Aircraft-Borne Thermal Imagery and Derived Terrain Analysis Layers, Pisgah Lava Field, California
Dates
Publication Date
2021-09-07
Start Date
2011-04-11
End Date
2011-04-13
Citation
Jenness, J.S., Wynne, J.J., Jhabvala, M.D., and Cabrol, N.A., 2021, Aircraft-Borne Thermal Imagery and Derived Terrain Analysis Layers, Pisgah Lava Field, California: U.S. Geological Survey data release, https://doi.org/10.5066/P9NF0L2I.
Summary
This dataset is one of many used in the development of the manuscript 'Advancing Cave Detection using Terrain Analysis Techniques and Thermal Imagery' by Wynne et al. 2021. Manuscript Abstract: Since the initial experiments nearly 50 years ago, techniques for detecting caves using airborne and spacecraft acquired thermal imagery have improved markedly. These advances are largely due to a combination of higher instrument sensitivity, modern computing systems, and processor-intensive analytical techniques. Through applying these advancements, our goals were to: (1) determine the utility of methods designed for terrain analysis and applied to thermal imagery; (2) analyze the usefulness of predawn and midday imagery for detecting caves; [...]
Summary
This dataset is one of many used in the development of the manuscript 'Advancing Cave Detection using Terrain Analysis Techniques and Thermal Imagery' by Wynne et al. 2021. Manuscript Abstract: Since the initial experiments nearly 50 years ago, techniques for detecting caves using airborne and spacecraft acquired thermal imagery have improved markedly. These advances are largely due to a combination of higher instrument sensitivity, modern computing systems, and processor-intensive analytical techniques. Through applying these advancements, our goals were to: (1) determine the utility of methods designed for terrain analysis and applied to thermal imagery; (2) analyze the usefulness of predawn and midday imagery for detecting caves; and, (3) determine which imagery type (predawn, midday, or the difference between the two) was most useful. Using forward stepwise logistic (FSL) and Least Absolute Shrinkage and Selection Operator (LASSO) regression analysis for model selection, and a thermal imagery dataset acquired from the Mojave Desert, California, we examined the efficacy of three well-known terrain descriptors (i.e., slope, topographic position index [TPI], and curvature) on thermal imagery for cave detection. We also included the actual, untransformed thermal DN values (hereafter “unenhanced thermal”) as a fourth dataset. We then compared the thermal signatures of known cave entrances to all non-cave surface locations. We determined these terrain-based analytical methods, which described the “shape” of the thermal landscape, hold significant promise for cave detection. All imagery types produced similar results. Down-selected covariates per imagery type, based upon the FSL models, were: predawn - Slope, TPI, Curvature at 0 m from cave entrance, as well as Slope at 1 m from cave entrance; midday - Slope, TPI, and unenhanced thermal at 0 m from cave entrance; and, difference - TPI and Slope at 0 m from cave entrance, as well as unenhanced thermal and TPI at 3.5 m from cave entrance. We provide recommendations for future research directions in terrestrial and planetary cave detection using thermal imagery.
