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At least 1,387 records · Page 77Linked to original sources

USGS1996

Spatial partitioning of environmental correlates of avian biodiversity in the conterminous United States

Classification and regression tree (CART) analysis was used to create hierarchically organized models of the distribution of bird species richness across the conterminous United States. Species richness data were taken from the Breeding Bird Survey and were related to climatic and land use data. We used a systematic spatial grid of approximately 12,500 hexagons, each approximately 640 square kilometres in area. Within each hexagon land use was characterized by the Loveland et al. land cover classification based on Advanced Very High Resolution Radiometer (AVHRR) data from NOAA polar orbiting meteorological satellites. These data were aggregated to yield fourteen land classes equivalent to an Anderson level II coverage; urban areas were added from the Digital Chart of the World. Each hexagon was characterized by climate data and landscape pattern metrics calculated from the land cover. A CART model then related the variation in species richness across the 1162 hexagons for which bird species richness data were available to the independent variables, yielding an R2-type goodness of fit metric of 47.5% deviance explained. The resulting model recognized eleven groups of hexagons, with species richness within each group determined by unique sequences of hierarchically constrained independent variables. Within the hierarchy, climate data accounted for more variability in the bird data, followed by land cover proportion, and then pattern metrics. The model was then used to predict species richness in all 12,500 hexagons of the conterminous United States yielding a map of the distribution of these eleven classes of bird species richness as determined by the environmental correlates. The potential for using this technique to interface biogeographic theory with the hierarchy theory of ecology is discussed. ?? 1996 Blackwell Science Ltd.

Biodiversity Letters↗

USGS2011

Chapter 8: Occurrence of large and medium-sized mammals: Occurrence but not count models predict pronghorn distribution

Management of medium to large-sized terrestrial mammals (Antilocapridae, Canidae, Cervidae, Leporidae, Mustelidae, Ochotonidae) in the western United States is multifaceted and complex. Species in this group generally are charismatic and provide economic opportunities, although others are considered a nuisance at one extreme or are listed as species of conservation concern at the other. Understanding the relative influence of land cover, habitat fragmentation, and human land use on their distribution during the breeding season is imperative to inform management decisions on land use and conservation planning for these species. We surveyed medium to large-sized sagebrush ( Artemisia spp.)-associated mammal species in 2005 and 2006 on 141 random transects (mean length = 1.1 km) in the Wyoming Basins, an area undergoing rapid land cover transformation due to human actions including energy development. Overall, we observed 10 species but only obtained enough observations of pronghorn ( Antilocapra americana ) to develop spatially explicit distribution models. For pronghorn, occurrence related positively to proportion of sagebrush land cover within 0.27 km, mixed shrubland land cover within 3 km, riparian land cover within 5 km, Normalized Difference Vegetation Index (NDVI) within 0.27 km, road density within 5 km, and decay distance to power line corridors at 1 km, but negatively to salt-desert shrubland cover within 18 km and an interaction between sagebrush and NDVI within 0.27 km. We found excellent predictive capability of this model when evaluated with independent test data. The model provides a basis for assessing the effects of proposed development on pronghorn and can aid planning efforts to avoid or mitigate adverse effects on pronghorn.

Wyoming↗

USGS2021

Sentinel-2 and WorldView-3 atmospheric correction and signal normalization based on ground-truth spectroradiometric measurements

Remote sensing satellite Earth Observing Systems (EOS) provide a variety of products for monitoring Earth surface processes at varying spatial and spectral resolutions. Combining information from high and medium spatial resolution images is valuable for monitoring ground cover and vegetation status in cropland, grassland, forests, and other natural settings. However, coupling information from different EOS requires compensating for atmospheric and view angle effects before integrating comparable surface reflectance (SR) values. The objectives of this study were i) to assess how different atmospheric constituents affect the atmospheric correction results in Sentinel-2 and WorldView-3 imagery, ii) to establish a relationship with field spectra measurements, and iii) to develop an empirical approach to ensure that SR values extracted from different EOS can be normalized for use in monitoring vegetation and land cover status. We compared surface reflectance values derived from Sentinel-2 images corrected with Sen2Cor, MODTRAN or FLAASH atmospheric correction approaches for the visible-to-near infrared regions. Additionally, this information was compared to SR values extracted from WorldView-3 imagery acquired from the same dates and location (Central Spain) and corrected with MODTRAN and FLAASH approaches. Assessment of the atmospheric correction was conducted by comparing satellite image SR with ground-truth spectra acquired with a FieldSpec hand-held spectroradiometer. The results emphasized the importance of using common atmospheric parameters collected from ancillary data sources (i.e. MODIS Atmosphere & Land products) to ensure a reliable SR comparison. When compared to field-collected spectral data, SR from corrected Sentinel-2 push-broom imagery showed a reliable match (<4% difference in the visible bands and <0.52% difference in the near infrared bands). However, SR imagery from the pointable WorldView-3 instrument showed significant deviation, likely resulting from the effects of steep off-nadir acquisition angles (24.6° to 39.1°) combined with surface anisotropy. The magnitude and sign of the deviation in SR differed depending on the vegetation type, wavelength and sun-surface-sensor geometry. Therefore, it was necessary to account for angular effects to ensure reliable comparisons of imagery from the different EOS. In this study, an empirical angular correction approach was developed based on calibrating each WorldView-3 band against the ground-truth spectra. This correction allowed for the accurate signal normalization of WorldView-3 and Sentinel-2 imagery SR in the visible-to-near infrared regions.

