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USGS2025

Unlocking ecological insights from sub-seasonal visible-to-shortwave infrared imaging spectroscopy: The SHIFT campaign

We stand at the threshold of a transformative era in Earth observation, marked by space-borne visible-to-shortwave infrared (VSWIR) imaging spectrometers that promise consistent global observations of ecosystem function, phenology, and inter- and intra-annual change. However, the full value of repeat spectroscopy, the information embedded within different temporal scales, and the reliability of existing algorithms across diverse ecosystem types and vegetation phenophases have remained elusive due to the absence of suitable sub-seasonal spectroscopy data. In response, the Surface Biology and Geology (SBG) High-Frequency Time Series (SHIFT) campaign was initiated during late February 2022 in Santa Barbara County, California. SHIFT, designed to support NASA's SBG mission, addressed mission scoping, scientific advancement, applications development, and community building. This ambitious endeavor included weekly Airborne Visible InfraRed Imaging Spectrometer-Next Generation (AVIRIS-NG) imagery acquisitions for 13 weeks (spanning February 24 to May 29, 2022), accompanied by coordinated terrestrial vegetation and coastal aquatic data collection. We describe the rich datasets collected and illustrate how the complex sub-seasonal patterns of change can be linked to biological science and applications, surpassing insights from multispectral observations. Leveraging open-source processing methods and cloud-based analysis tools, the SHIFT campaign showcases the readiness of the scientific community to harness ecological insights from remotely sensed hyperspectral time series. We provide an overview of SHIFT's goals, data collections, preliminary results, and the collaborative efforts of early career scientists committed to unlocking the transformative potential of high-frequency time series data from space-borne VSWIR imaging spectrometers.

Ecosphere↗

USGS2021

Exploring VIIRS continuity with MODIS in an expedited capability for monitoring drought-related vegetation conditions

Vegetation has been effectively monitored using remote sensing time-series vegetation index (VI) data for several decades. Drought monitoring has been a common application with algorithms tuned to capturing anomalous temporal and spatial vegetation patterns. Drought stress models, such as the Vegetation Drought Response Index (VegDRI), often use VIs like the Normalized Difference Vegetation Index (NDVI). The EROS expedited Moderate Resolution Imaging Spectroradiometer (eMODIS)-based, 7-day NDVI composites are integral to the VegDRI. As MODIS satellite platforms (Terra and Aqua) approach mission end, the Visible Infrared Imaging Radiometer Suite (VIIRS) presents an alternate NDVI source, with daily collection, similar band passes, and moderate spatial resolution. This study provides a statistical comparison between EROS expedited VIIRS (eVIIRS) 375-m and eMODIS 250-m and tests the suitability of replacing MODIS NDVI with VIIRS NDVI for drought monitoring and vegetation anomaly detection. For continuity with MODIS NDVI, we calculated a geometric mean regression adjustment algorithm using 375-m resolution for an eMODIS-like NDVI (eVIIRS’) eVIIRS’ = 0.9887 × eVIIRS − 0.0398. The resulting statistical comparisons (eVIIRS’ vs. eMODIS NDVI) showed correlations consistently greater than 0.84 throughout the three years studied. The eVIIRS’ VegDRI results characterized similar drought patterns and hotspots to the eMODIS-based VegDRI, with near zero bias.

Kansas↗

USGS2016

Selection and quality assessment of Landsat data for the North American forest dynamics forest history maps of the US

Using the NASA Earth Exchange platform, the North American Forest Dynamics (NAFD) project mapped forest history wall-to-wall, annually for the contiguous US (1986–2010) using the Vegetation Change Tracker algorithm. As with any effort to identify real changes in remotely sensed time-series, data gaps, shifts in seasonality, misregistration, inconsistent radiometry and cloud contamination can be sources of error. We discuss the NAFD image selection and processing stream (NISPS) that was designed to minimize these sources of error. The NISPS image quality assessments highlighted issues with the Landsat archive and metadata including inadequate georegistration, unreliability of the pre-2009 L5 cloud cover assessments algorithm, missing growing-season imagery and paucity of clear views. Assessment maps of Landsat 5–7 image quantities and qualities are presented that offer novel perspectives on the growing-season archive considered for this study. Over 150,000+ Landsat images were considered for the NAFD project. Optimally, one high quality cloud-free image in each year or a total of 12,152 images would be used. However, to accommodate data gaps and cloud/shadow contamination 23,338 images were needed. In 220 specific path-row image years no acceptable images were found resulting in data gaps in the annual national map products.

