Advertisement

Improved assessment of pasture availability in semi-arid grassland of South Africa

  • Mamokete N. V. Dingaan
  • Mitsuru TsuboEmail author
Article
  • 34 Downloads

Abstract

Satellite remote sensing technology has been successfully used to monitor grassland productivity, especially for estimating the green component of biomass using popular indices such as Normalized Difference Vegetation Index (NDVI). The non-green component, which includes senescent and dead standing material, has not been widely quantified. Our study aimed at devising a satellite remote sensing-based method that can distinguish between green and non-green herbage, in order to improve the accuracy of total aboveground biomass (TBM) estimations. This in turn can minimise under-estimations of pasture availability in semi-arid grasslands. MODIS satellite data was used to determine the relations of various indices to ground-measured green aboveground biomass (GBM) and non-green aboveground biomass (NBM) in South African semi-arid grasslands. We found a strong correlation of GBM to NDVI. We were then able to detect a correlation of NBM to Normalized Difference Water Index (NDWI), but a robust relationship was between NDWI and the ratio of NBM to TBM. NDVI and NDWI were used to estimate long-term TBM, which varies inter- and intra-seasonally. During the non-rainy season, NBM is important to maintain livestock grazing and in this regard monitoring of pasture availability in terms of green and non-green herbage is critical for sustainable grassland management.

Keywords

Biomass Dryland Pasture Remote sensing Vegetation 

Notes

Acknowledgements

We are grateful to Mr. Eric Economon and Mr. Amukelani Maluleke for assisting with field data collection.

Funding information

This research was partially funded by the National Research Foundation of South Africa and International Platform for Dryland Research and Education, Tottori University, Japan.

