Skip to main content

Advertisement

Log in

Exploring the relationship between vegetation spectra and eco-geo-environmental conditions in karst region, Southwest China

  • Published:
Environmental Monitoring and Assessment Aims and scope Submit manuscript

Abstract

Remote sensing of local environmental conditions is not accessible if substrates are covered with vegetation. This study explored the relationship between vegetation spectra and karst eco-geo-environmental conditions. Hyperspectral remote sensing techniques showed that there were significant differences between spectral features of vegetation mainly distributed in karst and non-karst regions, and combination of 1,300- to 2,500-nm reflectance and 400- to 680-nm first-derivative spectra could delineate karst and non-karst vegetation groups. Canonical correspondence analysis (CCA) successfully assessed to what extent the variation of vegetation spectral features can be explained by associated eco-geo-environmental variables, and it was found that soil moisture and calcium carbonate contents had the most significant effects on vegetation spectral features in karst region. Our study indicates that vegetation spectra is tightly linked to eco-geo-environmental conditions and CCA is an effective means of studying the relationship between vegetation spectral features and eco-geo-environmental variables. Employing a combination of spectral and spatial analysis, it is anticipated that hyperspectral imagery can be used in interpreting or mapping eco-geo-environmental conditions covered with vegetation in karst region.

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

Access this article

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

Instant access to the full article PDF.

Similar content being viewed by others

References

  • Armitage, R. P., Kent, M., & Weaver, R. E. (2004). Identification of the spectral characteristics of British semi-natural upland vegetation using direct ordination: A case study from Dartmoor, UK. International Journal of Remote Sensing, 25(17), 3369–3388. doi:10.1080/01431160310001654464.

    Article  Google Scholar 

  • Asner, G. P. (1998). Biophysical and biochemical sources of variability in canopy reflectance. Remote Sensing of Environment, 64, 234–253. doi:10.1016/S0034-4257(98)00014-5.

    Article  Google Scholar 

  • Asner, G. P., Jones, M. O., Martin, R. E., Knapp, D. E., & Hughes, R. F. (2008). Remote sensing of native and invasive species in Hawaiian forests. Remote Sensing of Environment, 112, 1912–1926. doi:10.1016/j.rse.2007.02.043.

    Article  Google Scholar 

  • Aspinall, R. J., Marcus, W. A., & Boardman, J. W. (2002). Considerations in collecting, processing and analyzing high spatial resolution hyperspectral data for environmental investigations. Journal of Geographical Systems, 4, 15–29. doi:10.1007/s101090100071.

    Article  Google Scholar 

  • Bao, S. D. (2000). Soil and agricultural chemistry analysis. Beijing: Agriculture Press of China.

    Google Scholar 

  • Baret, F., & Guyot, G. (1991). Potentials and limits of vegetation indexes for LAI and APAR assessment. Remote Sensing of Environment, 35, 161–173. doi:10.1016/0034-4257(91)90009-U.

    Article  Google Scholar 

  • Brown, H. E., Diuk-Wasser, M. A., Guan, Y. T., Caskey, S., & Fish, D. (2008). Comparison of three satellite sensors at three spatial scales to predict larval mosquito presence in Connecticut wetlands. Remote Sensing of Environment, 112(5), 2301–2308. doi:10.1016/j.rse.2007.10.008.

    Article  Google Scholar 

  • Cao, J. H., Yuan, D. X., & Pei, J. G. (2005). Karst ecosystem of Southwest China constrained by geological setting. Beijing: Geology Press.

    Google Scholar 

  • Ceccato, P., Flasse, S., Tarantola, S., Jacquemoud, S., & Gregoire, J. M. (2001). Detecting vegetation leaf water content using reflectance in the optical domain. Remote Sensing of Environment, 77, 22–33. doi:10.1016/S0034-4257(01)00191-2.

    Article  Google Scholar 

  • Chi, G. Y., Liu, X. H., Liu, S. H., & Yang, Z. F. (2005). The relationships between heavy metal pollution and spectral characteristics of Miscanthus floridulus in Dawu River Basin. Ecology and Environment, 14(4), 549–554.

