J.; Liu, Z.-Y. Spatial and seasonal characterization of net primary productivity and climate variables in southeastern China using MODIS data. J. Zhejiang Univ. Sci. B 2010, 11, 275–285. [Google Scholar] [CrossRef] [PubMed]
- Zeng, F.-W.; Collatz, G.J.; Pinzon, J.E.; Ivanoff, A. Evaluating and quantifying the climate-driven interannual variability in Global Inventory Modeling and Mapping Studies (GIMMS) Normalized Difference Vegetation Index (NDVI3g) at global scales. Remote Sens. 2013, 5, 3918–3950. [Google Scholar] [CrossRef]
- Wu, D.; Zhao, X.; Liang, S.; Zhou, T.; Huang, K.; Tang, B.; Zhao, W. Time-lag effects of global vegetation responses to climate change. Glob. Chang. Biol. 2015, 21, 3520–3531. [Google Scholar] [CrossRef] [PubMed]
- Vicente-Serrano, S.M.; Gouveia, C.; Camarero, J.J.; Beguería, S.; Trigo, R.; López-Moreno, J.I.; Azorín-Molina, C.; Pasho, E.; Lorenzo-Lacruz, J.; Revuelto, J.; et al. Response of vegetation to drought time-scales across global land biomes. Proc. Natl. Acad. Sci. USA 2013, 110, 52–57. [Google Scholar] [CrossRef]
- Jiang, W.; Niu, Z.; Wang, L.; Yao, R.; Gui, X.; Xiang, F.; Ji, Y. Impacts of Drought and Climatic Factors on Vegetation Dynamics in the Yellow River Basin and Yangtze River Basin, China. Remote Sens. 2022, 14, 930. [Google Scholar] [CrossRef]
- Braswell, B.; Schimel, D.S.; Linder, E.; Moore, B. The response of global terrestrial ecosystems to interannual temperature variability. Science 1997, 278, 870–873. [Google Scholar] [CrossRef]
- Wang, J.; Rich, P.M.; Price, K.P. Temporal responses of NDVI to precipitation and temperature in the central Great Plains, USA. Int. J. Remote Sens. 2003, 24, 2345–2364. [Google Scholar] [CrossRef]
- Zhou, Y.; Zhang, L.; Fensholt, R.; Wang, K.; Vitkovskaya, I.; Tian, F. Climate contributions to vegetation variations in central Asian drylands: Pre-and post-USSR collapse. Remote Sens. 2015, 7, 2449–2470. [Google Scholar] [CrossRef]
- Wen, Y.; Liu, X.; Yang, J.; Lin, K.; Du, G. NDVI indicated inter-seasonal non-uniform time-lag responses of terrestrial vegetation growth to daily maximum and minimum temperature. Glob. Planet. Chang. 2019, 177, 27–38. [Google Scholar] [CrossRef]
- Malo, A.R.; Nicholson, S.E. A study of rainfall and vegetation dynamics in the African Sahel using normalized difference vegetation index. J. Arid Environ. 1990, 19, 1–24. [Google Scholar] [CrossRef]
- Wang, J.; Price, K.; Rich, P. Spatial patterns of NDVI in response to precipitation and temperature in the central Great Plains. Int. J. Remote Sens. 2001, 22, 3827–3844. [Google Scholar] [CrossRef]
- Kuzyakov, Y.; Gavrichkova, O. Time lag between photosynthesis and carbon dioxide efflux from soil: A review of mechanisms and controls. Glob. Chang. Biol. 2010, 16, 3386–3406. [Google Scholar] [CrossRef]
- Saatchi, S.; Asefi-Najafabady, S.; Malhi, Y.; Aragão, L.E.; Anderson, L.O.; Myneni, R.B.; Nemani, R. Persistent effects of a severe drought on Amazonian forest canopy. Proc. Natl. Acad. Sci. USA 2013, 110, 565–570. [Google Scholar] [CrossRef] [PubMed]
