Skip to main content
SpringerLink
Log in

Exploring spatially variable relationships between NDVI and climatic factors in a transition zone using geographically weighted regression

  • Original Paper
  • Published:
Theoretical and Applied Climatology Aims and scope Submit manuscript

Abstract

At landscape scale, the normalized difference vegetation index (NDVI) can be used to indicate the vegetation’s dynamic characteristics and has been widely employed to develop correlated and dependent relationships with the climatic and environmental factors. However, studies show that NDVI-environment relationships always emerge with complex features such as nonlinearity, scale dependency, and nonstationarity, especially in highly heterogeneous areas. In this study, we used geographically weighted regression (GWR), a local modeling technique to estimate regression models with spatially varying relationships, to investigate the spatially nonstationary relationships between NDVI and climatic factors at multiple scales in North China. The results indicate that all GWR models with appropriate bandwidth represented significant improvements of model performance over the ordinary least squares (OLS) models. The spatial relationships between NDVI and climatic factors varied significantly over space and were more significant and sensitive in the ecogeographical transition zone. Clear spatial patterns of slope parameters and local coefficient of determination (R 2) were found from the results of the GWR models. Moreover, the spatial patterns of the local R 2 of NDVI-precipitation are much clearer than the R 2 of NDVI-temperature in the semi-arid and subhumid areas, which mean that precipitation has more significant influence on vegetation in these areas. In conclusion, the study revealed detailed site information on the variable relationships in different parts of the study area, especially in the ecogeographical transition zone, and the GWR model can improve model ability to address spatial, nonstationary, and scale-dependent problems in landscape ecology.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

Abbreviations

GWR:

Geographically weighted regression

OLS:

Ordinary least squares

NDVI:

Normalized different vegetation index

AP:

Annual precipitation

AMT:

Annual mean temperature

GLM:

Global regression model

MVC:

Maximum value composite

AICc :

Akaike information criterion

Moran’s I :

Moran indexes

RMSEE:

Root mean squared error estimate

References

  • Akaike H (1973) Information theory and an extension of the maximum likelihood principle. In: International Symposium on Information Theory, 2nd, Tsahkadsor, Armenian SSR. pp 267–281

  • Balmford A, Moore JL, Brooks T, Burgess N, Hansen LA, Williams P, Rahbek C (2001) Conservation conflicts across Africa. Science 291(5513):2616–2619

    Article  Google Scholar 

  • Brunsdon C, Fotheringham S, Charlton M (1998) Geographically weighted regression-modelling spatial non-stationarity. J Roy Stat Soc D-Sta 47:431–443

    Article  Google Scholar 

  • Brunsdon C, McClatchey J, Unwin D (2001) Spatial variations in the average rainfall–altitude relationship in Great Britain: an approach using geographically weighted regression. Int J Climatol 21(4):455–466

    Article  Google Scholar 

  • Calvo E, Escolar M (2003) The local voter: a geographically weighted approach to ecological inference. Am J Polit Sci 47(1):189–204

    Article  Google Scholar 

  • Chown SL, van Rensburg BJ, Gaston KJ, Rodrigues AS, van Jaarsveld AS (2003) Energy, species richness, and human population size: conservation implications at a national scale. Ecol Appl 13(5):1233–1241

    Article  Google Scholar 

  • Cui L, Shi J, Yang Y, FAN W (2009) Ten-day response of vegetation NDVI to the variations of temperature and precipitation in eastern China. Acta Geograph Sin 64(7):850–860

    Google Scholar 

  • Di Bella C, Rebella C, Paruelo J (2000) Evapotranspiration estimates using NOAA AVHRR imagery in the Pampa region of Argentina. Int J Remote Sens 21(4):791–797

    Article  Google Scholar 

  • Evans KL, James NA, Gaston KJ (2006) Abundance, species richness and energy availability in the North American avifauna. Global Ecol Biogeogr 15(4):372–385

    Article  Google Scholar 

  • Fang J, Piao S, Tang Z, Peng C, Ji W (2001) Interannual variability in net primary production and precipitation. Science 293(5536):1723–1723

    Article  Google Scholar 

  • Finley AO (2011) Comparing spatially-varying coefficients models for analysis of ecological data with non-stationary and anisotropic residual dependence. Methods Ecol Evol 2(2):143–154

    Article  Google Scholar 

  • Foody G (2003) Geographical weighting as a further refinement to regression modelling: an example focused on the NDVI–rainfall relationship. Remote Sens Environ 88(3):283–293

