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ISPRS Int. J. Geo-Inf. 2015, 4(4), 2792-2820; doi:10.3390/ijgi4042792

Estimating Plant Traits of Grasslands from UAV-Acquired Hyperspectral Images: A Comparison of Statistical Approaches

1
Department of Agricultural Sciences, University of Naples Federico II, Via Università 100, 80055 Portici, Italy
2
Laboratory of Geo-Information Science and Remote Sensing, Wageningen University, P.O. Box 47, 6700 AA Wageningen, The Netherlands
3
Versuchs- und Bildungszentrum Landwirtschaft Haus Riswick, Elsenpass 5, 47533 Kleve, Germany
*
Author to whom correspondence should be addressed.
Academic Editors: Gonzalo Pajares Martinsanz and Wolfgang Kainz
Received: 17 August 2015 / Revised: 23 November 2015 / Accepted: 30 November 2015 / Published: 10 December 2015
(This article belongs to the Special Issue Unmanned Aerial Vehicles in Geomatics)
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Abstract

Grassland ecosystems cover around 40% of the entire Earth’s surface. Therefore, it is necessary to guarantee good grassland management at field scale in order to improve its conservation and to achieve optimal growth. This study identified the most appropriate statistical strategy, between partial least squares regression (PLSR) and narrow vegetation indices, for estimating the structural and biochemical grassland traits from UAV-acquired hyperspectral images. Moreover, the influence of fertilizers on plant traits for grasslands was analyzed. Hyperspectral data were collected from an experimental field at the farm Haus Riswick, near Kleve in Germany, for two different flight campaigns in May and October. The collected image blocks were geometrically and radiometrically corrected for surface reflectance. Spectral signatures extracted for the plots were adopted to derive grassland traits by computing PLSR and the following narrow vegetation indices: the MERIS Terrestrial Chlorophyll Index (MTCI), the ratio of the Modified Chlorophyll Absorption in Reflectance and Optimized Soil-Adjusted Vegetation Index (MCARI/OSAVI) modified by Wu, the Red-edge Chlorophyll Index (CIred-edge), and the Normalized Difference Red Edge (NDRE). PLSR showed promising results for estimating grassland structural traits and gave less satisfying outcomes for the selected chemical traits (crude ash, crude fiber, crude protein, Na, K, metabolic energy). Established relations are not influenced by the type and the amount of fertilization, while they are affected by the grassland health status. PLSR is found to be the best strategy, among the approaches analyzed in this paper, for exploring structural and biochemical features of grasslands. Using UAV-based hyperspectral sensing allows for the highly detailed assessment of grassland experimental plots. View Full-Text
Keywords: grassland traits; spectroscopy; unmanned aerial vehicle (UAV); vegetation indices; partial least squares regression (PLSR) grassland traits; spectroscopy; unmanned aerial vehicle (UAV); vegetation indices; partial least squares regression (PLSR)
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Capolupo, A.; Kooistra, L.; Berendonk, C.; Boccia, L.; Suomalainen, J. Estimating Plant Traits of Grasslands from UAV-Acquired Hyperspectral Images: A Comparison of Statistical Approaches. ISPRS Int. J. Geo-Inf. 2015, 4, 2792-2820.

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