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Geology topics

Jason R. Kreitler

Publications and source records attributed to Jason R. Kreitler.

At least 19 recordsLinked to original sources
USGS2025

UAS and high-resolution satellite imagery improve the accuracy of cheatgrass detection across an invaded Yellowstone landscape

Context Cheatgrass ( Bromus tectorum L.) is a problem across the western United States, where it outcompetes and replaces native grass species, alters habitats, and increases the risk of wildfires. Cheatgrass greens up earlier in the growing season compared to native grasses, making it classifiable with multi-temporal and multi-spectral remote sensing. Objectives We mapped cheatgrass at different scales in the Greater Yellowstone Ecosystem using 10-m Sentinel-2 imagery, 3-m PlanetScope, and 10-cm Uncrewed Aerial Systems (UAS) imagery. We compared these maps to field-collected data to address 1) variation in seasonal phenological signals of native and cheatgrass patches, 2) the influence of scale on detectability and map accuracy across our study area. Results Model accuracy to predict cheatgrass presence increased with imagery resolution and ranged from 83% using 10-m Sentinel-2 to 94% with the integration of PlanetScope and UAS imagery. While there was spatial agreement across models, the fusion of UAS data with satellite sources allowed the detection of small cheatgrass with more precision. Our novel use of NExR and dNExR (a redness and differenced redness index) data in the classification of cheatgrass capitalizes on the senescence of cheatgrass during peak summer periods where cloud free imagery is more prevalent. Conclusions Our satellite and UAS-based models of cheatgrass prediction compare the fusion of very high resolution imagery and phenological time differencing to identify infested areas. Tradeoffs between accuracy and expense lead to important questions for management applications.

Montana↗
USGS2024

The feasibility of using national-scale datasets for classifying wetlands in Arizona with machine learning

The advent of machine learning techniques has led to a proliferation of landscape classification products. These approaches can fill gaps in wetland inventories across the United States (U.S.) provided that large reference datasets are available to develop accurate models. In this study, we tested the feasibility of expediting the classification process by sourcing requisite training and testing data from existing national-scale land cover maps instead of customized sample sets. We created a single map of water and wetland presence by intersecting water and wetland classes from available land cover products (National Wetland Inventory, Gap Analysis Project, National Land Cover Database and Dynamic Surface Water Extent) across the U.S. state of Arizona, which has fewer wetland-specific mapping products than other parts of the U.S. We derived classified samples for four wetland classes from the combined map: open water, herbaceous wetlands, wooded wetlands and non-wetland cover. In Google Earth Engine, we developed a random forest model that combined the training data with spatial predictor variables, including vegetation greenness indices, wetness indices, seasonal index variation, topographic parameters and vegetation height metrics. Results show that the final model separates the four classes with an overall accuracy of 86.2%. The accuracy suggests that existing datasets can be effectively used to compile machine learning training samples to map wetlands in arid landscapes in the U.S. These methods hold promise for the generation of wetland inventories at more frequent intervals, which could allow more nuanced investigations of wetland change over time in response to anthropogenic and climatic drivers.

Arizona↗
USGS2023

Human and infrastructure exposure to large wildfires in the United States

An increasing number of wildfire disasters have occurred in recent years in the United States. Here we demonstrate that cumulative primary human exposure—the population residing within the perimeters of large wildfires—was 594,850 people from 2000 to 2019 across the contiguous United States (CONUS), 82% of which occurred in the western United States. Primary population exposure increased by 125% in the CONUS in the past two decades; it was noted that there were large statistical uncertainties in the trend analysis due to the short study timeline. Population dynamics from 2000 to 2019 alone accounted for 24% of the observed increase rate in human exposure, and an increased wildfire extent drove the majority of the observed trends. In addition, we document the widespread exposure of roads (412,155 km) and transmission powerlines (14,835 km) to large wildfires in the CONUS, with a relative increase of 58% and 70% in the past two decades, respectively. Our results highlight that deliberate mitigation and adaptation efforts to help societies cope with wildfires are ever more needed.

