USGS Publications Warehouse

Historical ice jams and associated environmental conditions on Osoyoos Lake

Sutfin, Nicholas; Breen, Stephen — Thu, 16 Apr 2026 16:54:56

Ice jams occur regularly at the southern outlet of Osoyoos Lake, which spans the border between the State of Washington and British Columbia, Canada. In recent winters, ice jams caused (1) decreases in downstream discharge that may adversely affect salmon spawning habitat and (2) short-duration lake-level rise that can interfere with lake level management agreements. In response, water managers sought to understand the environmental conditions associated with the historical ice-jam occurrences on Osoyoos Lake. Researchers compiled datasets of discharge, lake level, and air temperature from four meteorological and three hydrologic stations near Oroville, Washington, to determine “ice-jam” or “non-ice-jam” days from 1942 to 2024.

After confirming known ice jams since 1994 using Landsat 8–9 and Sentinel–2 satellite imagery along with discharge, lake level, and air temperature data, researchers designated ice-jam days. They conducted statistical analyses to examine environmental conditions associated with ice-jam occurrences on Osoyoos Lake. Statistical tests indicated significant differences in wind speed, wind direction, and air temperature between ice-jam and non-ice-jam days. A linear discriminant-analysis model correctly predicted 12 of 13 historical ice-jam days since 1994 and determined that ice jams are more likely under westerly and northwesterly winds near or above 10 kilometers per hour (km/h) and minimum temperatures near or below –9.4 degrees Celsius (°C). An analysis of historical discharge suggests that ice jams have occurred since at least the 1940s, but 13 ice jam days occurred in the past decade (2014–2024), exceeding any previous decade. The daily minimum air temperature in the Osoyoos Lake region has increased at a rate of 0.021 °C per year since the 1940s, but ice jams usually occur in winters with colder average temperatures.

ECCOE Landsat quarterly calibration and validation report—Quarter 3, 2025

Fri, 10 Apr 2026 15:37:41

Executive Summary

The U.S. Geological Survey Earth Resources Observation and Science Calibration and Validation (Cal/Val) Center of Excellence (ECCOE) focuses on improving the accuracy, precision, calibration, and product quality of remote-sensing data, leveraging years of multiscale optical system geometric and radiometric calibration and characterization experience. The ECCOE Landsat Cal/Val Team continually monitors the geometric and radiometric performance of active Landsat missions and makes calibration adjustments, as needed, to maintain data quality at the highest level.

This report provides observed geometric and radiometric analysis results for Landsats 8 and 9 for quarter 3 (July–September) of 2025. All data used to compile the Cal/Val analysis results presented in this report are freely available from the U.S. Geological Survey EarthExplorer website: https://earthexplorer.usgs.gov.

Satellite time series analysis to quantify changing climax ciénegas using a state and transition model approach

Tue, 24 Mar 2026 17:12:07

Ciénegas are rare wetlands in arid landscapes of the North American Southwest, historically providing critical ecological and hydrological functions but increasingly threatened by changing climate and land use pressures. This study quantifies changes in ciénega condition and floodplain dynamics using a state-and-transition model (STM) informed by expert knowledge and remote sensing. Key factors include woody plant encroachment, water availability, and soil aggradation. We mapped 31 ciénegas with high-resolution imagery and analyzed Landsat data (1985–2023) to assess vegetation health and moisture using the Normalized Difference Vegetation Index (NDVI) and Normalized Difference Infrared Index (NDII). Results show substantial interannual variability in phenology, water stress, and soil moisture, with regional drying and elevation strongly influencing ciénega resilience. We classified ciénegas into three functional states—healthy, desiccated, and dormant—and mapped their 2023 condition. Trend analyses indicate most ciénegas exhibit greening despite drought, though localized variability underscores the need for site-specific management. None are in a stable climax (reference) state; rather, they transition among states in response to external drivers. Increasing woody plant cover and surface drying, likely linked to declining regional water tables, favor deep-rooted species over wetland grasses—a pattern mirrored in adjacent control plots. Spatially explicit analysis revealed intra-ciénega variability often masked by aggregated data, highlighting the importance of high-resolution monitoring. Seasonal and long-term trends provide context for understanding ciénega dynamics, including degradation and restoration pathways. This study emphasizes the importance of groundwater conservation and demonstrates how remote sensing supports long-term monitoring. The STM framework offers a practical tool for adaptive management to sustain freshwater resources in arid environments.

A framework for integrating spatiotemporal deep learning methods with landsat for annual land cover and impervious surface mapping

Thu, 19 Mar 2026 19:31:01

Land cover information is essential for understanding Earth’s surface dynamics and how vegetation, water, soil, climate, and terrain interact. The National Land Cover Database (NLCD) has been the authoritative source for consistent U.S. land cover mapping. To extend NLCD’s temporal resolution and reduce production latency, we developed the Land Cover Artificial Mapping System (LCAMS)—a prototype spatiotemporal deep learning framework piloted as the foundation for the new Annual NLCD.

LCAMS builds on concepts from legacy NLCD and the U.S. Geological Survey Land Change Monitoring, Assessment, and Projection (LCMAP) initiatives. It employs a loosely coupled two-stage architecture consisting of independent but functionally interdependent spatial and temporal models. Spatial models extract per-year information from Landsat data, while the temporal models refine the spatial outputs to enforce inter-annual consistency—critical for reliable land change monitoring. LCAMS produces annual 30 m resolution land cover and impervious surface outputs, with region-specific fine-tuning to generalize across diverse landscapes and temporal dynamics.