    Article  Google Scholar 

  • Foody GM (2004) Spatial nonstationarity and scale-dependency in the relationship between species richness and environmental determinants for the sub-Saharan endemic avifauna. Global Ecol Biogeogr 13(4):315–320

    Article  Google Scholar 

  • Fotheringham AS, Brunsdon C, Charlton M (2002) Geographically weighted regression : the analysis of spatially varying relationships. Wiley, Chichester

    Google Scholar 

  • Gao J, Li S (2011) Detecting spatially non-stationary and scale-dependent relationships between urban landscape fragmentation and related factors using geographically weighted regression. Appl Geogr 31(1):292–302

    Article  Google Scholar 

  • Gao J, Li S, Zhao Z, Cai Y (2012a) Investigating spatial variation in the relationships between NDVI and environmental factors at multi-scales: a case study of Guizhou Karst Plateau, China. Int J Remote Sens 33(7):2112–2129

    Article  Google Scholar 

  • Gao Y, Huang J, Li S, Li SC (2012b) Spatial pattern of non-stationarity and scale-dependent relationships between NDVI and climatic factors—a case study in Qinghai-Tibet Plateau, China. Ecol Indic 20:170–176. doi:10.1016/ecolind.2012.02.007

    Article  Google Scholar 

  • Gaughan AE, Stevens FR, Gibbes C, Southworth J, Binford MW (2012) Linking vegetation response to seasonal precipitation in the Okavango-Kwando-Zambezi catchment of southern Africa. Int J Remote Sens 33(21):6783–6804. doi:10.1080/01431161.2012.692831

    Article  Google Scholar 

  • Guo Z, Wang Z, Song K, Zhang B, Li F, Liu D (2007) Correlations between forest vegetation NDVI and water/thermal condition in Northeast China forest regions in 1982-2003. Chinese J Ecol 26(12):1930–1936

    Google Scholar 

  • Guo LGL, Ma ZMZ, Zhang LZL (2008) Comparison of bandwidth selection in application of geographically weighted regression: a case study. Can J For Res 38(9):2526–2534

    Article  Google Scholar 

  • Hijmans RJ, Cameron SE, Parra JL, Jones PG, Jarvis A (2005) Very high resolution interpolated climate surfaces for global land areas. Int J Climatol 25(15):1965–1978. doi:10.1002/Joc.1276

    Article  Google Scholar 

  • Holt JB, Lo C (2008) The geography of mortality in the Atlanta metropolitan area. Comput Environ Urban Syst 32(2):149–164

    Article  Google Scholar 

  • Hurvich CM, Tsai CL (1989) Regression and time series model selection in small samples. Biometrika 76(2):297–307

    Article  Google Scholar 

  • Jarlan L, Mangiarotti S, Mougin E, Mazzega P, Hiernaux P, Le Dantec V (2008) Assimilation of SPOT/VEGETATION NDVI data into a sahelian vegetation dynamics model. Remote Sens Environ 112(4):1381–1394. doi:10.1016/j.rse.2007.02.041

    Article  Google Scholar 

  • Kerr JT, Ostrovsky M (2003) From space to species: ecological applications for remote sensing. Trends Ecol Evol 18(6):299–305

    Article  Google Scholar 

  • Kottek M, Grieser J, Beck C, Rudolf B, Rubel F (2006) World map of the Koppen-Geiger climate classification updated. Meteorol Z 15(3):259–263

    Article  Google Scholar 

  • Lamb EG, Mallik AU (2003) Plant species traits across a riparian-zone/forest ecotone. J Veg Sci 14(6):853–858

    Article  Google Scholar 

  • Legendre P (1993) Spatial autocorrelation: trouble or new paradigm? Ecology 74(6):1659–1673

    Article  Google Scholar 

  • Leung Y, Chang-Lin M, Wen-Xiu Z (2000) Statistical tests for spatial nonstationarity based on the geographically weighted regression model. Environ Plan A 32(1):9–32

    Article  Google Scholar 

  • Li SC, Gao WM, Zhou QF, Liu FY (2006) Multi-scale spatial analysis on NDVI and topographical factors using wavelet transform. Acta Ecol Sin 26(12):4198–4203