International Journal of Digital Earth↗

USGS2024

Automated Cropland Fallow Algorithm (ACFA) for the Northern Great Plains of USA

Cropland fallowing is choosing not to plant a crop during a season when a crop is normally planted. It is an important component of many crop rotations and can improve soil moisture and health. Knowing which fields are fallow is critical to assess crop productivity and crop water productivity, needed for food security assessments. The annual spatial extent of cropland fallows is poorly understood within the United States (U.S.). The U.S. Department of Agriculture Cropland Data Layer does provide cropland fallow areas; however, at a significantly lower confidence than their cropland classes. This study developed a methodology to map cropland fallows within the Northern Great Plains region of the U.S. using an easily implementable decision tree algorithm leveraging training and validation data from wet (2019), normal (2015), and dry (2017) precipitation years to account for climatic variability. The decision trees automated cropland fallow algorithm (ACFA) was coded on a cloud platform utilizing remotely sensed, time-series data from the years 2010–2019 to separate cropland fallows from other land cover/land use classes. Overall accuracies varied between 96%-98%. Producer’s and user’s accuracies of cropland fallow class varied between 70-87%.

International Journal of Digital Earth↗

USGS2021

Geochemistry of coastal permafrost and erosion-driven organic matter fluxes to the Beaufort Sea near Drew Point, Alaska

Accelerating erosion of the Alaska Beaufort Sea coast is increasing inputs of organic matter from land to the Arctic Ocean, and improved estimates of organic matter stocks in eroding coastal permafrost are needed to assess their mobilization rates under contemporary conditions. We collected three permafrost cores (4.5–7.5 m long) along a geomorphic gradient near Drew Point, Alaska, where recent erosion rates average 17.2 m year −1 . Down-core patterns indicate that organic-rich soils and lacustrine sediments (12–45% total organic carbon; TOC) in the active layer and upper permafrost accumulated during the Holocene. Deeper permafrost (below 3 m elevation) mainly consists of Late Pleistocene marine sediments with lower organic matter content (∼1% TOC), lower C:N ratios, and higher δ 13 C values. Radiocarbon-based estimates of organic carbon accumulation rates were 11.3 ± 3.6 g TOC m −2 year −1 during the Holocene and 0.5 ± 0.1 g TOC m −2 year −1 during the Late Pleistocene (12–38 kyr BP). Within relict marine sediments, porewater salinities increased with depth. Elevated salinity near sea level (∼20–37 in thawed samples) inhibited freezing despite year-round temperatures below 0°C. We used organic matter stock estimates from the cores in combination with remote sensing time-series data to estimate carbon fluxes for a 9 km stretch of coastline near Drew Point. Erosional fluxes of TOC averaged 1,369 kg C m −1 year −1 during the 21st century (2002–2018), nearly doubling the average flux of the previous half-century (1955–2002). Our estimate of the 21st century erosional TOC flux year −1 from this 9 km coastline (12,318 metric tons C year −1 ) is similar to the annual TOC flux from the Kuparuk River, which drains a 8,107 km 2 area east of Drew Point and ranks as the third largest river on the North Slope of Alaska. Total nitrogen fluxes via coastal erosion at Drew Point were also quantified, and were similar to those from the Kuparuk River. This study emphasizes that coastal erosion represents a significant pathway for carbon and nitrogen trapped in permafrost to enter modern biogeochemical cycles, where it may fuel food webs and greenhouse gas emissions in the marine environment.

