Next Article in Journal
Semi-Automated Delineation of Stands in an Even-Age Dominated Forest: A LiDAR-GEOBIA Two-Stage Evaluation Strategy
Previous Article in Journal
The NEWA Ferry Lidar Experiment: Measuring Mesoscale Winds in the Southern Baltic Sea
Open AccessArticle

Imaging Spectroscopic Analysis of Biochemical Traits for Shrub Species in Great Basin, USA

1
School of Natural Resources, University of Nebraska-Lincoln, Lincoln, NE 68583, USA
2
Center for Spatial Technologies and Remote Sensing (CSTARS), Department of Land, Air and Water Resources, University of California, Davis, CA 95616, USA
3
Department of Geosciences, Boise State University, Boise Center Aerospace Lab, 1910 University Drive, Boise, ID 83725, USA
*
Author to whom correspondence should be addressed.
Remote Sens. 2018, 10(10), 1621; https://doi.org/10.3390/rs10101621
Received: 4 September 2018 / Revised: 8 October 2018 / Accepted: 10 October 2018 / Published: 12 October 2018
The biochemical traits of plant canopies are important predictors of photosynthetic capacity and nutrient cycling. However, remote sensing of biochemical traits in shrub species in dryland ecosystems has been limited mainly due to the sparse vegetation cover, manifold shrub structures, and complex light interaction between the land surface and canopy. In order to examine the performance of airborne imaging spectroscopy for retrieving biochemical traits in shrub species, we collected Airborne Visible Infrared Imaging Spectrometer—Next Generation (AVIRIS-NG) images and surveyed four foliar biochemical traits (leaf mass per area, water content, nitrogen content and carbon) of sagebrush (Artemesia tridentata) and bitterbrush (Purshia tridentata) in the Great Basin semi-desert ecoregion, USA, in October 2014 and May 2015. We examined the correlations between biochemical traits and developed partial least square regression (PLSR) models to compare spectral correlations with biochemical traits at canopy and plot levels. PLSR models for sagebrush showed comparable performance between calibration (R2: LMA = 0.66, water = 0.7, nitrogen = 0.42, carbon = 0.6) and validation (R2: LMA = 0.52, water = 0.41, nitrogen = 0.23, carbon = 0.57), while prediction for bitterbrush remained a challenge. Our results demonstrate the potential for airborne imaging spectroscopy to measure shrub biochemical traits over large shrubland regions. We also highlight challenges when estimating biochemical traits with airborne imaging spectroscopy data. View Full-Text
Keywords: imaging spectroscopy; biochemical traits; dryland ecosystem; shrub species; AVIRIS-NG imaging spectroscopy; biochemical traits; dryland ecosystem; shrub species; AVIRIS-NG
Show Figures

Graphical abstract

MDPI and ACS Style

Qi, Y.; Ustin, S.L.; Glenn, N.F. Imaging Spectroscopic Analysis of Biochemical Traits for Shrub Species in Great Basin, USA. Remote Sens. 2018, 10, 1621.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Search more from Scilit
 
Search
Back to TopTop