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At least 1,369 records · Page 76Linked to original sources
USGS2026

Is new actually better? A structural comparison of collaborative governance structures for the management of Atlantic salmon (Salmo salar) in Maine

Collaborative governance structures (CGS) have been increasingly adopted to address joint-jurisdictional management challenges. While the establishment of CGSs has been widely studied, their practical development has remained a conspicuous gap. CGSs are variable systems with several developmental stages (activation, collectivity, and institutionalization) which eventually lead to stabilization, decline, re-creation, or re-orientation. CGSs in decline may need to revisit earlier developmental stages if members choose to re-create or re-orient the structure. The Atlantic Salmon Recovery Framework (ASRF) was the CGS responsible for managing the Gulf of Maine Distinct Population Segment of Atlantic Salmon ( Salmo salar ) from 2011 until 2019 when it was replaced by the Collaborative Management Strategy (CMS) pilot program. The CMS was designed to address concerns identified in the ASRF; 1. slow and ineffective decision-making, 2. confusion surrounding leadership, and 3. low adaptive capacity. Building upon a previous evaluation of the ASRF, we used a parallel design to compare the structural components and participant perceptions of the two structures using a comparative case study methodology. The changes that occurred between the ASRF and the CMS constitute a re-creation, providing a unique opportunity to study this developmental phase of a CGS. The issues observed in the CMS may result from a misalignment between participant expectations and the current developmental stage of the re-created CGS. The CMS reorganized ASRF members and created roles for participants without direct management jurisdiction. The implementation of the CMS addressed many concerns identified in the ASRF while others remained (i.e., high membership uncertainty and confusion surrounding decision-making procedures). Formal leadership roles have created more equal representation, but participants still reported uncertainty regarding status and formalization of non-leadership roles.

Maine↗
USGS2026

Chronic, low concentration pesticide exposure alters reproduction and behavior in the intertidal sea anemone, Anthopleura elegantissima

Widespread pesticide and herbicide use paired with frequent transport away from application sites has led to pesticide presence in nearly all terrestrial and aquatic environments globally. Pesticides have unintentional toxic effects on non-target organisms by interfering with cellular processes, behavior, feeding, reproduction, and disrupting endocrine processes. The aggregating anemone, Anthopleura elegantissima, is an important species along the North American Pacific coast due to its symbiotic relationships that contribute to high productivity, and its clonal abundance that structures the rocky intertidal habitat. Commonly used pesticides, atrazine, diuron, and carbendazim were previously detected in coastal waters of Oregon, U.S.A. This study examined the potential effects of these pesticides at environmentally relevant concentrations on reproduction, symbionts, and behavior of A. elegantissima over an eight-week period. Pesticides significantly decreased gonad development in all treatments, having the most significant effect in individual treatments of atrazine ( p = 0.003) , carbendazim ( p = 0.003) , and the mixture of all three pesticides ( p = 0.008). All pesticide treatments significantly increased cloning behavior compared to the control, suggesting that cloning could be a stress response. Pesticide exposure also significantly increased tentacle retraction movement, suggesting possible metabolic or energy impairments. While other studies have previously found behavioral changes in anemones due to pollutants, our study is the first to document behavioral changes in anemones from pesticide exposure. All three pesticides significantly impacted a non-target marine invertebrate at environmentally relevant concentrations, which underscores the value of studies that focus on effects on marine invertebrates, paired with comprehensive pesticide monitoring in coastal areas.

Oregon↗
USGS2026

Ecotoxicological profile of a high prevalence marine disease area in Florida's Gulf coast

