@article {KNZ001934,
	title = {Hyperspectral analysis of leaf pigments and nutritional elements in tallgrass prairie vegetation},
	journal = {Frontiers in Plant Science},
	volume = {10},
	year = {2019},
	chapter = {142},
	abstract = {<p>Understanding the spatial distribution of forage quality is important to address critical research questions in grassland science. Due to its efficiency and accuracy, there has been a widespread interest in mapping the canopy vegetation characteristics using remote sensing methods. In this study, foliar chlorophylls, carotenoids, and nutritional elements across multiple tallgrass prairie functional groups were quantified at the leaf level using hyperspectral analysis in the region of 470\&ndash;800 nm, which was expected to be a precursor to further remote sensing of canopy vegetation quality. A method of spectral standardization was developed using a form of the normalized difference, which proved feasible to reduce the interference from background effects in the leaf reflectance measurements. Chlorophylls and carotenoids were retrieved through inverting the physical model PROSPECT 5. The foliar nutritional elements were modeled empirically. Partial least squares regression was used to build the linkages between the high-dimensional spectral predictor variables and the foliar biochemical contents. Results showed that the retrieval of leaf biochemistry through hyperspectral analysis can be accurate and robust across different tallgrass prairie functional groups. In addition, correlations were found between the leaf pigments and nutritional elements. Results provided insight into the use of pigment-related vegetation indices as the proxy of plant nutrition quality.</p>},
	keywords = {LTER-KNZ, Hyperspectral analysis, Leaf pigments, Nutritional elements, remote sensing, tallgrass prairie},
	doi = {10.3389/fpls.2019.00142},
	url = {https://www.frontiersin.org/articles/10.3389/fpls.2019.00142/full},
	author = {Ling, B.H. and Goodin, D.G. and Raynor, Edward J. and Anthony Joern}
}
@phdthesis {KNZ001880,
	title = {Remote sensing of vegetation characteristics and spatial analysis of pyric herbivory in a Tallgrass Prairie},
	volume = {PhD. Dissertation},
	year = {2018},
	school = {Kansas State University},
	type = {Ph.D. Thesis},
	address = {Manhattan, KS},
	keywords = {LTER-KNZ},
	url = {http://krex.k-state.edu/dspace/handle/2097/39091},
	author = {Ling, B.H.}
}
@article {KNZ001642,
	title = {Estimating canopy nitrogen content in a heterogeneous grassland with varying fire and grazing treatments: Konza Prairie, Kansas, USA},
	journal = {Remote Sensing},
	volume = {6},
	year = {2014},
	pages = {4430 -4453},
	abstract = {<p>Quantitative, spatially explicit estimates of canopy nitrogen are essential for understanding the structure and function of natural and managed ecosystems. Methods for extracting nitrogen estimates via hyperspectral remote sensing have been an active area of research. Much of this research has been conducted either in the laboratory, or in relatively uniform canopies such as crops. Efforts to assess the feasibility of the use of hyperspectral analysis in heterogeneous canopies with diverse plant species and canopy structures have been less extensive. In this study, we use in situ and aircraft hyperspectral data to assess several empirical methods for extracting canopy nitrogen from a tallgrass prairie with varying fire and grazing treatments. The remote sensing data were collected four times between May and September in 2011, and were then coupled with the field-measured leaf nitrogen levels for empirical modeling of canopy nitrogen content based on first derivatives, continuum-removed reflectance and ratio-based indices in the 562\&ndash;600 nm range. Results indicated that the best-performing model type varied between in situ and aircraft data in different months. However, models from the pooled samples over the growing season with acceptable accuracy suggested that these methods are robust with respect to canopy heterogeneity across spatial and temporal scales.</p>},
	keywords = {LTER-KNZ, heterogeneous canopy, hyperspectral remote sensing, nitrogen estimates},
	doi = {10.3390/rs6054430},
	url = {https://www.mdpi.com/2072-4292/6/5/4430},
	author = {Ling, B.H. and Goodin, D.G. and Mohler, R.L. and Laws, A.N. and Anthony Joern}
}
