Data for Vegetation Maps for Reynolds Creek Experimental Watershed (RCEW) for the Year 2015
Summary & Purpose
The sparse canopy cover and large contribution of bright background soil, along with the heterogeneous vegetation types in close proximity are common challenges for mapping dryland vegetation with remote sensing. Consequently, the results of a single classification algorithm or one type of sensor to characterize dryland vegetation typically show low accuracy and lack robustness. In our study, we improve classification accuracy in a semi-arid ecosystem based on the use of vegetation optical (hyperspectral) and structural (lidar) information combined with the environmental characteristics of the landscape. To accomplish this goal we used both spectral angle mapper (SAM) and multiple endmember spectral mixture analysis (MESMA) for optical vegetation classification. Lidar-derived maximum vegetation height and delineated riparian zones were then used to modify the optical classification. Incorporating the lidar information into the classification scheme increased the overall accuracy from 60% to 89%. Canopy structure can have a strong influence on spectral variability and the lidar provided complementary information for SAM's sensitivity to shape but not magnitude of the spectra. Similar approaches to map large regions of drylands with low uncertainty may be readily implemented with unmixing algorithms applied to upcoming space-based imaging spectroscopy and lidar. As such, widespread studies to develop and understand the nuances associated with these approaches will enable efficient adoption and application.
Date of Publication or Submission
Dashti, Hamid; Poley, Andrew; Glenn, Nancy F.; Ilangakoon, Nayani; Spaete, Lucas; Roberts, Dar; Enterkine, Josh; Flores, Alejandro N.; Ustin, Susan L.; and Mitchell, Jessica J.. (2019). Data for Vegetation Maps for Reynolds Creek Experimental Watershed (RCEW) for the Year 2015 [Data set]. Retrieved from 10.18122/bcal_data.5.boisestate
NASA Terrestrial Ecology, grant number NNX14AD81G Department of the Interior Northwest Climate Adaptation Science Center graduate fellowship
Single Dataset or Series?
*.tif, *.shp and *.docx
This is a compressed Windows Zip file containing five files: Xeric_Classes.tif; Mesic_Classes.tif and Riparian_Zone.tif and Readme.docx. The content of each file is explained in a separate Readme.docx file. The RCEW.shp is the boundary of the study area (RCEW).
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