Using Plot Photographs to Estimate Tundra Vegetation Cover in Northern Alaska
Plot photography can provide a quick, robust method to measure vegetation, especially in polar environments where logistics can be expensive and challenging. The success and widespread adoption of plot photography in the Arctic hinges on the accuracy of image analysis and data product interpretation...
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ftgvstateuniv:oai:scholarworks.gvsu.edu:theses-2076 2023-05-15T14:56:38+02:00 Using Plot Photographs to Estimate Tundra Vegetation Cover in Northern Alaska Christoffersen, Hana 2022-08-31T07:00:00Z application/pdf https://scholarworks.gvsu.edu/theses/1070 https://scholarworks.gvsu.edu/cgi/viewcontent.cgi?article=2076&context=theses unknown ScholarWorks@GVSU https://scholarworks.gvsu.edu/theses/1070 https://scholarworks.gvsu.edu/cgi/viewcontent.cgi?article=2076&context=theses Masters Theses Arctic Geographic Object-Based Image Analysis (GEOBIA) Handheld digital camera Plot photography Vegetation Change Vegetation Cover Terrestrial and Aquatic Ecology text 2022 ftgvstateuniv 2022-12-09T08:20:06Z Plot photography can provide a quick, robust method to measure vegetation, especially in polar environments where logistics can be expensive and challenging. The success and widespread adoption of plot photography in the Arctic hinges on the accuracy of image analysis and data product interpretation. The relative cover of eight vegetation classes was estimated using a point frame and digital camera across thirty, 1-m2 plots at Utqiaġvik, Alaska from 2012 to 2021. Geographic object-based image analysis (GEOBIA) was applied to generate objects and classify the three band (red, green, blue) images. Machine learning classifiers (random forest, gradient boosted model, classification and regression tree, support vector machine, k-nearest neighbor) were applied, and random forest performed the highest (60.5% overall accuracy). Objects were classified reliably in six out of the eight vegetation classes using the random forest classification, including bryophytes, forbs, graminoids, litter, shadow and standing dead. Deciduous shrubs and lichens were not reliably classified. We also assessed whether estimates of relative vegetation cover from plot photography were comparable to estimates using the point frame. Based on Spearman-Rank correlations within each year, graminoid cover was consistently, positively correlated. Most of the remaining vegetation classes showed moderate positive associations except for litter and standing dead, which showed a negative association. We then used multinomial regression models to gauge if the cover estimates from plot photography could accurately predict the abundance estimates from the point frame across space or time. Currently, our approach to image analysis is best suited to detect large shifts in composition over spatial gradients rather than the more subtle temporal shifts in vegetation over time. Together these results suggest that plot photography coupled with semi-automated image analysis maximizes time, funding, and available technology to monitor vegetation cover in the Arctic. Text Arctic Tundra Alaska Grand Valley State University: Scholar Works @ GVSU Arctic |
institution |
Open Polar |
collection |
Grand Valley State University: Scholar Works @ GVSU |
op_collection_id |
ftgvstateuniv |
language |
unknown |
topic |
Arctic Geographic Object-Based Image Analysis (GEOBIA) Handheld digital camera Plot photography Vegetation Change Vegetation Cover Terrestrial and Aquatic Ecology |
spellingShingle |
Arctic Geographic Object-Based Image Analysis (GEOBIA) Handheld digital camera Plot photography Vegetation Change Vegetation Cover Terrestrial and Aquatic Ecology Christoffersen, Hana Using Plot Photographs to Estimate Tundra Vegetation Cover in Northern Alaska |
topic_facet |
Arctic Geographic Object-Based Image Analysis (GEOBIA) Handheld digital camera Plot photography Vegetation Change Vegetation Cover Terrestrial and Aquatic Ecology |
description |
Plot photography can provide a quick, robust method to measure vegetation, especially in polar environments where logistics can be expensive and challenging. The success and widespread adoption of plot photography in the Arctic hinges on the accuracy of image analysis and data product interpretation. The relative cover of eight vegetation classes was estimated using a point frame and digital camera across thirty, 1-m2 plots at Utqiaġvik, Alaska from 2012 to 2021. Geographic object-based image analysis (GEOBIA) was applied to generate objects and classify the three band (red, green, blue) images. Machine learning classifiers (random forest, gradient boosted model, classification and regression tree, support vector machine, k-nearest neighbor) were applied, and random forest performed the highest (60.5% overall accuracy). Objects were classified reliably in six out of the eight vegetation classes using the random forest classification, including bryophytes, forbs, graminoids, litter, shadow and standing dead. Deciduous shrubs and lichens were not reliably classified. We also assessed whether estimates of relative vegetation cover from plot photography were comparable to estimates using the point frame. Based on Spearman-Rank correlations within each year, graminoid cover was consistently, positively correlated. Most of the remaining vegetation classes showed moderate positive associations except for litter and standing dead, which showed a negative association. We then used multinomial regression models to gauge if the cover estimates from plot photography could accurately predict the abundance estimates from the point frame across space or time. Currently, our approach to image analysis is best suited to detect large shifts in composition over spatial gradients rather than the more subtle temporal shifts in vegetation over time. Together these results suggest that plot photography coupled with semi-automated image analysis maximizes time, funding, and available technology to monitor vegetation cover in the Arctic. |
format |
Text |
author |
Christoffersen, Hana |
author_facet |
Christoffersen, Hana |
author_sort |
Christoffersen, Hana |
title |
Using Plot Photographs to Estimate Tundra Vegetation Cover in Northern Alaska |
title_short |
Using Plot Photographs to Estimate Tundra Vegetation Cover in Northern Alaska |
title_full |
Using Plot Photographs to Estimate Tundra Vegetation Cover in Northern Alaska |
title_fullStr |
Using Plot Photographs to Estimate Tundra Vegetation Cover in Northern Alaska |
title_full_unstemmed |
Using Plot Photographs to Estimate Tundra Vegetation Cover in Northern Alaska |
title_sort |
using plot photographs to estimate tundra vegetation cover in northern alaska |
publisher |
ScholarWorks@GVSU |
publishDate |
2022 |
url |
https://scholarworks.gvsu.edu/theses/1070 https://scholarworks.gvsu.edu/cgi/viewcontent.cgi?article=2076&context=theses |
geographic |
Arctic |
geographic_facet |
Arctic |
genre |
Arctic Tundra Alaska |
genre_facet |
Arctic Tundra Alaska |
op_source |
Masters Theses |
op_relation |
https://scholarworks.gvsu.edu/theses/1070 https://scholarworks.gvsu.edu/cgi/viewcontent.cgi?article=2076&context=theses |
_version_ |
1766328715722096640 |