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At least 901 records · Page 50Linked to original sources
USGS2020

Post-fire management-scale trials of bacterial soil amendment MB906 show inconsistent control of invasive annual grasses

Rangeland managers need tools to control invasive annual grasses, particularly following wildfire. We assessed responses of native and invasive/exotic grasses to the MB906 soil amendment containing live cultures of a purportedly weed-suppressive strain of the bacterium Pseudomonas fluorescens (“WSB”). MB906 was applied alone and in combination with the pre-emergent herbicide imazapic on >3000 ha across three sagebrush-steppe landscapes burned several months prior. Replicate plots of each treatment type were established and plant cover was measured in the following three years. Cover of invasive-annual grasses (“IAG”) was not responsive to MB906 when all IAG species were considered (“IAG-All”). However, MB906 led to a 54% reduction in the IAG's that were previously reported to be controlled by WSB (“IAG-Target”) in the second year following application (IAG-Target = cheatgrass, Bromus tectorum and medusahead, Taeniatherum caput-medusae; IAG-All also includes Vulpia myuros and Bromus arvensis ). MB906 reduced the effectiveness of co-applied imazapic: Imazapic alone reduced IAG-All by 83% and 68% in years 1 and 2, respectively, while imazapic+MB906 reduced IAG-All by 48% and 38% in years 1 and 2, respectively, across all landscapes, and a similar response pattern was observed for IAG-Target. Perennial grass cover was unaffected by the treatments except where it increased 4-fold in response to imazapic applied at a high rate (0.140 kg a.i. ha −1 ) in one of the landscapes. Tank mixing MB906 and herbicide may have lessened the biological activity of the herbicide by altering the pH or mineral content of the spray solution or by direct metabolism of the herbicide by the bacteria. These results do not provide strong support for MB906 as a tool for annual grass control, though they suggest further investigation may be warranted.

Rangeland Ecology and Management↗
USGS2023

Biocrusts indicators of livestock grazing effects on soil stability in sagebrush steppe: A case study from a long-term experiment in the northern Great Basin

Biocrusts are sensitive to changes in livestock grazing intensity in arid rangelands and may be useful indicators of ecosystem functions, particularly soil properties like soil stability, which may suggest the potential for soil erosion. We compared biocrust community composition and surface soil stability in a big sagebrush (Artemisia tridentata) steppe rangeland in the northwestern Great Basin in several paired sites, with or without long-term cattle grazing exclusion, and similar soils (mostly sandy loams), climate, and vegetation composition. We found that livestock grazing was associated with both lower surface soil stability and cover of several biocrust morphogroups, especially lichens, compared with sites with long-term livestock exclusion. Surface soil stability did not modify the effects of grazing on most biocrust components via interactive effects. Livestock grazing effects on total biocrust cover were partially mediated by changes in surface soil stability. Though lichens were more sensitive to grazing disturbance, our results suggest that moss (mostly Tortula ruralis in this site) might be a more readily observable indicator of grazing-related soil stability change in this area due to their relatively higher abundance compared with lichens (moss: mean, 8.5% cover, maximum, 96.1%, lichens: mean, 1.0% cover, maximum, 14.1%). These results highlight the potential for biocrust components as sensitive indicators of change in soil-related ecosystem functions in sagebrush steppe rangelands. However, further research is needed to identify relevant indicator groups across the wide range of biocrust community composition associated with site environmental characteristics, variable grazing systems, other rangeland health metrics, and other disturbance types such as wildfire.

Northern Great Basin↗
USGS2024

Systematic process for determining field-sampling effort required to know vegetation changes in large, disturbed rangelands where management treatments have been applied

