Department

Department of Ecosystem Science and Management/Department of Civil and Architectural Engineering

First Advisor

Ramesh Sivanpillai

Description

Since 2002, the beetle infestation has killed millions of lodgepole and spruce trees in the Medicine Bow National Forest and throughout the Rocky Mountain Region. Once attacked, first these trees lose their hydrologic conductivity, then the green needles turn to red and grey colors, which is followed by tree mortality. These changes in vegetation impact water flow and other ecosystem processes. In order to model these ecosystems, land cover maps depicting these changes are necessary. Remotely sensed imagery data can be used for mapping these changes in vegetation conditions by associating their spectral reflectance values with earth surface features such as different types of vegetation, and other classes. Through field surveys data are collected about different features and their location, and are associated with the imagery data for training the image processing algorithms. This relationship is used to generate the land cover map for the area of interest. Quality of field data in terms of location and description is important in order to generate reliable land cover maps. This presentation will describe how individual tree-level data within 46 plots were aggregated to plot-level. Next, using distance between plots, and their vegetation conditions (live, dying and dead trees), the plots were grouped to match the pixel resolution of Landsat images. As vegetation conditions changed every year these plots were regrouped from 2008-2015. This research highlights the importance of matching the image and field data characteristics prior to analyses.

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Mapping beetle infested stands in Medicine Bow National Forest: Importance of spatial and attribute accuracy of field data

Since 2002, the beetle infestation has killed millions of lodgepole and spruce trees in the Medicine Bow National Forest and throughout the Rocky Mountain Region. Once attacked, first these trees lose their hydrologic conductivity, then the green needles turn to red and grey colors, which is followed by tree mortality. These changes in vegetation impact water flow and other ecosystem processes. In order to model these ecosystems, land cover maps depicting these changes are necessary. Remotely sensed imagery data can be used for mapping these changes in vegetation conditions by associating their spectral reflectance values with earth surface features such as different types of vegetation, and other classes. Through field surveys data are collected about different features and their location, and are associated with the imagery data for training the image processing algorithms. This relationship is used to generate the land cover map for the area of interest. Quality of field data in terms of location and description is important in order to generate reliable land cover maps. This presentation will describe how individual tree-level data within 46 plots were aggregated to plot-level. Next, using distance between plots, and their vegetation conditions (live, dying and dead trees), the plots were grouped to match the pixel resolution of Landsat images. As vegetation conditions changed every year these plots were regrouped from 2008-2015. This research highlights the importance of matching the image and field data characteristics prior to analyses.