Spectral characteristics of lunar impact melts and inferred mineralogy

Abstract– Two suites of lunar impact melt samples have been measured in NASA’s Reflectance Experiment Laboratory (RELAB) at Brown University. Suite 1 comprises seven Apollo 17 crystalline impact melt breccias and seven quenched glass equivalents. Suite 2 is made up of 15 additional impact melt samples (from Apollo 12, 15, 16, and 17) which exhibit a range of textures and compositions related to cooling conditions and glass abundance. A few of these samples have cooled slowly and fully crystallized, and thus have the same spectral properties as igneous rocks of similar texture and composition; they cannot be uniquely distinguished without geologic context. However, most of the impact melts and melt breccias contain either quantities of quenched glass and/or have developed microcrystalline nonequilibrium textures with well‐defined, diagnostic spectral properties. The microcrystalline textures are associated with a distinctive 600 nm absorption feature, apparently due to submicroscopic ilmenite inclusions in a transparent host (typically fine‐grained plagioclase). The reflectance properties of these lunar sample suites contribute to and constrain the identification and characterization of impact melts in remote sensing data.     

Strukturböden,Solifluktion und Frostklimate der Erde

Ohne Zusammenfassung     

Reply to discussion on “Guidelines on the use of inverse velocity method as a tool for setting alarm thresholds and forecasting landslides and structure collapses” by F. Bozzano,P. Mazzanti,and S. Moretto

■■■The paper “Discussion to: Guidelines on the use of inverse velocity method as a tool for setting alarm thresholds and forecasting landslides and structure collapses by T. Carlà, E. Intrieri, F. Di Traglia, T. Nolesini, G. Gigli and N. Casagli” by Bozzano et al. brings forward new considerations on an issue of extreme concern in landslide risk management. To this day, the ability to predict catastrophic landslide failures from slope surface displacements is a problem dictated more by practical constraints rather than by theoretical uncertainties. In this sense, the development of data interpretation practices is crucial. This short reply provides a few further insights with regard to this subject, also in the context of the recently published literature.     

Tree species identification in mixed coniferous forest using airborne laser scanning

This study tests the capacity of relatively low density (<1 return/m²) airborne laser scanner data for discriminating between Douglas-fir, western larch, ponderosa pine, and lodgepole pine in a western North American montane forest and it evaluates the relative importance of intensity, height, and return type metrics for classifying tree species. Collectively, Exploratory Data Analysis, Pearson Correlation, ANOVA, and Linear Discriminant Analysis show that structural and intensity characteristics generated from LIDAR data are useful for classifying species at dominant and individual tree levels in multi-aged, mixed conifer forests. Proportions of return types and mean intensities are significantly different between species ($p$-value < 0.001) for plot-level dominant species and individual trees. Classification accuracies based on single variables range from 49%–61% at the dominant species level and 37%–52% for individual trees. The accuracy can be improved to 95% and 68% respectively by using multiple variables. The inclusion of proportion of return type greatly improves the classification accuracy at the dominant species level, but not for individual trees, while canopy height improves the accuracy at both levels. Overall differences in intensity and return type between species largely reflect variations in the physical structure of trees and stands. These results are consistent with the findings of others and point to airborne laser scanning as a useful source of data for species classification. However, there are still many knowledge gaps that prevent accurate mapping of species using ALS data alone, particularly with relatively sparse datasets like the one used in this study. Further investigations using other datasets in different forest types will likely result in improvements to species identification and mapping for some time to come.