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An automated (novel) algorithm for estimating green vegetation cover fraction from digital image: UIP-MGMEP

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Abstract

Green vegetation cover fraction (VCF) is an important indicator of vegetation status in ecology and agronomy. Digital image analysis (DIA) has been widely accepted as a new VCF measurement technique. In this study, we present a novel fully automatic threshold segmentation algorithm for VCF measurements, which is named as upper inflection point plus mean gradient magnitude of edge pixels (UIP-MGMEP). The algorithm performs VCF estimation upon the vegetation index Excess Green (EXG). UIP-MGMEP optimizes the EXG threshold by searching the upper inflection point (UIP) of the M-Et curve (mean gradient magnitude of edge pixels (MGMEP) vs. EXG threshold), based on the assumption that EXG variance of the boundary pixels between vegetation and background is larger than the variance of the background. Five typical sample images are used to illustrate how ground complexity reduces the distinctness of the UIP. Three controlled experiments are illustrated to test the robustness of UIP-MGMEP to resolution, exposure, and ground complexity. The results show that UIP-MGMEP is a promising algorithm for automatic VCF estimation upon digital images. Compared to broad-leaved grass, narrow-leaved grass is more sensitive to resolution and exposure. To reduce ground complexity, smaller footprint size while more images to cover the same area may be better than one image with large footprint size. UIP-MGMEP is fully automatic, making it promising for batch processing of VCF measurements that is very difficult in any wide-range field survey in the past. UIP-MGMEP algorithm can only extract green vegetation and is not suitable for non-green (even grayish-green) vegetation, due to the limits of vegetation index EXG. In addition, UIP-MGMEP is not recommended for images with VCF less than 0.5% or greater than 99.5%.

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References

  • Bold, K. C., Wood, F., Edwards, P. J., Williard, K. W. J., & Schoonover, J. E. (2009). Using photographic image analysis to assess ground cover: a case study of forest road cutbanks. Environmental Monitoring and Assessment, 163(1–4), 685–698.

    Google Scholar 

  • Booth, D. T., & Tueller, P. T. (2003). Rangeland monitoring using remote sensing. Arid Land Research and Management, 17(4), 455–467.

    Article  Google Scholar 

  • Booth, D. T., Cox, S. E., & Johnson, D. E. (2005). Detection-threshold calibration and other factors influencing digital measurements of ground cover. Rangeland Ecology & Management, 58(6), 598–604.

    Article  Google Scholar 

  • Booth, D. T., Cox, S. E., & Berryman, R. D. (2006a). Point sampling digital imagery with ‘Samplepoint’. Environmental Monitoring and Assessment, 123(1–3), 97–108.

    Article  Google Scholar 

  • Booth, D. T., Cox, S. E., Meikle, T. W., & Fitzgerald, C. (2006b). The accuracy of ground-cover measurements. Rangeland Ecology & Management, 59(2), 179–188.

    Article  Google Scholar 

  • Clark, R. N., & Roush, T. L. (1984). Reflectance spectroscopy: quantitative analysis techniques for remote sensing applications. Journal of Geophysical Research, 89(B7), 6329–6340.

    Article  CAS  Google Scholar 

  • Gitelson, A. A., Kaufman, Y. J., Stark, R., & Rundquist, D. (2002). Novel algorithms for remote estimation of vegetation fraction. Remote Sensing of Environment, 80(1), 76–87.

    Article  Google Scholar 

  • Golzarian, M., Desbiolles, J. M. A., Lee, M. K., & Stafford, J. V. (2007) Colour index evaluation method for plant segmentation from a soil background. In Precision agriculture '07. Papers presented at the European conference on precision agriculture, Skiathos, Greece, 3–6 June, 2007.

  • Golzarian, M. R., Lee, M. K., & Desbiolles, J. M. A. (2012). Evaluation of color indices for improved segmentation of plant images. Transactions of the ASABE, 55(1), 261–273.

    Article  Google Scholar 

  • Gutman, G., & Ignatov, A. (1998). The derivation of the green vegetation fraction from NOAA/AVHRR data for use in numerical weather prediction models. International Journal of Remote Sensing, 19(8), 1533–1543.

    Article  Google Scholar 

  • Hahn, I., & Scheuring, I. (2003). The effect of measurement scales on estimating vegetation cover. Community Ecology, 4(1), 29–33.

    Article  Google Scholar 

  • Korhonen, L., Korhonen, K. T., Rautiainen, M., & Stenberg, P. (2015). Estimation of forest canopy cover : a comparison of field measurement techniques. Silva Fennica, 40(4), 577–588.

