Estimates of canopy nitrogen content in heterogeneous grasslands of Konza Prairie by hyperspectral remote sensing

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dc.contributor.author Ling, Bohua
dc.date.accessioned 2013-04-25T19:17:04Z
dc.date.available 2013-04-25T19:17:04Z
dc.date.issued 2013-04-25
dc.identifier.uri http://hdl.handle.net/2097/15616
dc.description.abstract Hyperspectral data has been widely used for estimates of canopy biochemical content over the past decades. Most of these studies were conducted in forests or crops with relatively uniform canopies. Feasibility of the use of hyperspectral analysis in heterogeneous canopies with diverse plant species and canopy structures remains uncertain. Spectral data at the canopy level, with mixed background noise, canopy biochemical and biophysical properties create more problems in spectral analysis than that at the leaf level. Complications of heterogeneous canopies make biochemical retrieval through remote sensing even more difficult due to more uneven spatial distribution of biochemical constituents. The objective of my research was to map canopy nitrogen content in tallgrass prairie with mixed canopies by means of hyperspectral data from in-situ and airborne measurements. Research efforts were divided into three steps: (1) the green leaf area index (LAI) retrieval, given LAI is an important parameter in scaling nitrogen content from leaves to canopies; (2) canopy nitrogen modeling from analysis of in-situ hyperspectral data; and (3) canopy nitrogen mapping based on aerial hyperspectral imagery. Research results revealed that a fine chlorophyll absorption feature in the green-yellow region at wavelengths of 562 – 600 nm was sensitive to canopy nitrogen status. Specific spectral features from the normalized spectral data by the first derivative or continuum removal in this narrow spectral region could be selected by multivariate regression for nitrogen modeling. The optimal nitrogen models with high predictive accuracy measured as low values of root-mean-square error (RMSE) were applied to the aerial hyperspectral imagery for canopy nitrogen mapping during the growth seasons from May to September. These maps would be of great value in studies on the interactions between canopy vegetation quality and grazing patterns of large herbivores in tallgrass prairie. en_US
dc.language.iso en_US en_US
dc.publisher Kansas State University en
dc.subject Hyperspectral en_US
dc.subject Remote sensing en_US
dc.subject Canopy nitrogen en_US
dc.subject heterogeneous grasslands en_US
dc.title Estimates of canopy nitrogen content in heterogeneous grasslands of Konza Prairie by hyperspectral remote sensing en_US
dc.type Thesis en_US
dc.description.degree Master of Science en_US
dc.description.level Masters en_US
dc.description.department Department of Geography en_US
dc.description.advisor Douglas Goodin en_US
dc.subject.umi Geography (0366) en_US
dc.subject.umi Remote Sensing (0799) en_US
dc.date.published 2013 en_US
dc.date.graduationmonth May en_US


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