Alaska↗

USGS2019

Phenology patterns indicate recovery trajectories of ponderosa pine forests after high-severity fires

Post-fire recovery trajectories in ponderosa pine (Pinus ponderosa Laws.) forests of the US Southwest are increasingly shifting away from pre-burn vegetation communities. This study investigated whether phenological metrics derived from a multi-decade remotely sensed imagery time-series could differentiate among grass, evergreen shrub, deciduous, or conifer-dominated replacement pathways. We focused on 10 fires that burned ponderosa pine forests in Arizona and New Mexico, USA before the year 2000. A total of 29 sites with discernable post-fire recovery signals were selected within high-severity burn areas. At each site, we used Google Earth Engine to derive time-series of normalized difference vegetation index (NDVI) signals from Landsat Thematic Mapper, Enhanced Thematic Mapper+, and Operational Line Imager data from 1984 to 2017. We aggregated values to 8- and 16-day intervals, fit Savitzy-Golay filters to each sequence, and extracted annual phenology metrics of amplitude, base value, peak value, and timing of peak value in the Timesat analysis package. Results show that relative to post-fire conditions, pre-burn ponderosa pine forests exhibit significantly lower mean NDVI amplitude (0.14 vs. 0.21), higher mean base NDVI (0.47 vs. 0.22), higher mean peak NDVI (0.60 vs. 0.43), and later mean peak NDVI (day of year 277 vs. 237). Vegetation succession exhibits distinct phenometric characteristics as early as year five (amplitude) and as late as year 20 (timing of peak NDVI). This study confirms the feasibility of leveraging phenology metrics derived from long-term imagery time series to identify and monitor ecological outcomes. This information may be of benefit to land resource managers who seek indicators of future landscape composition to inform management strategies.

Arizona, New Mexico↗

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↗

USGS2022

High-resolution remote sensing and multistate occupancy estimation identify drivers of spawning site selection in fall chum salmon (Oncorhynchus keta) across a sub-Arctic riverscape

Groundwater upwellings provide warmer, stable overwinter temperatures for developing salmon embryos, which may be particularly important in cold, braided, gravel-bed sub-Arctic rivers. We used a three-year time series of aerial counts and remote sensing to estimate the distribution of low and high aggregations of spawning fall chum salmon ( Oncorhynchus keta ), classify approximately 0.5 km long river segments by geomorphic channel type, and map thermal variability along a 25.4 km stretch of the Teedriinjik River, Alaska. We used a dynamic multistate occupancy model to estimate detectability, occupancy, and the dynamics of spawning aggregations among river segments. Detectability was higher for large (>150) relative to smaller aggregations. Unoccupied segments were likely to remain so from year to year; low abundance spawning segments were dynamic and rarely remained in that state for multiple years, while ∼20%–35% of high abundance segments remained stable, indicating the presence of high-quality spawning habitat. Spawning habitat use was associated with warmer water temperatures likely caused by groundwater upwellings. We identified spawning habitat characteristics and trends in usage by fall chum salmon, which will inform land management decisions and assist in evaluating impacts of shifting climate conditions and resource management on Arctic salmon populations.

Alaska↗

USGS1993

Glaciers of Europe

This chapter, consisting of six independently authored subchapters, including one subchapter, Glaciers of the Alps, that has four independently authored sections, is the fourth to be released in U.S. Geological Survey Professional Paper 1386, Satellite Image Atlas of Glaciers of the World, a series of 11 chapters. In each chapter, remotely sensed images, primarily from the Landsat 1, 2, and 3 series of spacecraft, are used to study the glacierized regions of our planet and monitor glacier changes. Landsat images, acquired primarily during the middle to late 1970’s, were used by an international team of glaciologists and other scientists to study various geographic areas or discuss glaciological topics. In each geographic area the present areal distribution of glaciers was compared, where possible, with historical information about their past extent. The atlas provides an accurate regional inventory of the areal extent of glacier ice on our planet during the 1970’s as part of a growing international scientific effort to measure global environmental change on the Earth’s surface.