Adequate numbers of replicated, dispersed, and random samples are the basis for reliable sampling inference on resources of concern, particularly vegetation cover across large and heterogenous areas such as rangelands. Tools are needed to predict and assess data precision, specifically the sampling effort required to attain acceptable levels of precision, before and after sampling. We describe and evaluate a flexible and scalable process for assessing the sampling effort requirement for a common monitoring context (responses of rangeland vegetation cover to post-fire restoration treatments), using a custom R script called “SampleRange.” In SampleRange, vegetation cover is estimated from available digital-gridded or field data (e.g., using the satellite-derived cover from the Rangeland Assessment Platform). Next, the sampling effort required to estimate cover with 20% relative standard error (RSE) or to saturate sampling effort is determined using simulations across the environmental gradients in areas of interest to estimate the number of needed plots (“SampleRange quota”). Finally, the SampleRange quota are randomly identified for actual sampling. A 2022 full-cycle trial of SampleRange using the best available digital and prior field data for areas treated after a 2017 wildfire in sagebrush-steppe rangelands revealed that differences in the predicted compared with realized RSEs are inevitable. Future efforts to account for uncertainty in remotely sensed−based vegetative products will enhance tool utility.

Rangeland Ecology and Management↗
USGS2024

A strategic and science-based framework for management of invasive annual grasses in the sagebrush biome

In the last 20 years, the North American sagebrush biome has lost over 500,000 ha of intact and largely intact sagebrush plant communities on an annual basis. Much of this loss has been associated with expansion and infilling of invasive annual grasses (IAGs). These species are highly competitive against native perennial grasses in disturbed environments, and create fuel conditions that increase both the likelihood of fire ignition and the ease of wildfire spread across large landscapes. Given the current rate of IAG expansion in both burned and unburned rangelands, ameliorating the IAG threat will involve a range-wide paradigm shift from opportunistic and reactive management, to a framework that spatially prioritizes maintenance of largely intact, uninvaded areas and improvement of invaded habitats in strategic locations. We created a framework accompanied by biome-wide priority maps using geospatial overlays that target areas to MAINTAIN large, uninvaded areas as natural resource anchors through activities to prevent IAGs and IMPROVE areas to reduce invasions with the highest potential for management efforts to succeed in restoring large, intact landscapes. We then offer three case studies to illustrate the use of these concepts and map products at multiple scales. Our map products operate at the biome scale using regional data sources but additional data sources may be needed to inform local conservation planning. However, the basic strategic management principles of a) maintaining the intact and uninvaded areas that we can least afford to lose to IAGs and b) improving areas where we have a higher likelihood of restoration success, is timely, relevant, and scalable from the biome to local levels.

Rangeland Ecology & Management↗
USGS2024

Evaluating the sagebrush conservation design through the lens of a sagebrush indicator species

Sagebrush ecosystems support a suite of unique species such as the emblematic greater sage-grouse ( Centrocercus urophasianus ; sage-grouse) but are under increasing pressure from anthropogenic stressors such as annual grass invasion, conifer encroachment, altered wildfire regimes, and land use change. We examined the ability of an ecosystem-based framework for sagebrush conservation, the sagebrush conservation design (SCD) strategy, and the associated model of sagebrush ecological integrity (SEI), to identify and rank priority habitats for sage-grouse, a sagebrush indicator species. We compared sage-grouse population trends from 1996–2021 across the three ranked SEI categories. We then modeled those trends directly as a function of the same landcover predictors underlying SEI, used the median trend estimates to recategorize the sage-grouse’s range, and used spatial correlation methods to compare our sage-grouse performance categories with those of SEI. Finally, we compared the sage-grouse condition categories, predicted by our landcover-based model, to empirical trends derived from population count data. We found that the SCD and SEI were effective tools for identifying and ranking priority habitats for sage-grouse. Population trends were stable in the core areas identified by SEI but declining in the lower (i.e., growth and other) condition categories. As a result, core areas encompassed an increasingly larger share of the total sage-grouse population in a disproportionately smaller area. Our model supports the general functional relationships between landcover and sage-grouse performance suggested by SEI. We found strong spatial congruence between our categories of predicted sage-grouse population performance, the condition categories of SEI, and empirical trends derived from population count data. Our analysis demonstrates that proactive ecosystem-based approaches to the conservation of the sagebrush biome can help optimize the return on limited conservation resources and benefits for sagebrush obligate species and help reduce some of the real and perceived conflicts inherent in single-species management.

