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.

National Fire Danger Rating System fuel models were predicted from 1:80,000 scale aerial photographs with a greater than 90% accuracy using combinations of three variables: pine composition, pine basal area and total crown closure. These variables were measured from aerial photos and field checked using techniques common to forest and natural resource management. Graphical presentation from discriminate function analysis indicated good group separation. Misclassification of stands requiring field checking represented 3.3% of the total stand classification. The common use of both the fuel models and aerial photos make the procedures described in this study an easy method for classifying fuel models in the pine and pine-hardwood forests of East Texas.

Anaxyrus microscaphus (The Arizona Toad) is an at-risk species that is endemic to the southwestern United States. Despite conservation concerns, little is known about the ecological drivers of its distribution and habitat use. We investigated the potential distribution of A. microscaphus at the range-wide scale and local scales (i.e., Zion National Park), using MaxEnt to model habitat suitability under current and future climate scenarios. Our models incorporated 12 environmental variables, including climatic, geomorphological, and remotely sensed data. The results showed good model accuracy, with temperature and elevation being the top contributing variables. Currently, 42.6% of the park’s area provides a suitable habitat for A. microscaphus, but projections for 2050 and 2070 indicate a significant reduction in suitable habitat across its range. Temperature was the most influential variable, with habitat suitability decreasing as the annual mean temperatures exceeded 10 °C. Precipitation, vegetation, and topography variables also significantly contributed to the models. The most suitable habitat within Zion National Park occurred along sloped rivers and streams and in valleys with sandy soils, emphasizing the importance of riparian habitat conservation for A. microscaphus survival and persistence. As climate change progresses, the species’ habitat is expected to become increasingly constrained across local and range-wide scales. Our models demonstrated a shift in the suitable habitat towards major river systems, indicating a potential reliance on larger permanent river systems as smaller, more ephemeral habitats decrease in size and abundance. Future management strategies should prioritize conserving and enhancing the resilience of these habitats. MaxEnt models can guide population survey efforts and facilitate the identification of priority conservation areas, saving time and resources for species of concern such as A. microscaphus. Further research, including field surveys and large-scale analyses, is necessary to further refine our understanding of this species’ distribution and how it may be impacted by climate and habitat change.

Faculty within the Arthur Temple College of Forestry and Agriculture at Stephen F. Austin State University are heavily involved with using drones to quantify and qualify forests and natural resources. Drones are involved across the entire curriculum within the College of Forestry and Agriculture to enhance the educational experience of all students. Emphasis is placed on integrating drones, and their effective use in forest and natural resource management endeavors, within all three main focus areas of teaching, research, and service. In addition to the traditional three education focus areas, faculty within the College of Forestry and Agriculture also help train future drone pilots as part of their educational experience.

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Natural disasters, such as tornados, high wind damage, lightning strikes, ice storms, snow storms, and historic flood events, can cause extensive damage to natural resources. Drones, with their ability to observe the surface of the earth from a distance can provide individuals involved with disaster management evaluation and cleanup effort’s information not accessible or not possible with a traditional field-based site visit. Faculty within the Arthur Temple College of Forestry and Agriculture at Stephen F. Austin State University are researching and demonstrating how drones can be used in a host of natural resource activities including assisting disaster management efforts.