Pervasive marine pollution can have devastating effects on ocean health. Polychlorinated Biphenyls (PCB), Polycyclic Aromatic Hydrocarbons (PAH), and Organochlorine Pesticides (OCP) are of particular concern given their high level of toxicity and carcinogenicity. These compounds originate from anthropogenic activity, therefore are often ubiquitous in coastal environments close to human sources. Marine turtles in coastal Florida (USA) are affected by fibropapillomatosis, a tumor-causing panzootic with highly debilitating symptoms. Despite repeated associations between fibropapillomatosis occurrence and carcinogenic pollution, the ecotoxicological status of high disease areas in the Florida Gulf has been unknown. Our study deployed passive sampling devices along a structured inshore-to-offshore coastal grid to generate the first comprehensive dataset on the concentration and diversity of oncogenic pollutants in Crystal River, Florida, where juvenile green turtles exhibit a fibropapillomatosis prevalence of 74%. Multiple OCP, PAH, and PCB compounds were detected for a total chemical concentration of 16,086.98 pg/L (Σ OCP 45.51 pg/L + Σ PAH 16,014.53 pg/L + Σ PCB 26.94 pg/L). Density and composition of carcinogenic pollution was found to be significantly lower in offshore green turtle habitats compared to inshore habitats (p=0.02), suggesting multiple potential contamination sources along the coastline. The carcinogenic compounds detected have been shown to have associations with fibropapillomatosis in other parts of the world, as well as having wider ecosystem implications. Our work contributes to the body of knowledge correlating the emerging threat of harmful ocean pollution and marine wildlife diseases.

Florida↗
USGS2026

Statewide agent-based model for management of chronic wasting disease in white-tailed deer: PAOvCWD

Chronic wasting disease (CWD) is an always-fatal disease infecting wild cervids globally. Ecologically and economically important, CWD presents a challenge for managing white-tailed deer ( Odocoileus virginianus ). We built an agent-based model to simulate CWD transmission and assess potential management actions that could slow disease spread: PAOvCWD . We developed PAOvCWD using contact rates and other behavioral and ecological metrics estimated from deer monitored in Pennsylvania, USA. We programmed potential management responses (e.g., culling, altered hunter harvest) for all 22 Pennsylvania wildlife management units and validated the efficacy of PAOvCWD using a deer population in south-central Pennsylvania infected with CWD for > 10 years. To support applications of our model, we developed a user-friendly R pipeline that allows implementation with relatively minor modifications. Our pipeline includes four steps: • Steps 1 and 2 generate regional percent forest cover rasters and curate population-level demographics. • Step 3 initializes landscapes and agents using our PAOvPOP model. • Step 4 assesses CWD transmission and management responses using our PAOvCWD model.

Pennsylvania↗
USGS2026

Generating geochemical and mineralogy distributions of soil in the conterminous United States using Bayesian hierarchical spatial models

Characterizing geochemical and mineralogical soil distributions across large spatial extents is essential for understanding mineral resources, ecosystem processes, and environmental risks. Rasters of soil geochemical distributions for the conterminous United States, however, are limited. We present a Bayesian modeling workflow and tool for generating predictive geochemical and mineralogy distribution maps for the conterminous United States using integrated nested Laplace approximation (INLA) with the stochastic partial differential equation approach. By modeling soil geostatistical data with environmental covariates (soil properties, topography, climate, and land cover), we generate predictive distributions of soil geochemistry that can be mapped or extracted for further analyses. As an example, we model the spatial distribution of trace elements in soil relevant to vertebrate health (cobalt, copper, iron, manganese, selenium, and zinc) and provide a workflow that can be used to generate and visualize predictive distributions of 39 other major and trace elements and 21 minerals of the soil survey, supporting a variety of ecological, environmental, and agricultural applications.

MethodsX↗
USGS2026

Climate-smart wetlands can mitigate the wetland-methane feedback loop

Climate change is intensifying wetland methane emissions, reinforcing a feedback loop that accelerates warming and threatens wetlands’ role as natural climate solutions. We propose climate-smart interventions for wetland protection, restoration, and management that explicitly account for methane dynamics. Targeted interventions can weaken methane feedback while safeguarding ecological functions and services.