- Chen, T.; De Jeu, R.; Liu, Y.; Van der Werf, G.; Dolman, A. Using satellite based soil moisture to quantify the water driven variability in NDVI: A case study over mainland Australia. Remote Sens. Environ. 2014, 140, 330–338. [Google Scholar] [CrossRef]
- Wang, M.; An, Z. Regional and Phased Vegetation Responses to Climate Change Are Different in Southwest China. Land 2022, 11, 1179. [Google Scholar] [CrossRef]
- Bao, G.; Qin, Z.; Bao, Y.; Zhou, Y.; Li, W.; Sanjjav, A. NDVI-based long-term vegetation dynamics and its response to climatic change in the Mongolian Plateau. Remote Sens. 2014, 6, 8337–8358. [Google Scholar] [CrossRef]
- Eklundh, L. Estimating relations between AVHRR NDVI and rainfall in East Africa at 10-day and monthly time scales. Int. J. Remote Sens. 1998, 19, 563–570. [Google Scholar] [CrossRef]
- Ma, M.; Frank, V. Interannual variability of vegetation cover in the Chinese Heihe River Basin and its relation to meteorological parameters. Int. J. Remote Sens. 2006, 27, 3473–3486. [Google Scholar] [CrossRef]
- Muradyan, V.; Tepanosyan, G.; Asmaryan, S.; Saghatelyan, A.; Dell’Acqua, F. Relationships between NDVI and climatic factors in mountain ecosystems: A case study of Armenia. Remote Sens. Appl. Soc. Environ. 2019, 14, 158–169. [Google Scholar] [CrossRef]
- Anderson, L.O.; Malhi, Y.; Aragão, L.E.; Ladle, R.; Arai, E.; Barbier, N.; Phillips, O. Remote sensing detection of droughts in Amazonian forest canopies. New Phytol. 2010, 187, 733–750. [Google Scholar] [CrossRef] [PubMed]
- Bunting, E.L.; Munson, S.M.; Villarreal, M.L. Climate legacy and lag effects on dryland plant communities in the southwestern US. Ecol. Indic. 2017, 74, 216–229. [Google Scholar] [CrossRef]
- Philippon, N.; Mougin, E.; Jarlan, L.; Frison, P.L. Analysis of the linkages between rainfall and land surface conditions in the West African monsoon through CMAP, ERS-WSC, and NOAA-AVHRR data. J. Geophys. Res. Atmos. 2005, 110, D24. [Google Scholar] [CrossRef]
- Schwinning, S.; Sala, O.E. Hierarchy of responses to resource pulses in arid and semi-arid ecosystems. Oecologia 2004, 141, 211–220. [Google Scholar] [CrossRef] [PubMed]
- Piao, S.; Fang, J.; Zhou, L.; Guo, Q.; Henderson, M.; Ji, W.; Li, Y.; Tao, S. Interannual variations of monthly and seasonal normalized difference vegetation index (NDVI) in China from 1982 to 1999. J. Geophys. Res. Atmos. 2003, 108, D14. [Google Scholar] [CrossRef]
- Li, J.; Lewis, J.; Rowland, J.; Tappan, G.; Tieszen, L. Evaluation of land performance in Senegal using multi-temporal NDVI and rainfall series. J. Arid Environ. 2004, 59, 463–480. [Google Scholar] [CrossRef]
- Yu, L.; Wu, Z.; Du, Z.; Zhang, H.; Liu, Y. Insights on the roles of climate and human activities to vegetation degradation and restoration in Beijing-Tianjin sandstorm source region. Ecol. Eng. 2021, 159, 106105. [Google Scholar] [CrossRef]
