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Results for “Remote Sensing”

Search indexed USGS publications on groundwater, aquifers, geologic maps, mineral resources and earthquakes. Explore source records by subject and place.

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At least 361 records · Page 20Linked to original sources

USGS2004

Dispersal forcing of a southern California river plumes, based on field and remote sensing observations

River plumes are important pathways of terrestrial materials entering the sea. In southern California, rivers are known to be the dominant source of littoral, shelf and basin sediment and coastal pollution, although a basic understanding of the dynamics of these river inputs does not exist. Here we evaluate forcing parameters of a southern California river plume using ship-based hydrographic surveys and satellite remote sensing measurements to provide the first insights of river dispersal dynamics in southern California. Our results suggest that plumes of the Santa Clara River are strongly influenced by river inertia, producing jet-like structures ~10 km offshore during annual recurrence (~two-year) flood events and ~30 km during exceptional (~10-year recurrence) floods. Upwelling-favorable winds may be strong following stormwater events and can alter dispersal pathways of thse plumes. Due to similar runoff relationships and other reported satellite observations, we hypothesize that interia-dominated dispersal may be an important characteristic of the small, mountainous rivers throughout southern California.

California↗

USGS2012

A simple method for estimating basin-scale groundwater discharge by vegetation in the basin and range province of Arizona using remote sensing information and geographic information systems

Groundwater is a vital water resource in the arid to semi-arid southwestern United States. Accurate accounting of inflows to and outflows from the groundwater system is necessary to effectively manage this shared resource, including the important outflow component of groundwater discharge by vegetation. A simple method for estimating basin-scale groundwater discharge by vegetation is presented that uses remote sensing data from satellites, geographic information systems (GIS) land cover and stream location information, and a regression equation developed within the Southern Arizona study area relating the Enhanced Vegetation Index from the MODIS sensors on the Terra satellite to measured evapotranspiration. Results computed for 16-day composited satellite passes over the study area during the 2000 through 2007 time period demonstrate a sinusoidal pattern of annual groundwater discharge by vegetation with median values ranging from around 0.3 mm per day in the cooler winter months to around 1.5 mm per day during summer. Maximum estimated annual volume of groundwater discharge by vegetation was between 1.4 and 1.9 billion m 3 per year with an annual average of 1.6 billion m 3 . A simplified accounting of the contribution of precipitation to vegetation greenness was developed whereby monthly precipitation data were subtracted from computed vegetation discharge values, resulting in estimates of minimum groundwater discharge by vegetation. Basin-scale estimates of minimum and maximum groundwater discharge by vegetation produced by this simple method are useful bounding values for groundwater budgets and groundwater flow models, and the method may be applicable to other areas with similar vegetation types.

Arizona↗

USGS2015

Global view of remote sensing of rangelands: Evolution, applications, future pathways

The term “rangeland” is rather nebulous, and there is no single definition of rangeland that is universally accepted by land managers, scientists, or international bodies (Lund, 2007; Reeves and Mitchell, 2011). Dozens and possibly hundreds (Lund, 2007) of definitions and ideologies exist because various stakeholders often have unique objectives requiring different information. For the purpose of describing the role of remote sensing in a global context, it is, however, necessary to provide definitions to orient the reader. The Food and Agricultural Organization (FAO) of the United Nations convened a conference in 2002 and again in 2013 to begin addressing the issue of harmonizing definitions of forest- related activities. Based on this concept, here rangelands are considered lands usually dominated by nonforest vegetation. The Society for Range Management defines rangelands as (SRM, 1998) Land on which the indigenous vegetation (climax or natural potential) is predominantly grasses, grass-like plants, forbs, or shrubs and is managed as a natural ecosystem. If plants are introduced, they are managed similarly. Rangelands include natural grasslands, savannas, shrublands, many deserts, tundra, alpine communities, marshes, and wet meadows.

