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54 records · Page 3Linked to original sources
USGS2025

Hydrologic variability and plant composition drive relative abundance of marsh birds at created and reference marshes in southeastern Louisiana, U.S.A.

Coastal marsh loss occurs at an alarming pace globally, with extremely high rates along the northern Gulf of Mexico, particularly in Louisiana. In Louisiana, marsh creation projects combat wetland loss; however, biotic responses of vegetation and wildlife receive little to no consideration during and after construction. Habitat characteristics such as hydrologic processes, plant composition, and habitat structure affect the abundance of marsh birds, and understanding these features is important when creating suitable habitat for marsh birds. Our study compared hydrologic characteristics, plant composition, and habitat structure between created ( n = 10) and reference ( n = 9) sites across southeastern Louisiana and determined the relationship of these habitat characteristics to marsh bird relative abundance. We performed bird surveys ( n = 766), including call-back surveys for secretive marsh birds, at all sites across three breeding seasons (2021–2023). We used drone imagery to determine plant composition and used water level recording devices to assess hydrologic characteristics. Our results indicate that hydrologic variation and plant composition are drivers of marsh bird relative abundance regardless of whether a marsh is created or not. While some habitat features differed between created and reference sites, our results indicated that created marshes can support similar abundances of marsh birds as reference sites, depending on what habitat features are present at the site. Our study demonstrates the importance of creating marshes that promote hydrologic connectivity and water level variability, which in turn supports diverse emergent vegetation communities and provides suitable habitat for a variety of marsh bird species.

Louisiana↗
USGS2023

The 2022 Hunga-Tonga megatsunami: Near-field simulation of a once-in-a-century event

The Hunga Tonga–Hunga Ha’apai (HTHH) volcanic eruption in January 2022 generated catastrophic tsunami and contends for the largest natural explosion in more than a century. The main island, Tongatapu, suffered waves up to 17 m, and Tofua Island suffered waves up to 45 m, comfortably placing HTHH in the “megatsunami” league. We present a tsunami simulation of the Tongan Archipelago calibrated by field observations, drone, and satellite data. Our simulation emphasizes how the complex shallow bathymetry of the area acted as a low-velocity wave trap, capturing tsunami for more than 1 hour. Despite its size and long duration, few lives were lost. Simulation suggests that HTHH’s location relative to urban centers saved Tonga from a worse outcome. Whereas 2022 seems to have been a lucky escape, other oceanic volcanoes have the capacity to spawn future tsunami at HTHH scale. Our simulation amplifies the state of understanding of volcanic explosion tsunami and provides a framework for assessment of future hazards.

Science Advances↗
USGS2021

Honey bee (Apis mellifera) colonies benefit from grassland/ pasture while bumble bee (Bombus impatiens) colonies in the same landscapes benefit from non-corn/soybean cropland

Agriculturally important commercially managed pollinators including honey bees ( Apis mellifera L., 1758) and bumble bees ( Bombus impatiens Cresson, 1863) rely on the surrounding landscape to fulfill their dietary needs. A previous study in Europe demonstrated that managed honey bee foragers and unmanaged native bumble bee foragers are associated with different land uses. However, it is unclear how response to land use compares between managed honey bees and a managed native bumble bee species in the United States, where honey bees are an imported species. Furthermore, to our knowledge, no such direct comparisons of bee responses to land use have been made at the colony level. To better understand how two different social bees respond to variation in land use, we monitored the weights of A . mellifera and B . impatiens colonies placed in 12 apiaries across a range of land use in Michigan, United States in 2017. Bombus impatiens colonies gained more weight and produced more drones when surrounded by diverse agricultural land (i.e., non-corn/soybean cropland such as tree fruits and grapes), while honey bee colonies gained more weight when surrounded by more grassland/pasture land. These findings add to our understanding of how different bee species respond to agricultural landscapes, highlighting the need for further species-specific land use studies to inform tailored land management.

