The resurgence of oil and gas extraction in the Appalachian Basin has resulted in an excess of oil and gas brines in Pennsylvania, West Virginia, and Ohio. Primarily driven by unconventional development, this expansion has also impacted conventional wells and consequently, created economic pressure to develop effective and cheap disposal options. Using brine as a road treatment, directly or as a processed deicer, however, creates substantial concern that naturally occurring radioactive material in the brines can contaminate roads and road-side areas. Current decision making is based on risk exposure scenarios developed by regulatory agencies based on recreational users in rural areas and exposures to drivers during a typical commute. These scenarios are not appropriate for evaluating exposures to residential deicer users or people living near treated streets. More appropriate exposure scenarios were developed in this work and exposures predicted with these models based on laboratory measurements and literature data. Exposure scenarios currently used for regulatory assessment of brine road treatment result in predicted exposures of 0.4–0.6 mrem/year. Residential exposures predicted by the scenarios developed in this work are 4.6 mrem/year. If the maximum range of near-road soil radium concentrations observed in the region is used in this residential scenario (60 pCi/g226Ra, 50 pCi/g228Ra), residents living near these roads would be exposed to an estimated 296 mrems/year, above regulatory exposure thresholds used in nuclear facility siting assessments. These results underline the urgent need to clarify exposure risks from the use of oil and gas brines as a road treatment, particularly given the existing disparities in the distribution of road impacts across socioeconomic status.
Proximity to roads is one of the main determinants of deforestation in the Amazon basin. Determining the construction year of roads (CYR) is critical to improve the understanding of the drivers of road construction and to enable predictions of the expansion of the road network and its consequent impact on ecosystems. While recent artificial intelligence approaches have been successfully used for road extraction, they have typically relied on high spatial‐resolution imagery, precluding their adoption for the determination of CYR for older roads. In this article, we developed a new approach to automate the process of determining CYR that relies on the approximate position of the current road network and a time‐series of the proportion of exposed soil based on the multidecadal remote sensing imagery from the Landsat program. Starting with these inputs, our methodology relies on the Least Cost Path algorithm to co‐register the road network and on a Before‐After Control‐Impact design to circumvent the inherent image‐to‐image variability in the estimated amount of exposed soil. We demonstrate this approach for a 357 000 km2area around the Transamazon highway (BR‐230) in the Brazilian Amazon, encompassing 36 240 road segments. The reliability of this approach is assessed by comparing the estimated CYR using our approach to the observed CYR based on a time‐series of Landsat images. This exercise reveals a close correspondence between the estimated and observed CYR (). Finally, we show how these data can be used to assess the effectiveness of protected areas (PAs) in reducing the yearly rate of road construction and thus their vulnerability to future degradation. In particular, we find that integral protection PAs in this region were generally more effective in reducing the expansion of the road network when compared to sustainable use PAs.
more » « less- NSF-PAR ID:
- 10482243
- Publisher / Repository:
- Wiley Blackwell (John Wiley & Sons)
- Date Published:
- Journal Name:
- Remote Sensing in Ecology and Conservation
- ISSN:
- 2056-3485
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Abstract -
Landsat 5 has produced imagery for decades that can now be viewed and manipulated in Google Earth Engine, but a general, automated way of producing a coherent time series from these images—particularly over cloudy areas in the distant past—is elusive. Here, we create a land use and land cover (LULC) time series for part of tropical Mato Grosso, Brazil, using the Bayesian Updating of Land Cover: Unsupervised (BULC-U) technique. The algorithm built backward in time from the GlobCover 2009 data set, a multi-category global LULC data set at 300 m resolution for the year 2009, combining it with Landsat time series imagery to create a land cover time series for the period 1986–2000. Despite the substantial LULC differences between the 1990s and 2009 in this area, much of the landscape remained the same: we asked whether we could harness those similarities and differences to recreate an accurate version of the earlier LULC. The GlobCover basis and the Landsat-5 images shared neither a common spatial resolution nor time frame, But BULC-U successfully combined the labels from the coarser classification with the spatial detail of Landsat. The result was an accurate fine-scale time series that quantified the expansion of deforestation in the study area, which more than doubled in size during this time. Earth Engine directly enabled the fusion of these different data sets held in its catalog: its flexible treatment of spatial resolution, rapid prototyping, and overall processing speed permitted the development and testing of this study. Many would-be users of remote sensing data are currently limited by the need to have highly specialized knowledge to create classifications of older data. The approach shown here presents fewer obstacles to participation and allows a wide audience to create their own time series of past decades. By leveraging both the varied data catalog and the processing speed of Earth Engine, this research can contribute to the rapid advances underway in multi-temporal image classification techniques. Given Earth Engine’s power and deep catalog, this research further opens up remote sensing to a rapidly growing community of researchers and managers who need to understand the long-term dynamics of terrestrial systems.more » « less
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Abstract Approximately 20% of the Brazilian Amazon has now been deforested, and the Amazon is currently experiencing the highest rates of deforestation in a decade, leading to large‐scale land‐use changes. Roads have consistently been implicated as drivers of ongoing Amazon deforestation and may act as corridors to facilitate species invasions. Long‐term data, however, are necessary to determine how ecological succession alters avian communities following deforestation and whether established roads lead to a constant influx of new species.
