An ongoing spruce beetle (Dendroctonus rufipennis Kirby.) epidemic in southern Colorado has resulted in the death of thousands of acres of forests primarily dominated by Engelmann spruce (Picea engelmannii Parry.). To evaluate the ecological and economic impacts of this massive mortality event, researchers and land managers need to efficiently track its progression, spread, and severity across large spatial extents. In this study, mortality severity (0–100% dead) was successfully mapped at the Landsat pixel scale (30 × 30 m) across a large (5000 km²), persistently cloud-covered study area using multi-sensor (Landsat 5 Thematic Mapper (TM), Landsat 7 Enhanced Thematic Mapper Plus (ETM+) and Landsat 8 Operational Land Imager (OLI)) harmonized tasseled cap image composites as spectral predictors of gray stage spruce beetle mortality. Our maps display the distribution and severity of this landscape-scale mortality event in 2011 (R² = 0.48, root mean squared error (RMSE) = 7.7) and 2015 (R² = 0.55, RMSE = 11.6). Potential applications of this study include efficient landscape-scale forest health monitoring, targeted forest and timber management, and assessment of ecological impacts of bark beetle outbreaks.

This dataset provides a shapefile containing approximately 3500 polygons with the location, extent, size, and year of clearcut harvest events occurring between 1984 and 2015 in forested areas of the northern Colorado, Landsat WRS-2 scene Path 034, Row 032. Harvest events were modeled and mapped using a 32 year time series of Landsat imagery, the LandTrendr algorithm, and ancillary datasets. The dataset also conveys information on the elevation, aspect, ownership, distance to roads, and the watershed where each harvest event occurred.

Non-native and invasive tamarisk (Tamarix spp.) and Russian olive (Elaeagnus angustifolia) are common in riparian areas of the Colorado River Basin and are regarded as problematic by many land and water managers. Widespread location data showing current distribution of these species, especially data suitable for remote sensing analyses, are lacking. This dataset contains 3476 species occurrence and absence point records for tamarisk and Russian olive along rivers within the Colorado River Basin in Arizona, California, Colorado, Nevada, New Mexico, and Utah. Data were collected in the field in the summer of 2017 with high-resolution imagery loaded on computer tablets. This dataset includes status (live, dead, defoliated, etc.) of observed tamarisk to capture variability in tamarisk health across the basin, in part attributable to the tamarisk beetle (Diorhabda spp.). For absence points, vegetation or land cover were recorded. These data have a range of applications including serving as a baseline for the current distribution of these species, species distribution modeling, species detection with remote sensing, and invasive species management.

The invasive shrub, Russian olive (Elaeagnus augustifolia), is widely established within riparian areas across North America and eastern Europe. Limited information on its distribution and invasion dynamics in northern regions has hampered understanding and management efforts. Given this lack of spatial and ecological information we worked with local stakeholders and developed two main objectives: (1) map the distribution of Russian olive along the Powder River (Montana and Wyoming, United States) as of 2020 with field data and remote sensing; and (2) relate that distribution to environmental variables to understand its habitat suitability and community/invasion dynamics. Field data showed Russian olive has reached near equal canopy cover (18.3%) to native Plains cottonwood (Populus deltoides; 19.1%) and has a broader distribution. At the watershed scale, we modeled Russian olive distribution using field surveys, ocular sampling of aerial imagery, and spectral variables from Sentinel-2 MultiSpectral Instrument using a random forest model (RMSE = 15.42, R² = 0.64). A statistical model linking the resulting Russian olive percent cover detection map to environmental variables for the entire watershed indicated Russian olive cover increased with flow accumulation and decreased with elevation, and was associated with poorer soil types. We attribute the success of Russian olive to its broad habitat suitability combined with changing hydrologic conditions favoring it over natives. The maps of Russian olive cover along the Powder River and its main tributaries in northern Wyoming and southern Montana revealed regions of the watershed with high and low cover, which can guide landscape-scale management prioritization. This study provides a repeatable Russian olive detection method due to the use of Sentinel-2 imagery that is available worldwide and provides insight into Russian olive’s ecological relationships and success with relevance for management across areas with similar environmental conditions.

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.