Michigan↗
USGS2019

Problems of Large Spatial Databases

Large spatial databases often labeled as geospatial big data exceed the capacity of commonly used computing systems as a result of data volume, variety, velocity, and veracity. Additional problems also labeled with V’s are cited, but the four primary ones are the most problematic and focus of this chapter (Li et al., 2016, Panimalar et al., 2017). Sources include satellites, aircraft and drone platforms, vehicles, geosocial networking services, mobile devices, and cameras. The problems in processing these data to extract useful information include query, analysis, and visualization. Data mining techniques and machine learning algorithms, such as deep convolutional neural networks, often are used with geospatial big data. The obvious problem is handling the large data volumes, particularly for input and output operations, requiring parallel read and write of the data, as well as high speed computers, disk services, and network transfer speeds. Additional problems of large spatial databases include the variety and heterogeneity of data requiring advanced algorithms to handle different data types and characteristics, and integration with other data. The velocity at which the data are acquired is a challenge, especially using today’s advanced sensors and the Internet of Things that includes millions of devices creating data on short temporal scales of micro seconds to minutes. Finally, the veracity, or truthfulness of large spatial databases is difficult to establish and validate, particularly for all data elements in the database.

Book chapter↗
USGS2025

Assessment of coastal and fluvial morphodynamic changes using Structure-for-Motion: A case study of the Sfȃntu Gheorghe Mouth (Danube Delta, Romania)

The ability to accurately map erosion, flooding, and habitat loss in coastal environments is crucial for formulating national strategies aimed at preventing and mitigating the impacts of natural disasters. A fundamental component of this process is the implementation of coastal morphodynamics monitoring through Structure-from-Motion (SfM) techniques, utilizing high-resolution 2D/3D data obtained from aerial photogrammetry. To assess morphodynamic changes over a three-year period (2022 – 2024), several SfM-based photogrammetric studies were conducted, each year, in the Romanian sector of the Danube-Black Sea coastal zone, specifically at the mouth of one of the Danube River distributaries (Sf Gheorghe branch) into the Black Sea, and along the left bank, near Sf Gheorghe locality, located within the Danube Delta Biosphere Reserve (DDBR). The essential equipment for aerial photogrammetry comprises Unmanned Aerial Vehicles (UAVs) and Global Navigation Satellite Systems (GNSS). In this study, the UAV used was a DJI Mavic 3T (Enterprise/Thermal) drone, complemented by two Trimble R12i and R4 GNSS systems, as well as approximately 10 Ground Control Points (GCPs). Data acquisition and processing were carried out using specialized photogrammetric software (Agisoft Metashape) along with various GIS tools (e.g., Blue Marble Geographics Global Mapper and ESRI ArcMap). The photogrammetric products generated for the study, as detailed in this paper, include Digital Elevation Models (DEMs), Digital Terrain Models (DTMs), orthomosaics (orthophotos), and others. At Sfântu Gheorghe beach, a comparison between 2023 and 2024 photogrammetric surveys revealed that the left bank of the Sf. Gheorghe Arm, at the river mouth into the Black Sea, suffered from a twist (erosion) of up to 64 metres. Additionally, on the selected perimetre (total area of 31,910 square meters ) from the beach and dune zone of Sf. Gheorghe, an area of up to 16,202 square meters was eroded between 2023 and 2024. This contrasts with the period between 2022 and 2023, during which deposition predominated. Erosion at the Danube mouths and the adjacent Black Sea coastline is driven by a complex interaction of natural and anthropogenic factors. Natural processes, including subsidence, sea-level rise, and episodic extreme storm events, contribute significantly to coastal dynamics. Meanwhile, human-induced factors, such as upstream hydrotechnical works that limits sediment transport, cutting of navigation canals, as well as the exacerbating effects of climate change, further accelerate erosion. The recent Structure-from-Motion (SfM) surveys provide essential quantitative data, enabling a detailed analysis of both short-term and long-term morphodynamic changes influenced by seasonal variations and extreme hydrometeorological events in this highly dynamic coastal system.