We used data across nearly 40 years from a large‐scale deforestation experiment in the central Amazon to examine the avian colonization process in a spatial and temporal framework, considering the role that roads may play in facilitating colonization.
Since 1979, 139 species that are not part of the original forest avifauna have been recorded, including more secondary forest species than expected based on the regional species pool. Among the 35 species considered to have colonized and become established, a disproportionate number were secondary forest birds (63%), almost all of which first appeared during the 1980s. These new residents comprise about 13% of the current community of permanent residents.
Widespread generalists associated with secondary forest colonized quickly following deforestation, with few new species added after the first decade, despite a stable road connection. Few species associated with riverine forest or specialized habitats colonized, despite road connection to their preferred source habitat. Colonizing species remained restricted to anthropogenic habitats and did not infiltrate old‐growth forests nor displace forest birds.
Deforestation and expansion of road networks into
terra firme rainforest will continue to create degraded anthropogenic habitat. Even so, the initial pulse of colonization by nonprimary forest bird species was not the beginning of a protracted series of invasions in this study, and the process appears to be reversible by forest succession. -
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Beyond organisms experiencing direct impacts (mortality) from the presence of anthropogenic features, interactive relationships may exacerbate the effects of anthropogenic disturbance within the context of these features. For example, mortality risk may be affected by the road infrastructure associated with energy development by influencing space use of predators including human hunters. To assess these relationships, we conducted research on northern bobwhite
Colinus virginianus across a hunted and non‐hunted area of Beaver River Wildlife Management Area, Oklahoma, using radiotelemetry from 2012–2015. We found that bobwhite mortality risk decreased as the distance from primary roads (m) increased across weeks (hazard ratio [HR] = 1.008, 95% confidence interval [CI] = 1.0003 to 1.0013). The interaction between unit (hunted and non‐hunted) and distance from primary roads was not significant (HR = 1.00, 95% CI = 0.999 to 1.001) indicating that hunting pressure was not a likely explanation for the observed decrease in survival related to primary roads. Bobwhite on the hunted unit avoided exposed soil/sparse vegetation (= ‐0.01, CI = ‐0.02 to ‐0.002) and bare ground ( =‐0.01, CI =‐0.02 to ‐0.002) more than bobwhite on the non‐hunted unit, however these were weak relationships. No other differences in bobwhite space use were detected related to hunting. Though we were limited to estimating theoretical rather than empirical amounts of hunting pressure during our study, we were unable to detect any negative compounding effects of anthropogenic development and hunting pressure on bobwhite ecology during the hunting season. -
null (Ed.)Abstract Wildfires are a major disturbance to forest carbon (C) balance through both immediate combustion emissions and post-fire ecosystem dynamics. Here we used a process-based biogeochemistry model, the Terrestrial Ecosystem Model (TEM), to simulate C budget in Alaska and Canada during 1986–2016, as impacted by fire disturbances. We extracted the data of difference Normalized Burn Ratio (dNBR) for fires from Landsat TM/ETM imagery and estimated the proportion of vegetation and soil C combustion. We observed that the region was a C source of 2.74 Pg C during the 31-year period. The observed C loss, 57.1 Tg C year −1 , was attributed to fire emissions, overwhelming the net ecosystem production (1.9 Tg C year −1 ) in the region. Our simulated direct emissions for Alaska and Canada are within the range of field measurements and other model estimates. As burn severity increased, combustion emission tended to switch from vegetation origin towards soil origin. When dNBR is below 300, fires increase soil temperature and decrease soil moisture and thus, enhance soil respiration. However, the post-fire soil respiration decreases for moderate or high burn severity. The proportion of post-fire soil emission in total emissions increased with burn severity. Net nitrogen mineralization gradually recovered after fire, enhancing net primary production. Net ecosystem production recovered fast under higher burn severities. The impact of fire disturbance on the C balance of northern ecosystems and the associated uncertainties can be better characterized with long-term, prior-, during- and post-disturbance data across the geospatial spectrum. Our findings suggest that the regional source of carbon to the atmosphere will persist if the observed forest wildfire occurrence and severity continues into the future.more » « less
