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Full-Text Articles in Forest Sciences
Accuracy Assessment Of Pictometry® Height Measurements Stratified By Cardinal Direction And Image Magnification Factor, Daniel Unger, David Kulhavy, I-Kuai Hung, Yanli Zhang
Accuracy Assessment Of Pictometry® Height Measurements Stratified By Cardinal Direction And Image Magnification Factor, Daniel Unger, David Kulhavy, I-Kuai Hung, Yanli Zhang
Faculty Publications
The aim of this project was to ascertain if Pictometry® estimated height could be used in lieu of field-based height estimation. Height of a light pole measured with a telescopic height pole was compared to Pictometry® hyperspatial 4-inch (10.2 centimeters) multispectral imagery estimated light pole height on the campus of Stephen F. Austin State University, Nacogdoches, Texas. Average percent agreement between light pole height and Pictometry® estimated light pole height summarized by Pictometry® image magnification factors at 100%, 125%, 150%, 200%, and 300% magnification were within 98% of light pole height with percent disagreement ranging from …
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 …
Measuring Tree Height Using Pictometry Hyperspatial Imagery, Daniel Unger, David Kulhavy, Matthew A. Wade, I-Kuai Hung
Measuring Tree Height Using Pictometry Hyperspatial Imagery, Daniel Unger, David Kulhavy, Matthew A. Wade, I-Kuai Hung
Faculty Publications
Trees within Nacogdoches, Texas were measured for height using Pictometry hyperspatial imagery at 4 inch spatial resolution. Trees measured included baldcypress located on LaNana Creek as part of a hybrid analysis study. Baldcypress, Taxodiumdistichum, was planted along La Nana Creek, Nacogdoches, Texas, for erosion control and as a test bank for growth of the species genotypes. Each tree was located with GPS and entered into the GIS data base in the Arthur Temple College of Forestry and Agriculture, Stephen F. Austin State University. Actual tree height, measured using a height pole in 0.1 inch increments, was compared to …
Geolite, An Arcgis Extension To Assist In Lidar Data Processing, Yanli Zhang, Jason Grogan, I-Kuai Hung, Ramanathan Sugumaran
Geolite, An Arcgis Extension To Assist In Lidar Data Processing, Yanli Zhang, Jason Grogan, I-Kuai Hung, Ramanathan Sugumaran
Faculty Publications
No abstract provided.
Advanced Digital Image Processing Techniques For Natural Resource Assessment At Stephen F. Austin State University, Daniel Unger
Advanced Digital Image Processing Techniques For Natural Resource Assessment At Stephen F. Austin State University, Daniel Unger
Faculty Presentations
Graduate course work concentrating on land cover classification and digital image processing within the Arthur Temple College of Forestry and Agriculture at Stephen F. Austin State University is presented.
Accuracy Assessment Of Land Cover Maps Derived From Multiple Data Sources, Daniel Unger, Hillary Tribby, Hans Michael Williams, I-Kuai Hung
Accuracy Assessment Of Land Cover Maps Derived From Multiple Data Sources, Daniel Unger, Hillary Tribby, Hans Michael Williams, I-Kuai Hung
Faculty Publications
Maximum Likelihood (ML) and Artificial Neural Network (ANN) supervised classification methods were used to demarcate land cover types within IKONOS and Landsat ETM+ imagery. Three additional data sources were integrated into the classification process: Canopy Height Model (CHM), Digital Terrain Model (DTM) and Thermal data. Both the CHM and DTM were derived from multiple return small footprint LIDAR. Forty maps were created and assessed for overall map accuracy, user's accuracy, producer's accuracy, kappa statistic and Z statistic using classification schemes from U.S.G.S. 1976 levels 1 and 2 and T.G.l.C. 1999 levels 2 and 4. Results for overall accuracy of land …
Accuracy Assessment Of Classified Maps Derived From High And Midspatial Resolution Multispectral Data, Daniel Unger, Sean O'Melveny
Accuracy Assessment Of Classified Maps Derived From High And Midspatial Resolution Multispectral Data, Daniel Unger, Sean O'Melveny
Faculty Presentations
No abstract provided.
Assessing The Quantity And Quality Of Forested Resources In East Texas Using Remotely Sensed Data, Daniel Unger
Assessing The Quantity And Quality Of Forested Resources In East Texas Using Remotely Sensed Data, Daniel Unger
Faculty Publications
OBJECTIVES: Development of new or enhanced remote sensing methodologies for assessing the quantity of east Texas forests and their associated ecosystems. Development of new or enhanced remote sensing methodologies for assessing the quality of east Texas forests and their associated ecosystems. Application of temporal analysis to assess the change in the quantity/quality of east Texas forests and their associated ecosystems over time.
Seasonal Comparison Of Remotely Sensed Relative Forest Ecosystem Temperature Zones With Topography And Forest Biomass In The Clear Springs Wilderness Area Of The Shawnee National Forest, Daniel Unger
Faculty Presentations
The use of thermal infrared data to delineate seasonal relative forest ecosystem temperature zones as a tool for forest ecological studies was analyzed. Analysis involved: (1) delineating relative seasonal forest ecosystem temperature zones within the Clear Springs Wilderness Area of the Shawnee National Forest using Landsat Thematic Mapper thermal infrared data; and, (2) quantifying the effect of topography and forest biomass on relative forest ecosystem temperature zones within seasons. Results indicate that slope was statistically uncorrelated with relative temperature zones within any season, aspect was statistically correlated with relative temperature zones during fall and winter, and forest biomass was statistically …