Book chapter↗

USGS2022

Remote sensing of field-scale irrigation withdrawals in the central Ogallala aquifer region

For agricultural areas facing water scarcity, sustainable water use policy relies on irrigation information that is timely and at a high resolution, but existing publicly available water use data are often insufficient for monitoring compliance or understanding the influence of policy on individual farmer decisions. This study attempts to fill this data gap by using remote sensing to map annual irrigation quantity at the field-scale within the central Ogallala aquifer region of the United States. We compiled in situ annual irrigation volume data at the field scale in the Republican River Basin of Colorado for 2015–2018 and at the Public Land Survey System (PLSS) section scale in western Kansas for 2000–2016, which served as reference data in random forest models that relied on Landsat-based actual evapotranspiration from the Operational Simplified Surface Energy Balance model (SSEBop) along with maps of irrigated area, Landsat spectral indices, climate, soils, and derived hydrologic variables. The models explained 87% of the variability in irrigation volume in Colorado and 75% in Kansas, but accuracy declined when transferring the models in spatial cross-validation (Colorado R 2 =0.81; Kansas R 2 =0.51) and temporal cross-validation (Colorado R 2 =0.82; Kansas R 2 =0.68). Predicted annual totals of irrigation volume in western Kansas had a mean absolute error of 11.9%, which was slightly higher than the average annual change of 11%. Use of predicted irrigation maps also lead to an underestimated effect size for a water use restriction policy in Kansas. These results indicate that field- and section-scale irrigation can be mapped with reasonable accuracy within a region and time period that has adequate sample data, but that methods may need to be improved for applying the models more broadly in areas that lack extensive in situ irrigation data to support further research on water use and aid in structuring policy.

Colorado Kansas↗

USGS2021

Mapping the vulnerability of giant sequoias after extreme drought in California using remote sensing

Between 2012 and 2016, California suffered one of the most severe droughts on record. During this period Sequoiadendron giganteum (giant sequoias) in the Sequoia and Kings Canyon National Parks (SEKI), California, USA experienced canopy water content (CWC) loss, unprecedented foliage senescence, and, in a few cases, death. We present an assessment of the vulnerability of giant sequoia populations to droughts that is currently lacking and needed for management. We used a temporal trend of remotely sensed CWC obtained between 2015 and 2017, and recently georeferenced giant sequoia crowns to quantify the vulnerability of 7,408 individuals in 10 groves in the northern portion of SEKI. CWC is sensitive to changes in liquid water in tree canopies; therefore, it is a useful metric for quantifying the response of sequoia trees to drought. Temporal trends indicated that 9% of giant sequoias had a significant decline or consistently low CWC, suggesting these trees were likely operating at low photosynthetic capacity and potentially at high risk to drought stress. We also found that 20% of the giant sequoias had an increase or consistently high level of CWC, indicating these trees were at low risk to drought stress. These vulnerability categories were used in a random forest model with a combination of topographic, fire-related, and climate variables to generate high-resolution vulnerability risk maps. These maps show that higher risk is associated with lower elevation and higher climate water deficit. We also found that sequoias at higher elevations but located near meadows had higher vulnerability risk. These results and the vulnerability maps can identify vulnerable sequoias that may be difficult to save or locations of refugia to be protected, and thus may aid forest managers in preparation for future droughts.

California↗

USGS1981

Objectives, accomplishments, and future plans of IGCP project 143, remote sensing and mineral exploration

The International Geological Correlation Programme (IGCP) is a worldwide cooperative research programme that began in 1974 under the auspices of the International Union of Geological Sciences. Because of the global availability of Earth resources data collected by satellites and the great interest among geologists in taking advantage of these new sources of information, a project was begun in 1976 to improve the rate of technology transfer in the field of remote-sensing exploration for energy and mineral resources. Conducting joint workshops in cooperation with COSPAR has been an important part of this project. It is to be hoped the project will improve our capability to explore, identify, and develop new resources to meet the burgeoning demands of society. ?? 1981.

Advances in Space Research↗

USGS2012

Wetland fire remote sensing research--The Greater Everglades example

Fire is a major factor in the Everglades ecosystem. For thousands of years, lightning-strike fires from summer thunderstorms have helped create and maintain a dynamic landscape suited both to withstand fire and recover quickly in the wake of frequent fires. Today, managers in the Everglades National Park are implementing controlled burns to promote healthy, sustainable vegetation patterns and ecosystem functions. The U.S. Geological Survey (USGS) is using remote sensing to improve fire-management databases in the Everglades, gain insights into post-fire land-cover dynamics, and develop spatially and temporally explicit fire-scar data for habitat and hydrologic modeling.

