Integrating imaging spectroscopy and neural networks to map grass quality in the Kruger National Park, South Africa

A new integrated approach, involving continuum-removed absorption features, the red edge position and neural networks, is developed and applied to map grass nitrogen concentration in an African savanna rangeland. Nitrogen, which largely determines the nutritional quality of grasslands, is commonly t...

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Veröffentlicht in:	Remote sensing of environment Jg. 90; H. 1; S. 104 - 115
Hauptverfasser:	Mutanga, O, Skidmore, A.K
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York, NY Elsevier Inc 15.03.2004 Elsevier Science
Schlagworte:	Absorption features Animal, plant and microbial ecology Applied geophysics Areal geology. Maps Biological and medical sciences Continuum removal Earth sciences Earth, ocean, space Exact sciences and technology Foliar nitrogen concentration Fundamental and applied biological sciences. Psychology General aspects. Techniques Geologic maps, cartography Hymap imagery Internal geophysics Red edge position Savanna rangelands Teledetection and vegetation maps South Africa Southern Africa South Africa, Mpumalanga, Kruger Natl. Park Foliar nitrogen concentration Continuum removal Absorption features Savanna rangelands Red edge position Hymap imagery wavelength algorithms absorption nitrogen land cover maps Africa neural networks spectral analysis remote sensing concentration roots image analysis spatial distribution nutrients quality airborne methods grasslands leaves spectroscopy savanna errors national parks
ISSN:	0034-4257, 1879-0704
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Zusammenfassung:	A new integrated approach, involving continuum-removed absorption features, the red edge position and neural networks, is developed and applied to map grass nitrogen concentration in an African savanna rangeland. Nitrogen, which largely determines the nutritional quality of grasslands, is commonly the most limiting nutrient for grazers. Therefore, the remote sensing of foliar nitrogen concentration in savanna rangelands is important for an improved understanding of the distribution and feeding patterns of wildlife. Continuum removal was applied on two absorption features located in the visible ( R 550–757) and the SWIR ( R 2015–2199) from an atmospherically corrected HYMAP MKI image. A feature selection algorithm was used to select wavelength variables from the absorption features. Selected band depths from the absorption features as well as the red edge position (REP) were input into a backpropagation neural network. The best-trained neural network was used to map nitrogen concentration over the whole study area. Results indicate that the new integrated approach could explain 60% of the variation in savanna grass nitrogen concentration on an independent test data set, with a root mean square error (rmse) of 0.13 (±8.30% of the mean observed nitrogen concentration). This result is better compared to the result obtained using multiple linear regression, which yielded an R 2 of 38%, with a RMSE of 0.16 (±10.30% of the mean observed nitrogen concentration) on an independent test data set. The study demonstrates the potential of airborne hyperspectral data and neural networks to estimate and ultimately to map nitrogen concentration in the mixed species environments of Southern Africa.
Bibliographie:	ObjectType-Article-2 SourceType-Scholarly Journals-1 ObjectType-Feature-1 content type line 23
ISSN:	0034-4257 1879-0704
DOI:	10.1016/j.rse.2003.12.004