References

  1. Anderson, G., Hanson, J., & Haas, R. (1993). Evaluating Landsat thematic mapper derived vegetation indices for estimating above-ground biomass on semiarid rangelands. Remote Sensing of Environment, 45, 165–175.  https://doi.org/10.1016/0034-4257(93)90040-5.CrossRefGoogle Scholar
  2. Beeri, O., Phillips, R., Hendrickson, J., Frank, A. B., & Kronberg, S. (2007). Estimating forage quantity and quality using aerial hyperspectral imagery for northern mixed-grass prairie. Remote Sensing of Environment, 110, 216–225.  https://doi.org/10.1016/j.rse.2007.02.027.CrossRefGoogle Scholar
  3. Boutton, T. W., Tieszen, L. L., & Imbamba, S. K. (1988). Seasonal changes in the nutrient content of East African grassland vegetation. African Journal of Ecology, 26, 103–115.  https://doi.org/10.1111/j.1365-2028.1988.tb00961.x.CrossRefGoogle Scholar
  4. Cao, X., Chen, J., Matsushita, B., & Imura, H. (2010). Developing a MODIS-based index to discriminate dead fuel from photosynthetic vegetation and soil background in the Asian steppe area. International Journal of Remote Sensing, 31, 1589–1604.  https://doi.org/10.1080/01431160903475274.CrossRefGoogle Scholar
  5. Cox, J. R. (1985). Above-ground biomass and nitrogen quantities in a big sacaton [Sporobolus wrightii] grassland. Journal of Range Management, 38, 273–276.  https://doi.org/10.2307/3898984.CrossRefGoogle Scholar
  6. Craine, J. M., Nippert, J. B., Elmore, A. J., Skibbe, A. M., Hutchinson, S. L., & Brunsell, N. A. (2012). Timing of climate variability and grassland productivity. Proceedings of the National Academy of Sciences of the United States of America, 109, 3401–2405.  https://doi.org/10.1073/pnas.1118438109.CrossRefGoogle Scholar
  7. Danielson, J. J., & Gesch, D. B. (2011). Global multi-resolution terrain elevation data 2010 (GMTED2010), Open-File Report 2011–1073. Reston, Virginia: U.S. Geological Survey.Google Scholar
  8. Dingaan, M. N. V., Walker, S., Tsubo, M., & Newby, T. (2016). Influence of grazing on plant diversity-productivity relationship in semi-arid grassland of South Africa. Applied Ecology and Environmental Research, 14, 1–13.  https://doi.org/10.15666/aeer/1404_001013.CrossRefGoogle Scholar
  9. Dingaan, M. N. V., Tsubo, M., Walker, S., & Newby, T. (2017). Soil chemical properties and plant species diversity along a rainfall gradient in semi-arid grassland of South Africa. Plant Ecology and Evolution, 150, 35–44.  https://doi.org/10.5091/plecevo.2017.1260.CrossRefGoogle Scholar
  10. FAO/IIASA/ISRIC/ISSCAS/JRC. (2012). Harmonized World Soil Database, Version 1.2. Rome & Luxenburg: FAO & IIASA.Google Scholar
  11. Fensholt, R., & Sandholt, I. (2005). Evaluation of MODIS and NOAA AVHRR vegetation indices with in situ measurements in semi-arid environment. International Journal of Remote Sensing, 26, 2561–2594.  https://doi.org/10.1080/01431160500033724.CrossRefGoogle Scholar
  12. Fetzel, T., Havlik, P., Herrero, M., & Erb, K. H. (2017). Seasonality constraints to livestock grazing intensity. Global Change Biology, 23, 1636–1647.  https://doi.org/10.1111/gcb.13591.CrossRefGoogle Scholar
  13. Gao, B.-C. (1996). NDWI - a normalized difference water index for remote sensing of vegetation liquid water from space. Remote Sensing of Environment, 58, 257–266.  https://doi.org/10.1016/S0034-4257(96)00067-3.CrossRefGoogle Scholar
  14. Gelder, B. K., Kaleita, A. L., & Cruse, R. M. (2009). Estimating mean field residue cover on Midwestern soils using satellite imagery. Agronomy Journal, 101, 635–643.  https://doi.org/10.2134/agronj2007.0249.CrossRefGoogle Scholar
  15. Gitelson, A. A., Kaufman, Y. J., & Merzlyak, M. N. (1996). Use of a green channel in remote sensing of global vegetation from EOS-MODIS. Remote Sensing of Environment, 58, 289–298.  https://doi.org/10.1016/S0034-4257(96)00072-7.CrossRefGoogle Scholar
  16. Green, S., Cawkwell, F., & Dwyer, E. (2016). Cattle stocking rates estimated in temperate intensive grasslands with a spring growth model derived from MODIS NDVI time-series. International Journal of Applied Earth Observation and Geoinformation, 52, 166–174.  https://doi.org/10.1016/j.jag.2016.06.012.CrossRefGoogle Scholar