- Yang, L.; Guan, Q.; Lin, J.; Tian, J.; Tan, Z.; Li, H. Evolution of NDVI secular trends and responses to climate change: A perspective from nonlinearity and nonstationarity characteristics. Remote Sens. Environ. 2021, 254, 112247. [Google Scholar] [CrossRef]
- Xue, J.; Wang, Y.; Teng, H.; Wang, N.; Li, D.; Peng, J.; Biswas, A.; Shi, Z. Dynamics of vegetation greenness and its response to climate change in Xinjiang over the past two decades. Remote Sens. 2021, 13, 4063. [Google Scholar] [CrossRef]
- Tucker, C.J.; Vanpraet, C.L.; Sharman, M.; Van Ittersum, G. Satellite remote sensing of total herbaceous biomass production in the Senegalese Sahel: 1980–1984. Remote Sens. Environ. 1985, 17, 233–249. [Google Scholar] [CrossRef]
- Tucker, C.; Sellers, P. Satellite remote sensing of primary production. Int. J. Remote Sens. 1986, 7, 1395–1416. [Google Scholar] [CrossRef] [Green Version]
- Jonsson, P.; Eklundh, L. Seasonality extraction by function fitting to time-series of satellite sensor data. IEEE Trans. Geosci. Remote Sens. 2002, 40, 1824–1832. [Google Scholar] [CrossRef]
- Fensholt, R.; Rasmussen, K.; Nielsen, T.T.; Mbow, C. Evaluation of earth observation based long term vegetation trends—Intercomparing NDVI time series trend analysis consistency of Sahel from AVHRR GIMMS, Terra MODIS and SPOT VGT data. Remote Sens. Environ. 2009, 113, 1886–1898. [Google Scholar] [CrossRef]
- Lu, C.; van Ittersum, M.K.; Rabbinge, R. A scenario exploration of strategic land use options for the Loess Plateau in northern China. Agric. Syst. 2004, 79, 145–170. [Google Scholar] [CrossRef]
- Holben, B.N. Characteristics of maximum-value composite images from temporal AVHRR data. Int. J. Remote Sens. 1986, 7, 1417–1434. [Google Scholar] [CrossRef]
- Gitelson, A.A. Wide dynamic range vegetation index for remote quantification of biophysical characteristics of vegetation. J. Plant Physiol. 2004, 161, 165–173. [Google Scholar] [CrossRef]
- Piao, S.; Mohammat, A.; Fang, J.; Cai, Q.; Feng, J. NDVI-based increase in growth of temperate grasslands and its responses to climate changes in China. Glob. Environ. Chang. 2006, 16, 340–348. [Google Scholar] [CrossRef]
- Foote, K.C.; Schaedle, M. Physiological Characteristics of Photosynthesis and Respiration in Stems of Populus tremuloides Michx. Plant Physiol. 1976, 58, 91–94. [Google Scholar] [CrossRef]
- Nemani, R.R.; Keeling, C.D.; Hashimoto, H.; Jolly, W.M.; Piper, S.C.; Tucker, C.J.; Myneni, R.B.; Running, S.W. Climate-driven increases in global terrestrial net primary production from 1982 to 1999. Science 2003, 300, 1560–1563. [Google Scholar] [CrossRef]
- Pearson, R.G.; Phillips, S.J.; Loranty, M.M.; Beck, P.S.; Damoulas, T.; Knight, S.J.; Goetz, S.J. Shifts in Arctic vegetation and associated feedbacks under climate change. Nat. Clim. Chang. 2013, 3, 673–677. [Google Scholar] [CrossRef]