Professional Paper↗

USGS2022

A novel regression method for harmonic analysis of time series

Harmonic analysis of time series is an important technique in remote sensing to reveal seasonal land surface dynamics. However, frequency selection in the harmonic analysis is often difficult because high-frequency components are useful for delineating seasonal dynamics but sensitive to noise and gaps in time series. On the other hand, it is challenging to obtain temporally continuous satellite data with high quality because of atmospheric contamination. We developed a novel regression method named Harmonic Adaptive Penalty Operator (HAPO) for harmonic analysis of unevenly distributed time series. We introduced a new penalty function to minimize unexpected fluctuations in the model, which can substantially reduce the overfitting issue of regression in time series with temporal gaps. Specifically, the new penalty function minimizes the length of the model curve and the value range difference between the model and the time series observations. We compared HAPO with three widely used regression methods (OLS: Ordinary Least Squares; LASSO: Least Absolute Shrinkage and Selection Operator; and Ridge) in different scenarios using Landsat time series data across the United States. First, we evaluated methods using the Landsat surface reflectance time series within a single year. HAPO showed low and consistent monthly Root Mean Square Deviation (RMSD) values, in which most of the time RMSD of predicted reflectance were less than 0.04. More importantly, HAPO showed consistent and less bias given varying density and irregularity of time series. Second, we evaluated methods using multi-year time series. HAPO was a better predictor of relatively short time series (< 4 years) with steady low RMSD values. When a longer time series ( 4 years) was used, all four methods showed similar RMSD values, but HAPO outperformed the other methods if there were temporal gaps. Therefore, for places with large seasonal observation gaps or for time series that are relatively short (less than 4 years), HAPO can provide more consistent and accurate results in harmonic analysis of time series.

ISPRS Journal of Photogrammetry and Remote Sensing↗

USGS2022

What determines the effectiveness of Pinyon-Juniper clearing treatments? Evidence from the remote sensing archive and counter-factual scenarios

In the intermountain western US, expansion of Pinyon ( Pinus edulis) and Juniper ( Juniperus spp. ) woodlands (PJ) into grasslands and shrublands is a pervasive phenomenon, and an example of the global trend towards enhanced woody growth in drylands. Due to the perceived impacts of these expansions on ecosystem services related to biodiversity, hydrology, soil stability, fire prevention, and livestock forage, mechanical and chemical PJ reduction treatments have been a long-standing practice in the region. More recently, PJ reduction practices have come under enhanced public scrutiny, due to potential impacts on PJ-dependent wildlife, risk of erosion due to soil disturbance, and cost effectiveness due to variable rates of long-term success. Moreover, there is growing interest in understanding the biotic, abiotic, and management conditions under which PJ reduction treatments are effective. Here, we evaluated PJ reduction treatment outcomes leveraging large, curated databases of land treatments, new remotely sensed fractional cover time-series products, gridded climate and soils data, and analytical approaches adopted from the econometric literature. From 302 treatment events and 1569 distinct treatment polygons we found evidence that treatments reduced tree cover and largely increased shrub and perennial herbaceous cover for 10 or more years. However, treatments were also associated with increases in annual, likely non-native, herbaceous cover . Importantly, we noted treatment outcomes varied by landscape context, with some soil and geomorphic settings exhibiting consistent returns to pre-treatment conditions within 10–15 years, and others exhibiting more persistent changes in functional type composition. Despite the overall trends we observed, there was considerable unexplained variability in outcomes from treatment to treatment, highlighting the need for caution and attention to local geomorphic and biological context in planning future treatments.