  17. Hobbs, T. J. (1995). The use of NOAA-AVHRR NDVI data to assess herbage production in the arid rangelands of Central Australia. International Journal of Remote Sensing, 16, 1289–1302.  https://doi.org/10.1080/01431169508954477.CrossRefGoogle Scholar
  18. Holechek, J. L. (1988). An approach for setting the stocking rate. Rangelands, 10, 10–14.Google Scholar
  19. Huang, N., He, J. S., & Niu, Z. (2013). Estimating the spatial pattern of soil respiration in Tibetan alpine grasslands using Landsat TM and MODIS data. Ecological Indicators, 26, 117–125.  https://doi.org/10.1016/j.ecolind.2012.10.027.CrossRefGoogle Scholar
  20. Huete, A., Didan, K., Miura, T., Rodriguez, E. P., Gao, X., & Ferreira, L. G. (2002). Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote Sensing of Environment, 83, 195–213.  https://doi.org/10.1016/S0034-4257(02)00096-2.CrossRefGoogle Scholar
  21. Jiang, Y., Tao, J., Huang, Y., Zhu, J., Tian, L., & Zhang, Y. (2015). The spatial pattern of grassland aboveground biomass on Xizang Plateau and its climatic controls. Journal of Plant Ecology, 8, 30–40.  https://doi.org/10.1093/jpe/rtu002.CrossRefGoogle Scholar
  22. Kawamura, K., Akiyama, T., Yokota, H., Tsustuni, M., Yasuda, T., Watanabe, O., & Wang, S. (2005). Comparing MODIS vegetation indices with AVHRR NDVI for monitoring the forage quality and quantity in Inner Mongolia grassland, China. Grassland Science, 51, 33–40.  https://doi.org/10.1111/j.1744-697X.2005.00006.x.CrossRefGoogle Scholar
  23. Le, Q. B., Nkonya, E., & Mirzabaev, A. (2016). Biomass productivity-based mapping of global land degradation hotspots. In E. Nkonya, A. Mirzabaev, & J. von Braun (Eds.), Economics of land degradation and improvement – a global assessment for sustainable development (pp. 55–84). Heidelberg: Springer.  https://doi.org/10.1007/978-3-319-19168-3_4.CrossRefGoogle Scholar
  24. Li, F., Zeng, Y., Luo, J., Ma, R., & Wu, B. (2016). Modeling grassland aboveground biomass using a pure vegetation index. Ecological Indicators, 62, 279–288.  https://doi.org/10.1016/j.ecolind.2015.11.005.CrossRefGoogle Scholar
  25. Liu, S., Cheng, F., Dong, S., Zhao, H., Hou, X., & Wu, X. (2017). Spatiotemporal dynamics of grassland aboveground biomass on the Qinghai-Tibet Plateau based on validated MODIS NDVI. Scientific Reports, 7, 4182.  https://doi.org/10.1038/s41598-017-04038-4.CrossRefGoogle Scholar
  26. Loris, V., & Damiano, G. (2006). Mapping the green herbage ratio of grasslands using both aerial and satellite-derived spectral reflectance. Agriculture, Ecosystems and Environment, 115, 141–149.  https://doi.org/10.1016/j.agee.2005.12.018.CrossRefGoogle Scholar
  27. Mbow, C., Fensholt, R., Rasmussen, K., & Diop, D. (2013). Can vegetation productivity be derived from greenness in a semi-arid environment? Evidence from ground-based measurements. Journal of Arid Environments, 97, 56–65.  https://doi.org/10.1016/j.jaridenv.2013.05.011.CrossRefGoogle Scholar
  28. McNairn, H., & Protz, R. (1993). Mapping corn residue cover on agricultural fields in Oxford County, Ontario, using thematic mapper. Canadian Journal of Remote Sensing, 19, 152–159.  https://doi.org/10.1080/07038992.1993.10874543.CrossRefGoogle Scholar
  29. McNaughton, S. J. (1985). Ecology of a grazing ecosystem: the Serengeti. Ecological Monographs, 55, 259–294.  https://doi.org/10.2307/1942578.CrossRefGoogle Scholar
  30. Merzlyak, M. N., Gitelson, A. A., Chivkmova, O. B., & Rakitin, V. Y. (1999). Non-destructive optical detection of pigment changes during leaf senescence and fruit ripening. Physiologia Plantarum, 106, 135–141.  https://doi.org/10.1034/j.1399-3054.1999.106119.x.CrossRefGoogle Scholar
  31. Nakano, T., Bavuudorj, G., Urianhai, N. G., & Shinoda, M. (2013). Monitoring aboveground biomass in semiarid grasslands using MODIS images. Journal of Agricultural Meteorology, 69, 33–39.  https://doi.org/10.2480/agrmet.69.1.1.CrossRefGoogle Scholar
