Geology topics

Carlos Ramirez

Publications and source records attributed to Carlos Ramirez.

3 recordsLinked to original sources

USGS2025

Origins and fluxes of gas emissions from the Central Volcanic Zone of the Andes

We present geochemical data from gas samples from ∼1200 km of arc in the Central Volcanic Zone of the Andes (CVZA), the volcanic arc with the thickest (∼70 km) continental crust globally. The primary goals of this study are to characterize and understand how magmatic gases interact with hydrothermal systems, assess the origins of the major gas species, and constrain gas emission rates. To this end, we use gas chemistry, isotope compositions of H, O, He, C, and S, and SO 2 fluxes from the CVZA. Gas and isotope ratios (CO 2 /S T , CO 2 /CH 4 , H 2 O/S T , δ 13 C, δ 34 S, 3 He/ 4 He) vary dramatically as magmatic gases are progressively affected by hydrothermal processes, reflecting removal and crustal sequestration of reactive species (e.g., S) and addition of less reactive meteoric and crustal components (e.g., He). The observed variations are similar in magnitude to those expected during the magmatic reactivation of volcanoes with hydrothermal systems. Carbon and sulfur isotope compositions of the highest temperature emissions (97–408 °C) are typical of arc magmatic gases. Helium isotope compositions reach values similar to upper mantle in some volcanic gases indicating that transcustal magma systems are effective conduits for volatiles, even through very thick continental crust. However, He isotopes are highly sensitive to even low degrees of hydrothermal interaction and radiogenic overprinting. Previous work has significantly underestimated volatile fluxes from the CVZA; however, emission rates from this study also appear to be lower than typical arcs, which may be related to crustal thickness.

Central Volcanic Zone of the Andes↗

USGS2018

Use of imaging spectroscopy and LIDAR to characterize fuels for fire behavior prediction

To protect ecosystem services and the increasing wildland urban interface in a world with fire, comprehensive maps of wildland fuels are needed to predict fire behavior and effects. Traditionally, fuels have been categorized into a classification scheme whereby a single metric represents vegetation composition and structure, which can then be parameterized based on variable vegetation amount and condition. Remote sensing has been used to extrapolate between known field plots across the landscape, however until recently, those technologies have had limited ability to characterize fuels (e.g., composition, horizontal and vertical connectivity). Using new technologies (imaging spectroscopy and LIDAR), the objectives of this study are to assess: 1) how fuel characteristics observed from remote sensing affect categorical fuel classifications, and 2) how fuel characteristics affect landscape-scale fire behavior (spread rate, areal extent and perimeter). The analysis was conducted over the 2014 California King Fire that burned ~40,000 ha over lands with varying use and history and has unique remote sensing observations from before and after the fire. This analysis compares fuel classifications from a synergistic field, model, and Landsat approach (LANDFIRE) and products derived from the Airborne Visible/Infrared Imaging Spectrometer and LIDAR (MapFUELS). Each classification focuses on different fuel characteristics, which were then used to compare differences in a fire simulation model (CAWFE) and actual fire behavior. The results show that fuel characteristic inputs such as horizontal connectivity or fuel type and vertical structure affect fire spread rate and final fire extent (respectively). These results present the opportunity for future integration of fuel characteristics observed at coarser resolutions (900 m 2 ) into predictions of fire behavior a similar spatial resolutions (as opposed to the current standard based on empirical relationships between fuel and fire behavior at ~12 m 2 resolution).

Remote Sensing Applications: Society and Environme↗

USGS2016

Mapping site index and volume increment from forest inventory, Landsat, and ecological variables in Tahoe National Forest, California, USA

High-resolution site index (SI) and mean annual increment (MAI) maps are desired for local forest management. We integrated field inventory, Landsat, and ecological variables to produce 30 m SI and MAI maps for the Tahoe National Forest (TNF) where different tree species coexist. We converted species-specific SI using adjustment factors. Then, the SI map was produced by ( i ) intensifying plots to expand the training sets to more climatic, topographic, soil, and forest reflective classes, ( ii ) using results from a stepwise regression to enable a weighted imputation that minimized the effects of outlier plots within classes, and ( iii ) local interpolation and strata median filling to assign values to pixels without direct imputations. The SI (reference age is 50 years) map had an R 2 of 0.7637, a root-mean-square error (RMSE) of 3.60, and a mean absolute error (MAE) of 3.07 m. The MAI map was similarly produced with an R 2 of 0.6882, an RMSE of 1.73, and a MAE of 1.20 m 3 ·ha −1 ·year −1 . Spatial patterns and trends of SI and MAI were analyzed to be related to elevation, aspect, slope, soil productivity, and forest type. The 30 m SI and MAI maps can be used to support decisions on fire, plantation, biodiversity, and carbon.

Canadian Journal of Forest Research↗