Abstract
The primary indicator of forage quality in any rangeland environment is grass nitrogen. Remote sensing has been used to map grass quality by predicting nitrogen in order to facilitate effective management and conservation of rangeland ecosystems. Using Telperion Game Reserve as the study site, this study sought to understand the extent to which the application of Sentinel-2 (S2) MSI sensor, together with new red edge bands and vegetation indices can improve the estimation accuracy of grass nitrogen (N) at a landscape scale when compared to the RapidEye sensor. This study was particularly interested in understanding if animal distribution in a rangeland environment is significantly correlated to grass quality across different grass communities at a landscape level. The performances of the simple ratios (SR), normalised vegetation difference indices (NDVI) and random forest (RF) regression models were evaluated and used to map and predict grass nitrogen concentration. The results indicate that the model developed using the SR indices could predict nitrogen with a mean R2 of 0.92 for S2 data and 0.53 from RapidEye data using combined models. The results also showed that nitrogen maps from remote sensing could be used to explain animal aggregation and grazing patterns at a landscape level.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Abdel-Rahman EM, Ahmed FB, van den Berg M (2010) Estimation of sugarcane leaf nitrogen concentration using in situ spectroscopy. Int J Appl Earth Obs Geoinf 12(SUPPL. 1):52–57. https://doi.org/10.1016/j.jag.2009.11.003
Adam E et al (2014) Estimating standing biomass in papyrus ( Cyperus papyrus L.) swamp: exploratory of in situ hyperspectral indices and random forest regression. Int J Remote Sens. Taylor & Francis 35(2):693–714. https://doi.org/10.1080/01431161.2013.870676
Adam E, Mutanga O (2009) Spectral discrimination of papyrus vegetation (Cyperus papyrus L.) in swamp wetlands using field spectrometry. ISPRS J Photogramm Remote Sens. Elsevier B.V. 64(6):612–620. https://doi.org/10.1016/j.isprsjprs.2009.04.004
Adjorlolo C, Mutanga O, Cho MA (2015) Predicting C3 and C4 grass nutrient variability using in situ canopy reflectance and partial least squares regression. Int J Remote Sens 36(6):1743–1761. https://doi.org/10.1080/01431161.2015.1024893
Avgar T, Mosser A, Brown GS, Fryxell JM (2013) Environmental and individual drivers of animal movement patterns across a wide geographical gradient. J Anim Ecol 82(1):96–106. https://doi.org/10.1111/j.1365-2656.2012.02035.x
Beeri O et al (2007) Estimating forage quantity and quality using aerial hyperspectral imagery for northern mixed-grass prairie. Remote Sens Environ 110(2):216–225. https://doi.org/10.1016/j.rse.2007.02.027
Breiman L (2001) Random forests. Mach Learn 45:5–32
Burkepile DE et al (2013) Habitat selection by large herbivores in a southern African savanna: the relative roles of bottom-up and top-down forces. Ecosphere 4(11):139. https://doi.org/10.1890/ES13-00078.1
Cho MA et al (2007) Estimation of green grass/herb biomass from airborne hyperspectral imagery using spectral indices and partial least squares regression. Int J Appl Earth Obs Geoinf 9(4):414–424. https://doi.org/10.1016/j.jag.2007.02.001
Cho MA et al (2012) Mapping tree species composition in south African savannas using an integrated airborne spectral and LiDAR system. Remote Sens Environ. Elsevier Inc. 125:214–226. https://doi.org/10.1016/j.rse.2012.07.010
Cho MA, Skidmore AK (2006) A new technique for extracting the red edge position from hyperspectral data: the linear extrapolation method. Remote Sens Environ 101(2):181–193. https://doi.org/10.1016/j.rse.2005.12.011
Clevers, J. G. P. W. and Gitelson, A. A. (2012) ‘Using the red-edge bands on Sentinel-2 for retrieving canopy chlorophyll and nitrogen content’, European Space Agency, (Special Publication) ESA SP, 707 SP, p. 58588. https://doi.org/10.1109/JSTARS.2011.2176468
