Geology topics

Research about northern Arizona

Source-linked reports with geographic coverage including northern Arizona.

3 recordsLinked to original sources

USGS2025

UAV-derived models of vegetation characteristics do not transfer to extreme drought and wet conditions across a northern Arizona landscape

Context Shifts in precipitation regimes due to climate change are significantly impacting dryland ecosystems, including vegetation composition and structure. Unoccupied aerial vehicles (UAVs) are widely used to monitor vegetation, but whether models built to predict changes in these characteristics are robust under extreme precipitation regimes is unclear. Objectives We aimed to predict key vegetation characteristics under three precipitation regimes (ambient, drought, and water addition) and assess model performance across these moisture conditions. We also evaluated how models built under ambient conditions predicted vegetation characteristics under extreme precipitation regimes. Methods UAV surveys were conducted at five sites subject to long-term precipitation manipulation along an elevation gradient in northern Arizona, United States (U.S.). Twenty-one vegetation indices and point cloud data from the UAV imagery were used to develop models to predict vegetation structure and composition characteristics. Model performance and transferability were assessed via error and directional bias within each treatment (i.e., in situ) and from ambient to precipitation treatments (i.e., model transfer). Results UAV-based models accurately measured vegetation characteristics across all regimes, but maximum height showed significantly higher error under drought conditions. Models developed under ambient precipitation and applied to extreme precipitation treatments exhibited significant differences in the error and directional bias, indicating they may not be suitable under climate change. Conclusions UAV-based models are effective for monitoring vegetation characteristics but may lose accuracy under extreme precipitation regimes expected under climate change. This study emphasizes the need to improve model transferability and suggests refining landscape monitoring approaches to consider extreme changes in precipitation and associated vegetation responses.

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USGS1988

An improved dark-object subtraction technique for atmospheric scattering correction of multispectral data

Digital analysis of remotely sensed data has become an important component of many earth-science studies. These data are often processed through a set of preprocessing or “clean-up” routines that includes a correction for atmospheric scattering, often called haze. Various methods to correct or remove the additive haze component have been developed, including the widely used dark-object subtraction technique. A problem with most of these methods is that the haze values for each spectral band are selected independently. This can create problems because atmospheric scattering is highly wavelength-dependent in the visible part of the electromagnetic spectrum and the scattering values are correlated with each other. Therefore, multispectral data such as from the Landsat Thematic Mapper and Multispectral Scanner must be corrected with haze values that are spectral band dependent. An improved dark-object subtraction technique is demonstrated that allows the user to select a relative atmospheric scattering model to predict the haze values for all the spectral bands from a selected starting band haze value. The improved method normalizes the predicted haze values for the different gain and offset parameters used by the imaging system. Examples of haze value differences between the old and improved methods for Thematic Mapper Bands 1, 2, 3, 4, 5, and 7 are 40.0, 13.0, 12.0, 8.0, 5.0, and 2.0 vs. 40.0, 13.2, 8.9, 4.9, 16.7, and 3.3, respectively, using a relative scattering model of a clear atmosphere. In one Landsat multispectral scanner image the haze value differences for Bands 4, 5, 6, and 7 were 30.0, 50.0, 50.0, and 40.0 for the old method vs. 30.0, 34.4, 43.6, and 6.4 for the new method using a relative scattering model of a hazy atmosphere.

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USGS1960

Detrital-appearing uraninite grains in the shinarump member of the chinle formation in northern Arizona

A sample of uranium-bearing conglomerate from the Shinarump member of the Chinle formation in northern Arizona was disaggregated in an ultrasonic separator. The sample, weighing 187 grams, contained about 60 grains of uraninite (from 0.1 to 1 mm diameter) which have the sphericity and polish suggestive of detrital grains . The detrital-appearing grains are associated with abundant interstitial uraninite , sphalerite, and pyrite, which presents conflicting evidence as to the origin of the uranium deposit. No definite conclusions are presented for the occurrence of these grains . However, the evidence strongly suggests that they are probably not detrital in origin, but are uraninite replacements of detrital grains ; the original mineral may have been quartz.

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