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Full-Text Articles in Life Sciences
Incorporating Applied Undergraduate Research In Senior To Graduate Level Remote Sensing Courses, Richard Henley, Daniel Unger, David Kulhavy, I-Kuai Hung
Incorporating Applied Undergraduate Research In Senior To Graduate Level Remote Sensing Courses, Richard Henley, Daniel Unger, David Kulhavy, I-Kuai Hung
Faculty Publications
An Arthur Temple College of Forestry and Agriculture (ATCOFA) senior spatial science undergraduate student engaged in a multi-course undergraduate research project to expand his expertise in remote sensing and assess the applied instruction methodology employed within ATCOFA. The project consisted of performing a change detection land-use/land-cover classification for Nacogdoches and Angelina counties in Texas using satellite imagery. The dates for the imagery were spaced approximately ten years apart and consisted of four different acquisitions between 1984 and 2013. The classification procedure followed and expanded upon a series of concrete theoretical remote sensing principles, transforming the four remotely sensed raster images …
Quantifying Land Cover Change Due To Petroleum Exploration And Production In The Haynesville Shale Region Using Remote Sensing, Daniel Unger, I-Kuai Hung, Kenneth W. Farrish, Darinda Dans
Quantifying Land Cover Change Due To Petroleum Exploration And Production In The Haynesville Shale Region Using Remote Sensing, Daniel Unger, I-Kuai Hung, Kenneth W. Farrish, Darinda Dans
Faculty Publications
The Haynesville Shale lies under areas of Louisiana and Texas and is one of the largest gas plays in the U.S. Encompassing approximately 2.9 million ha, this area has been subject to intensive exploration for oil and gas, while over 90% of it has traditionally been used for forestry and agriculture. In order to detect the landscape change in the past few decades, Landsat Thematic Mapper (TM) imagery for six years (1984, 1989, 1994, 2000, 2006, and 2011) was acquired. Unsupervised classifications were performed to classify each image into four cover types: agriculture, forest, well pad, and other. Change detection …
Accuracy Assessment Of Land Cover Maps Of Forests Within An Urban And Rural Environment, Daniel Unger, I-Kuai Hung, David L. Kulhavy
Accuracy Assessment Of Land Cover Maps Of Forests Within An Urban And Rural Environment, Daniel Unger, I-Kuai Hung, David L. Kulhavy
Faculty Publications
Land cover maps of forests within an urban and rural environment derived from high spatial resolution multispectral data (QuickBird) and medium spatial resolution multispectral data (Landsat ETM+ and SPOJ 4) were compared to ascertain whether increased spatial resolution increases map accuracy of forests and whether map accuracy varies across land cover classification schemes. It is commonly assumed that increased spatial resolution would probably increase land cover map accuracy regardless of land cover classification methodology. This study assessed whether that assumption is correct within a rural and an urban environment. Map accuracy for modified National Land Cover Data (NLCD) 2001 Level …
Standardized, Cost-Effective, And Repeatable Remote Sensing Methodology To Quantify Forested Resources In Texas, Daniel Unger, James Kroll, I-Kuai Hung, Jeffrey M. Williams, Dean W. Coble
Standardized, Cost-Effective, And Repeatable Remote Sensing Methodology To Quantify Forested Resources In Texas, Daniel Unger, James Kroll, I-Kuai Hung, Jeffrey M. Williams, Dean W. Coble
Faculty Publications
A standardized remote sensing methodology was evaluated for its use in quantifying the forested resources of the state of Texas in a timely and cost-effective manner. Landsat data from 2002 were used to create a land cover base map encompassing a four-county study area in East Texas. Site-specific and non-site-specific accuracy assessments of the classified map indicate that overall the 2002 base map accuracy of 72.78% was within acceptable remote sensing standards for Landsat data and that forest cover types derived from 2002, 1987, and 1980 Landsat data were within 4.4, 0.5, and 7.4% agreement with Forest Inventory and Analysis …
Multitemporal Analysis Using Landsat Thematic Mapper (Tm) Bands For Forest Cover Classification In East Texas, Jason Grogan, I-Kuai Hung, James Kroll
Multitemporal Analysis Using Landsat Thematic Mapper (Tm) Bands For Forest Cover Classification In East Texas, Jason Grogan, I-Kuai Hung, James Kroll
Faculty Publications
Land cover maps have been produced using satellite imagery to monitor forest resources since the launch of Landsat 1. Research has shown that stacking leaf-on and leaf-off imagery (combining two separate images into one image for processing) may improve classification accuracy. It is assumed that the combination of data will aid in differentiation between forest types. In this study we explored potential benefits of using multidate imagery versus single-date imagery for operational forest cover classification as part of an annual remote sensing forest inventory system. Landsat Thematic Mapper (TM) imagery was used to classify land cover into four classes. Six …