Advertisement

Integrating multiple vegetation indices via an artificial neural network model for estimating the leaf chlorophyll content of Spartina alterniflora under interspecies competition

  • Pudong Liu
  • Runhe ShiEmail author
  • Chao Zhang
  • Yuyan Zeng
  • Jiapeng Wang
  • Zhu Tao
  • Wei Gao
Article

Abstract

The invasive species Spartina alterniflora and native species Phragmites australis display a significant co-occurrence zonation pattern and this co-exist region exerts most competitive situations between these two species, competing for the limited space, directly influencing the co-exist distribution in the future. However, these two species have different growth ratios in this area, which increase the difficulty to detect the distribution situation directly by remote sensing. As chlorophyll content is a key indicator of plant growth and physiological status, the objective of this study was to reduce the effect of interspecies competition when estimating Cab content; we evaluated 79 published representative indices to determine the optimal indices for estimating the chlorophyll a and b (Cab) content. After performing a sensitivity analysis for all 79 spectral indices, five spectral indices were selected and integrated using an artificial neural network (ANN) to estimate the Cab content of different competition ratios: the Gitelson ratio green index, the transformed chlorophyll absorption ratio index/optimized soil-adjusted vegetation index, the modified normalized difference vegetation index, the chlorophyll fluorescence index, and the Vogelmann chlorophyll index. The ANN method yielded better results (R 2 = 0.7110 and RMSE = 8.3829 μg cm−2) on average than the best single spectral index (R 2 = 0.6319 and RMSE = 9.3535 μg cm−2), representing an increase of 10.78% in R 2 and a decrease of 10.38% in RMSE. Our results indicated that integrating multiple vegetation indices with an ANN can alleviate the impact of interspecies competition and achieve higher estimation accuracy than the traditional approach using a single index.

Keywords

Spartina alterniflora Interspecies competition Multiple vegetation indices Artificial neural network Chlorophyll 

Notes

Funding information

This work was partially supported by the National Key Research and Development Program of China (No. 2016YFC1302602), the Science and Technology Commission of Shanghai Municipality (Grant No. 15dz1207805), the Shanghai Municipal Commission of Health and Family Planning (Grant No. 15GWZK0201), the National Science Foundation of China (No. 31500392), the General Financial Grant from the Chinese Postdoctoral Science Foundation (No. 2015M 581569), the Director Grant of Key Laboratory of Geographic Information Science, Ministry of Education (No. KLGIS2015C01), the 2017 Open Research Fund of the Shanghai Key Laboratory of Urbanization and Ecological Restoration (No. SHUES2017B01) and the Fundamental Research Funds for the Central Universities of China.

Supplementary material

10661_2017_6323_MOESM1_ESM.docx (43 kb)
ESM 1 (DOCX 43 kb)