One Earth↗
USGS2026

Constraining the onset of carboniferous cyclicity in the Arkoma Basin of the Midcontinent, North America: Implications for calibrating a globally significant latest Bashkirian transgression

Cyclothems are defined by the repeat juxtaposition of littoral and open marine successions over short stratigraphic distances (meters to 10's of meters) and are interpreted to be driven by glacioeustatic forcing of sea level during the late Paleozoic Ice Age. The concept of cyclothems was defined in the Midcontinent region of the United States. However, correlating the Midcontinent region to other cyclic successions is difficult, which is the result of no geochronologic control for the Midcontinent biostratigraphic framework. We present the first high-resolution U Pb zircon CA-ID-TIMS and feldspar 40 Ar/ 39 Ar age control for the onset of Midcontinent cyclothem deposition in the Arkoma Basin, Arkansas USA. Geochronologic control is obtained from a volcaniclastic unit preserved in the newly recovered Dare Creek #1 core. We integrate these data with biostratigraphic, lithostratigraphic and trace element analyses to investigate the timing, stratigraphic and geochemical response to late Paleozoic climate forcing. The lowermost Atoka Formation is associated with the onset of five high frequency transgressive-regressive cycles, which are defined by nearshore sandstones juxtaposed on top of offshore marine mudstones and are associated with changes in salinity and redox conditions. The Trace Creek Member of the lower Atoka Formation hosts a thick, organic-rich black shale, which defines the last and maximum transgression of the lower Atoka Formation in the Arkoma Basin, in the latest Bashkirian. Base-level records from time equivalent stratigraphic successions from Arrow Canyon, Nevada, U.S.A. and the Donets Basin, Ukraine also record a maximum transgression in the latest Bashkirian. The synchroneity of maximum flooding events from multiple basins which span the low latitudes in the latest Bashkirian support that cyclothem deposition was controlled by allostratigraphic forcing mechanisms, likely glacioeustatic forcing resulting from dynamic glaciation in high-latitude Gondwana.

Arkansas, Oklahoma↗
USGS2026

Challenges and opportunities for Sendai framework disaster loss reporting in the United States

The Sendai Framework for Disaster Risk Reduction provides quantitative indicators for nations to measure progress in the reduction of disaster losses. The collection and analysis of disaster loss data under the Sendai Framework improves our understanding of the effectiveness of national disaster risk reduction strategies and interventions. The Sendai Framework has enhanced cooperation among Federal agencies to collect and track disaster loss data in the U.S., yet challenges remain for reporting disaster losses. Based on our experiences collecting and reporting U.S. data to the Sendai Monitor, we identify opportunities to improve disaster loss reporting in the U.S.

Progress in Disaster Science↗
USGS2026

Can a physics-based fire behavior model predict burn severity and post-fire debris flow hazard?

Post-wildfire flooding and debris flows pose a threat to ecosystems and infrastructure. As the frequency and size of severe wildfires increase, there is a growing need for predictive debris flow hazard modeling in unburned but fire-prone landscapes. However, no crosswalks have been established between physics-based models of fire behavior and the remotely sensed burn severity inputs needed for debris flow modeling. Here, we explore this connection by creating a large ensemble of fire behavior simulations using QUIC-Fire – a fast-running three-dimensional fire model – in basins across five major-disaster wildfires in California and Washington. We then conducted generalized linear mixed modeling (GLMM) and Random Forest modeling to identify QUIC-Fire outputs that best predicted remotely sensed burn severity. We found that the selected linear predictors exhibited weak and unintuitive relationships with soil burn severity and differenced Normalized Burn Ratio. Moreover, model evaluations of GLMMs and Random Forests showed they were poorly predictive of both metrics of remotely sensed burn severity. This suggests that there are barriers to integrating QUIC-Fire into a wildfire-hazards modeling framework, which could include mismatches in scale between fire models and remotely sensing, or simplifications of weather data and fire initiation. Further research is needed to establish connections between the fire physics outputs of QUIC-Fire and post-fire effects to vegetation and soils that are essential for evaluating debris flow hazard.