    Google Scholar 

  • Clark, R. N., Swayze, G. A., Livo, K. E., et al. (2003). Imaging spectroscopy: Earth and planetary remote sensing with the USGS tetracorder and expert systems. Journal of Geophysical Research, 108, 5.1–5.44. doi:10.1029/2002JE001847.

    Google Scholar 

  • Collins, W., Chang, S. H., Raines, G. L., & Ashley, R. (1983). Airborne biogeophysical mapping of hidden mineral deposits. Economic Geology, 78(4), 737–749.

    Article  CAS  Google Scholar 

  • Galvao, L. S., Formaggio, A. R., & Tisot, D. A. (2005). Discrimination of sugarcane varieties in southeastern Brazil with EO-1 Hyperion data. Remote Sensing of Environment, 94, 523–534. doi:10.1016/j.rse.2004.11.012.

    Article  Google Scholar 

  • Garnier, E., Shipley, B., Roumet, C., & Laurent, G. (2001). A standardized protocol for the determination of species leaf area and leaf dry matter content. Functional Ecology, 15, 688–695. doi:10.1046/j.0269-8463.2001.00563.x.

    Article  Google Scholar 

  • Goetz, A. F. H., Solomon, G. V. J., & Rock, B. N. (1985). Imaging spectrometry for earth remote sensing. Science, 228(4704), 1147–1153. doi:10.1126/science.228.4704.1147.

    Article  Google Scholar 

  • Guangxi Institute of Botany (1982). Manual of vegetation in karst region. Nanning: Guangxi People Press.

    Google Scholar 

  • Guisan, A., Weiss, S. B., & Weiss, A. D. (1999). GLM versus CCA spatial modeling of plant species distribution. Plant Ecology, 143, 107–122. doi:10.1023/A:1009841519580.

    Article  Google Scholar 

  • Jacquemoud, S., & Baret, F. (1990). PROSPECT: A model of leaf optical properties spectra. Remote Sensing of Environment, 34, 74–91. doi:10.1016/0034-4257(90)90100-Z.

    Article  Google Scholar 

  • Jan, L., & Peter, S. (2003). Multivariate analysis of ecological data using CANOCO. Cambridge: Cambridge University Press.

    Google Scholar 

  • LeGrand, H. E. (1973). Hydrological and ecological problems of karst regions. Science, 179, 859–864. doi:10.1126/science.179.4076.859.

    Article  Google Scholar 

  • Li, X. K., Su, Z. M., Lu, S. H., Ou, Z. L., Xiang, W. S., Ou, Z., et al. (2003). The spatial pattern of natural vegetation in the karst regions of Guangxi and the ecological signality for ecosystem rehabilitation and reconstruction. Journal of Mountain Science, 21(2), 129–139 (in Chinese).

    Google Scholar 

  • Mars, J. C., & Crowley, J. K. (2003). Mapping mine wastes and analyzing areas affected by selenium-rich water runoff in southeast Idaho using AVIRIS imagery and digital elevation data. Remote Sensing of Environment, 84(3), 422–436. doi:10.1016/S0034-4257(02)00132-3.

    Article  Google Scholar 

  • Martin, M. E., Newman, S. D., Aber, J. D., & Congalton, R. G. (1998). Determining forest species composition using high spectral resolution remote sensing data. Remote Sensing of Environment, 65, 249–254. doi:10.1016/S0034-4257(98)00035-2.

    Article  Google Scholar 

  • Milton, N. M., & Mouat, D. A. (1989). Remote sensing of vegetation responses to natural and cultural environmental conditions. Photogrammetric Engineering and Remote Sensing, 55, 1167–1173.

    Google Scholar 

  • Ou, Z. L., Su, Z. M., & Li, X. K. (2004). Flora of karst vegetation in Guangxi. Guihaia, 24(4), 302–310.

    Google Scholar 

  • Rong, L., Wang, S. J., & Liu, N. (2005). Leaf anatomical characters and its ecological adaptation of the pioneer species in the karst mountain area. Journal of Mountain Science, 23(1), 35–42 (in Chinese).

    Google Scholar 

  • Schmidt, K. S., & Skidmore, A. K. (2003). Spectral discrimination of vegetation types in a coastal wetland. Remote Sensing of Environment, 85, 92–108. doi:10.1016/S0034-4257(02)00196-7.

