Non-linear partial least square regression increases the estimation accuracy of grass nitrogen and phosphorus using in situ hyperspectral and environmental data

A. Ramoelo, A.K. Skidmore, M.A. Cho, R. Mathieu, I.M.A. Heitkonig, N. Dudeni-Tlhone, M. Schlerf, H.H.T. Prins

Research output: Contribution to journalArticleAcademicpeer-review

61 Citations (Scopus)

Abstract

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.
Original languageEnglish
Pages (from-to)27-40
JournalISPRS Journal of Photogrammetry and Remote Sensing
Volume82
DOIs
Publication statusPublished - 2013

Keywords

  • kruger-national-park
  • multiple linear-regression
  • band-depth analysis
  • vegetation indexes
  • south-africa
  • chlorophyll estimation
  • imaging spectroscopy
  • absorption features
  • biochemical content
  • mineral-nutrition

Fingerprint Dive into the research topics of 'Non-linear partial least square regression increases the estimation accuracy of grass nitrogen and phosphorus using in situ hyperspectral and environmental data'. Together they form a unique fingerprint.

  • Cite this