Sfȃntu Gheorghe Mouth (Danube Delta)↗
USGS2024

Landsat geometric and radiometric calibration and characterization

The U.S. Geological Survey (USGS) 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 ( Haque and others, 2024 ). The accuracy of the ECCOE team calibration adjustments gives other civil and commercial satellite programs around the globe a trusted criterion and reference point. The ECCOE team works with U.S. and international government agencies and commercial vendors to help harmonize data sources as more frequent, consistent views of Earth benefits scientific research. Since the program started, more than 50 years ago, Landsat data have improved. When advances in calibration and validation today are applied to past satellite missions, researchers can consistently see how land changes over decades. To maintain this criterion, the ECCOE team continues to seek new and better ways to calibrate and validate data, which includes using the moon for calibration and drones for ground validation (fig. 1).

Fact Sheet↗
USGS2019

Earth as art 6

Earth has a stunning variety of landscapes. The colors, patterns, textures, and shapes all make for intriguing artwork as seen from the perspective of space. Earth As Art shows not only what satellites capture in the visible wavelengths of light you and I can see, but also what’s hiding in the invisible wavelengths that Landsat sensors can detect in the infrared part of the electromagnetic spectrum. Those combinations can bring out much more scientific value, but also can produce imagery of breathtaking beauty. Earth As Art 6 even includes images from U.S. Geological Survey (USGS) Unmanned Aircraft Systems (UAS), commonly known as drones. Sensors attached to a UAS also capture visible and infrared light and have proven their value at monitoring change over time alongside their spaceborne partners. Besides, their images look great, too. Enjoy the latest from Earth As Art! https://eros.usgs.gov/image-gallery/earth-art-6

General Information Product↗
USGS2007

Sm-Nd and U-Pb isotopic constraints for crustal evolution during Late Neoproterozic from rocks of the Schirmacher Oasis, East Antarctica: geodynamic development coeval with the East African Orogeny

Recent post-750 Ma continental reconstructions constrain models for East African Orogeny formation and also the scattered remnants of ~640 Ma granulites, whose genesis is controversial. One such Neoproterozoic granulite belt is the Schirmacher Oasis in East Antarctica, isolated from the distinctly younger Pan-African orogen to the south in the central Droning Maud Land. To ascertain the duration of granulite-facies events in these remnants, garnet Sm-Nd and monazite and titanite U-Pb IDTIMS geochronology was carried out on a range of metamorphic rocks. Garnet formation ages from a websterite enclave and gabbro were 660±48 Ma and 587±9 Ma respectively, and those from Stype granites were 598±4 Ma and 577±4 Ma. Monazites from metapelite and metaquartzite yielded lower intercept UPb ages of 629±3 Ma and 639±5 Ma, respectively. U-Pb titanite age from calcsilicate gneiss was 580±5 Ma. These indicate peak metamorphism to have occurred between 640 and 630 Ma, followed by near isobaric cooling to ~580 Ma. Though an origin as an exotic terrane from the East African Orogen cannot be discounted, from the present data there is a greater likelihood that Mesoproterozoic microplate collision between Maud orogen and a northerly Lurio-Nampula block resulted in formation of these granulite belt(s).