Florida↗

USGS2025

Development of ‘SedCam’— A close-range remote sensing method of estimating suspended-sediment concentration in small rivers

The adaptation of suspended-sediment surrogate technologies continues to rapidly expand across geomorphology and fluvial sediment monitoring efforts. Over a decade of research and development shows increased reliability and accuracy of in-situ surrogates with reduced program cost as compared to traditional sample-based methods, but environmental fouling and probe damage can be problematic. The SedCam technique is a unique non-contact close-range remote sensing method to estimate suspended-sediment concentration from multispectral imagery of a river surface. In contrast to typical airborne- or satellite-based platforms, SedCam uses broadband sensors with lower spectral resolution (three bands covering wavelengths of 340 to 1100 nm) but greater spatial resolution (0.5 mm pixel size; equivalent to medium to coarse sand) and temporal resolution (15-min intervals during daylight hours). This paper summarizes lessons learned from two studies, utilizing three consumer-grade digital cameras (each with different spectral signatures) at two different rivers (each with different sediment characteristics). >90,000 images and 174 concurrent physical samples represent a collective period of 26 months. A subset of these data pairs supports the development of four regression models. Statistical diagnostics show model error can be <40 % when surface point samples are used, with coefficients of determination ≥0.90. This novel approach shows similar accuracy to other surrogate methods such as instream turbidity. Results of this study indicate that optimizing spectra based on expected suspended-sediment concentration increases model performance.

Geomorphology↗

USGS2025

A novel approach to increase accuracy in remotely sensed evapotranspiration through basin water balance and flux tower constraints

Remote sensing-derived evapotranspiration (RSET) products capture the spatiotemporal variations of evapotranspiration (ET) from field to basin scales with unprecedented details. However, their accuracy varies across RSET estimation methods and diverse hydroclimate regions. While ET modeling efforts to account for biophysical processes and controlling parameters have made good progress in recent years, a parallel approach of integrating in-situ ET with RSET could reduce biases in RSET products. Basin water balance ET (WBET) and flux tower ET are widely applied to evaluate RSET accuracy, yet such ET measurements are rarely used for RSET bias corrections, especially for large area applications. To address this issue, we propose a novel approach: the water balance equivalence (WABE) method, which generates spatially continuous WBET for correcting biases in RSET products. The WABE method computes synthetic WBET by integrating observed WBET and flux tower-derived FLUXCOM ET, which fills the spatial gaps of observed WBET and generates a spatially continuous WBET dataset. Synthetic WBET (2002–2015 annual average) of eight-digit hydrologic unit code (HUC8) basins across the conterminous United States (CONUS), constituting 44 % (887 out of 2035 basins) of CONUS basins, was determined within 2.0 % (RMSE = 12 %) of observed WBET at CONUS and between 1–12 % (RMSE = 3–33 %) across 18 regions in CONUS. With WABE-based bias corrections, the overall annual bias of RSET decreased from 10 % (RMSE = 34 %) to 6 % (RMSE = 26 %) across 37 flux tower sites. The WABE method offers a new approach for RSET accuracy improvement and shows great promise for large area implementations with a potential to yield substantial benefits for building accurate basin water budgets and water management decisions.

Conterminous United States↗

USGS2013

Estimating suitable environments for invasive plant species across large landscapes: a remote sensing strategy using Landsat 7 ETM+

The key to reducing ecological and economic damage caused by invasive plant species is to locate and eradicate new invasions before they threaten native biodiversity and ecological processes. We used Landsat Enhanced Thematic Mapper Plus imagery to estimate suitable environments for four invasive plants in Big Bend National Park, southwest Texas, using a presence-only modeling approach. Giant reed ( Arundo donax ), Lehmann lovegrass ( Eragrostis lehmanniana ), horehound ( Marrubium vulgare ) and buffelgrass ( Pennisteum ciliare ) were selected for remote sensing spatial analyses. Multiple dates/seasons of imagery were used to account for habitat conditions within the study area and to capture phenological differences among targeted species and the surrounding landscape. Individual species models had high (0.91 to 0.99) discriminative ability to differentiate invasive plant suitable environments from random background locations. Average test area under the receiver operating characteristic curve (AUC) ranged from 0.91 to 0.99, indicating that plant predictive models exhibited high discriminative ability to differentiate suitable environments for invasive plant species from random locations. Omission rates ranged from <1.0 to 18%. We demonstrated that useful models estimating suitable environments for invasive plants may be created with <50 occurrence locations and that reliable modeling using presence-only datasets can be powerful tools for land managers.

