The integration of geophysical and enhanced Moderate Resolution Imaging Spectroradiometer Normalized Difference Vegetation Index data into a rule-based, piecewise regression-tree model to estimate cheatgrass beginning of spring growth

International Journal of Digital Earth
By: , and 

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Abstract

Cheatgrass exhibits spatial and temporal phenological variability across the Great Basin as described by ecological models formed using remote sensing and other spatial data-sets. We developed a rule-based, piecewise regression-tree model trained on 99 points that used three data-sets – latitude, elevation, and start of season time based on remote sensing input data – to estimate cheatgrass beginning of spring growth (BOSG) in the northern Great Basin. The model was then applied to map the location and timing of cheatgrass spring growth for the entire area. The model was strong (R2 = 0.85) and predicted an average cheatgrass BOSG across the study area of 29 March–4 April. Of early cheatgrass BOSG areas, 65% occurred at elevations below 1452 m. The highest proportion of cheatgrass BOSG occurred between mid-April and late May. Predicted cheatgrass BOSG in this study matched well with previous Great Basin cheatgrass green-up studies.

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Additional publication details

Publication type Article
Publication Subtype Journal Article
Title The integration of geophysical and enhanced Moderate Resolution Imaging Spectroradiometer Normalized Difference Vegetation Index data into a rule-based, piecewise regression-tree model to estimate cheatgrass beginning of spring growth
Series title International Journal of Digital Earth
DOI 10.1080/17538947.2013.860196
Volume 8
Issue 2
Year Published 2015
Language English
Publisher Taylor & Francis
Publisher location Abingdon, UK
Contributing office(s) Earth Resources Observation and Science (EROS) Center
Description 15 p.
First page 116
Last page 130
Country United States
Other Geospatial Great Basin
Online Only (Y/N) N
Additional Online Files (Y/N) N