Open-File Report↗
USGS2018

Promoting synergy in the innovative use of environmental data—Workshop summary

From December 2 to 4, 2015, NatureServe and the U.S. Geological Survey organized and hosted a biodiversity and ecological informatics workshop at the U.S. Department of the Interior in Washington, D.C. The workshop objective was to identify user-driven future directions and areas of collaboration in advanced applications of environmental data applied to forecasting and decision making for the sustainability of biodiversity and ecosystem services. Substantial effort to recruit attendees from diverse Federal, State, and private sector organizations successfully attracted participants from 20 Federal agencies and 48 different institutions in the academic, nonprofit, State government, and commercial sectors; the total number of attendees ranged from 100 to 144 during the 3-day workshop. The first one-half of the workshop was divided into 7 plenary sessions and 3 sets of lightning talk sessions organized by sector, providing 48 oral and visual plenary presentations that shared diverse perspectives on biodiversity and ecological informatics, including original biospatial analyses from 6 graduate student map contest winners. The second one-half of the workshop focused on 10 breakout sessions with participant-driven themes from the environmental data sphere and concluded with an address by the Director of the U.S. Fish and Wildlife Service. The workshop was structured to encourage interactivity. About 80–90 percent of attendees provided direct feedback using clicker devices for specific questions related to biodiversity and ecological data uses and needs, and 10 breakout session leaders shared the highlights of their group discussions during the final workshop plenary sessions. Participants were encouraged to use the Twitter hashtag #ShareUrData. Over lunch on day 2 there were 20 simultaneous presentations of tools and apps during a special “Tools Café” session. The 10 participant-defined breakout session topics are listed below: Ecosystem services and ecological indicators Inventory and monitoring Biogeographic map of the Nation Pollinators Invasive species Remote sensing Drivers of agricultural change Citizen science Climate Hydrology and watersheds Numerous common themes that emerged from the workshop include the following: The vital importance of completing foundational environmental datasets that are nationally consistent and are essential to multiple sectors, such as the Soil Survey Geographic database high-resolution soils data, a minimum 5-meter resolution digital elevation model, national hydrographic data, high-resolution land cover data, time series high-resolution spatial climate data from historical to future time steps, and a national wetland inventory. Improved, nationally consistent environmental datasets (integrated with targeted observations) will dramatically advance forecasting capacity and support early warning systems (that is, drought, forest disease); however, multiagency coordination should focus on decision support tools that convey appropriate actions and responses to adapt to, and mitigate, potential negative consequences. Digitizing and providing access to the vast stores of underused historical data that can be leveraged for this purpose is of national importance. Modern computational techniques and the ever-increasing flow of environmental data from ground and remote observations can support improved understanding of environmental change. Success of understanding patterns of change for decision making requires establishing baselines from which change can be measured. The value of digitized historical data is greater than ever before. There is a need to recognize the multifaceted potential of citizen science to engage the public in resource stewardship, to create the next generation of science, technology, engineering, math, and environmental leaders, and to have sufficient field personnel to monitor environmental trends, including early detection of alien invasive species, phenological shifts, shifting distribution and abundance of indicator species, and species inventories. The Federal government has an essential role in creating the infrastructure to dramatically improve mobilization of citizen science (and other) data by fostering the following: creation of data standards, creation of nationally consistent framework datasets, vertical integration of observation data, visualization and dissemination of aggregated datasets, and calculation and communication of derived trends. Current and near future trends in the availability of remotely sensed data (rapid expansion of satellite fleets and drones) is revolutionizing access to near-real-time ecological data. Targeted integration with ground-based observations and instrumentation has an extremely valuable role in validating remotely sensed data, filling data gaps, improving data quality, and fully realizing the potential of the near-real-time monitoring of environmental indicator trends. Integrated management of environmental data at the landscape scale is required even as specific actions on the ground are largely local in nature. The workshop highlighted numerous success stories; however, almost every breakout group pointed out the still-too-fragmented nature of the current data landscape. Management and delivery of the necessary data, tools, and analyses to sustain our Nation’s environmental capital must be a collaborative effort between Federal, State, and local governments, academia, nonprofits, and the commercial sector, even though the responsibilities of each sector are different.

Open-File Report↗
USGS2018

Shoreline erosion at selected areas along Lake Sharpe on the Lower Brule Reservation in South Dakota, 1966–2015