Texas↗

USGS2014

Remote sensing analysis of riparian vegetation response to desert marsh restoration in the Mexican Highlands

Desert marshes, or cienegas, are extremely biodiverse habitats imperiled by anthropogenic demands for water and changing climates. Given their widespread loss and increased recognition, remarkably little is known about restoration techniques. In this study, we examine the effects of gabions (wire baskets filled with rocks used as dams) on vegetation in the Cienega San Bernardino, in the Arizona, Sonora portion of the US-Mexico border, using a remote-sensing analysis coupled with field data. The Normalized Difference Vegetation Index (NDVI), used here as a proxy for plant biomass, is compared at gabion and control sites over a 27-year period during the driest months (May/June). Over this period, green-up occurred at most sites where there were gabions and at a few of the control sites where gabions had not been constructed. When we statistically controlled for differences among sites in source area, stream order, elevation, and interannual winter rainfall, as well as comparisons of before and after the initiation of gabion construction, vegetation increased around gabions yet did not change (or decreased) where there were no gabions. We found that NDVI does not vary with precipitation inputs prior to construction of gabions but demonstrates a strong response to precipitation after the gabions are built. Field data describing plant cover, species richness, and species composition document increases from 2000 to 2012 and corroborate reestablished biomass at gabions. Our findings validate that gabions can be used to restore riparian vegetation and potentially ameliorate drought conditions in a desert cienega.

Ecological Engineering↗

USGS1982

Evaulation of remote sensing, geological and geophysical data for south-central New York and northern Pennsylvania

A study was made of the relationship between lineaments observed on Landsat satellite images and the geologic framework of a portion of the Allegheny Plateau of south-central New York and northern Pennsylvania. The area is underlain by a relatively thick sequence of salt and other evaporites in the Silurian Salina Group and is a potential site for deep-storage of solid nuclear waste. A combination of remote sensing techniques, detailed geologic mapping and geophysical investigations were applied to the problem. Because of the premature termination of the Department of Energy contract, only a portion of the total work was completed. The completed portion of the project included 1) digital contrast enhancement of several Landsat multispectral scanner (MSS) images, 2) analysis of lineament patterns from a Landsat MSS-7 mosaic, 3) field mapping of bedrock joint patterns, 4) compilation and analysis of surface and subsurface structure and isopach maps, 5) collection and digital analysis of aeromagnetic data for southern New York, 6) compilation and analysis of aeromagnetic and gravity data for much of New York and Pennsylvania, and 7) analysis of seismic reflection survey lines for selected portions of New York and Pennsylvania. We identified eight major lineaments or lineament zones and studied them in detail. They typically represent linear alignments of the most conspicuous or prominent physiographic features observable on Landsat images. The Cortland-Ithaca, Watkins Glen-Tanghannock, Seneca Lake-Elmira, Painted Post-Blossburg and Endicott-Syracuse conspicuous lineaments include the Corning-Bath, Van Etten-Towanda, Van Etten-Candor and Van Etten-Odessa lineaments. In addition, a major fault system--the West Danby fault zone--was further defined by geologic and geophysical investigations during our study; the fault zone was not recognizable on satellite images. The lineaments and lineament zones were categorized by their azimuthal trends. Those with a northerly orientation (e.g. Van Etten-Towanda, Seneca Lake-Elmira, Painted Post-Blossburg and Endicott-Syracuse) are most common. Northeasterly lineaments (e.g. Cortland-Ithaca and Watkins Glen-Taughannock) also are common. The Corning-Bath and Van Etten-Odessa lineaments have a northwesterly orientation and the Van Etten-Candor lineament is the sole representative of the east-west direction. The West Danby fault system also trends east-west. All the lineaments or lineament zones studied appear to be related, in one fashion or another, to structural disturbances, because changes in the structural attitude of beds or thickness of rocks commonly occur along their extent. The changes in many instances occur on multiple stratigraphic horizons and in a manner suggestive of several different styles of tectonism, leading to the conclusion that the lineaments and lineament zones have been periodically reactivated during the Paleozoic and Mesozoic Eras. Aeromagnetic data commonly show a parallel alignment of contours juxtaposed or on line with lineaments and lineament zones, suggesting that these physiographic alignments owe their origin to features within the crystalline basement. Pre-Alleghanian faulting and depositional patterns show that the study area was affected by basement-controlled adjustments along and in the same direction as most of the lineaments and lineament zones. Actual displacements cannot be proven for all of the lineaments or lineament zones, but changes in dip of the rocks across these features suggest either basement hingelines or small displacement faulting which cannot be resolved by the present data. These displacements occurred in at least Middle-Late Ordovician, Middle-Late Silurian and the Early-Middle Devonian times. The major northeast trending lineament zones (Cortland-Ithaca and Watkins Glen-Taughannock) correspond in orientation and lie along the northeastward projection of the Rome Trough, a postulated Paleozoic aulacogen. Results of