  32. Numata, I., Roberts, D. A., Chadwick, O. A., Schimel, J., Sampaio, F. R., Leonidas, F. C., & Soares, J. V. (2007). Characterization of pasture biophysical properties and the impact of grazing intensity using remotely sensed data. Remote Sensing of Environment, 109, 314–327.  https://doi.org/10.1016/j.rse.2007.01.013.CrossRefGoogle Scholar
  33. Oesterheld, M., Sala, O. E., & McNaughton, S. J. (1992). Effect of animal husbandry on herbivore-carrying capacity at a regional scale. Nature, 356, 234–236.  https://doi.org/10.1038/356234a0.CrossRefGoogle Scholar
  34. Paruelo, J. M., Epstein, H. E., Lauenroth, W. K., & Burke, I. C. (1997). ANPP estimates from NDVI for the central grassland region of the United States. Ecology, 78, 953–958.CrossRefGoogle Scholar
  35. Peñuelas, J., Baret, F., & Filella, I. (1995). Semi-empirical indices to assess carotenoids/chlorophyll a ratio from leaf spectral reflectance. Photosynthetica, 31, 221–230.Google Scholar
  36. Qi, J., Marsett, R., Heilman, P., Bieden-bender, S., Moran, S., Goodrich, D., & Weltz, M. (2002). RANGES improves satellite-based information and land cover assessments in southwest United States. Eos, Transactions American Geophysical Union, 83, 601 & 605–601 & 606.  https://doi.org/10.1029/2002EO000411.CrossRefGoogle Scholar
  37. Ren, H., Zhang, B., & Guo, X. (2018). Estimation of litter mass in nongrowing seasons in arid grasslands using MODIS satellite data. European Journal of Remote Sensing, 51, 222–230.  https://doi.org/10.1080/22797254.2017.1418186.CrossRefGoogle Scholar
  38. Schino, G., Borfecchia, F., De Cecco, L., Dibari, C., Iannetta, M., Martini, S., & Pedrotti, F. (2003). Satellite estimate of grass biomass in a mountainous range in central Italy. Agroforestry Systems, 59, 157–162.  https://doi.org/10.1023/A:1026308928874.CrossRefGoogle Scholar
  39. Sharma, V., Irmak, S., Kilic, A., Sharma, V., Gilley, J. E., Meyer, G. E., Knezevic, S. Z., & Marx, D. (2016). Quantification and mapping of surface residue cover for maize and soybean fields in south central Nebraska. Transactions of the ASABE, 59, 925–939.  https://doi.org/10.13031/trans.59.11489.CrossRefGoogle Scholar
  40. 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.  https://doi.org/10.1016/S0034-4257(02)00010-X.CrossRefGoogle Scholar
  41. Todd, S. W., Hoffer, R. M., & Milchunas, D. G. (1998). Biomass estimation on grazed and ungrazed rangelands using spectral indices. International Journal of Remote Sensing, 19, 427–438.  https://doi.org/10.1080/014311698216071.CrossRefGoogle Scholar
  42. Tucker, C. J. (1979). Red and photographic infrared linear combinations for monitoring vegetation. Remote Sensing of Environment, 8, 127–150.  https://doi.org/10.1016/0034-4257(79)90013-0.CrossRefGoogle Scholar
  43. Tucker, C. J., Vanpraet, C., Boerwinkel, E., & Gaston, A. (1983). Satellite remote sensing of total dry matter production in the Senegalese Sahel. Remote Sensing of Environment, 13, 461–474.  https://doi.org/10.1016/0034-4257(83)90053-6.CrossRefGoogle Scholar
  44. Van Deventer, A. P., Ward, A. D., Gowda, P. H., & Lyon, J. G. (1997). Using thematic mapper data to identify contrasting soil plains and tillage practices. Photogrammetric Engineering and Remote Sensing, 63, 87–93.Google Scholar
  45. Xiu, L., Yan, C., Li, X., Qian, D., & Feng, K. (2018). Monitoring the response of vegetation dynamics to ecological engineering in the Mu Us Sandy Land of China from 1982 to 2014. Environmental Monitoring and Assessment, 190, 543.  https://doi.org/10.1007/s10661-018-6931-9.CrossRefGoogle Scholar
  46. Xu, D., Guo, X., Li, Z., Yang, X., & Yin, H. (2014). Measuring the dead component of mixed grassland with Landsat imagery. Remote Sensing of Environment, 142, 33–43.  https://doi.org/10.1016/j.rse.2013.11.017.CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Department of Life and Consumer SciencesUniversity of South AfricaJohannesburgSouth Africa
  2. 2.Arid Land Research CenterTottori UniversityTottoriJapan

Personalised recommendations