Estimating tree characteristics with field plots located in remote and inaccessible areas can be a costly and timely endeavor. Light Detection and Ranging (Lidar) remote sensing allowing for the estimation of the 3-dimensional structure of forest vegetation offers an alternative to traditional ground based forest measurements. This project assessed the utility of using Lidar data to estimate number of trees, tree height and crown width within Barksdale Air Force Base forest management area, Bossier City, Louisiana. Two programs, Lidar Data Filtering and Forest Studies (Tiffs) and Lidar Analyst were used to derive forest measurements, which were compared to field measurements. Based on Root Mean Square Error (RMSE), Lidar Analyst (3.81 trees) performed better than Tiffs (5.71 trees) at estimating average tree count per plot. Tiffs was better at deriving average tree height than Lidar Analyst with an RMSE of 19.08 feet to Lidar Analyst’s RMSE of 21.20 feet. Lidar Analyst, with a RMSE of 25.41 feet, was better in deriving average crown diameter over Tiffs RMSE of 30.54 feet. All linear correlation coefficients between average field measured tree height and Lidar derived average tree height were highly significant at the 0.01 probability level for both Tiffs and Lidar Analyst on hardwood, conifers and a combined hardwood-conifer comparison.

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.

Fuel load prediction equations that made use of aerial photographs were developed for Mixed Conifer, Ponderosa Pine ( Pinus ponderosa Dougl. ex Laws.) and Pinyon–Juniper ( Pinus edulis Engelm.)–( Juniperus monosperma Engelm.) cover types from one-time measurements made in the Santa Fe watershed (SFWS) located in the Sangre de Cristo Mountains of northern New Mexico, and at Los Alamos National Laboratory (LANL) located in the Jemez Mountains of northern New Mexico. The results of the watershed data set were favorable and exhibited a high degree of relative accuracy. The results from the LANL data set did not share the same degree of accuracy, but rather exhibited a high degree of error. Use of these or similar prediction equations may be limited to certain regions and community types that exhibit similar regional characteristics such as terrain, soil, and weather conditions. Applied use of the prediction equations required less time than traditional fuel sampling performed onsite, but suffered from a loss of accuracy. It is strongly suggested that additional study of this method be undertaken to generate more accurate and reliable equations. Hopefully, more accurate equations may augment existing fuel sampling techniques and be put to practical use for fire planning purposes.

Undergraduate students pursuing a Bachelor of Science in Forestry (BSF) at Stephen F. Austin State University (SFA) within the Arthur Temple College of Forestry and Agriculture (ATCOFA) attend an intensive 6-week hands-on instruction in applied field methods. The second week of field station is focused on land measurement activities to introduce students to practical, hands-on, and technology based ways to survey forest boundaries. On Monday of the second week students are introduced to the concepts of how to use a handheld compass to navigate from point to point, use a consumer-grade handheld Global Positioning System (GPS) unit for collecting the geographic coordinates of given locations, use a GPS unit to calculate the area of a forest opening, use a GPS unit to walk and record a forest hiking trail, and evaluate the accuracy of their GPS derived locations via a Root Mean Square Error (RMSE) analysis. RMSE analysis between a students collected geographic coordinates and the instructors collected geographic coordinates indicated that the students were sufficient in correctly recording the geographic coordinates of point, line, and polygon features identified in the field. Grades on the student submitted reports summarizing Monday’s activities resulted in 33 of 56 students (59.0%) receiving a high A, 14 of 56 students (25.0%) receiving a low A, and 9 of 56 students (16.0%) receiving a high B indicating that the interactive hands-on nature of ATCOFA’s field station is effective at providing students with real-world applications whereby they will be ready to make a difference the day after graduation. Interactive drone imagery and video integrated into the daily activities in the field to enhance a student’s understanding of their specific objectives provided the students in the field with a bird’s eye perspective of the landscape to aid their understanding and planning of the field tasks assigned. In conclusion, employers can have confidence that when hiring recent BSF graduates from ATCOFA that the students have been introduced to geospatial technologies within a proven one-on-one instruction methodology designed to increase cognitive retention and can traverse from location to location accurately and record the geographic coordinates of earth surface features correctly.

Uses of high spatial resolution data obtained from satellite-based sensors include creating land cover maps, deriving large-scale quantitative assessments such as vegetation indices, and visually assessing an area for qualitative information only assessable from large-scale digital data. One of the more popular uses of high spatial resolution data is to use the image as a base map for on-screen digitizing spatially dependent vector products. Since most geographic information system (GIS) databases store a variety of current and historical data, the accuracy of any on-screen digitized product is dependent on the spatial accuracy of the reference data. Therefore, it is important to understand and validate the accuracy of data used to create spatially referenced product, even though the data come with high spatial resolution. One of the more popular and historical high spatial resolution data within most GIS labs is QuickBird's multispectral data at 2.44 × 2.44 m². Although there are current sensors available with a higher spatial resolution, the sometimes prohibitive expense of obtaining high spatial resolution data necessitates the need to utilize and assess historic data. Since the QuickBird has been the mainstay of high spatial resolution data since 2001, understanding the geometric accuracy of the DigitalGlobe's QuickBird user-defined panchromatic and multispectral image bundle product remains relevant. In this study, we assessed the positional accuracy of this product for its utility as an “off the shelf” base map for creating other spatially referenced products. The average Euclidean distance, RMSE (root mean square error), and RSME (root square mean error) between QuickBird-identified Universal Transverse Mercator (UTM) coordinates and coincident in situ GPS-collected UTM coordinates were calculated at 33 systematically selected locations throughout the city of Nacogdoches, Texas, USA. The average Euclidean distance, RMSE, and RSME between QuickBird's projected UTM coordinates and its corresponding GPS-collected UTM coordinates measured at 5.34 meters, 5.79 meters, and 4.05 meters, respectively. They were well within DigitalGlobe's stated RMSE positional accuracy of 14.0 meters for a panchromatic and multispectral QuickBird image bundle.