Open-File Report↗

USGS2017

Using remote sensing to characterize and compare evapotranspiration from different irrigation regimes in the Smith River Watershed of central Montana

According to the 2005 U.S. Geological Survey national water use compilation, irrigation is the second largest use of fresh water in the United States, accounting for 37%, or 484.48 million cubic meters per day, of total freshwater withdrawal. Accurately estimating the amount of water withdrawals and actual consumptive water use (the difference between water withdrawals and return flow) for irrigation at a regional scale is difficult. Remote sensing methods make it possible to compare actual ET (ETa) rates which can serve as a proxy for consumptive water use from different irrigation regimes at a regional scale in a systematic manner. This study investigates crucial components of water use from irrigation such as the difference of ETa rates from flood- and sprinkler-irrigated fields, spatial variability of ETa within a watershed, and the effect of sprinkler irrigation on the water budget of the study area. The mean accumulated ETa depth for the 1,051 square kilometer study area within the upper Smith River watershed was about 467 mm 30-meter per pixel for the 2007 growing season (April through mid-October). The total accumulated volume of ETa for the study area was about 474.705 million cubic meters. The mean accumulated ETa depth from sprinkler-irrigated land was about 687 mm and from flood-irrigated land was about 621 mm from flood-irrigated land. On average, the ETa rate from sprinkler-irrigated fields was 0.25 mm per day higher than flood-irrigated fields over the growing season. Spatial analysis showed that ETa rates within individual fields of a single crop type that are irrigated with a single method (sprinkler or flood) can vary up to about 8 mm per day. It was estimated that the amount of sprinkler irrigation in 2007 accounted for approximately 3% of the total volume of ETa in the study area. When compared to non-irrigated dryland, sprinkler irrigation increases ETa by about 59 to 82% per unit area.

Montana↗

USGS2018

Remote sensing vegetation index methods to evaluate changes in greenness and evapotranspiration in riparian vegetation in response to the Minute 319 environmental pulse flow to Mexico

During the spring of 2014, 130 million m 3 of water were released from the United States' Morelos Dam on the lower Colorado River to Mexico, allowing water to reach the Gulf of California for the first time in 13 years. Our study assessed the effects of water transfer or ecological environmental flows from one nation to another, using remote sensing. Spatial applications for water resource evaluation are important for binational, integrated water resources management and planning for the Colorado River, which includes seven basin states in the US plus two states in Mexico. Our study examined the effects of the historic binational experiment (the Minute 319 agreement) on vegetative response along the riparian corridor. We used 250 m Moderate Resolution Imaging Spectroradiometer (MODIS), Enhanced Vegetation Index (EVI) and 30 m Landsat 8 satellite imagery to track evapotranspiration (ET) and the normalized difference vegetation index (NDVI). Our analysis showed an overall increase in NDVI and evapotranspiration (ET) in the year following the 2014 pulse, which reversed a decline in those metrics since the last major flood in 2000. NDVI and ET levels decreased in 2015, but were still significantly higher ( P < 0.001) than pre-pulse (2013) levels. Preliminary findings show that the decline in 2015 persisted into 2016 and 2017. We continue to analyse results for 2018 in comparison to short-term (2013–2018) and long-term (2000–2018) trends. Our results support the conclusion that these environmental flows from the US to Mexico via the Minute 319 “pulse” had a positive, but short-lived (1 year), impact on vegetation growth in the delta.

Colorado River Delta↗

USGS2015

Evaluating the relationship between biomass, percent groundcover and remote sensing indices across six winter cover crop fields in Maryland, United States

