Ramoelo, AbelSkidmore, AKCho, Moses AMathieu, Renaud SAHeitkönig, IMADudeni-Tlhone, NSchlerf, MPrins, HHT2013-08-052013-08-052013-06Ramoelo, A., Skidmore, A.K., Cho, M.A., Mathieu, R., Heitkönig, I.M.A., Dudeni-Tlhone, N, Schlerf, M. and Prins, H.H.T. 2013. Non-linear partial least square regression increases the estimation accuracy of grass nitrogen and phosphorus using in situ hyperspectral and environmental data. ISPRS Journal of Photogrammetry and Remote Sensing, vol. 82, pp 27-400924-2716http://www.sciencedirect.com/science/article/pii/S0924271613001214http://hdl.handle.net/10204/6924Copyright: 2013 Elsevier. This is the Pre/post print version of the work. The definitive version is published in ISPRS Journal of Photogrammetry and Remote Sensing, vol. 82, pp 27-40Grass nitrogen (N) and phosphorus (P) concentrations are direct indicators of rangeland quality and provide imperative information for sound management of wildlife and livestock. It is challenging to estimate grass N and P concentrations using remote sensing in the savanna ecosystems. These areas are diverse and heterogeneous in soil and plant moisture, soil nutrients, grazing pressures, and human activities. The objective of the study is to test the performance of non-linear partial least squares regression (PLSR) for predicting grass N and P concentrations through integrating in situ hyperspectral remote sensing and environmental variables (climatic, edaphic and topographic). Data were collected along a land use gradient in the greater Kruger National Park region. The data consisted of: (i) in situ-measured hyperspectral spectra, (ii) environmental variables and measured grass N and P concentrations. The hyperspectral variables included published starch, N and protein spectral absorption features, red edge position, narrow-band indices such as simple ratio (SR) and normalized difference vegetation index (NDVI). The results of the non-linear PLSR were compared to those of conventional linear PLSR. Using non-linear PLSR, integrating in situ hyperspectral and environmental variables yielded the highest grass N and P estimation accuracy (R2 = 0.81, root mean square error (RMSE) = 0.08, and R2 = 0.80, RMSE = 0.03, respectively) as compared to using remote sensing variables only, and conventional PLSR. The study demonstrates the importance of an integrated modeling approach for estimating grass quality which is a crucial effort towards effective management and planning of protected and communal savanna ecosystems.enIn situ hyperspectral remote sensingEcosystemPartial least square regressionRadial basis neural networkNitrogen concentrationsPhosphorus concentrationsArticleRamoelo, A., Skidmore, A., Cho, M. A., Mathieu, R. S., Heitkönig, I., Dudeni-Tlhone, N., ... Prins, H. (2013). Non-linear partial least square regression increases the estimation accuracy of grass nitrogen and phosphorus using in situ hyperspectral and environmental data. http://hdl.handle.net/10204/6924Ramoelo, Abel, AK Skidmore, Moses A Cho, Renaud SA Mathieu, IMA Heitkönig, N Dudeni-Tlhone, M Schlerf, and HHT Prins "Non-linear partial least square regression increases the estimation accuracy of grass nitrogen and phosphorus using in situ hyperspectral and environmental data." (2013) http://hdl.handle.net/10204/6924Ramoelo A, Skidmore A, Cho MA, Mathieu RS, Heitkönig I, Dudeni-Tlhone N, et al. Non-linear partial least square regression increases the estimation accuracy of grass nitrogen and phosphorus using in situ hyperspectral and environmental data. 2013; http://hdl.handle.net/10204/6924.TY - Article AU - Ramoelo, Abel AU - Skidmore, AK AU - Cho, Moses A AU - Mathieu, Renaud SA AU - Heitkönig, IMA AU - Dudeni-Tlhone, N AU - Schlerf, M AU - Prins, HHT AB - Grass nitrogen (N) and phosphorus (P) concentrations are direct indicators of rangeland quality and provide imperative information for sound management of wildlife and livestock. It is challenging to estimate grass N and P concentrations using remote sensing in the savanna ecosystems. These areas are diverse and heterogeneous in soil and plant moisture, soil nutrients, grazing pressures, and human activities. The objective of the study is to test the performance of non-linear partial least squares regression (PLSR) for predicting grass N and P concentrations through integrating in situ hyperspectral remote sensing and environmental variables (climatic, edaphic and topographic). Data were collected along a land use gradient in the greater Kruger National Park region. The data consisted of: (i) in situ-measured hyperspectral spectra, (ii) environmental variables and measured grass N and P concentrations. The hyperspectral variables included published starch, N and protein spectral absorption features, red edge position, narrow-band indices such as simple ratio (SR) and normalized difference vegetation index (NDVI). The results of the non-linear PLSR were compared to those of conventional linear PLSR. Using non-linear PLSR, integrating in situ hyperspectral and environmental variables yielded the highest grass N and P estimation accuracy (R2 = 0.81, root mean square error (RMSE) = 0.08, and R2 = 0.80, RMSE = 0.03, respectively) as compared to using remote sensing variables only, and conventional PLSR. The study demonstrates the importance of an integrated modeling approach for estimating grass quality which is a crucial effort towards effective management and planning of protected and communal savanna ecosystems. DA - 2013-06 DB - ResearchSpace DP - CSIR KW - In situ hyperspectral remote sensing KW - Ecosystem KW - Partial least square regression KW - Radial basis neural network KW - Nitrogen concentrations KW - Phosphorus concentrations LK - https://researchspace.csir.co.za PY - 2013 SM - 0924-2716 T1 - Non-linear partial least square regression increases the estimation accuracy of grass nitrogen and phosphorus using in situ hyperspectral and environmental data TI - Non-linear partial least square regression increases the estimation accuracy of grass nitrogen and phosphorus using in situ hyperspectral and environmental data UR - http://hdl.handle.net/10204/6924 ER -