California, Washington↗
USGS2026

Cyanobacteria and aquatic ecosystem dynamics across 28,000 years of environmental changes in subtropical North America

Ecological pressures on aquatic ecosystems have increased over recent centuries due to human activities and climate change. However, contextualizing ecosystem deterioration is often challenging due to limited knowledge of environmental changes over millennial timescales. Subtropical Carolina bays in North Carolina, USA, have remained unglaciated, preserving paleolimnological records that extend back to the last glacial period. Here, we analyzed a sediment core from the ecologically rich Lake Waccamaw spanning more than 28,000 years for aquatic proxies of nutrients, photosynthetic pigments, cyanotoxins, carbon isotopes, and terrestrial proxies of pollen and charcoal. The study explored paleolimnological changes in the aquatic environment connected to land changes and climate during the late Quaternary in the southeastern Atlantic Coastal Plain. Results reveal that while current levels of colonial cyanobacteria are high, past levels of cyanobacteria, other primary producers, and cyanotoxins were higher under natural climate variability. Abrupt ecosystem responses to increasing trophic conditions during Interstadial 3 (27.8–26.4 cal ka BP) and the early Holocene (11.4–7 cal ka BP) were marked by increases in primary producer abundance, deciduous vegetation expansion, and fire activity. Cyanobacteria remained dominant throughout the record, with colonial forms prevailing during the Holocene. Increases in pigment concentrations aligned with Quercus and were primarily driven by hydroclimatic variability and nutrient stoichiometry. Transitions between Pinus and Quercus pollen matched stadials and interstadials in the δ 18 Ο record from the North Greenland Ice Core Project (NGRIP). This study highlights the value of multi-proxy millennial-scale paleolimnological records for understanding aquatic ecosystem responses to climate conditions during the late Pleistocene.

North Carolina↗
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↗
USGS2026

Regreening, restoring, and reconnecting a southwestern wetland ecosystem – the Zeedyk wetland

Alluvial wetland ecosystems are vital as biodiversity hotspots but are increasingly threatened by anthropogenic stressors and drought. These pressures are especially acute in arid and semi-arid regions, where eco-hydrologic connectivity is fragile and recovery is slow. This study quantifies the efficacy of nature-based solutions, particularly the ‘Zeedyk approach,’ which employs low-tech Natural Infrastructure in Dryland Streams (NIDS)—including rock detention structures—to slow surface water, raise groundwater tables, and restore wetland function at a spring-fed wetland in Cebolla Canyon, New Mexico, U.S.A. Our results depict a Restoration Feedback Loop that captures stages of change from a healthy wetland in 1935, altered by 20th-century agriculture and grazing, to the re-establishment of the historical flow regime by 2024 documented through an 89-year archive of aerial imagery (1935–2024). By the end of our study period, the Spring-Fed Wetland had expanded by roughly 229% of the original 1935 area, to 4.13 ha. Using 40 years of satellite data, we assess changes in vegetation and hydrology with remote sensing indices. Spatial and temporal analyses reveal significant increases in vegetation greenness and wetness, particularly in an Expanded Wetland subregion, which exhibited ∼3.5x higher wetness and ∼1.5x higher greenness trends compared to adjacent areas. Monthly metrics highlight seasonal variability, with increases in greenness linked to monsoonal rainfall and lateral water redistribution, indicating that restoration impacts extend beyond the primary wetland. This study demonstrates the utility of cloud-based platforms like Google Earth Engine and USGS EarthExplorer for long-term monitoring of wetland restoration, while quantifying the efficacy of the ‘Zeedyk approach’ and demonstrating its potential as a scalable method to restore and conserve wetland meadows in other arid and semi-arid landscapes.

New Mexico↗
USGS2026

Comparative crop yield forecasting using satellite-derived biophysical and agro-climatic predictors in Sub-Saharan Africa

Timely and accurate crop yield forecasting is central to food security early warning systems, particularly in climate-vulnerable regions. While operational forecasting frameworks commonly rely on precipitation and vegetation indices such as NDVI, their ability to provide actionable lead time remains limited. Here, we evaluate the added value of satellite-derived biophysical Essential Climate Variables (ECVs): Leaf Area Index (LAI) and Fraction of Photosynthetically Active Radiation (FAPAR), for forecasting millet yield in Burkina Faso (BF) and maize yield in South Africa (ZA) and Malawi (MW). Using Random Forest models, we quantify forecast skill across the growing season at both national and sub-national scales. Results show that LAI and FAPAR provide effective forecast lead times of approximately 4 months in BF, 2 months in ZA, and up to 6 months in MW relative to harvest. At peak performance, Mean Absolute Percentage Error (MAPE) reaches 19.8% (LAI) and 23.8% (FAPAR) in BF, 12.0% and 9.8% in ZA, and 21.8% and 20.8% in MW, respectively. Across countries, biophysical parameters often outperform NDVI and precipitation, particularly in arid and semi-arid regions. At the sub-national level, LAI and FAPAR enable classification of administrative units into high and moderate-skill forecast units, revealing strong spatial heterogeneity linked to crop dominance. However, forecast skill declines where the target crop is not the dominant type, highlighting an important limitation for operational deployment. Overall, the findings suggest that satellite-derived biophysical parameters can provide earlier and more spatially resolved yield signals than commonly used predictors, with potential to improve the timeliness and effectiveness of food security early warning systems.