Arizona, California, Colorado, Idaho, New Mexico, ↗
USGS2025

Quantifying regional ecological dynamics using agency monitoring data, ecological site descriptions, and ecological site groups

Information about what ecological conditions are likely, causes or drivers of degradation, and potential management actions to restore degraded lands may support land conservation and restoration decisions. State-and-transition models (STMs) describe persistent plant and ecological conditions that are possible (the “state”) within a given abiotic setting and drivers or actions that can cause shifts between states (the “transitions”). These primarily conceptual models are widely used to inform resource and conservation decisions. Data-driven STMs have been developed for some lands, but not at regional or national scales. Here, we demonstrate a new repeatable workflow for developing data-driven STMs in the United States (US). The approach leverages predictive maps of Ecological Site Groups (ESGs), extensive field-based Federal monitoring databases, information from Ecological Site Description (ESD) STMs, soil erosion models, remotely sensed productivity, and other available spatial information (fire, land protection, and drought) to provide context and descriptions of the data-driven states, including likely drivers of transitions. Results of this workflow applied to one dryland ESG in the Upper Colorado River Basin in the southwestern US suggest that an Invaded state (16% of 1352 plots) and some occurrences of a Grassland state (30% of plots) are in a degraded or at-risk condition with reduced ecosystem services. The most common drivers of state transitions in the associated ESDs ( n = 26) are related to livestock grazing and fire. The Invaded state in the ESG has evidence of degraded habitat quality and accelerated run-off while the Grassland state occurrences show reduced richness, productivity, and elevated erosion risk by wind. Areas subject to wildfire and with lower protection status had greater probability of Invaded state occurrence, generally supporting drivers in ESDs. The workflow presented here can serve as a template for describing ecological dynamics at regional scales, and support prioritization of land for conservation and climate adaptation activities.

Rangeland Ecology & Management↗
USGS2025

Correspondence between satellite-derived and long-term field observations of vegetation cover at Great Basin experimental treatments.

Vegetation treatments are frequently utilized in Western US rangelands to reduce woody plant cover in sagebrush stands threatened by increased wildfire risk and in pinyon-juniper woodlands expanding into formerly high-value sagebrush habitats. Despite widespread use of these treatments, monitoring data to evaluate long-term vegetation responses are often insufficient or absent. Long-term field experiments and remote-sensing based vegetation data may be complementary for assessing treatment effectiveness across temporal and spatial scales. The SageSTEP project experimentally implemented treatments at numerous sites across the Intermountain West and monitored the subsequent response of vegetation cover components with 15+ yr of field observations. However, while pretreatment data were collected in the year of implementation, long-term observations of pretreatment vegetation conditions are lacking in the SageSTEP database. Remote-sensing based time-series maps (1985–2023) of vegetation cover from Rangeland Condition Monitoring Assessment and Projection (RCMAP) could fill temporal gaps in monitoring data and scale findings across broader extents. We evaluate the relationship between pretreatment vegetation cover in the RCMAP data and the post-treatment response in both the RCMAP and field observations. Additionally, we explore the correspondence between SageSTEP field observations and RCMAP at various scales, and examine key factors related to the strength of relationships. Overall, SageSTEP and RCMAP data show a similar direction of treatment effect for each component, and to a lesser extent the magnitude of effect. SageSTEP and RCMAP data tended to agree most strongly where treatment effects were strong; when averaged across broader spatial scales; and for components such as tree and bare ground that are more easily distinguished spectrally. Remote sensing tools such as RCMAP, in combination with field-based climate and vegetation observations, can help assess postdisturbance recovery trajectories and facilitate regional decision-making around treatment alternatives, fire risk reduction, and protection of critical habitats.

