Geology topics

Travis B. Poitras

Publications and source records attributed to Travis B. Poitras.

4 recordsLinked to original sources

USGS2019

Distant neighbors: recent wildfire patterns of the Madrean Sky Islands of southwestern United States and northwestern Mexico

Background Information about contemporary fire regimes across the Sky Island mountain ranges of the Madrean Archipelago Ecoregion in the southwestern United States and northern Mexico can provide insight into how historical fire management and land use have influenced fire regimes, and can be used to guide fuels management, ecological restoration, and habitat conservation. To contribute to a better understanding of spatial and temporal patterns of fires in the region relative to environmental and anthropogenic influences, we augmented existing fire perimeter data for the US by mapping wildfires that occurred in the Mexican Sky Islands from 1985 to 2011. Results A total of 254 fires were identified across the region: 99 fires in Mexico (μ = 3901 ha, σ = 5066 ha) and 155 in the US (μ = 3808 ha, σ = 8368 ha). The Animas, Chiricahua, Huachuca-Patagonia, and Santa Catalina mountains in the US, and El Pinito in Mexico had the highest proportion of total area burned (>50%) relative to Sky Island size. Sky Islands adjacent to the border had the greatest number of fires, and many of these fires were large with complex shapes. Wildfire occurred more often in remote biomes, characterized by evergreen woodlands and conifer forests with cooler, wetter conditions. The five largest fires (>25 000 ha) all occurred during twenty-first century droughts (2002 to 2003 and 2011); four of these were in the US and one in Mexico. Overall, high variation in fire shape and size were observed in both wetter and drier years, contributing to landscape heterogeneity across the region. Conclusions Future research on regional fire patterns, including fire severity, will enhance opportunities for collaborative efforts between countries, improve knowledge about ecological patterns and processes in the borderlands, and support long-term planning and restoration efforts.

Madrean Sky Islands↗

USGS2018

Landsat time series analysis of fractional plant cover changes on abandoned energy development sites

Oil and natural gas development in the western United States has increased substantially in recent decades as technological advances like horizontal drilling and hydraulic fracturing have made extraction more commercially viable. Oil and gas pads are often developed for production, and then capped, reclaimed, and left to recover when no longer productive. Understanding the rates, controls, and degree of recovery of these reclaimed well sites to a state similar to pre-development conditions is critical for energy development and land management decision processes. Here we use a multi-decadal time series of satellite imagery (Landsat 5, 1984–2011) to assess vegetation regrowth on 365 abandoned well pads located across the Colorado Plateau in Utah, Colorado, and New Mexico. We developed high-frequency time series of the Soil-Adjusted Total Vegetation Index (SATVI) for each well pad using the Google Earth Engine cloud computing platform. BFAST time-series models were used to fit temporal trends, identifying when vegetation was cleared from the site and the magnitudes and rates of vegetation change after abandonment. The time series metrics are used to calculate the relative fractional vegetation cover (RFVC) of each pad, a measure of post-abandonment vegetation cover relative to pre-drilling condition. Mean and median RFVC were 36% (s.d. 33%) and 26%, respectively, five years after abandonment, with one third of well pads having RFVC greater than 50%. Statistical analyses suggest that much of the high vegetation cover is associated with weedy invasive annual species such as cheatgrass (Bromus tectorum) and Russian thistle (Salsola spp.). Climate conditions and the year of abandonment also play a role, with increased cover in later years associated with a wetter period. Non-linear change at many pads suggests longer recovery times than would be estimated by linear extrapolation. New techniques implemented here address a complex response of cover change to soils, management, and climate over time, and can be extended to the operational monitoring of energy development across large areas.

International Journal of Applied Earth Observation↗

USGS2018

Identifying optimal remotely-sensed variables for ecosystem monitoring in Colorado Plateau drylands

Water-limited ecosystems often recover slowly following anthropogenic or natural disturbance. Multitemporal remote sensing can be used to monitor ecosystem recovery after disturbance; however, dryland vegetation cover can be challenging to accurately measure due to sparse cover and spectral confusion between soils and non-photosynthetic vegetation. With the goal of optimizing a monitoring approach for identifying both abrupt and gradual vegetation changes, we evaluated the ability of Landsat-derived spectral variables to characterize surface variability of vegetation cover and bare ground across a range of vegetation community types. Using three year composites of Landsat data, we modeled relationships between spectral information and field data collected at monitoring sites near Canyonlands National Park, UT. We also developed multiple regression models to assess improvement over single variables. We found that for all vegetation types, percent cover bare ground could be accurately modeled with single indices that included a combination of red and shortwave infrared bands, while near infrared-based vegetation indices like NDVI worked best for quantifying tree cover and total live vegetation cover in woodlands. We applied four models to characterize the spatial distribution of putative grassland ecological states across our study area, illustrating how this approach can be implemented to guide dryland ecosystem management.

Colorado Plateau↗

USGS2017

Disturbance automated reference toolset (DART): Assessing patterns in ecological recovery from energy development on the Colorado Plateau

A new disturbance automated reference toolset (DART) was developed to monitor human land surface impacts using soil-type and ecological context. DART identifies reference areas with similar soils, topography, and geology; and compares the disturbance condition to the reference area condition using a quantile-based approach based on a satellite vegetation index. DART was able to represent 26–55% of variation of relative differences in bare ground and 26–41% of variation in total foliar cover when comparing sites with nearby ecological reference areas using the Soil Adjusted Total Vegetation Index (SATVI). Assessment of ecological recovery at oil and gas pads on the Colorado Plateau with DART revealed that more than half of well-pads were below the 25th percentile of reference areas. Machine learning trend analysis of poorly recovering well-pads (quantile < 0.23) had out-of-bag error rates between 37 and 40% indicating moderate association with environmental and management variables hypothesized to influence recovery. Well-pads in grasslands (median quantile [MQ] = 13%), blackbrush ( Coleogyne ramosissima ) shrublands (MQ = 18%), arid canyon complexes (MQ = 18%), warmer areas with more summer-dominated precipitation, and state administered areas (MQ = 12%) had low recovery rates. Results showcase the usefulness of DART for assessing discrete surface land disturbances, and highlight the need for more targeted rehabilitation efforts at oil and gas well-pads in the arid southwest US.

Colorado Plateau↗