Integrating multiple vegetation indices via an artificial neural network model for estimating the leaf chlorophyll content of Spartina alterniflora under interspecies competition
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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.
KeywordsSpartina alterniflora Interspecies competition Multiple vegetation indices Artificial neural network Chlorophyll
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.
- 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
- 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
- Fu, X. H., & Zhao, H. (2010). Application of MATLAB neural network design. Beijing: Machinery Industry Press.Google Scholar
- 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
- 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
- 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
- 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.
- 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
- Kent, M., Coker, P. (1992). Vegetation description and analysis: a practical approach (pp. 363). Chichester: John Wiley and Sons.Google Scholar
- 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
- 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
- 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.
- 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
- 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
- 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
- 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.
- 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
- 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
- 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