Geology topics

Kevin Robertson

Publications and source records attributed to Kevin Robertson.

5 recordsLinked to original sources

USGS2025

Modelling and mapping burn severity of prescribed and wildfires across the southeastern United States (2000-2022)

Background The southeastern United States (‘Southeast’) experiences high levels of fire activity, but the preponderance of small and prescribed fires means that existing burn severity products are incomplete across the region. Aims We developed and applied a burn severity model across the Southeast to enhance our understanding of regional burn severity patterns. Methods We used Composite Burn Index (CBI) plot data from across the conterminous US (CONUS) to train a gradient-boosted decision tree model. The model was optimised for the Southeast and applied to the annual Landsat Burned Area product for 2000–2022 across the region. Key results The burn severity model had a root mean square error (RMSE) of 0.48 ( R 2 = 0.70) and 0.50 ( R 2 = 0.37) for the CONUS and Southeast, respectively. The Southeast, relative to CONUS, had lower mean absolute residuals in low and moderate burn severity categories. Burn severity was consistently lower in areas affected by prescribed burns relative to wildfires. Conclusions Although regional performance was limited by a lack of high burn severity CBI plots, the burn severity dataset demonstrated patterns consistent with low-severity, frequent fire regimes characteristic of Southeastern ecosystems. Implications More complete data on burn severity will enhance regional management of fire-dependent ecosystems and improve estimates of fuels and fire emissions.

Southeastern United States↗

USGS2022

Reimagine fire science for the anthropocene

Fire is an integral component of ecosystems globally and a tool that humans have harnessed for millennia. Altered fire regimes are a fundamental cause and consequence of global change, impacting people and the biophysical systems on which they depend. As part of the newly emerging Anthropocene, marked by human-caused climate change and radical changes to ecosystems, fire danger is increasing, and fires are having increasingly devastating impacts on human health, infrastructure, and ecosystem services. Increasing fire danger is a vexing problem that requires deep transdisciplinary, trans-sector, and inclusive partnerships to address. Here, we outline barriers and opportunities in the next generation of fire science and provide guidance for investment in future research. We synthesize insights needed to better address the long-standing challenges of innovation across disciplines to (i) promote coordinated research efforts; (ii) embrace different ways of knowing and knowledge generation; (iii) promote exploration of fundamental science; (iv) capitalize on the “firehose” of data for societal benefit; and (v) integrate human and natural systems into models across multiple scales. Fire science is thus at a critical transitional moment. We need to shift from observation and modeled representations of varying components of climate, people, vegetation, and fire to more integrative and predictive approaches that support pathways toward mitigating and adapting to our increasingly flammable world, including the utilization of fire for human safety and benefit. Only through overcoming institutional silos and accessing knowledge across diverse communities can we effectively undertake research that improves outcomes in our more fiery future.

PNAS Nexus↗

USGS2021

Determination of burn severity models ranging from regional to continental scales for the conterminous United States

Identifying meaningful measures of ecological change over large areas is dependent on the quantification of robust relationships between ecological metrics and remote sensing products. Over the past several decades, ground observations of wildfire and prescribed fire severity have been acquired across hundreds of wildland fires in the United States, primarily utilizing the Composite Burn Index (CBI) plot protocol. These observations have been coupled to spaceborne passive spectral reflectance indices (e.g. Landsat-derived variations of the Normalized Burn Ratio [NBR]) to produce regression models describing their relationship. Here we develop regression models by vegetation type for multiple vegetation classification systems representing a range of spatial scales, and a decision tree framework for evaluating these regression models. Our overall goals were to determine which scale of ecological classifications provided the best estimate of burn severity from Landsat data and how to choose the best regression model. We aggregated a total of 6280 CBI plots for 234 wildland fires that burned between 1994 and 2017 and produced Landsat-derived NBR and differenced NBR (dNBR) values for each plot. We then calculated best fit linear or higher order regression equations between CBI and NBR/dNBR for each landcover classification system from smallest to largest scale: LANDFIRE Biophysical Settings (BPS), National Vegetation Classification macrogroup (NVC) landcover classifications, Omernick III, II, and I ecoregions, LANDFIRE Fire Regime Groups (FRG), and the entire conterminous United States (CONUS) dataset. The CONUS regression model goodness of fit was moderate (R 2 = 0.55, P < 0.001) for dNBR and poor (R 2 = 0.30, P < 0.001) for NBR. Within landcover classifications, CBI was better fit by dNBR than NBR. Finer scale regional regression models including BPS (dNBR R 2 ¯ "> R2¯ = 0.56 and 0.00–0.83 R 2 range; NBR R 2 ¯ "> R(bar) 2 = 0.43 and 0.00–0.82 R 2 range) and NVC (dNBR R 2 ¯ "> R(bar) 2 = 0.55 and 0.15–0.78 R 2 range; NBR R 2 ¯ "> R(bar) 2 = 0.41 and 0.00–0.79 R 2 range) were on average the same or better than the CONUS models for dNBR and NBR, with the strongest fit models exhibiting R 2 ≥ 0.70, whereas larger scale regional models R 2 ¯ "> R(bar) 2 ranged from 0.28 to 0.5. However, variation in accuracy among landcover types indicate that dNBR and NBR regression models could be used to effectively estimate CBI for future fires in certain regions, while for other regions models may require additional field observations or alternative spectral transformations. Our decision tree schema can be used to help users determine which scale is likely to produce the most accurate results using our models. The CBI regression models developed here, paired with the decision tree, provide users with a simple method to estimate burn severity in units of CBI for any fire within CONUS with moderate to high levels of confidence and provide a template for further development of models with new data going forward.