Great Basin↗
USGS2026

Spatial variation in effectiveness and costs of sagebrush restoration treatments across the western United States

Natural resource managers strive to improve restoration efficiency across the vast network of public lands of the United States, seeking the best return on investment through innovation, geographic prioritization, and adaptive management. This challenge is exemplified by ecosystems dominated by sagebrush ( Artemisia spp.), which occur across the western United States and have been degraded by increasing wildfire frequency driven by invasive plant spread, among other factors. However, both costs and effectiveness of sagebrush restoration can vary spatially due to management practices in addition to biotic and abiotic factors, and characterizing this variation could inform broad-scale planning. We leveraged published models of sagebrush recovery and treatment costs to predict and evaluate cost-effectiveness of aerial and drill seeding Artemisia spp. across 429 718 km 2 that overlapped 12 US states. Compared to natural recovery, effectiveness for both methods was generally low, yet effectiveness was often greater, and costs were always greater, for drill than aerial seeding, resulting in slightly higher cost-effectiveness on average for drill seeding. Cost-effectiveness for both treatments increased near major roads and in areas with more repeated burns. Cost-effectiveness also increased with predisturbance cover of sagebrush but decreased with herbaceous cover. However, we also identified areas where aerial seeding was more cost-effective than drill seeding, particularly with greater slope. Our results provide spatially explicit estimates and potential mechanisms of cost-effectiveness for two common seeding methods for sagebrush, which can help prioritize limited resources, guide land use, and improve restoration efficiency and effectiveness across public rangelands of the western United States.

western United States↗
USGS2013

Quantifying tree mortality in a mixed species woodland using multitemporal high spatial resolution satellite imagery

Widespread tree mortality events have recently been observed in several biomes. To effectively quantify the severity and extent of these events, tools that allow for rapid assessment at the landscape scale are required. Past studies using high spatial resolution satellite imagery have primarily focused on detecting green, red, and gray tree canopies during and shortly after tree damage or mortality has occurred. However, detecting trees in various stages of death is not always possible due to limited availability of archived satellite imagery. Here we assess the capability of high spatial resolution satellite imagery for tree mortality detection in a southwestern U.S. mixed species woodland using archived satellite images acquired prior to mortality and well after dead trees had dropped their leaves. We developed a multistep classification approach that uses: supervised masking of non-tree image elements; bi-temporal (pre- and post-mortality) differencing of normalized difference vegetation index (NDVI) and red:green ratio (RGI); and unsupervised multivariate clustering of pixels into live and dead tree classes using a Gaussian mixture model. Classification accuracies were improved in a final step by tuning the rules of pixel classification using the posterior probabilities of class membership obtained from the Gaussian mixture model. Classifications were produced for two images acquired post-mortality with overall accuracies of 97.9% and 98.5%, respectively. Classified images were combined with land cover data to characterize the spatiotemporal characteristics of tree mortality across areas with differences in tree species composition. We found that 38% of tree crown area was lost during the drought period between 2002 and 2006. The majority of tree mortality during this period was concentrated in piñon-juniper ( Pinus edulis-Juniperus monosperma ) woodlands. An additional 20% of the tree canopy died or was removed between 2006 and 2011, primarily in areas experiencing wildfire and management activity. -Our results demonstrate that unsupervised clustering of bi-temporal NDVI and RGI differences can be used to detect tree mortality resulting from numerous causes and in several forest cover types.

Remote Sensing of Environment↗
USGS2013

Modeling spatially explicit fire impact on gross primary production in interior Alaska using satellite images coupled with eddy covariance

In interior Alaska, wildfires change gross primary production (GPP) after the initial disturbance. The impact of fires on GPP is spatially heterogeneous, which is difficult to evaluate by limited point-based comparisons or is insufficient to assess by satellite vegetation index. The direct prefire and postfire comparison is widely used, but the recovery identification may become biased due to interannual climate variability. The objective of this study is to propose a method to quantify the spatially explicit GPP change caused by fires and succession. We collected three Landsat images acquired on 13 July 2004, 5 August 2004, and 6 September 2004 to examine the GPP recovery of burned area from 1987 to 2004. A prefire Landsat image acquired in 1986 was used to reconstruct satellite images assuming that the fires of 1987–2004 had not occurred. We used a light-use efficiency model to estimate the GPP. This model was driven by maximum light-use efficiency (E max ) and fraction of photosynthetically active radiation absorbed by vegetation (F PAR ). We applied this model to two scenarios (i.e., an actual postfire scenario and an assuming-no-fire scenario), where the changes in E max and F PAR were taken into account. The changes in E max were represented by the change in land cover of evergreen needleleaf forest, deciduous broadleaf forest, and shrub/grass mixed, whose E max was determined from three fire chronosequence flux towers as 1.1556, 1.3336, and 0.5098 gC/MJ PAR. The changes in F PAR were inferred from NDVI change between the actual postfire NDVI and the reconstructed NDVI. After GPP quantification for July, August, and September 2004, we calculated the difference between the two scenarios in absolute and percent GPP changes. Our results showed rapid recovery of GPP post-fire with a 24% recovery immediately after burning and 43% one year later. For the fire scars with an age range of 2–17 years, the recovery rate ranged from 54% to 95%. In addition to the averaging, our approach further revealed the spatial heterogeneity of fire impact on GPP, allowing one to examine the spatially explicit GPP change caused by fires.