The Lower Brule Reservation in central South Dakota is losing land because of shoreline erosion along Lake Sharpe, a reservoir on the Missouri River, which has caused detrimental effects for the Lower Brule Sioux Tribe including losses of cultural sites, recreation access points, wildlife habitat, irrigated cropland, and landmass. To better understand and quantify shoreline erosion, the Lower Brule Sioux Tribe and the U.S. Geological Survey cooperated on a series of data-collection efforts and study of shoreline erosion along Lake Sharpe. Data collected or compiled for 1966–2015 were used to describe and quantify shoreline erosion along Lake Sharpe. The progression of shoreline erosion near the community of Lower Brule, South Dakota, was tracked by comparing current or recent aerial imagery with existing historical maps. At 33 evaluation lines along a 7-mile reach of Lake Sharpe shoreline near Lower Brule, cumulative change of shoreline from 1966 to 2010 ranged from about −224 feet of deposition to 770 feet of erosion. Photographic and location data were collected for this study to understand the processes affecting erosion and estimate erosion rates. Photographs were collected only in the 7-mile reach near Lower Brule, but locations of the bank over time were collected at the 7-mile reach and two additional reaches within the Lower Brule Reservation. Global navigation satellite system equipment was used in real-time kinematic mode to collect bank locations along three reaches of interest. Reach-length data were collected four times between November 2011 and November 2012. A small, unmanned aerial system (drone) was used to capture digital video along the shoreline of the 7-mile reach. Water-level fluctuations contribute to the number of wet-dry cycles experienced by the soils at the shoreline or bank. The soils present under the current (2017) location of the reservoir are predominantly terrace alluvium, consisting of sand and silt. Detailed soils data for Lyman County indicate that the dominant soil type along the southern part of the shoreline in the 7-mile reach is Bullcreek clay. Weather within the study area can affect the erosion rate. Air temperature can potentially affect erosion rates by freezing and thawing water and soils. Mean hourly wind speeds vary somewhat throughout the year but averaged 13.3 miles per hour. The direction of prevailing winds near Lower Brule indicates that there are several miles of fetch to build large waves. Annual erosion rates calculated or measured throughout this study varied by location. Long-term annual average erosion rates of the 7-mile reach, as calculated by image analysis, ranged from −5.1 feet per year (deposition) to 17.5 feet per year (erosion). Short-term annual erosion rates measured using global navigation satellite system equipment during 2010–12 ranged from about 0 to 31.7 feet per year for the 7-mile reach. Existing scour countermeasures have been effective variably. Fieldstone rip-rap seems to have stabilized the shoreline, whereas tree strips paralleling the shoreline seem to have slowed erosion.

South Dakota↗
USGS2018

The potential of unmanned aerial systems for sea turtle research and conservation: a review and future directions

The use of satellite systems and manned aircraft surveys for remote data collection has been shown to be transformative for sea turtle conservation and research by enabling the collection of data on turtles and their habitats over larger areas than can be achieved by surveys on foot or by boat. Unmanned aerial vehicles (UAVs) or drones are increasingly being adopted to gather data, at previously unprecedented spatial and temporal resolutions in diverse geographic locations. This easily accessible, low-cost tool is improving existing research methods and enabling novel approaches in marine turtle ecology and conservation. Here we review the diverse ways in which incorporating inexpensive UAVs may reduce costs and field time while improving safety and data quality and quantity over existing methods for studies on turtle nesting, at-sea distribution and behaviour surveys, as well as expanding into new avenues such as surveillance against illegal take. Furthermore, we highlight the impact that high-quality aerial imagery captured by UAVs can have for public outreach and engagement. This technology does not come without challenges. We discuss the potential constraints of these systems within the ethical and legal frameworks which researchers must operate and the difficulties that can result with regard to storage and analysis of large amounts of imagery. We then suggest areas where technological development could further expand the utility of UAVs as data-gathering tools; for example, functioning as downloading nodes for data collected by sensors placed on turtles. Development of methods for the use of UAVs in sea turtle research will serve as case studies for use with other marine and terrestrial taxa.

Endangered Species Research↗
USGS2022

Keeping an eye on water quality from the sky

You can learn a lot about rivers, lakes, estuaries, and oceans by looking down at them from the sky. Scientists use a technique called remote sensing to measure the amount of light or heat energy reflected and emitted from the Earth. Sensors can be on satellites or mounted on airplanes, helicopters, or drones. Scientists use this information to map the quality of water in the San Francisco Bay-Delta estuary. Remote sensing helps scientists see where and when there might be problems for human health or for the plants and animals living in the estuary.