- Peng, S.; Chen, A.; Xu, L.; Cao, C.; Fang, J.; Myneni, R.B.; Pinzon, J.E.; Tucker, C.J.; Piao, S. Recent change of vegetation growth trend in China. Environ. Res. Lett. 2011, 6, 044027. [Google Scholar] [CrossRef]
- Peteet, D. Sensitivity and rapidity of vegetational response to abrupt climate change. Proc. Natl. Acad. Sci. USA 2000, 97, 1359–1361. [Google Scholar] [CrossRef] [PubMed]
- Bala, G.; Caldeira, K.; Wickett, M.; Phillips, T.; Lobell, D.; Delire, C.; Mirin, A. Combined climate and carbon-cycle effects of large-scale deforestation. Proc. Natl. Acad. Sci. USA 2007, 104, 6550–6555. [Google Scholar] [CrossRef]
- Brando, P.M.; Balch, J.K.; Nepstad, D.C.; Morton, D.C.; Putz, F.E.; Coe, M.T.; Silvério, D.; Macedo, M.N.; Davidson, E.A.; Nóbrega, C.C.; et al. Abrupt increases in Amazonian tree mortality due to drought–fire interactions. Proc. Natl. Acad. Sci. USA 2014, 111, 6347–6352. [Google Scholar] [CrossRef]
- Choat, B.; Jansen, S.; Brodribb, T.J.; Cochard, H.; Delzon, S.; Bhaskar, R.; Bucci, S.J.; Feild, T.S.; Gleason, S.M.; Hacke, U.G.; et al. Global convergence in the vulnerability of forests to drought. Nature 2012, 491, 752–755. [Google Scholar] [CrossRef]
- Malhi, Y.; Roberts, J.T.; Betts, R.A.; Killeen, T.J.; Li, W.; Nobre, C.A. Climate change, deforestation, and the fate of the Amazon. Science 2008, 319, 169–172. [Google Scholar] [CrossRef]
- Sterling, S.M.; Ducharne, A.; Polcher, J. The impact of global land-cover change on the terrestrial water cycle. Nat. Clim. Chang. 2013, 3, 385–390. [Google Scholar] [CrossRef]
- Guo, L.; Wu, S.; Zhao, D.; Yin, Y.; Leng, G.; Zhang, Q. NDVI-based vegetation change in Inner Mongolia from 1982 to 2006 and its relationship to climate at the biome scale. Adv. Meteorol. 2014, 2014, 692068. [Google Scholar] [CrossRef]
- Mulder, C.P.; Iles, D.T.; Rockwell, R.F. Increased variance in temperature and lag effects alter phenological responses to rapid warming in a subarctic plant community. Glob. Chang. Biol. 2017, 23, 801–814. [Google Scholar] [CrossRef]
- Xu, G.; Zhang, H.; Chen, B.; Zhang, H.; Innes, J.L.; Wang, G.; Yan, J.; Zheng, Y.; Zhu, Z.; Myneni, R.B. Changes in vegetation growth dynamics and relations with climate over China’s landmass from 1982 to 2011. Remote Sens. 2014, 6, 3263–3283. [Google Scholar] [CrossRef]
- Barber, V.A.; Juday, G.P.; Finney, B.P. Reduced growth of Alaskan white spruce in the twentieth century from temperature-induced drought stress. Nature 2000, 405, 668–673. [Google Scholar] [CrossRef]
- Piao, S.; Wang, X.; Ciais, P.; Zhu, B.; Wang, T.; Liu, J. Changes in satellite-derived vegetation growth trend in temperate and boreal Eurasia from 1982 to 2006. Glob. Chang. Biol. 2011, 17, 3228–3239. [Google Scholar] [CrossRef]
- ShengPei, D.; Zhang, B.; HaiJun, W.; YaMin, W.; LingXia, G.; XingMei, W.; Dan, L. Vegetation cover change and the driving factors over northwest China. J. Arid Land 2011, 3, 25–33. [Google Scholar] [CrossRef]
- Tan, Z.; Tao, H.; Jiang, J.; Zhang, Q. Influences of climate extremes on NDVI (normalized difference vegetation index) in the Poyang Lake Basin, China. Wetlands 2015, 35, 1033–1042. [Google Scholar] [CrossRef]