Remote Sensing of Environment↗
USGS2014

Modeling a historical mountain pine beetle outbreak using Landsat MSS and multiple lines of evidence

Mountain pine beetles are significant forest disturbance agents, capable of inducing widespread mortality in coniferous forests in western North America. Various remote sensing approaches have assessed the impacts of beetle outbreaks over the last two decades. However, few studies have addressed the impacts of historical mountain pine beetle outbreaks, including the 1970s event that impacted Glacier National Park. The lack of spatially explicit data on this disturbance represents both a major data gap and a critical research challenge in that wildfire has removed some of the evidence from the landscape. We utilized multiple lines of evidence to model forest canopy mortality as a proxy for outbreak severity. We incorporate historical aerial and landscape photos, aerial detection survey data, a nine-year collection of satellite imagery and abiotic data. This study presents a remote sensing based framework to (1) relate measurements of canopy mortality from fine-scale aerial photography to coarse-scale multispectral imagery and (2) classify the severity of mountain pine beetle affected areas using a temporal sequence of Landsat data and other landscape variables. We sampled canopy mortality in 261 plots from aerial photos and found that insect effects on mortality were evident in changes to the Normalized Difference Vegetation Index (NDVI) over time. We tested multiple spectral indices and found that a combination of NDVI and the green band resulted in the strongest model. We report a two-step process where we utilize a generalized least squares model to account for the large-scale variability in the data and a binary regression tree to describe the small-scale variability. The final model had a root mean square error estimate of 9.8% canopy mortality, a mean absolute error of 7.6% and an R 2 of 0.82. The results demonstrate that a model of percent canopy mortality as a continuous variable can be developed to identify a gradient of mountain pine beetle severity on the landscape.

Montana↗
USGS2016

Multi-index time series monitoring of drought and fire effects on desert grasslands

The Western United States is expected to undergo both extended periods of drought and longer wildfire seasons under forecasted global climate change and it is important to understand how these disturbances will interact and affect recovery and composition of plant communities in the future. In this research paper we describe the temporal response of grassland communities to drought and fire in southern Arizona, where land managers are using repeated, prescribed fire as a habitat restoration tool. Using a 25-year atlas of fire locations, we paired sites with multiple fires to unburned control areas and compare satellite and field-based estimates of vegetation cover over time. Two hundred and fifty Landsat TM images, dating from 1985–2011, were used to derive estimates of Total Vegetation Fractional Cover (TVFC) of live and senescent grass using the Soil-Adjusted Total Vegetation Index (SATVI) and post-fire vegetation greenness using the Normalized Difference Vegetation Index (NDVI). We also implemented a Greenness to Cover Index that is the difference of time-standardized SATVI-TVFC and NDVI values at a given time and location to identify post-fire shifts in native, non-native, and annual plant cover. The results highlight anomalous greening and browning during drought periods related to amounts of annual and non-native plant cover present. Results suggest that aggressive application of prescribed fire may encourage spread of non-native perennial grasses and annual plants, particularly during droughts.