References

  1. Aber, J. D., & Federer, C. A. (1992). A generalized, lumped-parameter model of photosynthesis, evapotranspiration and net primary production intemperate and boreal forest ecosystems. Oecologia, 92, 463–474.  https://doi.org/10.1007/BF00317837.CrossRefGoogle Scholar
  2. An, S. Q., Guà, B. H., Zhou, C. F., Wang, Z. S., Deng, Z. F., Zhi, Y. B., et al. (2007). Spartina invasion in China—implications for invasive species management and future research. Weed Research, 47, 183–191.CrossRefGoogle Scholar
  3. Butera, M. K. (1983). Remote sensing of wetlands. IEEE Transactions on Geoscience and Remote Sensing, GE-21(3), 383–392.  https://doi.org/10.1109/TGRS.1983.350471.CrossRefGoogle Scholar
  4. Chen, L., Huang, J. F., Wang, F. M., & Tang, Y. L. (2007). Comparison between back propagation neural network and regression models for the estimation of pigment content in rice leaves and panicles using hyperspectral data. International Journal of Remote Sensing, 28(16), 3457–3478.  https://doi.org/10.1080/01431160601024242.CrossRefGoogle Scholar
  5. Chen, J., Quan, W., Cui, T., & Song, Q. (2015). Estimation of total suspended matter concentration from MODIS data using a neural network model in the China eastern coastal zone. Estuarine, Coastal and Shelf Science, 155, 104–113.  https://doi.org/10.1016/j.ecss.2015.01.018.CrossRefGoogle Scholar
  6. Duarte, C. M., Middelburg, J. J., & Caraco, N. (2005). Major role of marine vegetation on the oceanic carbon cycle. Biogeosciences, 2, 1–8.  https://doi.org/10.5194/bgd-1-659-2004.CrossRefGoogle Scholar
  7. Fagherazzi, S., Kirwan, M. L., Mudd, S. M., Guntenspergen, G. R., Temmerman, S., Rybczyk, J. M., et al. (2012). Numerical models of salt marsh evolution-ecological geomorphic. Reviews of Geophysics, 50(2011), 1–28.  https://doi.org/10.1029/2011RG000359.Google Scholar
  8. Féret, J.-B., François, C., Gitelson, A., Asner, G. P., Barry, K. M., Panigada, C., et al. (2011). Optimizing spectral indices and chemometric analysis of leaf chemical properties using radiative transfer modeling. Remote Sensing of Environment, 115(10), 2742–2750.  https://doi.org/10.1016/j.rse.2011.06.016.CrossRefGoogle Scholar
  9. Fu, X. H., & Zhao, H. (2010). Application of MATLAB neural network design. Beijing: Machinery Industry Press.Google Scholar
  10. Gao, Z. G., & Zhang, L. Q. (2006). Multi-seasonal spectral characteristics analysis of coastal salt marsh vegetation in Shanghai, China. Estuarine, Coastal and Shelf Science, 69(1–2), 217–224.  https://doi.org/10.1016/j.ecss.2006.04.016.CrossRefGoogle Scholar
  11. Ge, Z.-M., Wang, T.-H., Wang, K.-Y., & Wang, X.-M. (2008). Characteristics of coastal wetland ecosystem of the Yangtze Estuary and conservation for key communities (pp. 189). Beijing: Science Press.Google Scholar
  12. Ge, Z., Cao, H., & Zhang, L. (2013). A process-based grid model for the simulation of range expansion of Spartina alterniflora on the coastal saltmarshes in the Yangtze Estuary. Ecological Engineering, 58, 105–112.  https://doi.org/10.1016/j.ecoleng.2013.06.024.CrossRefGoogle Scholar
  13. Ge, Z.-M., Guo, H.-Q., Zhao, B., & Zhang, L.-Q. (2015). Plant invasion impacts on the gross and net primary production of the salt marsh on eastern coast of China: insights from leaf to ecosystem. Journal of Geophysical Research – Biogeosciences, 120(1), 169–186.  https://doi.org/10.1002/2014jg002736.CrossRefGoogle Scholar
  14. Goetz, S. J., & Prince, S. D. (1996). Remote sensing of net primary production in boreal forest stands. Agricultural and Forest Meteorology, 78(3–4), 149–179.  https://doi.org/10.1016/0168-1923(95)02268-6.CrossRefGoogle Scholar
  15. Gumbricht, T., Roman-cuesta, R. M., Murdiyarso, D., Verchot, L., Herold, M., Wittmann, F., et al. (2017). An expert system model for mapping tropical wetlands and peatlands reveals South America as the largest contributor. Global Change Biology, 23(9), 3581–3599.  https://doi.org/10.1111/gcb.13689.CrossRefGoogle Scholar