Nature Sustainability↗
USGS2023

Assessing large landscape patterns of potential fire connectivity using circuit methods

Context Minimizing negative impacts of wildfire is a major societal objective in fire-prone landscapes. Models of fire connectivity can aid in understanding and managing wildfires by analyzing potential fire spread and conductance patterns. We define ‘fire connectivity’ as the landscape’s capacity to facilitate fire transmission from one point on the landscape to another. Objectives Our objective was to develop an approach for modeling fire connectivity patterns representing potential fire spread and relative flow across a broad landscape extent, particularly in the management-relevant context of fuel breaks. Methods We applied an omnidirectional circuit theory algorithm to model fire connectivity in the Great Basin of the western United States. We used predicted rates of fire spread to approximate conductance and calculated current densities to identify connections among areas with high spread rates. We compared existing and planned fuel breaks with fire connectivity patterns. Results Fire connectivity and relative flow outputs were characterized by spatial heterogeneity in the landscape’s capacity to transmit fire. We found that existing fuel break networks were denser in areas with relatively diffuse and impeded flow patterns, rather than in locations with channelized flow. Conclusions This approach could be paired with traditional fire behavior and risk analyses to better understand wildfire spread as well as direct strategic placement of individual fuel breaks within larger networks to constrain fire spread. Thus, our findings may offer local- to landscape-level support for management actions that aim to disrupt fire spread and mitigate the costs of fire on the landscape.

California, Idaho, Nevada, Oregon, Utah↗
USGS2022

Anthropogenic stressors compound climate impacts on inland lake dynamics: The case of Hamun Lakes

Inland lakes face unprecedented pressures from climatic and anthropogenic stresses, causing their recession and desiccation globally. Climate change is increasingly blamed for such environmental degradation, but in many regions, direct anthropogenic pressures compound, and sometimes supersede, climatic factors. This study examined a human-environmental system – the terminal Hamun Lakes on the Iran-Afghanistan border – that embodies amplified challenges of inland waters. Satellite and climatic data from 1984 to 2019 were fused, which documented that the Hamun Lakes lost 89% of their surface area between 1999 and 2001 (3809 km 2 versus 410 km 2 ), coincident with a basin-wide, multi-year meteorological drought. The lakes continued to shrink afterwards and desiccated in 2012, despite the above-average precipitation in the upstream basin. Rapid growth in irrigated agricultural lands occurred in upstream Afghanistan in the recent decade, consuming water that otherwise would have fed the Hamun Lakes. Compounding upstream anthropogenic stressors, Iran began storing flood water that would have otherwise drained to the lakes, for urban and agricultural consumption in 2009. Results from a deep Learning model of Hamun Lakes' dynamics indicate that the average lakes' surface area from 2010 to 2019 would have been 2.5 times larger without increasing anthropogenic stresses across the basin. The Hamun Lakes' desiccation had major socio-environmental consequences, including loss of livelihood, out-migration, dust-storms, and loss of important species in the region.

Science of the Total Envionrment↗
USGS2021

Augmented normalized difference water index for improved monitoring of surface water

We present a comprehensive critical review of well-established satellite remote sensing water indices and offer a novel, robust Augmented Normalized Difference Water Index (ANDWI). ANDWI employs an expanded set of spectral bands, RGB, NIR, and SWIR 1-2 , to maximize the contrast between water and non-water pixels. Further, we implement a dynamic thresholding method, the Otsu algorithm, to enhance ANDWI's performance. Applied to a variety of environmental conditions, ANDWI with Otsu-thresholding offered the highest overall accuracy (accuracy = 0.98, F1 = 0.98, and Kappa = 0.96) compared to other indices (NDWI, MNDWI, AWEI, WI). We also propose a novel cloud filtering algorithm that substantially increases the number of useable images compared to the conventional cloud-free composites (124% increased observations in the studied area) and resolves inappropriate masking of water bodies and hot sands as clouds by conventional methods. Finally, we develop a Google Earth Engine App to readily delineate 16-day surface water bodies across the globe.

