Accurate assessment of burn severity is a critical need for an improved understanding of fire behavior and ecology and effective post-fire management. Although NASA Landsat satellites have a long history of use for remotely sensed mapping of burn severity, the recently launched (2015 and 2017) European Space Agency Sentinel-2 satellite constellation offers increased temporal and spatial resolution with global coverage, combined with free data access. Evaluations of burn severity derived from Landsat and Sentinel generally show comparable results, but these studies only assessed a small number of fires with limited field data. We used 912 ground calibration plots from 26 fires that burned between 2016 and 2019 in western North America to compare Sentinel- and Landsat-derived burn severity estimates with the field-based composite burn index. We mapped burn severity using two methods; the well-established paired scene approach, in which a single pre- and post-fire scene are selected for each fire, and also a mean image compositing approach that automatically integrates multiple scenes using the cloud-based remote sensing platform Google Earth Engine. We found that Sentinel generally performed as well or better than Landsat for four spectral indices of burn severity, particularly when using atmospherically corrected Sentinel imagery. Additionally, we tested the effects of mapping burn severity at Sentinel’s finer spatial resolution (10 m) on estimates of the spatial complexity of stand-replacing fire, resulting in a 5% average reduction per-fire in area mapped as high-severity patch interiors (24,273 ha total) compared to mapping at the resolution of Landsat (30 m). These findings suggest Sentinel may improve ecological discrimination of fine-scale fire effects, but also warrant caution when comparing estimates of burn severity spatial patterns derived at different resolutions. Overall, these results indicate that burn severity mapping will benefit substantially from the integration of Sentinel imagery through increased imagery availability, and that Sentinel’s higher spatial resolution improves opportunities for examining finer-scale fire effects across ecosystems.

Peroxisome proliferator-activated receptor γ (PPARγ) is expressed at low levels in skeletal muscle, where it protects against adiposity and insulin resistance via unclear mechanisms. To test the hypothesis that PPARγ directly modulates skeletal muscle metabolism, we created two models that isolate direct PPARγ actions on skeletal myocytes. PPARγ was overexpressed in murine myotubes by adenotransfection and in mouse skeletal muscle by plasmid electroporation. In cultured myotubes, PPARγ action increased fatty acid uptake and incorporation into myocellular lipids, dependent upon a 154 ± 20-fold up-regulation of CD36 expression. PPARγ overexpression more than doubled insulin-stimulated thymoma viral proto-oncogene (AKT) phosphorylation during low lipid availability. Furthermore, in myotubes exposed to palmitate levels that inhibit insulin signaling, PPARγ overexpression increased insulin-stimulated AKT phosphorylation and glycogen synthesis over 3-fold despite simultaneously increasing myocellular palmitate uptake. The insulin signaling enhancement was associated with an increase in activating phosphorylation of phosphoinositide-dependent protein kinase 1 and a normalized expression of palmitate-induced genes that antagonize AKT phosphorylation. In vivo, PPARγ overexpression more than doubled insulin-dependent AKT phosphorylation in lipid-treated mice but did not augment insulin-stimulated glucose uptake. We conclude that direct PPARγ action promotes myocellular storage of energy by increasing fatty acid uptake and esterification while simultaneously enhancing insulin signaling and glycogen formation. However, direct PPARγ action in skeletal muscle is not sufficient to account for the hypoglycemic actions of PPARγ agonists during lipotoxicity.

California must adapt to increasing wildfire activity concurrent with climate change and expanding housing development in fire-prone areas. Recent decades have seen record-breaking fire activity, economic costs, and human health impacts. Residents more frequently face home evacuations, prolonged periods of unhealthy air quality, and power shut-offs. Understanding how these experiences influence support for risk mitigation policies is essential to inform action on climate and fire adaptation. To better understand linkages between experience and policy support, we surveyed California residents (n = 645) about their wildfire-related experiences, risk perceptions, and support for 18 wildfire risk mitigation policies. To assess how the relationship between policy support and wildfire experience is modulated by preexisting worldviews, we measured the extent to which respondents are motivated by individualistic or communitarian values as proposed in the cultural theory of risk. We surveyed residents across a gradient of wildfire impacts, spatially stratifying residences based on wildland-urban-interface type and proximity to large 2020 wildfires. Support was generally high for most policies, though most respondents opposed incorporating future risk into insurance rates and coverage. Policy support models showed that communitarian worldviews were more consistently associated with greater support for diverse wildfire mitigation policies than were measures of recent experience with wildfire. These results suggest that California residents within our sample regions already support many wildfire risk mitigation strategies, and preexisting societal beliefs are a stronger predictor of these views than personal experiences with wildfire. Policy-makers can utilize this understanding to focus on crafting policies and messaging that resonates with individualistic values.

Effective wildfire impact assessment and post-fire management rely on burn-severity maps that accurately characterize fire effects on vegetation and soils. These maps inform time-sensitive activities such as post-fire flood and debris-flow modeling, risk mitigation for homes and communities, and ecosystem recovery planning, but the satellite observations used to generate them are often obstructed or delayed by smoke, clouds, and snow. Across 9,129 large fires (≥ 404 ha) in Canada and the United States from 2016–2024, we quantified post-fire data obstruction and evaluated observational latency – defined as the time required to acquire the first usable scene – for four freely available moderate-resolution remote sensing datasets: Landsat, Sentinel-1, Sentinel-2, and the Harmonized Landsat Sentinel-2 (HLS) product. For multispectral datasets (Landsat, Sentinel-2, and HLS), cloud cover was the primary source of data obstruction, particularly in mesic and coastal regions, whereas Sentinel-1 (synthetic aperture radar) can penetrate clouds. Consequently, Sentinel-1 had the shortest observational latency (median = 4 days), followed by HLS (7 days), Sentinel-2 (10 days), and Landsat (17 days). Likewise, one-month composite imagery from Sentinel-1 (89.6%) and HLS (82.8%) achieved near-complete coverage for most fires, substantially better than Sentinel-2 (70.5%) and Landsat (56.1%). Despite its widespread use in operational burn-severity mapping, Landsat provided the slowest and least complete early post-fire coverage. For 77 fires in forested ecosystems, we also evaluated relationships between early post-fire imagery and field-derived burn-severity measurements collected 0–3 years after fire. All multispectral datasets corresponded well with field data and reliably identified areas of near-total canopy loss using imagery acquired within two months after fire (> 80% balanced accuracy), performing nearly as well as extended assessments using imagery from a full post-fire growing season. Sentinel-1 produced less accurate burn-severity estimates but may remain valuable where persistent cloud cover limits observations, including eastern Canada and the Pacific Northwest. Overall, HLS provided the best combination of latency, spatial coverage, and burn-severity mapping performance, making it a strong candidate for operational use and ecological monitoring across diverse North American ecosystems.