JDJCSET | Sigma Xi Poster Competition

Title

Vegetation Mapping of Mammoth Cave National Park Using Multispectral Imagery

Presenter Information

Hongli YangFollow
Robin Q. ZhangFollow

Academic Level at Time of Presentation

Graduate

Major

Geosciences

Minor

Geography

List all Project Mentors & Advisor(s)

Robin Q. Zhang

Presentation Format

Poster Presentation

Abstract/Description

Vegetation Mapping of Mammoth Cave National Park Using Multispectral Imagery

Author: Hongli Yang and Robin Q. Zhang (Mentor)

Up-to-date and detailed vegetation map provides critical information for habitat management. In addition, a vegetation map is necessary for the Park’s Fire Management, for classification of fuel types, and for delineation of fire management units. There have been several attempts of vegetation mapping in 1934, 1975 and 1997. Recent advancements in mapping technology and the availability of high resolution Lidar data call for a new vegetation map for the Park’s management team.

For my research, I will use Landsat-8 Operational Land Imager (OLI) imagery as the main data source for analysis. Obtained from USGS between June 2015 and February 2016, Landsat-8 dataset will cover Mammoth Cave National Park in 30m pixels for the multispectral band and 15m for the panchromatic band, during both the leaf-on and leaf-off seasons. I will apply Landsat-8 dataset to categorize vegetation types according to their different reflectance characteristics. Utilizing both leaf-off and leaf-on images will distinguish coniferous and deciduous trees better. In addition, I plan to use a vegetation index to identify specific vegetation species. To test classification accuracy, I will use the field plots for different vegetation categories. The classification results will also be compared to 2011 National Land Cover Image.

In conclusion, my research will produce a new vegetation map for the Mammoth Cave National Park. These maps will provide critical information for habitat and fire management.

Keywords: Mammoth Cave National Park, Landsat-8 OLI, vegetation mapping, National Land Cover Image

Affiliations

Sigma Xi Poster Competition--ONLY

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Vegetation Mapping of Mammoth Cave National Park Using Multispectral Imagery

Vegetation Mapping of Mammoth Cave National Park Using Multispectral Imagery

Author: Hongli Yang and Robin Q. Zhang (Mentor)

Up-to-date and detailed vegetation map provides critical information for habitat management. In addition, a vegetation map is necessary for the Park’s Fire Management, for classification of fuel types, and for delineation of fire management units. There have been several attempts of vegetation mapping in 1934, 1975 and 1997. Recent advancements in mapping technology and the availability of high resolution Lidar data call for a new vegetation map for the Park’s management team.

For my research, I will use Landsat-8 Operational Land Imager (OLI) imagery as the main data source for analysis. Obtained from USGS between June 2015 and February 2016, Landsat-8 dataset will cover Mammoth Cave National Park in 30m pixels for the multispectral band and 15m for the panchromatic band, during both the leaf-on and leaf-off seasons. I will apply Landsat-8 dataset to categorize vegetation types according to their different reflectance characteristics. Utilizing both leaf-off and leaf-on images will distinguish coniferous and deciduous trees better. In addition, I plan to use a vegetation index to identify specific vegetation species. To test classification accuracy, I will use the field plots for different vegetation categories. The classification results will also be compared to 2011 National Land Cover Image.

In conclusion, my research will produce a new vegetation map for the Mammoth Cave National Park. These maps will provide critical information for habitat and fire management.

Keywords: Mammoth Cave National Park, Landsat-8 OLI, vegetation mapping, National Land Cover Image