State of Texas: Ten Largest Billboard Company Sign Locations

Spatial analysis of the ten largest billboard company sign locations in Texas. The analysis was undertaken in conjunction with SFA\u27s billboard campaign to increase enrollment. Results indicate visually that the majority of billboards in Texas are within close proximity to major metropolitan areas

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

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 correlated with relative temperature zones during fall and spring which may indicate the use of thermal infrared data as an aid in identifying forest structure/age

Advanced Digital Image Processing Techniques for Natural Resource Assessment at Stephen F. Austin State University

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

Assessing the Quantity and Quality of Forested Resources in East Texas Using Remotely Sensed Data

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

SPATIALLY EXPLICIT MODEL OF AREAS BETWEEN SUITABLE BLACK BEAR HABITAT IN EAST TEXAS AND BLACK BEAR POPULATIONS IN LOUISIANA, ARKANSAS, AND OKLAHOMA

Although black bears (Ursus americanus, Ursus americanus luteolus) were once found throughout the south-central United States, unregulated harvest and habitat loss resulted in severe range retractions and by the beginning of the twentieth century populations in Oklahoma, Louisiana, Texas and Arkansas were nearing extirpation. In response to these losses, translocation programs were initiated in Arkansas (1958-1968 & 2000-2006) and Louisiana (1964-1967 & 2001-2009). These programs successfully restored bears to portions of Louisiana and Arkansas, and, as populations in Arkansas began dispersing, to Oklahoma. In contrast, east Texas remains unoccupied despite the existence of suitable habitat in the region. To facilitate the establishment of a breeding population in east Texas, I sought to identify suitable habitat which bears could use for dispersal between known bear locations in Louisiana, Arkansas and Oklahoma and the east Texas recovery units. I utilized Maxent, a machine learning software, to model habitat suitability in this region. I collected known black bear presence locations (n=18,241) from state agencies in Louisiana, Oklahoma, Arkansas and east Texas and filtered them to reduce spatial autocorrelation (n=664). I also collected spatial data sets based on known black bear ecology to serve as environmental predictor variables. The model was developed at 30-m resolution and encompassed 417,076 km 2. The final model was selected to minimize model over-fitting while maintaining a high test Area Under the Receiver Operating Curve (AUC TEST)score. For final model interpretation and analysis, I used the 10th percentile training threshold available in Maxent which excludes the lowest 10% of predicted presence suitability scores from the binary predictive map, thus resulting in a more conservative predictive map. The final 10th percentile model predicted 43.7% of the pixels in the study area as suitable and 53.7 % percent of the pixels identified as potential recovery units by Kaminski et al. (2013, 2014) as suitable. To focus management efforts, I identified three movement zones with a high proportion of suitable habitat within which connectivity analyses were performed. Suitable patches greater than or equal to 12 km2 were classified within ArcGIS as stepping stone patches. Buffers of 3,500 m were generated around these patches to determine the level of functional connectivity in each zone. The final Maxent model confirmed that suitable bear habitat exists between source populations and the east Texas recovery units. The importance of percent of mast producing forest, percentage of cultivated crops and percentage of protected lands reflect what is known about basic bear biology and ecology. Furthermore, 153 stepping stone patches were identified within the movement zones, demonstrating that there is a reasonable chance of bears naturally dispersing to east Texas using the habitat identified in this study. Thus, protection of existing bear habitat and the stepping stone patches identified in this study should be a priority for managers seeking to facilitate natural bear recolonization of east Texas

Comparison of Presettlement and Present Vegetation Cover of Marion County, Illinois using a Geographic Information System

The vegetation types of Marion County, Illinois prior to European settlement, which were derived from soil association surveys, were compared to a satellite map of current vegetation types to quantify their change over time. GIS analysis indicates that 10% of presettlement prairie is currently grassland, 2% of presettlement prairie has been converted to forest, 32% of original prairie has been converted to agriculture and urban use, 17% of presettlement forest remains intact, grassland comprises 18% of presettlement forest while 21% of presettlement forest has been converted to agricultural and urban use

Positional Precision Analysis of Orthomosaics Derived from Drone Captured Aerial Imagery