Conterminous United States↗

USGS2015

Hydrothermal alteration and diagenesis of terrestrial lacustrine pillow basalts: Coordination of hyperspectral imaging with laboratory measurements

We investigate an outcrop of &sim;187 Ma lacustrine pillow basalts of the Talcott Formation exposed in Meriden, Connecticut, USA, focusing on coordinated analyses of one pillow lava to characterize the aqueous history of these basalts in the Hartford Basin. This work uses a suite of multidisciplinary measurements, including hyperspectral imaging, other spectroscopic techniques, and chemical and mineralogical analyses, from the microscopic scale up to the scale of an outcrop. The phases identified in the sample are albite, large iron oxides, and titanite throughout; calcite in vesicles; calcic clinopyroxene, aegirine, and Fe/Mg-bearing clay in the rind; and fine-grained hematite and pyroxenes in the interior. Using imaging spectroscopy, the chemistry and mineralogy results extend to the hand sample and larger outcrop. From all of the analyses, we suggest that the pillow basalts were altered initially after emplacement, either by heated lake water or magmatic fluids, at temperatures of at least 400-600°C, and the calcic clinopyroxenes and aegirine identified in the rind are a preserved record of that alteration. As the hydrothermal system cooled to slightly lower temperatures, clays formed in the rind, and, during this alteration, the sample oxidized to form hematite in the matrix of the interior and Fe 3+ in the pyroxenes in the rind. During the waning stages of the hydrothermal system, calcite precipitated in vesicles within the rind. Later, diagenetic processes albitized the sample, with albite replacing plagioclase, lining vesicles, and accreting onto the exterior of the sample. This albitization or Na-metasomatism occurred when the lake within the Hartford Basin evaporated during a drier past climatic era, resulting in Na-rich brines. As Ca-rich plagioclase altered to albite, Ca was released into solution, eventually precipitating as calcite in previously-unfilled vesicles, dominantly in the interior of the pillow. Coordinated analyses of this sample permit identification of the alteration phases and help synthesize the aqueous history of pillow lavas of the Talcott formation. These results are also relevant to Mars, where volcanically-resurfaced open basin lakes have been found, and this Hartford Basin outcrop may be a valuable analog for any potential volcano-lacustrine interactions. The results can also help to inform the utility and optimization of potentially complementary, synergistic, and uniquely-suited techniques for characterization of hydrothermally-altered terrains.

Connecticut↗

USGS2011

Timing constraints on remote sensing of wildland fire burned area in the southeastern US

Remote sensing using Landsat Thematic Mapper (TM) satellite imagery is increasingly used for mapping wildland fire burned area and burn severity, owing to its frequency of collection, relatively high resolution, and availability free of charge. However, rapid response of vegetation following fire and frequent cloud cover pose challenges to this approach in the southeastern US. We assessed these timing constraints by using a series of Landsat TM images to determine how rapidly the remotely sensed burn scar signature fades following prescribed burns in wet flatwoods and depression swamp community types in the Apalachicola National Forest, Florida, USA during 2006. We used both the Normalized Burn Ratio (NBR) of reflectance bands sensitive to vegetation and exposed soil cover, as well as the change in NBR from before to after fire (dNBR), to estimate burned area. We also determined the average and maximum amount of time following fire required to obtain a cloud-free image for burns in each month of the year, as well as the predicted effect of this time lag on percent accuracy of burn scar estimates. Using both NBR and dNBR, the detectable area decreased linearly 9% per month on average over the first four months following fire. Our findings suggest that the NBR and dNBR methods for monitoring burned area in common southeastern US vegetation community types are limited to an average of 78–90% accuracy among months of the year, with individual burns having values as low as 38%, if restricted to use of Landsat 5 TM imagery. However, the majority of burns can still be mapped at accuracies similar to those in other regions of the US, and access to additional sources of satellite imagery would improve overall accuracy.

Remote Sensing↗