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RCCZO -- GIS / Map Data, LiDAR, Land Cover, Vegetation -- Data for Vegetation Maps for RCEW -- Reynolds Creek Experimental Watershed -- (2015-2015)

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Created: Feb 20, 2020 at 5:01 p.m.
Last updated: Apr 24, 2020 at 5:33 p.m.
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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.

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Resource Level Coverage


Coordinate System/Geographic Projection:
WGS 84 EPSG:4326
Coordinate Units:
Decimal degrees


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Additional Metadata

Name Value
DOI 10.18122/bcal_data/5/boisestate
Recommended Citation Dashti, Hamid; Poley, Andrew; Glenn, Nancy; Ilangakoon, Nayani; Spaete, Lucas; Roberts, Dar; Enterkine, Josh; Flores, Alejandro; Ustin, Susan and Mitchell, Jessica. (2019). Vegetation maps for Reynolds Creek Experimental Watershed (RCEW) for the year 2015. [Data set].
BSU ScholarWorks Link https://scholarworks.boisestate.edu/bcal_data/5/


Funding Agencies

This resource was created using funding from the following sources:
Agency Name Award Title Award Number
NASA Terrestrial Ecology NNX14AD81G
Department of the Interior Northwest Climate Adaptation Science Center Graduate Fellowship


People that contributed technically, materially, financially, or provided general support for the creation of the resource's content but are not considered authors.

Name Organization Address Phone Author Identifiers
Boise State University Boise Center Aerospace Laboratory (BCAL)
Reynolds Creek Critical Zone Observatory
USDA-ARS Northwest Watershed Research Center Reynolds Creek Experimental Watershed

How to Cite

Dashti, H., N. Glenn, N. Ilangakoon, L. Spaete, D. Roberts, J. Enterkine, A. Flores, J. Mitchell (2020). RCCZO -- GIS / Map Data, LiDAR, Land Cover, Vegetation -- Data for Vegetation Maps for RCEW -- Reynolds Creek Experimental Watershed -- (2015-2015), HydroShare, http://www.hydroshare.org/resource/325556f92ee14bbb90a2b516b8ffcbaa

This resource is shared under the Creative Commons Attribution CC BY.



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