    Google Scholar 

  • Li SC, Zhao ZQ, Xie MM, Wang YL (2010) Investigating spatial non-stationary and scale-dependent relationships between urban surface temperature and environmental factors using geographically weighted regression. Environ Model Softw 25(12):1789–1800. doi:10.1016/j.envsoft.2010.06.011

    Article  Google Scholar 

  • Li SC, Zhao ZQ, Wang Y, Wang YL (2011) Identifying spatial patterns of synchronization between NDVI and climatic determinants using joint recurrence plots. Environ Earth Sci 64(3):851–859. doi:10.1007/s12665-011-0909-z

    Article  Google Scholar 

  • Liu D, Fu N, Fan J (2008) Dynamic change of vegetation cover and its reponse to climate change in recent 20 years in Tianjin area. Ecol Environ 17(2):798–801

    Google Scholar 

  • Longley PA, Tobón C (2004) Spatial dependence and heterogeneity in patterns of hardship: an intra-urban analysis. Ann Assoc Am Geogr 94(3):503–519

    Article  Google Scholar 

  • Luck GW (2007) The relationships between net primary productivity, human population density and species conservation. J Biogeogr 34(2):201–212

    Article  Google Scholar 

  • Maselli F, Chiesi M (2006) Integration of multi-source NDVI data for the estimation of Mediterranean forest productivity. Int J Remote Sens 27(1):55–72

    Article  Google Scholar 

  • Mei C, Wang N, Zhang W (2006) Testing the importance of the explanatory variables in a mixed geographically weighted regression model. Environ Plan A 38(3):587

    Article  Google Scholar 

  • Mennis JL, Jordan L (2005) The distribution of environmental equity: exploring spatial nonstationarity in multivariate models of air toxic releases. Ann Assoc Am Geogr 95(2):249–268

    Article  Google Scholar 

  • Moody A, Johnson DM (2001) Land-surface phenologies from AVHRR using the discrete fourier transform. Remote Sens Environ 75(3):305–323

    Article  Google Scholar 

  • Morawitz DF, Blewett TM, Cohen A, Alberti M (2006) Using NDVI to assess vegetative land cover change in central Puget Sound. Environ Monit Assess 114(1–3):85–106

    Article  Google Scholar 

  • Myneni R, Tucker C, Asrar G, Keeling C (1998) Interannual variations in satellite-sensed vegetation index data from 1981 to 1991. J Geophys Res 103(D6):6145–6160

    Article  Google Scholar 

  • Onema JMK, Taigbenu A (2009) NDVI-rainfall relationship in the Semliki watershed of the equatorial Nile. Phys Chem Earth 34(13–16):711–721. doi:10.1016/j.pce.2009.06.004

    Article  Google Scholar 

  • Osborne PE, Foody GM, Suarez-Seoane S (2007) Non-stationarity and local approaches to modelling the distributions of wildlife. Divers Distrib 13(3):313–323. doi:10.1111/j.1472-4642.2007.00344.x

    Article  Google Scholar 

  • Peel MC, Finlayson BL, McMahon TA (2007) Updated world map of the Köppen-Geiger climate classification. Hydrol Earth Syst Sci Disc 4(2):439–473

    Article  Google Scholar 

  • Piao SL, Fang JY, Zhou LM, Guo QH, Henderson M, Ji W, Li Y, Tao S (2003) Interannual variations of monthly and seasonal normalized difference vegetation index (NDVI) in China from 1982 to 1999. J Geophys Res-Atmos 108 (D14)

  • Piao S, Mohammat A, Fang J, Cai Q, Feng J (2006) NDVI-based increase in growth of temperate grasslands and its responses to climate changes in China. Glob Environ Chang 16(4):340–348

    Article  Google Scholar 

  • Propastin PA (2009) Spatial non-stationarity and scale-dependency of prediction accuracy in the remote estimation of LAI over a tropical rainforest in Sulawesi, Indonesia. Remote Sens Environ 113(10):2234–2242

    Article  Google Scholar 

  • Propastin P, Kappas M, Erasmi S (2008) Application of geographically weighted regression to investigate the impact of scale on prediction uncertainty by modelling relationship between vegetation and climate. Int J Spat Data Infrastructures Res 3:73–94

    Google Scholar 

  • Raynolds MK, Comiso JC, Walker DA, Verbyla D (2008) Relationship between satellite-derived land surface temperatures, arctic vegetation types, and NDVI. Remote Sens Environ 112(4):1884–1894