    Google Scholar 

  • Laliberte, A. S., Rango, A., Herrick, J. E., Fredrickson, E. L., & Burkett, L. (2007). An object-based image analysis approach for determining fractional cover of senescent and green vegetation with digital plot photography. Journal of Arid Environments, 69, 1–14.

    Article  Google Scholar 

  • Louhaichi, M., Borman, M. M., & Johnson, D. E. (2001). Spatially located platform and aerial photography for documentation of grazing impacts on wheat. Geocarto International, 16(1), 65–70.

    Article  Google Scholar 

  • Lukina, E. V., Stone, M. L., & Rann, W. R. (1999). Estimating vegetation coverage in wheat using digital images. Journal of Plant Nutrition, 22(2), 341–350.

    Article  CAS  Google Scholar 

  • Macfarlane, C., & Ogden, G. N. (2012). Automated estimation of foliage cover in forest understorey from digital nadir images. Methods in Ecology and Evolution, 3(2), 405–415.

    Article  Google Scholar 

  • Pan, G., Li, F. M., & Sun, G. J. (2007) Digital camera based measurement of crop cover for wheat yield prediction. In Geoscience and Remote Sensing Symposium (pp. 797–800): IEEE.

  • Patrignani, A., & Ochsner, T. E. (2015). Canopeo: A powerful new tool for measuring fractional green canopy cover. Agronomy Journal, 107(6), 2312–2320.

    Article  CAS  Google Scholar 

  • Purcell, N. B. S. P., & Larry, C. (2000). Soybean canopy coverage and light interception measurements using digital imagery. Crop Science, 40(3), 834–837.

    Article  Google Scholar 

  • Rasmussen, J., Norremark, M., & Bibby, B. M. (2007). Assessment of leaf cover and crop soil cover in weed harrowing research using digital images. Weed Research, 47(4), 299–310.

    Article  Google Scholar 

  • Richardson, M. D., Karcher, D. E., & Purcell, L. C. (2001). Quantifying turfgrass cover using digital image analysis. Crop Science, 41(6), 1884–1888.

    Article  Google Scholar 

  • Song, W., Mu, X., Yan, G., & Huang, S. (2015). Extracting the green fractional vegetation cover from digital images using a shadow-resistant algorithm (SHAR-LABFVC). Remote Sensing, 7(8), 10425–10443.

    Article  Google Scholar 

  • Sykes, J. M., Horrill, A. D., & Mountford, M. D. (1983). Use of visual cover assessments as quantitative estimators of some British woodland taxa. Journal of Ecology, 71(2), 437–450.

    Article  Google Scholar 

  • Thorp, K. R., & Tian, L. F. (2004). A review on remote sensing of weeds in agriculture. Precision Agriculture, 5(5), 477–508.

    Article  Google Scholar 

  • Woebbecke, D. M., Meyer, G. E., Von Bargen, K., & Mortensen, D. A. (1995). Color indices for weed identification under various soil, residue, and lighting conditions. Transactions of ASAE, 38(1), 259–269.

    Article  Google Scholar 

  • Zhou, Q., & Robson, M. (2001). Automated rangeland vegetation cover and density estimation using ground digital images and a spectral-contextual classifier. International Journal of Remote Sensing, 22(17), 3457–3470.

    Article  Google Scholar 

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Acknowledgments

We would like to thank Dr. Alison Beamish from the University of British Columbia and Prof. Zhang Jian from the East China Normal University for their assistance with the English language and grammatical editing of the manuscript.

Funding

This work was supported by the TIWTE Research and Innovation Fund Project under Grant TKS160112 and TKS170211, Qinghai Provincial Transportation Science and Technology Fund Project under Grant 31118022.

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Authors and Affiliations

  1. Laboratory of Environmental Protection in Water Transport Engineering, Tianjin Research Institute for Water Transport Engineering, Ministry of Transport, Tianjin, 300456, China

    J. B. Hu, M. X. Dai & S. T. Peng

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  1. J. B. Hu
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  2. M. X. Dai
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  3. S. T. Peng
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Correspondence to J. B. Hu.

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Hu, J.B., Dai, M.X. & Peng, S.T. An automated (novel) algorithm for estimating green vegetation cover fraction from digital image: UIP-MGMEP. Environ Monit Assess 190, 687 (2018). https://doi.org/10.1007/s10661-018-7075-7

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