Winter cover crops are an essential part of managing nutrient and sediment losses from agricultural lands. Cover crops lessen sedimentation by reducing erosion, and the accumulation of nitrogen in aboveground biomass results in reduced nutrient runoff. Winter cover crops are planted in the fall and are usually terminated in early spring, making them susceptible to senescence, frost burn, and leaf yellowing due to wintertime conditions. This study sought to determine to what extent remote sensing indices are capable of accurately estimating the percent groundcover and biomass of winter cover crops, and to analyze under what critical ranges these relationships are strong and under which conditions they break down. Cover crop growth on six fields planted to barley, rye, ryegrass, triticale or wheat was measured over the 2012–2013 winter growing season. Data collection included spectral reflectance measurements, aboveground biomass, and percent groundcover. Ten vegetation indices were evaluated using surface reflectance data from a 16-band CROPSCAN sensor. Restricting analysis to sampling dates before the onset of prolonged freezing temperatures and leaf yellowing resulted in increased estimation accuracy. There was a strong relationship between the normalized difference vegetation index (NDVI) and percent groundcover ( r 2 = 0.93) suggesting that date restrictions effectively eliminate yellowing vegetation from analysis. The triangular vegetation index (TVI) was most accurate in estimating high ranges of biomass ( r 2 = 0.86), while NDVI did not experience a clustering of values in the low and medium biomass ranges but saturated in the higher range (>1500 kg/ha). The results of this study show that accounting for index saturation, senescence, and frost burn on leaves can greatly increase the accuracy of estimates of percent groundcover and biomass for winter cover crops.

Maryland↗

USGS2026

National-scale remotely sensed lake surface temperature in the contiguous United States

Temperature is a master variable in aquatic ecosystems. Despite its importance for ecosystem function, our capacity to measure temperature is often limited to focal systems with intensive in situ measurements or larger systems with readily accessible data from disparate data streams. These limitations are consequential for holistically understanding aquatic ecosystem status and change, especially considering that most lakes globally do not have consistent in situ data. To empower researchers with macroscale surface temperature observations, we present the first remotely sensed lake temperature dataset that includes measurements from Landsat 8 made every ~ 16 d for 137,394 lakes of at least 4 ha in surface area throughout the contiguous United States from 2013 through 2025. By aggregating data in a cloud computing framework, the dataset is amenable for continual update, thereby allowing end users to track local-to-continental scale changes in lake water temperature over time.

contiguous United States↗

USGS1971

Application of remote sensing techniques for appraising changes in wildlife habitat

An attempt was made to investigate the potential of airborne, multispectral, line scanner data acquisition and computer-implemented automatic recognition techniques for providing useful information about waterfowl breeding habitat in North Dakota. The spectral characteristics of the components of a landscape containing waterfowl habitat can be detected with airborne scanners. By analyzing these spectral characteristics it is possible to identify and map the landscape components through analog and digital processing methods. At the present stage of development multispectral remote sensing techniques are not ready for operational application to surveys of migratory bird habitat and other such resources. Further developments are needed to: (1) increase accuracy; (2) decrease retrieval and processing time; and (3) reduce costs.

North Dakota↗

USGS2024

Using remote sensing to identify habitat for wintering Henslow's Sparrows (Centronyx henslowii)

The Henslow's Sparrow ( Centronyx henslowii ) is a grassland bird species that overwinters in the southeastern United States and is a species of conservation concern due to population declines primarily caused by habitat loss. Henslow's Sparrows often overwinter in marginal habitats, such as powerline rights-of-way (ROWs), clear cuts, and field edges that provide some of their desired habitat characteristics, such as low-to-no tree cover and a diverse herbaceous understory. Using remote sensing methods, we evaluated the habitat characteristics of Henslow's Sparrow–occupied ROWs in southeastern Georgia. We calculated 22 satellite imagery metrics from Sentinel 2-L2 10 m resolution imagery, including single-pixel variables (e.g., Enhanced Vegetation Index, EVI) as well as “image texture” metrics that represent spatial heterogeneity. Using Random Forest models, we evaluated whether satellite imagery metrics could be used to discriminate between Henslow's Sparrow used areas (delineated from telemetry data) and surrounding available areas. Satellite imagery metrics were successful in predicting habitat characteristics in the ROWs (as evaluated by out-of-bag error and 3 goodness-of-fit tests), with image texture metrics performing better than single-pixel metrics. Image texture metrics were 9 of the top 10 most important predictors of habitat use in the best performing model that had a 500 m available buffer around use areas (out-of-bag error rate 21.21%). The most important image texture metric, cluster shade, was positively correlated with tree cover; Henslow's Sparrows were more likely to use areas with intermediate levels of cluster shade. From our results, we concluded that image texture metrics derived from 10 m satellite imagery could be used to predict sites that have suitable overwintering habitat for Henslow's Sparrows, but only at coarse resolutions and broader extents (hundreds of meters to kilometers). Therefore, this tool could be used to identify other ROWs (and possibly non-ROWs) with habitat that may support this and other declining grassland species.

Georgia↗