California↗
USGS2020

QCam: sUAS-based doppler radar for measuring river discharge

The U.S. Geological Survey is actively investigating remote sensing of surface velocity and river discharge (discharge) from satellite-, high altitude-, small, unmanned aircraft systems- (sUAS or drone), and permanent (fixed) deployments. This initiative is important in ungaged basins and river reaches that lack the infrastructure to deploy conventional streamgaging equipment. By coupling alternative discharge algorithms with sensors capable of measuring surface velocity, streamgage networks can be established in regions where data collection was previously impractical or impossible. To differentiate from satellite or high-altitude platforms, near-field remote sensing is conducted from sUAS or fixed platforms. QCam is a Doppler (velocity) radar mounted and integrated on a 3DR © Solo sUAS. It measures the along-track surface velocity by spot dwelling in a river cross section at a vertical where the maximum surface velocity is recorded. The surface velocity is translated to a mean-channel (mean) velocity using the probability concept (PC), and discharge is computed using the PC-derived mean velocity and cross-sectional area. Factors including surface-scatterer quality, flight altitude, propwash, wind drift, and sample duration may affect the radar-returns and the subsequent computation of mean velocity and river discharge. To evaluate the extensibility of the method, five science flights were conducted on four rivers of varying size and dynamics and included the Arkansas River, Colorado (CO), USA (two events); Salcha River near Salchaket, Alaska (AK), USA; South Platte River, CO, USA; and the Tanana River, AK, USA. QCam surface velocities and river discharges were compared to conventional streamgaging methods, which represented truth. QCam surface velocities for the Arkansas River, Salcha River, South Platte River, and Tanana River were 1.02 meters per second (m/s) and 1.43 m/s; 1.58 m/s; 0.90 m/s; and 2.17 m/s, respectively. QCam discharges (and percent differences) were 9.48 (0.3%) and 20.3 cubic meters per second (m 3 /s) (2.5%); 62.1 m 3 /s (−10.4%); 3.42 m 3 /s (7.3%), and 1579 m 3 /s (−18.8%). QCam results compare favorably with conventional streamgaging and are a viable near-field remote sensing technology that can be operationalized to deliver real-time surface velocity, mean velocity, and river discharge, if cross-sectional area is available.

Remote Sensing↗
USGS2016

Towards establishing compact imaging spectrometer standards

Remote sensing science is currently undergoing a tremendous expansion in the area of hyperspectral imaging (HSI) technology. Spurred largely by the explosive growth of Unmanned Aerial Vehicles (UAV), sometimes called Unmanned Aircraft Systems (UAS), or drones, HSI capabilities that once required access to one of only a handful of very specialized and expensive sensor systems are now miniaturized and widely available commercially. Small compact imaging spectrometers (CIS) now on the market offer a number of hyperspectral imaging capabilities in terms of spectral range and sampling. The potential uses of HSI/CIS on UAVs/UASs seem limitless. However, the rapid expansion of unmanned aircraft and small hyperspectral sensor capabilities has created a number of questions related to technological, legal, and operational capabilities. Lightweight sensor systems suitable for UAV platforms are being advertised in the trade literature at an ever-expanding rate with no standardization of system performance specifications or terms of reference. To address this issue, both the U.S. Geological Survey and the National Institute of Standards and Technology are eveloping draft standards to meet these issues. This paper presents the outline of a combined USGS/NIST cooperative strategy to develop and test a characterization methodology to meet the needs of a new and expanding UAV/CIS/HSI user community.