Arizona, Colorado, New Mexico, Utah↗

USGS2005

Mars exploration rover geologic traverse by the spirit rover in the plains of Gusev crater, Mars

The Spirit rover completed a 2.5 km traverse across gently sloping plains on the floor of Gusev crater from its location on the outer rim of Bonneville crater to the lower slopes of the Columbia Hills, Mars. Using the Athena suite of instruments in a transect approach, a systematic series of overlapping panoramic mosaics, remote sensing observations, surface analyses, and trenching operations documented the lateral variations in landforms, geologic materials, and chemistry of the surface throughout the traverse, demonstrating the ability to apply the techniques of field geology by remote rover operations. Textures and shapes of rocks within the plains are consistent with derivation from impact excavation and mixing of the upper few meters of basaltic lavas. The contact between surrounding plains and crater ejecta is generally abrupt and marked by increases in clast abundance and decimeter-scale steps in relief. Basaltic materials of the plains overlie less indurated and more altered rock types at a time-stratigraphic contact between the plains and Columbia Hills that occurs over a distance of one to two meters. This implies that regional geologic contacts are well preserved and that Earth-like field geologic mapping will be possible on Mars despite eons of overturn by small impacts.

Geology↗

USGS2013

Phenology-based, remote sensing of post-burn disturbance windows in rangelands

Wildland fire activity has increased in many parts of the world in recent decades. Ecological disturbance by fire can accelerate ecosystem degradation processes such as erosion due to combustion of vegetation that otherwise provides protective cover to the soil surface. This study employed a novel ecological indicator based on remote sensing of vegetation greenness dynamics (phenology) to estimate variability in the window of time between fire and the reemergence of green vegetation. The indicator was applied as a proxy for short-term, post-fire disturbance windows in rangelands; where a disturbance window is defined as the time required for an ecological or geomorphic process that is altered to return to pre-disturbance levels. We examined variability in the indicator determined for time series of MODIS and AVHRR NDVI remote sensing data for a database of ∼100 historical wildland fires, with associated post-fire reseeding treatments, that burned 1990–2003 in cold desert shrub steppe of the Great Basin and Columbia Plateau of the western USA. The indicator-based estimates of disturbance window length were examined relative to the day of the year that fires burned and seeding treatments to consider effects of contemporary variability in fire regime and management activities in this environment. A key finding was that contemporary changes of increased length of the annual fire season could have indirect effects on ecosystem degradation, as early season fires appeared to result in longer time that soils remained relatively bare of the protective cover of vegetation after fires. Also important was that reemergence of vegetation did not occur more quickly after fire in sites treated with post-fire seeding, which is a strategy commonly employed to accelerate post-fire vegetation recovery and stabilize soil. Future work with the indicator could examine other ecological factors that are dynamic in space and time following disturbance – such as nutrient cycling, carbon storage, microbial community composition, or soil hydrology – as a function of disturbance windows, possibly using simulation modeling and historical wildfire information.

California;Idaho;Nevada;Oregon;Utah;Washington↗

USGS2007

Color shaded-relief and surface-classification maps of the Fish Creek Area, Harrison Bay Quadrangle, Northern Alaska

The northeastern part of the National Petroleum Reserve in Alaska (NPRA) has become an area of active petroleum exploration during the past five years. Recent leasing and exploration drilling in the NPRA requires the U.S. Bureau of Land Management (BLM) to manage and monitor a variety of surface activities that include seismic surveying, exploration drilling, oil-field development drilling, construction of oil-production facilities, and construction of pipelines and access roads. BLM evaluates a variety of permit applications, environmental impact studies, and other documents that require rapid compilation and analysis of data pertaining to surface and subsurface geology, hydrology, and biology. In addition, BLM must monitor these activities and assess their impacts on the natural environment. Timely and accurate completion of these land-management tasks requires elevation, hydrologic, geologic, petroleum-activity, and cadastral data, all integrated in digital formats at a higher resolution than is currently available in nondigital (paper) formats. To support these land-management tasks, a series of maps was generated from remotely sensed data in an area of high petroleum-industry activity (fig. 1). The maps cover an area from approximately latitude 70°00' N. to 70°30' N. and from longitude 151°00' W. to 153°10' W. The area includes the Alpine oil field in the east, the Husky Inigok exploration well (site of a landing strip) in the west, many of the exploration wells drilled in NPRA since 2000, and the route of a proposed pipeline to carry oil from discovery wells in NPRA to the Alpine oil field. This map area is referred to as the "Fish Creek area" after a creek that flows through the region. The map series includes (1) a color shaded-relief map based on 5-m-resolution data (sheet 1), (2) a surface-classification map based on 30-m-resolution data (sheet 2), and (3) a 5-m-resolution shaded relief-surface classification map that combines the shaded-relief and surface-classification data (sheet 3). Remote sensing datasets that were used to compile the maps include Landsat 7 Enhanced Thematic Mapper+ (ETM+), and interferometric synthetic aperture radar (IFSAR) data. In addition, a 1:250,000-scale geologic map of the Harrison Bay quadrangle, Alaska (Carter and Galloway, 1985, 2005) was used in conjunction with ETM+ and IFSAR data.