The advancement of drones has revolutionized the production of aerial imagery. Using a drone with its associated flight control and image processing applications, a high resolution orthorectified mosaic from multiple individual aerial images can be produced within just a few hours. However, the positional precision and accuracy of any orthomosaic produced should not be overlooked. In this project, we flew a DJI Phantom drone once a month over a seven-month period over Oak Grove Cemetery in Nacogdoches, Texas, USA resulting in seven orthomosaics of the same location. We identified 30 ground control points (GCPs) based on permanent features in the cemetery and recorded the geographic coordinates of each GCP on each of the seven orthomosaics. Analyzing the cluster of each GCP containing seven coincident positions depicts the positional precision of the orthomosaics. Our analysis is an attempt to answer the fundamental question, “Are we obtaining the same geographic coordinates for the same feature found on every aerial image mosaic captured by a drone over time?” The results showed that the positional precision was higher at the center of the orthomosaic compared to the edge areas. In addition, the positional precision was lower parallel to the direction of the drone flight

Master of Science

thesisThe 2012 Great Utah Shakeout highlighted the necessity for increased coordination in the collection and sharing of spatial data related to disaster response during an event. Multiple agencies must quickly relay scientific and damage observations between teams in the field and command centers. Spatial Data Infrastructure (SDI) is a framework that directly supports information discovery and access and use of the data in decision making processes. An SDI contains five core components: policies, access networks, data handling facilities, standards, and human resources needed for the effective collection, management, access, delivery, and utilization of spatial data for a specific area. Implementation of an SDI will increase communication between agencies, field-based reconnaissance teams, first responders, and individuals in the event of a disaster. The increasing popularity of location-based mobile social networks has led to spatial data from these sources being used in the context of managing disaster response and recovery. Spatial data acquired from social networks, or Volunteer Geographic Information (VGI), could potentially contribute thousands of low-cost observations to aid in damage assessment and recovery efforts that may otherwise be unreported. The objective of this research is to design and develop an SDI to allow the incorporation of VGI, professional Geographic Information System (GIS) layers, a mobile application, and scientific reports to aid in the disaster management process. A secondary goal is to assess the utility of the resulting SDI. The end result of combining the three systems (e.g., SDE, a mobile application, and VGI), along with the network of relevant users, is an SDI that improves the volume, quality, currency, accuracy, and access to vital spatial and scientific information following a hazard event

Validating One-On-One GPS Instruction Methodology for Natural Resource Area Assessments Using Forestry Undergraduate Students

Undergraduate students pursuing a Bachelor of Science in Forestry (BSF) at Stephen F. Austin State University (SFA) attend an intensive 6-week residential hands-on instruction in applied field methods. The intensive 6-week instruction includes learning how to use the Global Positioning System (GPS) with a Garmin eTrex HCx GPS unit to accurately calculate area. Students were instructed how to assess the accuracy of their GPS collected waypoints by calculating the Root Mean Square Error (RMSE) comparing their GPS collected area measurements with instructor on-screen digitized area. Student’s average area RMSE between digitized and GPS derived area was 0.015 hectares, whereas instructor’s average area RMSE between digitized and GPS derived area was 0.015 hectares. Over 76% of students measured GPS area was within 5% of instructor on-screen digitized area. No difference between the students and instructors area RMSE of 0.015 hectares and high level of agreement between student measured GPS area and instructor on-screen digitized area: (1) indicates students receiving hands-on instruction in GPS applications can record accurate area measurements after only a limited 2 hour introduction; (2) the accuracy of the Garmin eTrex HCx GPS unit is not user dependent; and, (3) validates the interactive hands-on instruction methodology employed at SFA

Accuracy Assessment on Drone Measured Heights at Different Height Levels

The advancement in unmanned aerial system (UAS) technology has made it possible to attain an aerial unit, commonly known as a drone, at an affordable price with increasing precision and accuracy in positioning and photographing. While aerial photography is the most common use of a drone, many of the models available in the market are also capable of measuring height, the height of the drone above ground, or the altitude above the mean sea level. On board a drone, a barometer is used to control the flight height by detecting the atmospheric pressure change; while a GPS receiver is mainly used to determine the horizontal position of the drone. While both barometer and GPS are capable of measuring height, they are based on different algorithms. Our study goal was to assess the accuracy of height measurement by a drone, with different landing procedures and GPS settings