    Article  Google Scholar 

  • Ross AL, Foster BL, Loving GS (2003) Contrasting effects of plant neighbours on invading Ulmus rubra seedlings in a successional grassland. Ecoscience 10(4):525–531

    Google Scholar 

  • Shi H, Laurent EJ, LeBouton J, Racevskis L, Hall KR, Donovan M, Doepker RV, Walters MB, Lupi F, Liu J (2006a) Local spatial modeling of white-tailed deer distribution. Ecol Model 190(1):171–189

    Article  Google Scholar 

  • Shi H, Zhang L, Liu J (2006b) A new spatial-attribute weighting function for geographically weighted regression. Can J For Res 36(4):996–1005

    Article  Google Scholar 

  • Sun Y, Guo P (2012) Variation of vegetation coverage and its relationship with climate change in north China from 1982 to 2006. Ecol Environ Sci 21(1):7–12

    Google Scholar 

  • Sun H, Shen Y, Yu Q, Flerchinger GN, Zhang Y, Liu C, Zhang X (2010) Effect of precipitation change on water balance and WUE of the winter wheat–summer maize rotation in the North China Plain. Agric Water Manag 97(8):1139–1145

    Article  Google Scholar 

  • Tobler WR (1970) A computer movie simulating urban growth in the Detroit region. Econ Geogr 46:234–240

    Article  Google Scholar 

  • Udelhoven T, Stellmes M, Del Barrio G, Hill J (2009) Assessment of rainfall and NDVI anomalies in Spain (1989–1999) using distributed lag models. Int J Remote Sens 30(8):1961–1976

    Article  Google Scholar 

  • Wang J, Xu X, Liu P (1999) Study on land use and population load in inter-distributing area of agricultural and grazing lands in northern China. Res Sci 21(5):19–24

    Google Scholar 

  • Wheeler DC, Calder CA (2007) An assessment of coefficient accuracy in linear regression models with spatially varying coefficients. J Geogr Syst 9(2):145–166

    Article  Google Scholar 

  • Wheeler D, Tiefelsdorf M (2005) Multicollinearity and correlation among local regression coefficients in geographically weighted regression. J Geogr Syst 7(2):161–187

    Article  Google Scholar 

  • Wu Y, Li Z, Wang Y, Luan Q, Tian G (2009) Responses of vegetation index (NDVI) in typical ecological areas of Shanxi Province to climate change. Chinese J Ecol 5:024

    Google Scholar 

  • Xiao J, Moody A (2004) Trends in vegetation activity and their climatic correlates: China 1982 to 1998. Int J Remote Sens 25(24):5669–5689

    Article  Google Scholar 

  • Zhang X, Hu Y, Zhuang D, Qi Y (2009) The spatial pattern and differentiation of NDVI in Mongolia Plateau. Geogr Res-Aust 1:002

    Google Scholar 

  • Zhao HL, Zhao XY, Zhang TH, Zhou R (2002) Boundary line on agropasture zigzag zone in north China and its problems on ecoenvironment. Adv Earth Sci 17(5):739–748

    Google Scholar 

  • Zheng D (2008) Research on eco-geographical region systems of China. The Commercial Press, Beijing

    Google Scholar 

Download references

Acknowledgments

The research for this study was financially supported by the National Natural Science Foundation of China (nos. 41130534 and 41440747). The authors are grateful to the anonymous reviewers for offering valuable suggestions to improve the manuscript.

Author information

Authors and Affiliations

  1. Laboratory for Earth Surface Processes Ministry of Education, College of Urban and Environmental Sciences, Peking University, No. 2 Yifu Building 3345, No. 5 Yiheyuan Rd, Beijing, 100871, China

    Zhiqiang Zhao, Yanglin Wang & Shuangcheng Li

  2. Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, 100101, China

    Jiangbo Gao

  3. Center for Systems Integration and Sustainability, Department of Fisheries and Wildlife, Michigan State University, East Lansing, MI, 48823, USA

    Zhiqiang Zhao & Jianguo Liu

Authors
  1. Zhiqiang Zhao
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jiangbo Gao
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yanglin Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Jianguo Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Shuangcheng Li
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Zhiqiang Zhao or Shuangcheng Li.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zhao, Z., Gao, J., Wang, Y. et al. Exploring spatially variable relationships between NDVI and climatic factors in a transition zone using geographically weighted regression. Theor Appl Climatol 120, 507–519 (2015). https://doi.org/10.1007/s00704-014-1188-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00704-014-1188-x

Keywords

Search

Navigation