Validation was conducted using an independent dataset of 1925 randomly sampled plots from five U.S. Landsat Analysis Ready Data (ARD) tiles spanning 1985-2021, selected for spatial and temporal variability. This dataset was used consistently to evaluate LCAMS, Legacy NLCD, and LCMAP. Using the NLCD legend, LCAMS achieved 72.1 ± 1.60%

72.1 \pm 1.60 %

overall agreement, compared to 71.1 ± 1.7%

71.1 \pm 1.7 %

agreement for Legacy NLCD. Using the LCMAP legend, LCAMS achieved 83.4 ±

83.4 \pm 1.22 %

1.22% agreement, compared to 84.6 ±

84.6 \pm 1.11 %

1.11% agreement for LCMAP. Overall, LCAMS delivers comparable accuracy while offering higher thematic resolution, longer temporal coverage, and automated production of annual 30 m CONUS land cover.

Landsat 8–9 geometric and radiometric calibration and characterization

Anderson, Cody; Choate, Michael J.; Micijevic, Esad; Shaw, Jerad L. — Fri, 6 Mar 2026 14:39:50

The U.S. Geological Survey Earth Resources Observation and Science Cal/Val (Calibration and Validation) Center of Excellence is a global leader in improving the accuracy, precision, and quality of remote-sensing data. Calibration is the process of quantitatively defining a system’s response to known and controlled signal inputs. Validation is the process of assessing, by independent means, the quality of the calibrated data products derived from system outputs.

The Landsat Cal/Val team, comanaged by the Earth Resources Observation and Science Cal/Val Center of Excellence and the National Aeronautics and Space Administration Landsat Science Project, continually monitors the geometric and radiometric performance of active Landsat missions and makes calibration adjustments, as needed, to maintain data quality at the highest level, ensuring its reliability for scientific research. Landsat data quality is often referred to as the “gold standard” and gives other civil and commercial satellite programs a trusted reference point for measuring their own data quality.

The Landsat program started more than 50 years ago. Since then, Landsat missions have gone through multiple technological advances, which, together with improved calibration and validation techniques, have led to higher data quality over time. The Cal/Val team also maintains consistency in data calibration across the multiple generations of sensors, which is vital to many scientists for time-series analysis.

Groundwater dependency and hydroclimatic influences on riparian and upland vegetation productivity, Upper San Pedro, Arizona, United States

Fri, 13 Mar 2026 15:02:27

In arid and semi-arid regions, groundwater sustains vegetation through subsurface water access, yet the responses of groundwater-dependent ecosystems (GDEs) to changing hydroclimate and groundwater availability are relatively understudied. This study investigates seasonal and spatial patterns in vegetation greenness using Landsat Enhanced Vegetation Index (EVI) values across riparian and upland zones in the semi-arid Upper San Pedro (USP) watershed, southern Arizona, which experiences a bimodal precipitation regime. We paired 25 years (2000–2024) of EVI and depth to groundwater (DTG) data from 89 wells and climate metrics (precipitation and vapour pressure deficit) to quantify the sensitivity of vegetation to subsurface moisture as well as atmospheric moisture supply and demand. Vegetation at wells near the USP riparian area showed strong associations between EVI and DTG anomalies during the monsoon season, indicating sustained groundwater use even during this wet period when summer precipitation is abundant. In contrast, upland vegetation that lacked access to groundwater showed minimal sensitivity in EVI to DTG and was generally less responsive to vapour pressure deficit. Interestingly, the riparian GDEs were not decoupled from precipitation and climate variability. These results underscore the importance of groundwater for maintaining riparian productivity and highlight the utility of remote sensing in identifying vegetation-climate-groundwater linkages across heterogeneous dryland landscapes.

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

Wed, 15 Apr 2026 15:02:05

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.

Spatially concentrating logging could mitigate climate-magnified fragmentation risks to a globally endangered bird

Thu, 2 Apr 2026 17:00:56

1. Rising timber demand is transforming forest structure globally, profoundly affecting biodiversity and climate resilience. Logging-driven fragmentation is potentially a major driver of biodiversity loss in production landscapes, yet its interactions with escalating climate stressors remain poorly understood.

2. We combine two decades of Landsat-derived habitat metrics with 29,000 surveys of the marbled murrelet (Brachyramphus marmoratus)—an iconic Pacific Northwest old-forest specialist seabird affecting management of >10 million hectares. Controlling for habitat amount and detection probability, increasing landscape-scale forest edge amount sharply reduces murrelet occupancy, with impacts worsening under unfavourable climate-driven ocean conditions.

3. Comparing alternative landscape-scale timber harvest strategies, spatially concentrated logging consistently supports higher murrelet populations than fragmented approaches producing equivalent wood volumes, with benefits amplified under adverse ocean conditions. However, historical harvesting policies in the Pacific Northwest have instead driven severe habitat fragmentation, which we show is eroding the value of core set-aside forests on federal and conservation lands and ultimately rendering murrelets more vulnerable to climate change.

4. Synthesis and applications: We map key opportunities to boost populations by reducing edginess around remaining nesting habitat and investigate these opportunities' spatial distribution across land ownership and timber productivity gradients. Concentrating logging could be critical for mitigating fragmentation and climate threats for murrelets and potentially other forest-dependent species amid rising timber demand.