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We examine the coseismic influence of the 5 July 2019, MW7.1 Ridgecrest and the 24 June 2020 MW5.8 Owens Lake earthquakes on rockfall distributions in two undisturbed high-altitude areas of the southern Sierra Nevada Mountains, California, USA. These events occurred within the geologically recent (<2 Mya) Walker Lane/eastern California shear zone. While both study areas are characterized as plutonic, the Owens Lake event largely affected terrain that was formerly glaciated and oversteepened while the Ridgecrest event affected non-glaciated terrain. Our inventory of rockfall locations was derived from analysis of Sentinel-2 images acquired just prior to and immediately after the events. This difference mapping approach using readily-available Sentinel-2 imagery allows for rapid rockfall and landslide mapping. GIS analysis shows that even though the total area assessed for both earthquakes was similar (~1500 km2), the significantly lower magnitude Owens Lake event produced nearly twice as many (102) mappable rockslides as the significantly stronger Ridgecrest event (58), a difference likely due to slope oversteepening in the formerly glaciated area. Significant seismic amplification by topography and reactivation of preexisting failures was apparent for both areas. Inclusion of these factors may improve failure predictions and rockfall probability estimation. [ABSTRACT FROM AUTHOR]

To authenticate to the full-text of this article, please visit this link: http://dx.doi.org/10.1111/j.1745-5871.2008.00557.x Byline: ANDREW N. YUHAS (1,2), LOUIS A. SCUDERI (1,2) Keywords: NDVI; mortality; dieoff; NAM; MODIS; drought; monsoon Abstract: Abstract Vegetation stress or mortality can be the result of many factors including drought-induced water deficit, insect infestations and failures of, or fluctuations in, precipitation sources typical to an area. Reduction of cover and reduced health are identifiable in remotely-sensed multispectral satellite images. A suite of images from NASA's MODIS sensor was used to calculate the Normalised Difference Vegetation Index (NDVI) during the 2000-2006 North American growing seasons. Fluctuations in NDVI over this period show a significant decline in vegetative health in the region with specific areas showing changes linked to moisture sources, prevailing wind patterns, slope aspect and solar radiation receipt. Ground-truthing of these areas has confirmed the extent and magnitude of the dieoff signal. Historically, dieoff has been reversed through regeneration as climate conditions return to a normal regime. However, quantification of recent vegetative change in western North America suggests that the degree of change may be too severe for regrowth to occur and may have far-reaching impacts on a scale unseen in modern times. The loss of vegetative habitat and native species in semi-arid regions and lack of regeneration in these marginal ecosystems due to prolonged drought are growing global problems. Similar drought stress impacts on marginal ecotypes have also been observed in semi-arid regions of Australia, South America, Asia and Africa. Observations of the spatial pattern of temperate forest vegetation globally can be used to develop a precise picture of vegetative health in these regions and how they are reacting to global climate change. Author Affiliation: (1)Department of Earth and Planetary Sciences, Northrop Hall, MSC03-2040, University of New Mexico, Albuquerque, New Mexico, 87131, USA. (2)Center for Rapid Environmental Assessment and Terrain Evaluation, University of New Mexico, 1601 Central Ave. NE, Albuquerque, New Mexico, 87131, USA. Article History: Received 26 October 2007; Revised 6 June 2008; Accepted 22 September 2008 Article note: (*) Corresponding author. Email: ayuhas@unm.edu

The use of process based geographic information systems for decisions on water reallocation is discussed. These use biophysical, institutional and information for more accurate assessments of the impact of such decisions.

Morphological characteristics of cirques have been studied for decades; however, no repeatable set of metrics has been derived that can consistently identify them. Perhaps more importantly, there is no consensus definition of the form that distinguishes cirques and clusters of cirques from non-cirques. In our approach, we use Shuttle Radar Topography Mission (SRTM) digital elevation models (DEMs) in a Convolutional Neural Network (CNN) framework to identify cirques in 20 mountain ranges globally. We extracted bounding boxes of cirques in 19 of these study areas and used them to develop a training set for a cirque identification model. The trained model was applied to the Sierra Nevada California to assess whether this algorithmic approach derived from a global dataset could produce consistent results in complex terrain with mutually interacting cirque forms. Using commonalities revealed using this approach, we find that there is a basic, recognizable and morphometrically quantifiable cirque form. This approach can be used to automate the identification of cirque locations and to guide the quantification of cirque form independent of the subjective definitions of individual workers. The approach can also be used to understand cirque form under different environmental conditions, including similar forms on Mars. [ABSTRACT FROM AUTHOR]