    Article  Google Scholar 

  • Schmidtlein, S. (2005). Imaging spectroscopy as a tool for mapping Ellenberg indicator values. Journal of Applied Ecology, 42, 966–974. doi:10.1111/j.1365-2664.2005.01064.x.

    Article  Google Scholar 

  • Sims, D. A., & Gamon, J. A. (2002). Relationships between leaf pigment content and spectral reflectance across a wide range of species, leaf structures and developmental stages. Remote Sensing of Environment, 81, 337–354. doi:10.1016/S0034-4257(02)00010-X.

    Article  Google Scholar 

  • Smart, S. M., & Scott, W. A. (2004). Bias in Ellenberg indicator values—problems with detection of the effect of vegetation type. Journal of Vegetation Science, 15(6), 843–846. doi:http://dx.doi.org/10.1658/1100-9233(2004)015[0843:BIEIVP]2.0.CO;2.

    Google Scholar 

  • Su, Z. M. (1998). Classified system of natural vegetation in Guangxi. Guihaia, 18(3), 237–246 (in Chinese).

    Google Scholar 

  • Su, Z. M., & Li, X. K. (2003). Types of natural vegetation in karst region of Guangxi and its classified system. Guihaia, 23(4), 289–293 (in Chinese).

    Google Scholar 

  • ter Braak, C. J. F. (1986). Canonical correspondence analysis: New eigenvector techniques for multivariate direct gradient analysis. Ecology, 67, 1167–1179. doi:10.2307/1938672.

    Article  Google Scholar 

  • ter Braak, C. J. F., & Prentice, I. C. (1988). A theory of gradient analysis. Advances in Ecological Research, 18, 271–313. doi:10.1016/S0065-2504(08)60183-X.

    Article  Google Scholar 

  • ter Braak, C. J. F., & Verdonschot, P. F. M. (1995). Canonical correspondence analysis and related multivariate methods in aquatic ecology. Aquatic Sciences, 57, 255–289. doi:10.1007/BF00877430.

    Article  Google Scholar 

  • Tsai, F., & Philpot, W. (1998). Derivative analysis of hyperpsectral data. Remote Sensing of Environment, 66, 41–51. doi:10.1016/S0034-4257(98)00032-7.

    Article  Google Scholar 

  • van der Meer, F. D., Yang, H., & Lang, H. (2001). Imaging spectrometry and geological applications. In F. D. van der Meer, & S. M. de Jong (Eds.), Imaging spectrometry (pp. 201–218). Dordrecht, The Netherlands: Kluwer.

    Google Scholar 

  • Wamelink, G. W. W., Joosten, V., & van Dobben, H. F. (2002). Validity of Ellenberg indicator values judged from physico-chemical field measurements. Journal of Vegetation Science, 13(2), 269–278. doi:10.1658/1100-9233(2002)013[0269:VOEIVJ]2.0.CO;2.

    Article  Google Scholar 

  • Xie, L. P., Wang, S. J., & Xiao, D. A. (2007). Ca covariant relation in plant–soil system in a small karst catchment. Earth and Environment, 35(1), 26–32 (in Chinese).

    CAS  Google Scholar 

  • Yang, M. D. (1992). On the fragility of karst environment. Yunan Geographic Environment Research, 2(1), 21–29 (in Chinese).

    Google Scholar 

  • Yang, C., Liu, C. Q., Song, Z. L., & Liu, Z. M. (2007). Characteristics of the nutrient contents in plants from Guizhou karst mountainous area of China. Ecology and Environment, 16(2), 503–508.

    Google Scholar 

  • Yuan, D. X. (1993). Karst studies of China. Beijing: Geology Press.

    Google Scholar 

  • Zhang, J. T. (2004). Quantitative ecology. Beijing: Science Press.

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Yuemin Yue.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Yue, Y., Wang, K., Zhang, B. et al. Exploring the relationship between vegetation spectra and eco-geo-environmental conditions in karst region, Southwest China. Environ Monit Assess 160, 157–168 (2010). https://doi.org/10.1007/s10661-008-0665-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10661-008-0665-z

Keywords

Navigation