Remote Sensing of Environment↗
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 &#xAF; "> R2¯ = 0.56 and 0.00–0.83 R 2 range; NBR R 2 &#xAF; "> R(bar) 2 = 0.43 and 0.00–0.82 R 2 range) and NVC (dNBR R 2 &#xAF; "> R(bar) 2 = 0.55 and 0.15–0.78 R 2 range; NBR R 2 &#xAF; "> 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 &#xAF; "> 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↗
USGS2023

Quantification of wetland vegetation communities features with airborne AVIRIS-NG, UAVSAR, and UAV LiDAR data in Peace-Athabasca Delta

Arctic-boreal wetlands, important ecosystems for biodiversity and ecological services, are experiencing hydrological changes including permafrost thaw, earlier snowmelt, and increased wildfire susceptibility. These changes are affecting wetland productivity, species diversity, and biogeochemical cycles. However, given the diverse forms and structures of wetland vegetation communities, traditional wetland maps generated from lower spatial and spectral resolution satellite imagery lack community-level vegetation classification and miss spatially complex patterns. In this study, we built a cloud-based workflow to map wetland vegetation community of the Peace-Athabasca Delta (PAD), Canada, by leveraging high-resolution (5-m) airborne multi-sensor datasets, namely NASA's Airborne Visible/Infrared Imaging Spectrometer-Next Generation (AVIRIS-NG) and Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR), and a historical LiDAR archive. Validation of our classifications using ground references indicates that classifications derived from AVIRIS-NG have higher accuracies (≥87.9%) than either UAVSAR (65.6%) or LiDAR (75.9%) for mapping wetland vegetation communities. We also show improved classification accuracy when combining information from multiple sensors. In particular, incorporating AVIRIS-NG and UAVSAR datasets substantially reduced omission errors of wet graminoid and wet shrub classes from 29.6% to 20.5% and from 10.8% to 7.5%, respectively. Combining AVIRIS-NG and LiDAR datasets further improves overall accuracy (+2.2%) for most classifications, especially emergent vegetation, wet graminoid, and wet shrub. The best performing model, using features derived from all three sensors, achieved an overall accuracy of 93.5%. The framework established here can be used to leverage extensive airborne AVIRIS-NG and UAVSAR datasets collected across Alaska and northwest Canada to understand the spatial distribution of Arctic-Boreal wetland vegetation communities.

Remote Sensing of Environment↗
USGS2026

Basal area loss from fire using field-calibrated remote sensing refines western US fire severity measurements

The spatial patterns of fire effects and tree mortality have profound consequences for forest resilience. Cost-effective, medium-resolution, and spatiotemporally extensive fire severity measurements are essential for informing post-fire restoration and improving our understanding of wildfires—from forest stands to continents and from days to decades. Remote sensing advancements have improved burn severity mapping, but methods vary in interpretability, scalability, generalizability, and alignment with field measurements. One meaningful metric of fire effects on forests is proportion basal area loss, but existing methods are limited by a lack of region-specific field reference data and a scalable mapping framework. To address these issues, we compiled 3280 field reference plots from 123 fires in forests across the Western US to calculate the proportion of fire-induced basal area loss. We then used spatially cross-validated machine learning models with concurrent hyperparameter tuning to select a skillful, parsimonious model from a large candidate set of remotely-sensed, climatic, and topographic predictors. Spectral-only measures of severity over- or underestimated basal area loss in dry versus wet years and across aspects, demonstrating the value of incorporating climatic and topographic context. We also tested model performance on a separate holdout dataset in the Southwest US as a demonstration of reproducibility and transparency. We provide a Google Earth Engine tool for estimating proportional basal area loss for any fire perimeter in the Western US, enabling rapid map creation for land management and ecological modeling. All code, model parameters, and training data are released to support reproducibility, community adoption, regional refinement, and adaptation to new regions.

western United States↗
USGS2020

Estimating burn severity and carbon emissions from a megafire in boreal forests of China

Wildfires, especially those of large size, worsen air quality and alter the carbon cycle through combustion of large quantities of biomass and release of carbon into the atmosphere. The Black Dragon fire, which occurred in 1987 in the boreal forests of China is among the top five of such megafires ever recorded in the world. With over 30 years of accumulation of data and availability of new greenhouse gas emission accounting methods, carbon emissions from this megafire can now be estimated with improved precision and greater spatial resolution. To do this, we combined field and remote sensing data to map four burn severity classes and calculated combustion efficiency in terms of the biomass immediately consumed in the fire. Results of the study showed that 1.30 million hectares burned and 52% of that area burned with high severity. The emitted carbon dioxide equivalents (CO2e), accounted for approximately 10% of total fossil fuel emissions from China in 1987, along with CO (2%–3% of annual anthropogenic CO emissions from China) and non-methane hydrocarbons (NMHC) contributing to the atmospheric pollutants. Our study provides an important basis for carbon emission estimation and understanding the impacts of megafires.