International Journal of Experimental Spectroscopi↗
USGS2026

High-resolution transboundary vegetation community maps of the Sonoran and Mojave Desert ecoregion to support critical landscape conservation planning and habitat management needs

We produced a 30-m resolution binational land cover map of Bird Conservation Region 33 (BCR 33) for the U.S. North American Bird Conservation Initiative. The region covers large portions of the Sonoran and Mojave Deserts. The map can support the U.S. Fish and Wildlife Service (FWS) Migratory Bird Program’s recovery planning efforts and constitutes the first known binational land cover dataset spanning sections of the United States–Mexico border and using a consistent classification system for both countries. The mapped region includes 152 distinct land cover classes, covering a total area of 38,421,453 ha (148,345 mi 2 ), of which 13,148,345 ha (52,706 mi 2 ) are located in Mexico and 24,770,640 ha (95,639 mi 2 ) in the United States. We primarily used Landsat 8 (OLI) imagery, supplemented by limited ground surveys from two field campaigns, drone-based aerial data, and existing vegetation classification frameworks from both countries. The classification applied a data-fusion approach integrating 30-m Landsat 8 imagery, decadal phenology metrics from vegetation indices, and a random forest model trained mainly with datasets from a comprehensive national mapping project from the U.S. Geological Survey (USGS) GAP Analysis Project (GAP) and federal wildland fire agencies’ Landscape Fire and Resource Management Planning Tools (LANDFIRE) (GAP/LANDFIRE) [United States side] and the National Institute of Statistics and Geography (INEGI) [Mexico side] as well as land cover maps and opportunistic open-access and field observations. Mapping of the full BCR 33 region was carried out in two phases: 1) Phase I, the prototype map, covered a smaller portion of the transboundary area and identified 31 land cover classes, and 2) Phase II, the full BCR 33 map (refer to Figure 1), which resulted in 152 land cover classes. Using a Random Forest classifier, we achieved an overall prediction accuracy of 92% for the Phase I map and 87% for the Phase II full region map. This slight decrease can be attributed to working on a larger, more complex area with a greater number of land cover classes. No formal validation was conducted, aside from using a subset of the collected field observations and training data to assess model performance during and after training. The training sites were further verified using Google Earth (Google, 2026) imagery. Two undergraduate students who worked for over a year visually inspected imagery and open access public images to confirm each training site during model training using in-house developed, online, visual tools. A portion of this field training data was reserved for model validation, and the corresponding results are to be presented in later sections. The project developed an end-to-end, medium- and fine-resolution remote sensing–based data fusion mapping approach. This effort produced a map (Nagler et al., 2025) and the online tools to support a dynamic, live, online map for visualizing the transboundary vegetation communities in BCR 33. The toolset is currently hosted by the University of Arizona (UofA) Vegetation Index and Phenology (VIP) Lab to support FWS partners (https://vip.arizona.edu/viplab_data_explorer?LCM_BCR33). The online map is designed to allow rapid updates using new training, validation, or correction data, making it dynamic and maintainable. The approach we took established a framework for rapid updating and correction of land cover maps, as the model can be quickly retrained with new field observations, updated training data, or other sources. This enables dynamic mapping and change detection of the region’s vegetation. This framework is an advance in data fusion and crowdsourced mapping of complex, vulnerable regions, providing support to regional stakeholders and the wider user community. This transboundary map can inform the protection, conservation, and restoration of vegetation, habitat, and ecosystems, particularly for threatened and endangered species across the two nations using consistent and harmonized binational mapping systems. Beyond supporting land management decisions and stakeholders in the transboundary desert ecoregions, this BCR 33 mapping effort establishes a foundation for future rapid, low-cost, cross-border land cover mapping that can benefit and advance ecosystem management.

