Assessing the effects of subtropical forest fragmentation on leaf nitrogen distribution using remote sensing data

M.A. Cho, A. Ramoelo, P. Debba, O. Mutanga, R. Mathieu, H. van Deventer, N. Ndlovu

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29 Citations (Scopus)


Subtropical forest loss resulting from conversion of forest to other land-cover types such as grassland, secondary forest, subsistence crop farms and small forest patches affects leaf nitrogen (N) stocks in the landscape. This study explores the utility of new remote sensing tools to model the spatial distribution of leaf N concentration in a forested landscape undergoing deforestation in KwaZulu-Natal, South Africa. Leaf N was mapped using models developed from RapidEye imagery; a relatively new space-borne multispectral sensor. RapidEye consists of five spectral bands in the visible to near infra-red (NIR) and has a spatial resolution of 5 m. MERIS terrestrial chlorophyll index derived from the RapidEye explained 50 % of the variance in leaf N across different land-cover types with a model standard error of prediction of 29 % (i.e. of the observed mean leaf N) when assessed on an independent test data. The results showed that indigenous forest fragmentation leads to significant losses in leaf N as most of the land-cover types (e.g. grasslands and subsistence farmlands) resulting from forest degradation showed lower leaf N when compared to the original indigenous forest. Further analysis of the spatial variation of leaf N revealed an autocorrelation distance of about 50 m for leaf N in the fragmented landscape, a scale corresponding to the average dimension of subsistence fields (2,781 m2) in the region. The availability of new multispectral sensors such as RapidEye thus, moves remote sensing closer to widespread monitoring of the effect of tropical forest degradation on leaf N distribution.
Original languageEnglish
Pages (from-to)1479-1491
JournalLandscape Ecology
Issue number8
Publication statusPublished - 2013


  • red-edge
  • tropical deforestation
  • spatial heterogeneity
  • chlorophyll content
  • multispectral data
  • eucalyptus leaves
  • land-use
  • landscape
  • reflectance
  • savanna


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