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.
Highlights
Grassland covers roughly 40% of the total world land area and this extension corresponds to approximately 52.5 million km2 [1]
Na shows a low correlation with metabolic energy and crude fiber, a moderate correlation with K and crude ash, and a high correlation with crude protein
The results of this study have shown that hyperspectral data acquired from an Unmanned Aerial Vehicles (UAV) platform can be adopted to characterize both structural and biochemical traits of grasslands
Summary
Grassland covers roughly 40% of the total world land area and this extension corresponds to approximately 52.5 million km2 [1]. Grassland ecosystems have extremely variable features, since they are strongly influenced by environmental conditions, such as topographic location, and by anthropogenic effects, such as (in)organic fertilizer application [2]. Efficient management plans of grasslands require insight into the health status and spatial variation in order to improve conservation and to achieve optimal growth. The planning of grassland management is even more important if investigated at the field scale, since the farmers need a guide for identifying the optimal time for fertilizer application and for predicting the ideal time of harvest [3]. The integration of quantitative and spatial data into the already existing management plans could significantly improve the health status of grasslands and the feeding quality of the final harvest
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