Clevers JGPW, Gitelson AA (2013) Remote estimation of crop and grass chlorophyll and nitrogen content using red-edge bands on sentinel-2 and-3. Int J Appl Earth Obs Geoinf 23(1). https://doi.org/10.1016/j.jag.2012.10.008
Darvishzadeh R et al (2008) LAI and chlorophyll estimation for a heterogeneous grassland using hyperspectral measurements. ISPRS J Photogramm Remote Sens 63(4):409–426. https://doi.org/10.1016/j.isprsjprs.2008.01.001
Delegido J, Verrelst J, Meza CM, Rivera JP, Alonso L, Moreno J (2013) A red-edge spectral index for remote sensing estimation of green LAI over agroecosystems. Eur J Agron 46:42–52
Drusch, M. et al. (2012) ‘Sentinel-2: ESA’s optical high-resolution mission for GMES operational services’, Remote Sens Environ, 120, pp. 25–36. https://doi.org/10.1016/j.rse.2011.11.026
Dube T et al (2017) Evaluating the influence of the red edge band from RapidEye sensor in quantifying leaf area index for hydrological applications specifically focussing on plant canopy interception. Phys Chem Earth. Elsevier Ltd 100(May):73–80. https://doi.org/10.1016/j.pce.2017.02.016
ESA (2015) SENTINEL-2 User Handbook, pp 64
Fryxell JM (1991) Forage quality and aggregation by large herbivores. Am Nat 138(September):478–498. https://doi.org/10.1086/285227
Fryxell JM, Wilmshurst JF, Sinclair ARE (2004) Predictive models of movement. Ecology 85(9):2429–2435. https://doi.org/10.1890/04-0147
Fu Y et al (2014) Winter wheat biomass estimation based on spectral indices, band depth analysis and partial least squares regression using hyperspectral measurements. Comput Electron Agric. Elsevier B.V. 100:51–59. https://doi.org/10.1016/j.compag.2013.10.010
Fynn RWS, Chase M, Röder A (2013) Functional habitat heterogeneity and large herbivore seasonal habitat selection in northern Botswana. S Afr J Wildl Res 44(1):1–15. https://doi.org/10.3957/056.044.0103
Getis, Ord (1992) The analysis of spatial association by use of distance statistics. Geogr Anal 24:189–206
Gholizadeh A et al (2016) Assessment of red-edge position extraction techniques: a case study for Norway spruce forests using hymap and simulated sentinel-2 data. Forests 7(10). https://doi.org/10.3390/f7100226
Goecker ME, Heck KL, Valentine JF (2005) Effects of nitrogen concentrations in turtlegrass Thalassia testudinum on consumption by the bucktooth parrotfish Sparisoma radians. Mar Ecol Prog Ser 286:239–248. https://doi.org/10.3354/meps286239
Huang S et al (2017) Potential of RapidEye and WorldView-2 satellite data for improving rice nitrogen status monitoring at different growth stages. Remote Sens 9(3):227. https://doi.org/10.3390/rs9030227
Huete A (2014) Vegetation indices. Encycl Earth Sci Ser:883–886. https://doi.org/10.1007/978-0-387-36699-9_187
Kalacska M, Lalonde M, Moore TR (2015) Estimation of foliar chlorophyll and nitrogen content in an ombrotrophic bog from hyperspectral data: scaling from leaf to image. Remote Sens Environ. Elsevier Inc 169:270–279. https://doi.org/10.1016/j.rse.2015.08.012
Karlson M, Ostwald M (2016) Remote sensing of vegetation in the Sudano-Sahelian zone: a literature review from 1975 to 2014. J Arid Environ. Elsevier Ltd 124:257–269. https://doi.org/10.1016/j.jaridenv.2015.08.022
Kaszta Z et al (2016) Bulk feeder or selective grazer: African buffalo space use patterns based on fine-scale remotely sensed data on forage quality and quantity. Ecol Model 323:115–122. https://doi.org/10.1016/j.ecolmodel.2015.12.006
Knox NM et al (2011) Dry season mapping of savanna forage quality, using the hyperspectral Carnegie Airborne Observatory sensor. Remote Sens Environ. Elsevier Inc 115(6):1478–1488. https://doi.org/10.1016/j.rse.2011.02.007
Kumar et al (2002) Imaging spectrometry and vegetation science. In: Meer FD, Jong SMD (eds) Imaging spectrometry. Remote sensing and digital image processing, vol 4. Springer, Dordrecht