Alaska↗

USGS2017

Increased sediment load during a large-scale dam removal changes nearshore subtidal communities

The coastal marine ecosystem near the Elwha River was altered by a massive sediment influx—over 10 million tonnes—during the staged three-year removal of two hydropower dams. We used time series of bathymetry, substrate grain size, remotely sensed turbidity, scuba dive surveys, and towed video observations collected before and during dam removal to assess responses of the nearshore subtidal community (3 m to 17 m depth). Biological changes were primarily driven by sediment deposition and elevated suspended sediment concentrations. Macroalgae, predominantly kelp and foliose red algae, were abundant before dam removal with combined cover levels greater than 50%. Where persistent sediment deposits formed, macroalgae decreased greatly or were eliminated. In areas lacking deposition, macroalgae cover decreased inversely to suspended sediment concentration, suggesting impacts from light reduction or scour. Densities of most invertebrate and fish taxa decreased in areas with persistent sediment deposition; however, bivalve densities increased where mud deposited over sand, and flatfish and Pacific sand lance densities increased where sand deposited over gravel. In areas without sediment deposition, most invertebrate and fish taxa were unaffected by increased suspended sediment or the loss of algae cover associated with it; however, densities of tubeworms and flatfish, and primary cover of sessile invertebrates increased suggesting benefits of increased particulate matter or relaxed competition with macroalgae for space. As dam removal neared completion, we saw evidence of macroalgal recovery that likely owed to water column clearing, indicating that long-term recovery from dam removal effects may be starting. Our results are relevant to future dam removal projects in coastal areas and more generally to understanding effects of increased sedimentation on nearshore subtidal benthic communities.

Washington↗

USGS2003

Monitoring vegetation phenology using MODIS

Accurate measurements of regional to global scale vegetation dynamics (phenology) are required to improve models and understanding of inter-annual variability in terrestrial ecosystem carbon exchange and climate–biosphere interactions. Since the mid-1980s, satellite data have been used to study these processes. In this paper, a new methodology to monitor global vegetation phenology from time series of satellite data is presented. The method uses series of piecewise logistic functions, which are fit to remotely sensed vegetation index (VI) data, to represent intra-annual vegetation dynamics. Using this approach, transition dates for vegetation activity within annual time series of VI data can be determined from satellite data. The method allows vegetation dynamics to be monitored at large scales in a fashion that it is ecologically meaningful and does not require pre-smoothing of data or the use of user-defined thresholds. Preliminary results based on an annual time series of Moderate Resolution Imaging Spectroradiometer (MODIS) data for the northeastern United States demonstrate that the method is able to monitor vegetation phenology with good success.