Remote Sensing Applications: Society and Environme↗
USGS2026

Towards global mapping of dynamic surface water extents using Sentinel-1 SAR data

We introduce a fully automated and scalable method for mapping surface water extents from single-acquisition Sentinel-1 synthetic aperture radar (SAR) imagery. This approach integrates adaptive thresholding of radiometric terrain-corrected SAR backscatter data, fuzzy-logic classification, region growing, dark land estimation, and a bimodality test to minimize false positives in low-backscattering areas and false negatives in high-backscattering areas. By combining these steps, the algorithm achieves classification accuracies exceeding 85% in detecting surface water extents across diverse environmental conditions. Accuracy was first assessed at meter scale using 52 PlanetScope scenes acquired worldwide in September–October 2019; the algorithm achieved 93% overall accuracy, 86% user's accuracy, and 94% producer's accuracy. Global robustness was then evaluated by processing every Sentinel-1 acquisition from 1 to 12 November 2023 and cross-comparing the resulting maps with 6561 temporally matched observational products for end-users from remote sensing analysis (OPERA) dynamic surface water extent from Harmonized Landsat and Sentinel-2 (DSWx-HLS) products. This large-scale test yielded 90% user's and 94% producer's accuracies, confirming reliable performance at continental extent. Additional case studies demonstrate the algorithm's ability to handle surface water extent in sand-dominated deserts, to track seasonal amplitude in Folsom Lake (California), drought-induced loss in Cerro Prieto Reservoir (Mexico), and rapid filling of the Grand Ethiopian Renaissance Dam. These results show that the method scales across local to global domains and maintains high accuracy, providing a practical tool for near-real-time monitoring of floods, droughts, and water-resource management. Because the approach is sensor-agnostic, it can be ported to forthcoming L- and S-band missions such as NASA-ISRO synthetic aperture radar (NISAR), broadening its applicability to future hydrologic observations.

Remote Sensing of Environment↗
USGS2026

Hyperspectral retrieval of phytoplankton absorption and community composition from NASA’s PACE-OCI in estuarine–coastal waters using a hybrid framework combining mixture-of-experts and Variational Autoencoder

Retrieving the phytoplankton absorption coefficient (a phy ; m−1), one of the most spectrally rich inherent optical properties, remains challenging in optically complex coastal waters worldwide. Leveraging NASA's new hyperspectral mission, PACE, we introduce Hyper-MoE-VAE, a deep-learning architecture that integrates a Mixture-of-Experts with a Variational Autoencoder to retrieve high-dimensional a phy and subsequent estimation of phytoplankton community composition (PCC) from PACE-OCI hyperspectral remote sensing reflectance (R rs ). Pre-trained on global hyperspectral bio-optical datasets and fine-tuned using regional field R rs –a phy pairings from inland– estuarine–coastal waters, Hyper-MoE-VAE demonstrated strong transferability and effective adaptation across regions. Validation with in-situ Rrs showed accurate aphy retrievals in Lake Erie (NRMSE = 0.12, ε = 17.10), Lake Pontchartrain (NRMSE = 0.11, ε = 37.12), and the Barataria–Terrebonne Estuary (NRMSE = 0.14, ε = 38.89). Using same-day PACE-OCI Level 2 Rrs, the model achieved comparable performance in Lake Erie (NRMSE = 0.19, ε = 55.19), Lake Pontchartrain (NRMSE = 0.14, ε = 51.39), and the Barataria–Terrebonne Estuary (NRMSE = 0.17, ε = 47.92). Hyper-MoE-VAE derived PACE-OCI hyperspectral aphy was further decomposed against mass-specific absorption spectra to estimate group-specific contributions to total chlorophyll a. The resulting PCC showed strong agreement with HPLC–CHEMTAX in Lake Erie ( R 2 = 0.692) and Gulf estuarine–coastal systems ( R 2 = 0.732). Monte Carlo noise experiments further revealed group-dependent sensitivities, with diatoms and dinoflagellates showing moderate susceptibility to noise, while cyanobacteria and cryptophytes exhibited narrow uncertainty distributions. These results demonstrate Hyper-MoE-VAE's capability for regional, operational water-quality monitoring with PACE-OCI and its adaptability to current and future hyperspectral missions.