Lechmere-oertel RG (2003) The effects of goat browsing on ecosystem patterns and processes in succulent thicket, South Africa. Philosophiae Doctor University of Port Elizabeth, (October), p 120
Li F et al (2014) Improving estimation of summer maize nitrogen status with red edge-based spectral vegetation indices. Field Crops Research. Elsevier B.V. 157:111–123. https://doi.org/10.1016/j.fcr.2013.12.018
Matongera TN et al (2017) Detection and mapping the spatial distribution of bracken fern weeds using the Landsat 8 OLI new generation sensor. Int J Appl Earth Obs Geoinf. Elsevier B.V. 57:93–103. https://doi.org/10.1016/j.jag.2016.12.006
Mucina L, Rutherford MC eds (2006) The vegetation of South Africa, Lesotho and Swaziland. South African National Biodiversity Institute, Pretoria
Muñoz-Huerta RF, Guevara-Gonzalez RG, Contreras-Medina LM, Torres-Pacheco I, Prado-Olivarez J, Ocampo-Velazquez RV (2013) A review of methods for sensing the nitrogen status in plants: advantages, disadvantages and recent advances. Sensors (Basel, Switzerland) 13(8):10823–10843. https://doi.org/10.3390/s130810823
Mutanga O et al (2004a) Explaining grass-nutrient patterns in a savanna rangeland of southern Africa. J Biogeogr 31(5):819–829. https://doi.org/10.1111/j.1365-2699.2004.01072.x
Mutanga O et al (2015) Evaluating the robustness of models developed from field spectral data in predicting African grass foliar nitrogen concentration using WorldView-2 image as an independent test dataset. Int J Appl Earth Obs Geoinf. Elsevier B.V. 34(1):178–187. https://doi.org/10.1016/j.jag.2014.08.008
Mutanga O, Adam E, Cho MA (2012) High density biomass estimation for wetland vegetation using worldview-2 imagery and random forest regression algorithm. Int J Appl Earth Obs Geoinf. Elsevier B.V. 18(1):399–406. https://doi.org/10.1016/j.jag.2012.03.012
Mutanga O, Skidmore AK (2007) Red edge shift and biochemical content in grass canopies. ISPRS J Photogramm Remote Sens 62(1):34–42. https://doi.org/10.1016/j.isprsjprs.2007.02.001
Mutanga O, Skidmore AK, Prins HHT (2004b) Predicting in situ pasture quality in the Kruger National Park, South Africa, using continuum-removed absorption features. Remote Sens Environ 89(3):393–408. https://doi.org/10.1016/j.rse.2003.11.001
O’Connor TG et al (2010) Which grazing management practices are most appropriate for maintaining biodiversity in South African grassland? Afr J Range Forage Sci 27(2):67–76. https://doi.org/10.2989/10220119.2010.502646
Odindi J et al (2014) Comparison between {WorldView}-2 and {SPOT}-5 images in mapping the bracken fern using the random forest algorithm. J Appl Remote Sens 8(1):83527. https://doi.org/10.1117/1.JRS.8.083527
Oumar Z, Mutanga O (2014) Integrating environmental variables and WorldView-2 image data to improve the prediction and mapping of Thaumastocoris peregrinus (bronze bug) damage in plantation forests. ISPRS J Photogramm Remote Sens. International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS) 87:39–46. https://doi.org/10.1016/j.isprsjprs.2013.10.010
Parker A (2011). Surveying technical. The South African Coordinate Reference System ( Part 1 ). PositionIT, Nov/Dec(Part 1) 22–30
Ramoelo A, Skidmore AK, Cho MA, Schlerf M, Mathieu R, Heitkönig IMA (2012) Regional estimation of savanna grass nitrogen using the red-edge band of the spaceborne rapideye sensor. Int J Appl Earth Obs Geoinf. Elsevier B.V. 19(1):151–162. https://doi.org/10.1016/j.jag.2012.05.009
Ramoelo A, Cho MA et al (2015a) Monitoring grass nutrients and biomass as indicators of rangeland quality and quantity using random forest modelling and WorldView-2 data. Int J Appl Earth Obs Geoinf. Elsevier B.V. 43:43–54. https://doi.org/10.1016/j.jag.2014.12.010
Ramoelo A, Cho M et al (2015b) Potential of Sentinel-2 spectral configuration to assess rangeland quality. J Appl Remote Sens 9(1):094096. https://doi.org/10.1117/1.JRS.9.094096
Ramoelo A, Dzikiti S et al (2015c) Potential to monitor plant stress using remote sensing tools. J Arid Environ. Elsevier Ltd 113:134–144. https://doi.org/10.1016/j.jaridenv.2014.09.003