Remote Sensing of Environment↗

USGS2018

Spatiotemporal remote sensing of ecosystem change and causation across Alaska

Contemporary climate change in Alaska has resulted in amplified rates of press and pulse disturbances that drive ecosystem change with significant consequences for socio‐environmental systems. Despite the vulnerability of Arctic and boreal landscapes to change, little has been done to characterize landscape change and associated drivers across northern high‐latitude ecosystems. Here we characterize the historical sensitivity of Alaska's ecosystems to environmental change and anthropogenic disturbances using expert knowledge, remote sensing data, and spatiotemporal analyses and modeling. Time‐series analysis of moderate—and high‐resolution imagery was used to characterize land‐ and water‐surface dynamics across Alaska. Some 430,000 interpretations of ecological and geomorphological change were made using historical air photos and satellite imagery, and corroborate land‐surface greening, browning, and wetness/moisture trend parameters derived from peak‐growing season Landsat imagery acquired from 1984 to 2015. The time series of change metrics, together with climatic data and maps of landscape characteristics, were incorporated into a modeling framework for mapping and understanding of drivers of change throughout Alaska. According to our analysis, approximately 13% (~174,000 ± 8700 km 2 ) of Alaska has experienced directional change in the last 32 years (±95% confidence intervals). At the ecoregions level, substantial increases in remotely sensed vegetation productivity were most pronounced in western and northern foothills of Alaska, which is explained by vegetation growth associated with increasing air temperatures. Significant browning trends were largely the result of recent wildfires in interior Alaska, but browning trends are also driven by increases in evaporative demand and surface‐water gains that have predominately occurred over warming permafrost landscapes. Increased rates of photosynthetic activity are associated with stabilization and recovery processes following wildfire, timber harvesting, insect damage, thermokarst, glacial retreat, and lake infilling and drainage events. Our results fill a critical gap in the understanding of historical and potential future trajectories of change in northern high‐latitude regions.

Alaska↗

USGS2021

System characterization report on the China-Brazil Earth Resources Satellite-4A (CBERS–4A)

Executive Summary This report addresses system characterization of the China-Brazil Earth Resources Satellite-4A (CBERS–4A) multispectral remote sensing satellite and is part of a series of system characterization reports produced and delivered by the U.S. Geological Survey Earth Resources Observation and Science Cal/Val Center of Excellence in 2021. These reports present and detail the methodology and procedures for characterization; present technical and operational information about the specific sensing system being evaluated; and provide a summary of test measurements, data retention practices, data analysis results, and conclusions. CBERS–4A is a joint Chinese-Brazilian medium-resolution satellite launched in December 2019 by the China National Space Agency/National Institute for Space Research (Brazil) on a Chang Zheng 4B rocket from the Taiyuan Satellite Launch Center for Earth resources monitoring. The CBERS–4A mission continues the CBERS mission that has been in continual operation since the launch of CBERS–1 in 1999. The CBERS–4A satellite was designed and built by Academia Chinesa de Tecnologia Espacial/National Institute for Space Research and uses the Phoenix-Eye bus. CBERS–4A carries the multispectral camera and wide field imager sensors for medium-resolution land imaging and the wide swath panchromatic and multispectral camera sensor for high-resolution land imaging. This assessment focused on the multispectral camera sensor only. More information on CBERS sensors is available in the “ 2020 Joint Agency Commercial Imagery Evaluation—Remote Sensing Satellite Compendium ” and at https://www.gov.br/pt-br/servicos/obter-imagens-de-sensoriamento-remoto-da-terra-geradas-pelo-satelite-cbers-04a . The Earth Resources Observation and Science Cal/Val Center of Excellence system characterization team completed data analyses to characterize the geometric (interior and exterior), radiometric, and spatial performances. Results of these analyses indicate that CBERS–4A provides an interior (band-to-band) geometric performance in the range of −0.02 to −0.16 pixel; an exterior geometric accuracy performance of −22.02 (−1.47 pixels) to −16.06 meters (−1.07 pixels); a radiometric accuracy performance of –0.006 to 0.925 (offset and slope); and a spatial performance for relative edge response in the range of 0.39 to 0.44, for full width at half maximum in the range of 2.38 to 2.56 pixels, and for a modulation transfer function at a Nyquist frequency in the range of 0.001 to 0.013.

Open-File Report↗