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Faculty within the Arthur Temple College of Forestry and Agriculture (ATCOFA) at Stephen F. Austin State University in Nacogdoches, Texas are integrating drone technology into their curriculum to introduce students to the use of high-end technology within a natural-resource-based decisionmaking process. Drones are currently being integrated across the curriculum within ATCOFA, including 10 geographic information systems (GIS) courses for students pursuing the B.S. in Spatial Science and within six non-GIS specific courses for students pursuing the B.S. in Forestry. Results indicate that drone technology can be an effective tool in enhancing a student's academic experience and provides students with a skill set required for future natural-resource professionals. [ABSTRACT FROM AUTHOR]

The Parrot AR.Drone 2.0 unmanned aerial vehicle was used to assess the condition of 52 trees using a hazard rating system designed for an urban landscape. AR.Drone hazard ratings were compared to the Council of Tree and Landscape Appraisers (CTLA) method of tree condition based on six variables: Trunk Condition, Growth, Crown Structure, Insect and Disease, Crown Development, and Life Expectancy. For each tree, streaming video from the AR.Drone was captured onto an iPad2 using the AR.FreeFlight 2.4 app. The AR.Drone was stable in flight and easy to manipulate and produced video that could be stored for later study. The hazard rating of the trees within the AR.Drone video was compared with the visual hazard assessment of the CTLA method. Comparison of the six variables revealed no statistical difference in the measurements using a Wilcoxon paired-sample test at the 0.05 level of significance. A strong correlation was observed between the two approaches based on Spearman's rank-order analysis. Results indicate that urban trees can be effectively rated for hazard from a distance using a drone that can reach and assess areas of a tree not accessible or viewable from ground level. [ABSTRACT FROM AUTHOR]

Tree height is a critical variable of forest inventory assessments, and estimating the height of trees has been a component of forest inventory assessments for decades. The actual tree height of 60 open-grown baldcypress (Taxodium distichum) trees measured with a telescopic height pole were compared to Pictometry hyperspatial 4-in. multispectral imagery estimated tree height on the campus of Stephen F. Austin State University, Nacogdoches, Texas. Linear correlation coefficients (r) between actual tree height and Pictometry-estimated tree height for all 60 trees and the shortest 30 and tallest 30 trees were >0.997 for all r values. Pictometry-estimated tree height was within, on average, 1.77, 2.15, and 1.40% of actual tree height for all 60 trees, the shortest 30 trees, and the tallest 30 trees, respectively. All three paired t-tests, for all 60 trees, the shortest 30 trees, and the tallest 30 trees, resulted in a P value ≥0.08, indicating that there was no statistical significance between actual and estimated tree height at a 95% confidence level. Pictometry-estimated tree height can be used in lieu of field-based tree height estimation for open-grown urban forests. [ABSTRACT FROM AUTHOR]

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 SPOT 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 II, Level I, and Unique (a modified NLCD 2001 Level II and Level I combination) shows that 30-m Landsat ETM + data hod the highest overall map accuracy for rural, urban, and combined rural/urban land cover maps. Analysis of user's and producer's accuracies shows that Landsat ETM+ data had higher levels of producer's accuracy of >90.0% for the coniferous cover type for modified NLCD 2001 Level II and Unique, excluding one instance for which SPOT 4 had a user's accuracy of 98.5% for the rural coniferous cover type. Modified NLCD 2001 Level I Landsat ETM+ data had user's and producer's accuracies for a homogeneous forest cover type of 98.4 and 90.6%, respectively. Landsat ETM+ data also outperformed SPOT 4 and QuickBird within an urban environment, creating the only map products with forest cover type user's and producer's accuracies of >90.0%. [ABSTRACT FROM AUTHOR]

A review of the article "Accuracy Assessment of Land Cover Maps of Forests within an Urban and Rural Environment," by Daniel R. Unger, I.-Kuai Hung, and David L. Kulhavy, is presented.