  16. Hill, D. J., Tarasoff, C., Whitworth, G. E., Baron, J., Bradshaw, J. L., & Church, J. S. (2016). Utility of unmanned aerial vehicles for mapping invasive plant species: a case study on yellow flag iris (Iris pseudacorus L.) International Journal of Remote Sensing, 1–23.  https://doi.org/10.1080/01431161.2016.1264030.
  17. Hu, Z.-J., Ge, Z.-M., Ma, Q., Zhang, Z.-T., Tang, C.-D., Cao, H.-B., et al. (2015). Revegetation of a native species in a newly formed tidal marsh under varying hydrological conditions and planting densities in the Yangtze Estuary. Ecological Engineering, 83, 354–363.  https://doi.org/10.1016/j.ecoleng.2015.07.005.CrossRefGoogle Scholar
  18. Keiner, L. E., & Yan, X. (1998). A neural network model for estimating sea surface chlorophyll and sediments from thematic mapper imagery. Remote Sensing of Environment, 66(2), 153–165.  https://doi.org/10.1016/S0034-4257(98)00054-6.CrossRefGoogle Scholar
  19. Kent, M., Coker, P. (1992). Vegetation description and analysis: a practical approach (pp. 363). Chichester: John Wiley and Sons.Google Scholar
  20. Kirwan, M. L., & Mudd, S. M. (2012). Response of salt-marsh carbon accumulation to climate change. Nature, 489(7417), 550–553.  https://doi.org/10.1038/nature11440.CrossRefGoogle Scholar
  21. Li, B., Liao, C.-h., Zhang, X.-d., Chen, H.-l., Wang, Q., Chen, Z.-y., et al. (2009). Spartina alterniflora invasions in the Yangtze River estuary, China: an overview of current status and ecosystem effects. Ecological Engineering, 35(4), 511–520.  https://doi.org/10.1016/j.ecoleng.2008.05.013.CrossRefGoogle Scholar
  22. Li, H. L., Wang, Y. Y., An, S. Q., Zhi, Y. B., Lei, G. C., & Zhang, M. X. (2014). Sediment type affects competition between a native and an exotic species in coastal China. Scientific Reports, 4, 6748.  https://doi.org/10.1038/srep06748.CrossRefGoogle Scholar
  23. Lichtenthaler, H. K., & Wellburn, A. R. (1983). Determinations of total carotenoids and chlorophyll a and b of leaf extracts in different solvents. Biochemical Society Transactions, 11(1955), 591–592.  https://doi.org/10.1042/bst0110591.CrossRefGoogle Scholar
  24. Liu, M., Liu, X., Li, M., Fang, M., & Chi, W. (2010). Neural-network model for estimating leaf chlorophyll concentration in rice under stress from heavy metals using four spectral indices. Biosystems Engineering, 106(3), 223–233.  https://doi.org/10.1016/j.biosystemseng.2009.12.008.CrossRefGoogle Scholar
  25. Liu, P., Shi, R., & Gao, W. (2017). Estimating leaf chlorophyll contents by combining multiple spectral indices with an artificial neural network. Earth Science Informatics.  https://doi.org/10.1007/s12145-017-0319-1.
  26. Main, R., Cho, M. A., Mathieu, R., O’Kennedy, M. M., Ramoelo, A., & Koch, S. (2011). An investigation into robust spectral indices for leaf chlorophyll estimation. ISPRS Journal of Photogrammetry and Remote Sensing, 66(6), 751–761.  https://doi.org/10.1016/j.isprsjprs.2011.08.001.CrossRefGoogle Scholar
  27. Mänd, P., Hallik, L., Peñuelas, J., Nilson, T., Duce, P., Emmett, B. A., et al. (2010). Responses of the reflectance indices PRI and NDVI to experimental warming and drought in European shrublands along a north–south climatic gradient. Remote Sensing of Environment, 114(3), 626–636.  https://doi.org/10.1016/j.rse.2009.11.003.CrossRefGoogle Scholar
  28. Medeiros, D. L., White, D. S., & Howes, B. L. (2013). Replacement of Phragmites australis by Spartina alterniflora: the role of competition and salinity. Wetlands, 33(3), 421–430.  https://doi.org/10.1007/s13157-013-0400-6.CrossRefGoogle Scholar
  29. Morzaria-luna, H. N., & Zedler, J. B. (2014). Competitive interactions between two salt marsh halophytes across stress gradients. Wetlands, 34, 31–42.  https://doi.org/10.1007/s13157-013-0479-9.CrossRefGoogle Scholar
  30. Mudzengi, C. P., Murungweni, C., Dahwa, E., Poshiwa, X., Kativu, S., And, S. M. D. (2013). Woody species composition and structure in a semi-arid environment invaded by Dichrostachys cinerea (I.) Wight and Arn (Fabaceae), (pp. 1–10). International Journal of Scientific and Research Publications.Google Scholar