Great Lakes, Lake Pontchartrain↗
USGS2026

Advances in volcano monitoring driven by the first decade of Sentinel-1 observations

Sentinel-1 has transformed how satellite radar data (SAR and InSAR) are used in volcanology. The systematic, long-term archive and open-access policy means that volcano observatories and research organisations have invested in integrating Sentinel-1 datasets into their monitoring systems. We identify 233 high priority volcanoes and estimate that Sentinel-1 data has been used in peer-reviewed publications for 90 of them. We examine a global archive of 3.3 million automatically processed Sentinel-1 interferograms of volcanoes and use machine learning methods to identify eruptions and periods of unrest. We then review the ways in which InSAR data are being used in different contexts. At frequently erupting basaltic systems in Iceland, Hawaiʻi, the Galápagos , and Piton de la Fournaise, InSAR has become an effective monitoring tool and is integrated with other datasets and models to forecast magma pathways. For large explosive eruptions, deformation measurements often remain challenging, but SAR backscatter is increasingly used to map damaging flows and measure the changing shape of ocean islands. Sentinel-1's long archive provides critical baseline measurements that are vital for measuring slow deformation, capturing new periods of unrest and providing fresh insights into subsurface dynamics. Understanding the drivers of deformation remains challenging and typically relies on integration with external datasets. Future European Space Agency missions have the potential to improve both resolution and coverage providing an even richer dataset to further enhance global volcano monitoring

Erta Ale volcano↗
USGS2026

Mapping winter cover crops using remote sensing of crop phenology

Winter cover crops (WCC) are an effective agricultural conservation practice for improving soil health and water quality. Several U.S. states have established incentive programs to promote planting WCC between cash crop rotations. In recent years, the area planted with WCC has steadily increased due to these efforts, although the planting area varies among states. Since these programs are largely led by individual counties or state programs, there is no centralized data repository that quantifies the total WCC planted annually or their locations. Therefore, there is a practical need to identify fields that have been planted with WCC, as well as to quantify the total planted area both during and after the growing season. Furthermore, incentive programs often require verification that WCC were planted to release payments to participating growers in a timely manner. Satellite remote sensing time series can provide information on WCC growth status. However, it remains unclear what kind of WCC the remote sensing data detects. For example, separating WCC from weeds and winter commodity crops remains a challenge. Additionally, a binary classification of WCC presence and absence cannot accurately describe the category and performance of the WCC. This paper presents a new remote sensing phenology-based approach to map WCC likelihood. We assessed model predictions of WCC likelihood using WCC planting records and ground observations over fields at the Beltsville Agricultural Research Center (BARC) from 2018 to 2024. Regional WCC maps were assessed using the Maryland Department of Agriculture's records of WCC fields enrolled in the 2019 and 2020 incentive programs. Our results show that the balanced overall accuracy for WCC and non-WCC ranges from 75% to 85% but could exceed 90%, with limited training samples. However, the high accuracy of WCC corresponds with low accuracy for non-WCC due to confusion with weeds and perennial grasses. Early-season detection yields promising results, with overall accuracies comparable to late-season detection. The after-season detection can differentiate between winter cereal grain cash crops and incentive WCC based on the ending date of the growing season. With a small set of WCC samples and management information, the phenology-based mapping approach offers a rapid and scalable solution for mapping WCC over a large region during or after each WCC growing season.

Maryland↗
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↗