Environmental Modeling and Software↗
USGS2020

The Fire and Tree Mortality Database, for empirical modeling of individual tree mortality after fire

Wildland fires have a multitude of ecological effects in forests, woodlands, and savannas across the globe. A major focus of past research has been on tree mortality from fire, as trees provide a vast range of biological services. We assembled a database of individual-tree records from prescribed fires and wildfires in the United States. The Fire and Tree Mortality (FTM) database includes records from 164,293 individual trees with records of fire injury (crown scorch, bole char, etc.), tree diameter, and either mortality or top-kill up to ten years post-fire. Data span 142 species and 62 genera, from 409 fires occurring from 1981-2016. Additional variables such as insect attack are included when available. The FTM database can be used to evaluate individual fire-caused mortality models for pre-fire planning and post-fire decision support, to develop improved models, and to explore general patterns of individual fire-induced tree death. The database can also be used to identify knowledge gaps that could be addressed in future research.

Scientific Data↗
USGS2020

Estimating wildfire fuel consumption with multitemporal airborne laser scanning data and demonstrating linkage with MODIS-derived fire radiative energy

Characterizing pre- and post-fire fuels remains a key challenge for estimating biomass consumption and carbon emissions from wildfires. Airborne laser scanning (ALS) data have demonstrated effectiveness for estimating canopy, and to a lesser degree, surface fuel components at fine-scale (i.e., 30 m) across landscapes. Using pre- and post-fire ALS data and corresponding field data, this study estimated consumption of canopy fuel (ΔCF), understory fuel (ΔUF), total fuel (ΔTF), and canopy bulk density (ΔCBD) for the 2012 Pole Creek fire in Oregon, USA (10,760 ha), and portions of the 2011 Las Conchas fire in New Mexico, USA (4,934 ha). Additionally, the feasibility of predicting fuel consumption was tested using separate pre- and post-fire models (PrePost), models combining all pre- and post-fire data (Pooled), and models using all data from both fires (Global). Estimates of ΔTF were then compared to fire radiative energy (FRE, units: MJ) derived from Fire Radiative Power (FRP, units: MW) observations from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor onboard NASA Terra and Aqua satellites to mechanistically derive a biomass combustion coefficient (BCC, units: kg MJ −1 ). The PrePost and Pooled approaches yielded similar results at Las Conchas, but at Pole Creek insufficient pre-fire field data resulted in erroneous fuel consumption estimates outside the fire perimeter using the PrePost models. These results demonstrated that pre-fire field data were less important for these models than having field data which represent the full range of fuel conditions likely to exist across the landscape. Estimated total biomass consumed for the PrePost, Pooled, and Global models were 226 Gg, 224 Gg, and 224 Gg at Las Conchas, and 581 Gg, 713 Gg, and 552 Gg at Pole Creek. Comparisons between estimated ΔTF and FRE yielded an average BCC for both fires of 0.367 (s.d. ± 0.049) kg MJ −1 based on pixels with at least five MODIS observations. Both higher MODIS observations per pixel and accounting for canopy occlusion of FRE improved the relationship between ΔTF and MODIS-FRE. This study suggested a practical modelling approach for future efforts using only post-fire field observations and quantified a landscape-scale relationship between MODIS-derived FRE and fine-scale fuel consumption consistent with prior experiments.