  31. Peter, C. R., & Burdick, D. M. (2010). Can plant competition and diversity reduce the growth and survival of exotic Phragmites australis invading a tidal marsh? Estuaries and Coasts, 33(5), 1225–1236.  https://doi.org/10.1007/s12237-010-9328-8.CrossRefGoogle Scholar
  32. Powers, R. P., Hay, G. J., & Chen, G. (2012). How wetland type and area differ through scale: a GEOBIA case study in Alberta’s Boreal Plains. Remote Sensing of Environment, 117, 135–145.  https://doi.org/10.1016/j.rse.2011.07.009.CrossRefGoogle Scholar
  33. Rosso, P. H., Michel, U., Cronin, J. T., Civco, D. L., Ehlers, M., Stevens, R. D., et al. (2008). Monitoring the invasion of Phragmites australis in coastal marshes of Louisiana, USA, using multisource remote sensing data. Remote Sensing for Environmental Monitoring, GIS Applications, and Geology VIII, Proc. of SPIE, 7110, 71100B.  https://doi.org/10.1117/12.800269.
  34. Smith, S. M., & Lee, K. D. (2015). The influence of prolonged flooding on the growth of Spartina alterniflora in Cape Cod (Massachusetts, USA). Aquatic Botany, 127, 53–56.  https://doi.org/10.1016/j.aquabot.2015.08.002.CrossRefGoogle Scholar
  35. Tang, L. (2008). Control of Spartina alterniflora by an integrated approach of clipping, waterlogging and ecological replacement with reed: an experimental study of ecological mechanisms. Ph.D, Dissertation, Shanghai: fudan university.Google Scholar
  36. Tang, L., Gao, Y., Li, B., Wang, Q., Wang, C.-H., & Zhao, B. (2014). Spartina alterniflora with high tolerance to salt stress changes vegetation pattern by outcompeting native species. Ecosphere, 5(9), 1–18.  https://doi.org/10.1890/ES14-00166.1. CrossRefGoogle Scholar
  37. Wan, S., Qin, P., Liu, J., & Zhou, H. (2009). The positive and negative effects of exotic Spartina alterniflora in China. Ecological Engineering, 35(4), 444–452.  https://doi.org/10.1016/j.ecoleng.2008.05.020.CrossRefGoogle Scholar
  38. Wang, Q., Wang, C. H., Zhao, B., Ma, Z. J., Luo, Y. Q., Chen, J. K., et al. (2006). Effects of growing conditions on the growth of and interactions between salt marsh plants: implications for invasibility of habitats. Biological Invasions, 8(7), 1547–1560.  https://doi.org/10.1007/s10530-005-5846-x.CrossRefGoogle Scholar
  39. Yuan, Y., Wang, K., Li, D., Pan, Y., Lv, Y., Zhao, M., et al. (2013). Interspecific interactions between Phragmites australis and Spartina alterniflora along a tidal gradient in the Dongtan wetland, Eastern China. PLoS One, 8(1), e53843.  https://doi.org/10.1371/journal.pone.0053843.CrossRefGoogle Scholar
  40. Yuan, Y., Zhang, C., & Li, D. (2017). The effect of artificial mowing on the competition of Phragmites australis and Spartina alterniflora in the Yangtze Estuary. Scientifica (Cairo), 2017, 7853491.  https://doi.org/10.1155/2017/7853491.Google Scholar
  41. Zuo, P., Zhao, S., Liu, C. a., Wang, C., & Liang, Y. (2012). Distribution of Spartina spp. along China’s coast. Ecological Engineering, 40, 160–166.  https://doi.org/10.1016/j.ecoleng.2011.12.014.CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Pudong Liu
    • 1
    • 2
    • 3
    • 4
  • Runhe Shi
    • 1
    • 2
    • 3
    • 4
    Email author
  • Chao Zhang
    • 1
    • 2
    • 3
  • Yuyan Zeng
    • 1
    • 2
    • 3
  • Jiapeng Wang
    • 1
    • 2
    • 3
  • Zhu Tao
    • 1
    • 2
    • 3
  • Wei Gao
    • 1
    • 2
    • 3
    • 4
    • 5
  1. 1.Key Laboratory of Geographic Information Science, Ministry of EducationEast China Normal UniversityShanghaiChina
  2. 2.School of Geographic SciencesEast China Normal UniversityShanghaiChina
  3. 3.Joint Laboratory for Environment Remote Sensing and Data AssimilationEast China Normal University & Institute of Remote Sensing and Digital Earth Chinese Academy of SciencesShanghaiChina
  4. 4.Joint Research Institute for New Energy and the EnvironmentEast China Normal University and Colorado State UniversityShanghaiChina
  5. 5.Natural Resource Ecology Laboratory and Department of Ecosystem Science and SustainabilityColorado State UniversityFort CollinsUSA

Personalised recommendations