Abstract:
Earth Observing 1 (EO-1) Hyperion and Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) imagery were used to predict canopy nitrogen (N) concentration for mixed oak...Show MoreMetadata
Abstract:
Earth Observing 1 (EO-1) Hyperion and Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) imagery were used to predict canopy nitrogen (N) concentration for mixed oak forests of Green Ridge State Forest in Maryland. Nitrogen concentration was estimated for 27 ground plots using leaf samples of the dominant tree species from each plot that were dried, ground and analyzed in the laboratory for foliar N concentration. Foliar N data were composited based on relative species composition to determine overall canopy N concentration for the plot. Hyperion and AVIRIS images were converted to surface reflectance and related to canopy N using partial least squares (PLS) regression of first-derivative reflectance for wavelengths reported in the literature to be associated with N absorption features. The PLS model for Hyperion employed four factors and accounted for 97.8% of the variation in N concentrations and 40.4% of the variation in the spectral data whereas the AVIRIS model used three factors accounting for 84.9% of the variation in N and 72.4% of the variation in the spectral information. In the area of overlap between the AVIRIS and Hyperion images, >70% of the estimates from the two sensors were within 0.25%N of each other, indicating a very close fit between the models generated using data from Hyperion and AVIRIS. This research indicates the applicability of hyperspectral data in general and Hyperion data in particular for mapping canopy nitrogen concentration.
Published in: IEEE Transactions on Geoscience and Remote Sensing ( Volume: 41, Issue: 6, June 2003)
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- IEEE Keywords
- Index Terms
- Imaging Spectroscopy ,
- Central Appalachia ,
- Variance In The Data ,
- Species Richness ,
- Least Squares Regression ,
- Nitrogen Content ,
- Spectral Data ,
- Partial Least Squares ,
- Leaf Samples ,
- National Forest ,
- Partial Least Squares Regression ,
- Overlap Area ,
- Imaging Spectrometer ,
- Partial Least Squares Model ,
- Oak Forest ,
- Airborne Visible/Infrared Imaging Spectrometer ,
- Green State ,
- Prediction Model ,
- Number Of Factors ,
- Chlorophyll ,
- Leaf Area Index ,
- Forest Structure ,
- Basal Area ,
- Reflectance Spectra ,
- Remote Sensing ,
- Plant Canopy ,
- Pushbroom ,
- Ordinary Least Squares ,
- Field Plots ,
- Partial Least Squares Analysis
Keywords assist with retrieval of results and provide a means to discovering other relevant content. Learn more.
- IEEE Keywords
- Index Terms
- Imaging Spectroscopy ,
- Central Appalachia ,
- Variance In The Data ,
- Species Richness ,
- Least Squares Regression ,
- Nitrogen Content ,
- Spectral Data ,
- Partial Least Squares ,
- Leaf Samples ,
- National Forest ,
- Partial Least Squares Regression ,
- Overlap Area ,
- Imaging Spectrometer ,
- Partial Least Squares Model ,
- Oak Forest ,
- Airborne Visible/Infrared Imaging Spectrometer ,
- Green State ,
- Prediction Model ,
- Number Of Factors ,
- Chlorophyll ,
- Leaf Area Index ,
- Forest Structure ,
- Basal Area ,
- Reflectance Spectra ,
- Remote Sensing ,
- Plant Canopy ,
- Pushbroom ,
- Ordinary Least Squares ,
- Field Plots ,
- Partial Least Squares Analysis