Arizona, Baja California, California, Nevada, Sina↗
USGS2026

A methods framework for evaluating measurement consistency across spectrometers for multispectral uncrewed aerial system vegetation mapping applications

The U.S. Geological Survey collects remote sensing data to support national scientific assessments of natural resources, hazards, and landscape change. Spectrometers and spectroradiometers are essential for gathering point-based spectral measurements used in applications such as uncrewed aerial systems (UAS) multispectral image calibration, validation, and analysis. Evaluating how different instruments perform in laboratory and field environments helps determine whether they provide consistent, interoperable measurements. Such verification can expand access to spectral ground data during UAS operations by allowing scientists to use alternative instruments when budgets, logistics, or field conditions limit options. We propose and test a methodological framework for evaluating spectrometers for measurement consistency during UAS multispectral vegetation mapping applications. There are three central evaluation components to the framework: laboratory, field, and relative to UAS multispectral imagery. By evaluating the instruments in both relatively controlled and uncontrolled environments, we thoroughly examine measurement consistency and when/why measurements may differ. We opportunistically selected two instruments for a case study in a coastal marsh setting: a compact laboratory spectrometer we modified for field use and a field-ready spectroradiometer. The instruments produced consistent measurements in both environments. We found differences between the field spectra and UAS spectra that likely reflect the perspectives of ground vs. aerial data and indicate that further radiometric calibration may be needed.

Massachusetts↗
USGS2024

Detection probability and bias in machine-learning-based unoccupied aerial system non-breeding waterfowl surveys

Unoccupied aerial systems (UASs) may provide cheaper, safer, and more accurate and precise alternatives to traditional waterfowl survey techniques while also reducing disturbance to waterfowl. We evaluated availability and perception bias based on machine-learning-based non-breeding waterfowl count estimates derived from aerial imagery collected using a DJI Mavic Pro 2 on Missouri Department of Conservation intensively managed wetland Conservation Areas. UASs imagery was collected using a proprietary software for automated flight path planning in a back-and-forth transect flight pattern at ground sampling distances (GSDs) of 0.38–2.29 cm/pixel (15–90 m in altitude). The waterfowl in the images were labeled by trained labelers and simultaneously analyzed using a modified YOLONAS image object detection algorithm developed to detect waterfowl in aerial images. We used three generalized linear mixed models with Bernoulli distributions to model availability and perception (correct detection and false-positive) detection probabilities. The variation in waterfowl availability was best explained by the interaction of vegetation cover type, sky condition, and GSD, with more complex and taller vegetation cover types reducing availability at lower GSDs. The probability of the algorithm correctly detecting available birds showed no pattern in terms of vegetation cover type, GSD, or sky condition; however, the probability of the algorithm generating incorrect false-positive detections was best explained by vegetation cover types with features similar in size and shape to the birds. We used a modified Horvitz–Thompson estimator to account for availability and perception biases (including false positives), resulting in a corrected count error of 5.59 percent. Our results indicate that vegetation cover type, sky condition, and GSD influence the availability and detection of waterfowl in UAS surveys; however, using well-trained algorithms may produce accurate counts per image under a variety of conditions.

Missouri↗
USGS2025

Towards mobile wind measurements using joust configured ultrasonic anemometer for applications in gas flux quantification

Small uncrewed aerial systems (sUASs) can be used to quantify emissions of greenhouse and other gases, providing flexibility in quantifying these emissions from a multitude of sources, including oil and gas infrastructure, volcano plumes, wildfire emissions, and natural sources. However, sUAS-based emission estimates are sensitive to the accuracy of wind speed and direction measurements. In this study, we examined how filtering and correcting sUAS-based wind measurements affects data accuracy by comparing data from a miniature ultrasonic anemometer mounted on a sUAS in a joust configuration to highly accurate wind data taken from a nearby eddy covariance flux tower (aka the Tower). These corrections had a small effect on wind speed error, but reduced wind direction errors from 50° to >120° to 20–30°. A concurrent experiment examining the amount of error due to the sUAS and the Tower not being co-located showed that the impact of this separation was 0.16–0.21 ms − 1 "> ms − 1 , a small influence on wind speed errors. Lower wind speed errors were correlated with lower turbulence intensity and higher relative wind speeds. There were also some loose trends in diminished wind direction errors at higher relative wind speeds. Therefore, to improve the quality of sUAS-based wind measurements, our study suggested that flight planning consider optimizing conditions that can lower turbulence intensity and maximize relative wind speeds as well as include post-flight corrections.

Alaska↗