Science of the Total Environment↗
USGS2022

Post-fire temporal trends in soil-physical and -hydraulic properties and simulated runoff generation: Insights from different burn severities in the 2013 Black Forest Fire, CO, USA

Burn severity influences on post-fire recovery of soil-hydraulic properties controlling runoff generation are poorly understood despite the importance for parameterizing infiltration models. We measured soil-hydraulic properties of field-saturated hydraulic conductivity (Kfs), sorptivity (S), and wetting front potential (ψf) for four years after the 2013 Black Forest Fire, Colorado, USA at six sites across a gradient of initial remotely sensed burn severity using the change in the normalized burn ratio (dNBR). These measurements were correlated with soil-physical property measurements of bulk density (ρb), loss on ignition (LOI, a measure of soil organic matter), and ground cover composition to provide insight into causal factors for temporal changes in Kfs, S, and ψf . Modeled infiltration using the Smith-Parlange approach parameterized with measured Kfs, S, and ψf further discerned the role of precipitation intensity on runoff generation. Temporal trends of soil-physical properties and ground cover showed influences from initial burn severity. Trends in soil-hydraulic properties , surprisingly, were not strongly influenced by initial burn severity despite inferred effects of ρb, LOI, and ground cover on trends in Kfs and S. Calculations of dNBR at the time of sampling showed strong correlations with Kfs and S, demonstrating a new approach for estimating long-unburned Kfs and S values, infiltration model parameters after fire, and assessing the time of return to pre-fire values. Simulated infiltration-excess runoff, in contrast, did depend on initial burn severity. Time series of the ratio S2/Kfs ≈ ψf tended to converge between 1 to 10 mm four years after wildfire, potentially (i) defining a long-unburned forest domain of S2/Kfs and ψf from 1 to 10 mm with relatively high Kfs values, and (ii) providing a new post-fire soil-hydraulic property recovery metric (i.e. S2/Kfs ≈ ψf in the range of 1 to 10 mm) for sites in the Rocky Mountains of the USA.

Colorado↗
USGS2022

Natural infrastructure in dryland streams (NIDS) can establish regenerative wetland sinks that reverse desertification and strengthen climate resilience

In this article we describe the natural hydrogeomorphological and biogeochemical cycles of dryland fluvial ecosystems that make them unique, yet vulnerable to land use activities and climate change. We introduce Natural Infrastructure in Dryland Streams (NIDS), which are structures naturally or anthropogenically created from earth, wood, debris, or rock that can restore implicit function of these systems. This manuscript further discusses the capability of and functional similarities between beaver dams and anthropogenic NIDS, documented by decades of scientific study. In addition, we present the novel, evidence-based finding that NIDS can create wetlands in water-scarce riparian zones, with soil organic carbon stock as much as 200 to 1400 Mg C/ha in the top meter of soil. We identify the key restorative action of NIDS, which is to slow the drainage of water from the landscape such that more of it can infiltrate and be used to facilitate natural physical, chemical, and biological processes in fluvial environments. Specifically, we assert that the rapid drainage of water from such environments can be reversed through the restoration of natural infrastructure that once existed. We then explore how NIDS can be used to restore the natural biogeochemical feedback loops in these systems. We provide examples of how NIDS have been used to restore such feedback loops, the lessons learned from installation of NIDS in the dryland streams of the southwestern United States, how such efforts might be scaled up, and what the implications are for mitigating climate change effects. Our synthesis portrays how restoration using NIDS can support adaptation to and protection from climate-related disturbances and stressors such as drought, water shortages, flooding, heatwaves, dust storms, wildfire, biodiversity losses, and food insecurity.

Science of the Total Environment↗