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Using Targeted Training Data to Develop Site Potential for the Upper Colorado River Basin from 2000 - 2018

Dates

Publication Date
Start Date
2000-01-01
End Date
2018-12-31

Citation

Boyte, S.P., Benedict, T.D., Shrestha, D., and Brown, J.F., 2020, Using Targeted Training Data to Develop Site Potential for the Upper Colorado River Basin from 2000 - 2018: U.S. Geological Survey data release, https://doi.org/10.5066/P9IKI4XV.

Summary

Defining site potential for an area establishes its possible long-term vegetation growth productivity in a relatively undisturbed state, providing a realistic reference point for ecosystem performance. Modeling and mapping site potential helps to measure and identify naturally occurring variations on the landscape as opposed to variations caused by land management activities or disturbances (Rigge et al. 2020). We integrated remotely sensed data (250-m enhanced Moderate Resolution Imaging Spectroradiometer (eMODIS) Normalized Difference Vegetation Index (NDVI) (https://earthexplorer.usgs.gov/)) with land cover, biogeophysical (i.e., soils, topography) and climate data into regression-tree software (Cubist®). We integrated these data [...]

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Extension: grass_sitepot.zip
grass_sitepot.aux 7.62 KB
grass_sitepot.tfw 96 Bytes
grass_sitepot.tif 305.21 KB
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Extension: shrubsitepot.zip
shrubsitepot.tfw 96 Bytes
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shrubsitepot.tif-ColorRamp.SLD 2.07 KB

Purpose

The primary purpose for developing the site potential datasets was to use them as drivers in ecological models to account for spatial variation that are related to site conditions (Wylie et al. 2008). The secondary purpose for the development of the site potential data was to evaluate the use of specific datasets from the Land Change Monitoring Assessment and Projection (LCMAP) (https://www.usgs.gov/core-science-systems/eros/lcmap) project to filter the selection of training data pixels for mapping models that monitor shrub and grass dynamics in rangeland ecosystems in the UCRB. While weather provides the annual variation to the shrub and grass models, site potential provides spatial variation to these models. The data can be used to understand vegetation performance as measured by eMODIS NDVI, separating weather's influence from disturbances and management activites. As gridded, spatially explicit data, the data can be ingested into raster-based, machine-learning mapping models used to map land process phenomena. Wylie, BK., Zhang, L., Bliss, N., Ji, L., Tieszen, L.L., Jolly, W.M. 2008. Integrating modeling and remote sensing to identify ecosystem performance anomalies in the boreal forest, Yukon River Basin, Alaska. International Journal of Digital Earth 1:196-220. DOI: 10.1080/17538940802038366

Additional Information

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Type Scheme Key
DOI https://www.sciencebase.gov/vocab/category/item/identifier doi:10.5066/P9IKI4XV

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