Ramoelo A, Cho MA (2018) Explaining leaf nitrogen distribution in a semi-arid environment predicted on sentinel-2 imagery using a field spectroscopy derived modelss. Remote Sens 10(2). https://doi.org/10.3390/rs10020269
Sianga, K., Fynn, R. W. S. and Bonyongo, M. C. (2017) ‘Seasonal habitat selection by African buffalo Syncerus caffer in the Savuti–Mababe–Linyanti ecosystem of northern Botswana’, KOEDOE - African Protected Area Conservation and Science, 59(2), p. 10 pages. https://doi.org/10.4102/koedoe.v59i2.1382
Sibanda M, Mutanga O, Rouget M (2015) Examining the potential of Sentinel-2 MSI spectral resolution in quantifying above ground biomass across different fertilizer treatments. ISPRS J Photogramm Remote Sens. International Society for Photogrammetry and Remote Sensing, Inc (ISPRS) 110:55–65. https://doi.org/10.1016/j.isprsjprs.2015.10.005
Sibanda M, Mutanga O, Rouget M (2017) Testing the capabilities of the new WorldView-3 space-borne sensor’s red-edge spectral band in discriminating and mapping complex grassland management treatments. Int J Remote Sens. Taylor & Francis 38(1):1–22. https://doi.org/10.1080/01431161.2016.1259678
Skidmore et al (2010) Forage quality of savannas - Simultaneously mapping foliar protein and polyphenols for trees and grass using hyperspectral imagery. Remote Sens Environ 114(1):64–72
Simonne et al (1997) Could the dumas method replace the kjeldahl digestion for nitrogen and crude protein determinations in foods? J Sci Food Agr 73(1):39–45
Tshabalala T, Dube S, Lent PC (2010) Seasonal variation in forages utilized by the African buffalo (Syncerus caffer) in the succulent thicket of South Africa. Afr J Ecol 48(2):438–445. https://doi.org/10.1111/j.1365-2028.2009.01132.x
Verrelst J, Alonso L, Moreno J (2011) Evaluation of sentinel-2 red-edge bands for empirical estimation of green LAI and chlorophyll content. Sensors 11(7):7063–7081. https://doi.org/10.3390/s110707063
Watson ME, Galliher TL (2001) Comparison of Dumas and Kjeldahl methods with automatic analyzers on agricultural samples under routine rapid analysis conditions. Commun Soil Sci Plant Anal 32(13–14):2007–2019. https://doi.org/10.1081/CSS-120000265
van der Werff H, van der Meer F (2015) Sentinel-2 for mapping iron absorption feature parameters. Remote Sens 7(10):12635–12653. https://doi.org/10.3390/rs71012635
Zengeya FM et al (2015) Spatial overlap between sympatric wild and domestic herbivores links to resource gradients. Remote Sens Appl Soc Environ 2(2015):56–65. https://doi.org/10.1016/j.rsase.2015.11.001
Zengeya FM, Mutanga O, Murwira A (2012) Linking remotely sensed forage quality estimates from worldview-2 multispectral data with cattle distribution in a savanna landscape. Int J Appl Earth Obs Geoinf. Elsevier B.V. 21(1):513–524. https://doi.org/10.1016/j.jag.2012.07.008
Zengeya, F. M. and Science, E. (2014) ‘The distribution of cattle and their interaction with the African Buffalo at the wildlife-livestock interface understood using real-time global positioning systems ( GPS ) and remotely sensed data’, (April)
Acknowledgements
We thank Dr. Duncan MacFadyen, the manager of the Telperion Game Reserve for allowing us access into their game and Prof. Brown for coordinating the process. Special thanks go to Ms. Tendani Mashaba for assisting with data collection as well as Mr. Cassius Mmetle for the support during data collection and the provision of the GPS-based animal census data. We also thank the Cedara lab for free lab analysis.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Chabalala, Y., Adam, E., Oumar, Z. et al. Exploiting the capabilities of Sentinel-2 and RapidEye for predicting grass nitrogen across different grass communities in a protected area. Appl Geomat 12, 379–395 (2020). https://doi.org/10.1007/s12518-020-00305-8
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12518-020-00305-8