New Mexico, Oregon↗
USGS2020

Cost-effective fuel treatment planning: A theoretical justification and case-study

Modelling the spatial prioritisation of fuel treatments and their net effect on values at risk is an important area for applied work as economic damages from wildfire continue to grow. We model and demonstrate a cost-effective fuel treatment planning algorithm using two ecosystem services as benefits for which fuel treatments are prioritised. We create a surface of expected fuel treatment costs to incorporate the heterogeneity in factors affecting the revenue and costs of fuel treatments, and then prioritise treatments based on a cost-effectiveness ratio to maximise the averted loss of ecosystem services from fire. We compare treatment scenarios that employ cost-effectiveness with those that do not, and use common tools and models in a case study of the Sisters Ranger District on the Deschutes National Forest in central Oregon, USA. Using cost-effectiveness not only increases the expected averted losses from fuel treatments, but it also allows a larger area to be treated for the same cost, simply by incorporating costs and cost-effectiveness into the prioritisation routine. These results have considerable implications for policymakers and land managers trying to minimise risk. Incorporating costs into the spatial planning of treatments could allow more effective outcomes without increasing fuel treatment budgets.

Oregon↗
USGS2019

Effects of 21st century climate, land use, and disturbances on ecosystem carbon balance in California

Terrestrial ecosystems are an important sink for atmospheric carbon dioxide (CO 2 ), sequestering ~30% of annual anthropogenic emissions and slowing the rise of atmospheric CO 2 . However, the future direction and magnitude of the land sink is highly uncertain. We examined how historical and projected changes in climate, land use, and ecosystem disturbances affect the carbon balance of terrestrial ecosystems in California over the period 2001–2100. We modeled 32 unique scenarios, spanning 4 land use and 2 radiative forcing scenarios as simulated by four global climate models. Between 2001 and 2015, carbon storage in California's terrestrial ecosystems declined by −188.4 Tg C, with a mean annual flux ranging from a source of −89.8 Tg C/year to a sink of 60.1 Tg C/year. The large variability in the magnitude of the state's carbon source/sink was primarily attributable to interannual variability in weather and climate, which affected the rate of carbon uptake in vegetation and the rate of ecosystem respiration. Under nearly all future scenarios, carbon storage in terrestrial ecosystems was projected to decline, with an average loss of −9.4% (−432.3 Tg C) by the year 2100 from current stocks. However, uncertainty in the magnitude of carbon loss was high, with individual scenario projections ranging from −916.2 to 121.2 Tg C and was largely driven by differences in future climate conditions projected by climate models. Moving from a high to a low radiative forcing scenario reduced net ecosystem carbon loss by 21% and when combined with reductions in land‐use change (i.e., moving from a high to a low land‐use scenario), net carbon losses were reduced by 55% on average. However, reconciling large uncertainties associated with the effect of increasing atmospheric CO 2 is needed to better constrain models used to establish baseline conditions from which ecosystem‐based climate mitigation strategies can be evaluated.

California↗
USGS2019

Evaluating the mid-infrared bi-spectral index for improved assessment of low-severity fire effects in a conifer forest

Remote sensing products provide a vital understanding of wildfire effects across a landscape, but detection and delineation of low- and mixed-severity fire remains difficult. While data provided by the Monitoring Trends in Burn Severity project (MTBS) are frequently used to assess severity in the United States, alternative indices can offer improvement to the measurement of low-severity fire effects and should be considered for future product development and adoption. This research note evaluated one such alternative, the mid-infrared bi-spectral index (MIRBI), which was developed in savannah ecosystems to isolate spectral changes caused by burning and reduce noise from other factors. MIRBI, differenced MIRBI (dMIRBI) and burn severity indices used by MTBS were assessed for spectral optimality at distinguishing severity and the ability to separate unburned and burned canopy in a conifer forest. The MIRBI indices were better at isolating changes caused by burning and demonstrated higher spectral separability, particularly at low severity. These findings suggest that MIRBI indices can provide an enhanced alternative or compliment to current MTBS products in high canopy cover forests for applications such as discernment of fire perimeters and unburned islands, as well as identification of low-severity fire effects.

