Comparing stability in random forest models to map Northern Great Plains plant communities in pastures occupied by prairie dogs using Pleiades imagery
<p>Black-tailed prairie dogs (<i>Cynomys ludovicianus</i>) have been described as a keystone species and are important for grassland conservation, yet many concerns exist over the impact of prairie dogs on plant biomass production and consequently livestock production. The ability...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Copernicus Publications
2020-03-01
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| Series: | Biogeosciences |
| Online Access: | https://www.biogeosciences.net/17/1281/2020/bg-17-1281-2020.pdf |
| Summary: | <p>Black-tailed prairie dogs (<i>Cynomys ludovicianus</i>) have been described as a keystone species and are
important for grassland conservation, yet many concerns exist over the
impact of prairie dogs on plant biomass production and consequently
livestock production. The ability to map plant communities in pastures
colonized by prairie dogs can provide land managers with an opportunity to
optimize rangeland production while balancing conservation goals. The aim of
this study was to test the ability of random forest (RF) models to classify five
plant communities located on and off prairie dog towns in mixed-grass
prairie landscapes of north central South Dakota, assess the stability of RF
models among different years, and determine the utility of utilizing remote
sensing techniques to identify prairie dog colony extent. During 2015 and
2016, Pleiades satellites were tasked to image the study site for a total of
five monthly collections each summer (June–October). Training polygons were
mapped in 2016 for the five plant communities and used to train RF models.
Both the 2015 and 2016 RF models had low (1 %) out-of-bag error rates.
However, comparisons between the predicted plant community maps using the
2015 imagery and one created with the 2016 imagery indicate over 32.9 % of
pixels changed plant community class between 2015 and 2016. The results show
that while RF models may predict with a high degree of accuracy, overlap of
plant communities and interannual differences in rainfall may cause
instability in fitted models. A final RF model combining both 2015 and 2016
data yielded the lowest error rates and was also highly accurate in
determining prairie dog colony boundaries.</p> |
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| ISSN: | 1726-4170 1726-4189 |