- Tanja, S.; Berninger, F.; Vesala, T.; Markkanen, T.; Hari, P.; Mäkelä, A.; Ilvesniemi, H.; Hänninen, H.; Nikinmaa, E.; Huttula, T.; et al. Air temperature triggers the recovery of evergreen boreal forest photosynthesis in spring. Glob. Chang. Biol. 2003, 9, 1410–1426. [Google Scholar] [CrossRef]
- Wei, H.; Zhao, X.; Liang, S.; Zhou, T.; Wu, D.; Tang, B. Effects of warming hiatuses on vegetation growth in the Northern Hemisphere. Remote Sens. 2018, 10, 683. [Google Scholar] [CrossRef]
- Bao, G.; Bao, Y.; Qin, Z.; Zhou, Y.; Shiirev, A. Vegetation cover changes in Mongolian plateau and its response to seasonal climate changes in recent 10 years. Sci. Geogr. Sin. 2013, 33, 613–621. [Google Scholar]
- Zhang, Q.; Wu, S.; Zhao, D.; Dai, E. Responses of growing season vegetation changes to climatic factors in Inner Mongolia grassland. J. Nat. Resour. 2013, 28, 754–764. [Google Scholar]
- Zhao, X.; Tan, K.; Zhao, S.; Fang, J. Changing climate affects vegetation growth in the arid region of the northwestern China. J. Arid Environ. 2011, 75, 946–952. [Google Scholar] [CrossRef]
- Chen, D.; Huang, H.; Hu, M.; Dahlgren, R.A. Influence of lag effect, soil release, and climate change on watershed anthropogenic nitrogen inputs and riverine export dynamics. Environ. Sci. Technol. 2014, 48, 5683–5690. [Google Scholar] [CrossRef] [PubMed]
- Gessner, U.; Naeimi, V.; Klein, I.; Kuenzer, C.; Klein, D.; Dech, S. The relationship between precipitation anomalies and satellite-derived vegetation activity in Central Asia. Glob. Planet. Chang. 2013, 110, 74–87. [Google Scholar] [CrossRef]
- Xie, B.; Jia, X.; Qin, Z.; Shen, J.; Chang, Q. Vegetation dynamics and climate change on the Loess Plateau, China: 1982–2011. Reg. Environ. Chang. 2016, 16, 1583–1594. [Google Scholar] [CrossRef]
Climate Region | Statistical Correlation | April | May | June | July | August | September | October |
---|---|---|---|---|---|---|---|---|
Arid | R > 0, p > 0.05 | 20.05% | 38.84% | 21.30% | 19.12% | 26.31% | 19.44% | 33.31% |
R > 0, p < 0.05 | 19.97% | 27.88% | 51.84% | 63.32% | 62.41% | 74.37% | 57.52% | |
R < 0, p < 0.05 | 23.41% | 6.97% | 11.90% | 8.39% | 2.35% | 2.35% | 2.27% | |
R < 0, p < 0.05 | 36.57% | 26.31% | 14.96% | 9.17% | 8.93% | 3.84% | 6.90% | |
Rmax | 0.85 | 0.68 | 0.83 | 0.83 | 0.81 | 0.77 | 0.69 | |
Semiarid | R > 0, p < 0.05 | 37.57% | 41.98% | 38.24% | 21.64% | 23.88% | 14.84% | 22.43% |
R > 0, p < 0.05 | 32.01% | 32.95% | 44.66% | 70.07% | 59.61% | 82.07% | 66.78% | |
R > 0, p < 0.05 | 5.11% | 2.68% | 3.69% | 2.96% | 5.56% | 0.58% | 2.52% | |
R > 0, p < 0.05 | 25.31% | 22.39% | 13.42% | 5.34% | 10.95% | 2.52% | 8.28% | |
Rmax | 0.92 | 0.73 | 0.73 | 0.82 | 0.77 | 0.79 | 0.72 | |
Semi-humid | R > 0, p < 0.05 | 30.46% | 37.32% | 34.94% | 38.78% | 31.01% | 40.65% | 36.40% |
R < 0, p < 0.05 | 27.30% | 31.53% | 28.66% | 33.58% | 27.60% | 30.19% | 29.64% | |