International Journal of Wildland Fire↗
USGS2018

Sediment transport and deposition

Sediment transport and deposition (sedimentation) occurs from natural and anthropogenic sources in rivers, lakes, and reservoirs. Substantial changes in sediment transport (such as a major increase or decrease in sediment supply) can impact aquatic ecosystems that depend on a particular sediment quantity and particle size, for example, through altering stream-channel geomorphology or fish habitat. For human communities that rely on surface water resources, sedimentation can impact water supply and quality. Sedimentation in reservoirs affects water supply by reducing the reservoir volume available to store water. Sediment, as well as the nutrients and chemicals adsorbed in sediment, can serve as pollutants that decrease water quality and make water treatment necessary and costly.

California↗
USGS2017

Climate, wildfire, and erosion ensemble foretells more sediment in western USA watersheds

The area burned annually by wildfires is expected to increase worldwide due to climate change. Burned areas increase soil erosion rates within watersheds, which can increase sedimentation in downstream rivers and reservoirs. However, which watersheds will be impacted by future wildfires is largely unknown. Using an ensemble of climate, fire, and erosion models, we show that post-fire sedimentation is projected to increase for nearly nine-tenths of watersheds by > 10% and for more than one-third of watersheds by > 100% by the 2041 to 2050 decade in the western USA. The projected increases are statistically significant for more than eight-tenths of the watersheds. In the western USA, many human communities rely on water from rivers and reservoirs that originates in watersheds where sedimentation is projected to increase. Increased sedimentation could negatively impact water supply and quality for some communities, in addition to affecting stream channel stability and aquatic ecosystems.

Geophysical Research Letters↗
USGS2017

Sustainability of utility-scale solar energy – critical ecological concepts

Renewable energy development is an arena where ecological, political, and socioeconomic values collide. Advances in renewable energy will incur steep environmental costs to landscapes in which facilities are constructed and operated. Scientists – including those from academia, industry, and government agencies – have only recently begun to quantify trade-offs in this arena, often using ground-mounted, utility-scale solar energy facilities (USSE, ≥1 megawatt) as a model. Here, we discuss five critical ecological concepts applicable to the development of more sustainable USSE with benefits over fossil-fuel-generated energy: (1) more sustainable USSE development requires careful evaluation of trade-offs between land, energy, and ecology; (2) species responses to habitat modification by USSE vary; (3) cumulative and large-scale ecological impacts are complex and challenging to mitigate; (4) USSE development affects different types of ecosystems and requires customized design and management strategies; and (5) long-term ecological consequences associated with USSE sites must be carefully considered. These critical concepts provide a framework for reducing adverse environmental impacts, informing policy to establish and address conservation priorities, and improving energy production sustainability.

Frontiers in Ecology and the Environment↗
USGS2017

Landscape-scale quantification of fire-induced change in canopy cover following mountain pine beetle outbreak and timber harvest

Across the western United States, the three primary drivers of tree mortality and carbon balance are bark beetles, timber harvest, and wildfire. While these agents of forest change frequently overlap, uncertainty remains regarding their interactions and influence on specific subsequent fire effects such as change in canopy cover. Acquisition of pre- and post-fire Light Detection and Ranging (LiDAR) data on the 2012 Pole Creek Fire in central Oregon provided an opportunity to isolate and quantify fire effects coincident with specific agents of change. This study characterizes the influence of pre-fire mountain pine beetle (MPB; Dendroctonus ponderosae ) and timber harvest disturbances on LiDAR-estimated change in canopy cover. Observed canopy loss from fire was greater (higher severity) in areas experiencing pre-fire MPB (Δ 18.8%CC) than fire-only (Δ 11.1%CC). Additionally, increasing MPB intensity was directly related to greater canopy loss. Canopy loss was lower for all areas of pre-fire timber harvest (Δ 3.9%CC) than for fire-only, but among harvested areas, the greatest change was observed in the oldest treatments and the most intensive treatments [i.e., stand clearcut (Δ 5.0%CC) and combination of shelterwood establishment cuts and shelterwood removal cuts (Δ 7.7%CC)]. These results highlight the importance of accounting for and understanding the impact of pre-fire agents of change such as MPB and timber harvest on subsequent fire effects in land management planning. This work also demonstrates the utility of multi-temporal LiDAR as a tool for quantifying these landscape-scale interactions.

Oregon↗
USGS2017

Multi-temporal LiDAR and Landsat quantification of fire-induced changes to forest structure

Measuring post-fire effects at landscape scales is critical to an ecological understanding of wildfire effects. Predominantly this is accomplished with either multi-spectral remote sensing data or through ground-based field sampling plots. While these methods are important, field data is usually limited to opportunistic post-fire observations, and spectral data often lacks validation with specific variables of change. Additional uncertainty remains regarding how best to account for environmental variables influencing fire effects (e.g., weather) for which observational data cannot easily be acquired, and whether pre-fire agents of change such as bark beetle and timber harvest impact model accuracy. This study quantifies wildfire effects by correlating changes in forest structure derived from multi-temporal Light Detection and Ranging (LiDAR) acquisitions to multi-temporal spectral changes captured by the Landsat Thematic Mapper and Operational Land Imager for the 2012 Pole Creek Fire in central Oregon. Spatial regression modeling was assessed as a methodology to account for spatial autocorrelation, and model consistency was quantified across areas impacted by pre-fire mountain pine beetle and timber harvest. The strongest relationship (pseudo-r 2 = 0.86, p < 0.0001) was observed between the ratio of shortwave infrared and near infrared reflectance (d74) and LiDAR-derived estimate of canopy cover change. Relationships between percentage of LiDAR returns in forest strata and spectral indices generally increased in strength with strata height. Structural measurements made closer to the ground were not well correlated. The spatial regression approach improved all relationships, demonstrating its utility, but model performance declined across pre-fire agents of change, suggesting that such studies should stratify by pre-fire forest condition. This study establishes that spectral indices such as d74 and dNBR are most sensitive to wildfire-caused structural changes such as reduction in canopy cover and perform best when that structure has not been reduced pre-fire.

Oregon↗
USGS2015

Conservation planning for offsetting the impacts of development: a case study of biodiversity and renewable energy in the Mojave Desert

Balancing society&rsquo;s competing needs of development and conservation requires careful consideration of tradeoffs. Renewable energy development and biodiversity conservation are often considered beneficial environmental goals. The direct footprint and disturbance of renewable energy, however, can displace species&rsquo; habitat and negatively impact populations and natural communities if sited without ecological consideration. Offsets have emerged as a potentially useful tool to mitigate residual impacts after trying to avoid, minimize, or restore affected sites. Yet the problem of efficiently designing a set of offset sites becomes increasingly complex where many species or many sites are involved. Spatial conservation prioritization tools are designed to handle this problem, but have seen little application to offset siting and analysis. To address this need we designed an offset siting support tool for the Desert Renewable Energy Conservation Plan (DRECP) of California, and present a case study of hypothetical impacts from solar development in the Western Mojave subsection. We compare two offset scenarios designed to mitigate a hypothetical 15,331 ha derived from proposed utility-scale solar energy development (USSED) projects. The first scenario prioritizes offsets based precisely on impacted features, while the second scenario offsets impacts to maximize biodiversity conservation gains in the region. The two methods only agree on 28% of their prioritized sites and differ in meeting species-specific offset goals. Differences between the two scenarios highlight the importance of clearly specifying choices and priorities for offset siting and mitigation in general. Similarly, the effects of background climate and land use change may lessen the durability or effectiveness of offsets if not considered. Our offset siting support tool was designed specifically for the DRECP area, but with minor code modification could work well in other offset analyses, and could provide continuing support for a potentially innovative mitigation solution to environmental impacts.

California↗