R > 0, p < 0.05 | 12.31% | 12.65% | 17.81% | 9.78% | 20.69% | 9.06% | 8.81% | |
R > 0, p < 0.05 | 29.93% | 18.49% | 18.59% | 17.86% | 20.69% | 20.11% | 25.16% | |
Rmax | 0.42 | 0.47 | 0.63 | −0.51 | −0.59 | 0.65 | 0.45 |
Climate Region | Statistical Correlation | April | May | June | July | August | September | October |
---|---|---|---|---|---|---|---|---|
Arid | R > 0, p > 0.05 | 47.30% | 43.30% | 23.96% | 28.76% | 34.53% | 17.32% | 18.97% |
R > 0, p < 0.05 | 36.88% | 21.53% | 6.89% | 16.46% | 24.04% | 4.94% | 4.86% | |
R > 0, p < 0.05 | 2.51% | 8.93% | 27.88% | 12.30% | 16.91% | 49.53% | 37.85% | |
R < 0, p < 0.05 | 13.31% | 26.23% | 41.27% | 42.48% | 24.51% | 28.21% | 38.32% | |
Rmax | 0.85 | 0.68 | −0.70 | −0.56 | −0.57 | −0.64 | −0.64 | |
Semiarid | R > 0, p < 0.05 | 17.37% | 32.29% | 30.52% | 28.60% | 38.59% | 18.98% | 30.62% |
R > 0, p < 0.05 | 78.01% | 49.99% | 14.10% | 6.51% | 17.75% | 12.51% | 24.14% | |
R > 0, p < 0.05 | 1.12% | 5.56% | 25.36% | 18.70% | 13.47% | 30.27% | 12.91% | |
R > 0, p < 0.05 | 3.50% | 12.16% | 30.02% | 46.19% | 30.19% | 38.24% | 32.33% | |
Rmax | 0.92 | 0.77 | 0.71 | −0.77 | −0.77 | −0.72 | −0.71 | |
Semi-humid | R > 0, p < 0.05 | 14.99% | 32.99% | 25.35% | 29.54% | 38.51% | 19.43% | 42.29% |
R < 0, p < 0.05 | 69.29% | 37.96% | 14.60% | 5.06% | 9.40% | 2.82% | 30.95% | |
R > 0, p < 0.05 | 4.28% | 12.51% | 25.89% | 21.85% | 15.43% | 28.04% | 8.81% | |
R > 0, p < 0.05 | 11.44% | 16.55% | 34.16% | 43.55% | 36.66% | 49.71% | 17.96% | |
Rmax | 0.74 | 0.61 | −0.59 | −0.63 | −0.71 | −0.62 | 0.61 |
Vegetation Type | 4 | 5 | 6 | 7 | 8 | 9 | 10 |
---|---|---|---|---|---|---|---|
mixed forests | 0.55 | 0.57 | 0.57 | −0.49 | −0.59 | −0.46 | 0.52 |
grasslands | 0.92 | 0.73 | 0.83 | 0.83 | 0.81 | 0.79 | 0.72 |
barren or sparsely vegetated | 0.85 | 0.58 | 0.66 | 0.73 | 0.78 | 0.74 | 0.56 |
Vegetation Type | 4 | 5 | 6 | 7 | 8 | 9 | 10 |
---|---|---|---|---|---|---|---|
mixed forests | 0.74 | 0.61 | −0.53 | −0.46 | −0.48 | −0.40 | 0.61 |
grasslands | 0.92 | 0.77 | 0.71 | −0.77 | −0.77 | −0.72 | −0.71 |
barren or sparsely vegetated | 0.85 | 0.54 | −0.61 | −0.55 | −0.57 | −0.55 | −0.63 |
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. |
© 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Liu, C.; Liu, C.; Sun, Q.; Chen, T.; Fan, Y. Vegetation Dynamics and Climate from A Perspective of Lag-Effect: A Study Case in Loess Plateau, China. Sustainability 2022, 14, 12450. https://doi.org/10.3390/su141912450
Liu C, Liu C, Sun Q, Chen T, Fan Y. Vegetation Dynamics and Climate from A Perspective of Lag-Effect: A Study Case in Loess Plateau, China. Sustainability. 2022; 14(19):12450. https://doi.org/10.3390/su141912450
Chicago/Turabian StyleLiu, Chunyang, Chao Liu, Qianqian Sun, Tianyang Chen, and Ya Fan. 2022. "Vegetation Dynamics and Climate from A Perspective of Lag-Effect: A Study Case in Loess Plateau, China" Sustainability 14, no. 19: 12450. https://doi.org/10.3390/su141912450