ISPRS Journal of Photogrammetry and Remote Sensing↗

USGS2015

User manuals for the Delaware River Basin Water Availability Tool for Environmental Resources (DRB–WATER) and associated WATER application utilities

The Water Availability Tool for Environmental Resources (WATER) is a decision support system (DSS) for the nontidal part of the Delaware River Basin (DRB) that provides a consistent and objective method of simulating streamflow under historical, forecasted, and managed conditions. WATER integrates geospatial sampling of landscape characteristics, including topographic and soil properties, with a regionally calibrated hillslope-hydrology model, an impervious-surface model, and hydroclimatic models that have been parameterized using three hydrologic response units—forested, agricultural, and developed land cover. It is this integration that enables the regional hydrologic-modeling approach used in WATER without requiring site-specific optimization or those stationary conditions inferred when using a statistical model. The DSS provides a “historical” database, ideal for simulating streamflow for 2001–11, in addition to land-cover forecasts that focus on 2030 and 2060. The WATER Application Utilities are provided with the DSS and apply change factors for precipitation, temperature, and potential evapotranspiration to a 1981–2011 climatic record provided with the DSS. These change factors were derived from a suite of general circulation models (GCMs) and representative concentration pathway (RCP) emission scenarios. These change factors are based on 25-year monthly averages (normals) that are centere on 2030 and 2060. The WATER Application Utilities also can be used to apply a 2010 snapshot of water use for the DRB; a factorial approach enables scenario testing of increased or decreased water use for each simulation. Finally, the WATER Application Utilities can be used to reformat streamflow time series for input to statistical or reservoir management software.

Delaware, Maryland, New Jersey, New York, Pennsylv↗

USGS2002

Development of a circa 2000 land cover database for the United States

Multi-Resolution Land Characterization 2000 (MRLC 2000) is a second-generation federal consortium to create an updated pool of nation-wide Landsat 7 imagery, and derive a second-generation National Land Cover Database (NLCD 2000). This multi-layer, multisource database will include a suite of 30-meter resolution data that will serve as standardized ingredients for the production of land cover – both nationally and locally. This database will also provide the framework to allow flexibility in developing and applying suites of independent data layers. These nationally standardized independent data layers or components, will be useful not only within the land-cover classification but as data themes for other applications. This database will consist of the following components: (1) normalized tasseled cap (TC) transformations of Landsat 7 imagery for three time periods per scene (early, peak and late), (2) ancillary data layers, including 30m DEM derivatives of slope, aspect and elevation and three STATSCO soil derivatives, (4) image shape and texture information, (5) image derivatives of percent imperviousness and percent tree canopy per-pixel, (6) classified land-cover data derived from the Tassel Capped imagery, ancillary data and derivatives, (7) classification rules and metadata from the land cover classification, allowing future users the potential to modify rules to derive land cover products tailored to their specific local applications. In a pilot study application of the database concept, two mapping zones (Utah and Virginia) were selected for full generation of the above data components. Three derivative layers including, per-pixel imperviousness, per-pixel canopy and land cover were classified from the database. Cross validation accuracies for land cover ranged from 65-82%, and mean absolute error values of 10-15% were reported for percent tree canopy and imperviousness.

conterminous United States↗

USGS2022

The Hawai'i groundwater recharge tool

This article discusses the design and implementation of the Hawai’i Groundwater Recharge Tool, an application for providing data and analyses of the impacts of land-cover modifications and changes in precipitation on groundwater-recharge rates for the island of O’ahu. This application uses simulation data based on a set of 29 land-cover types and 2 precipitation conditions to provide users with real-time recharge calculations for interactively defined land-cover modifications. The tool provides two visualizations, representing the land cover for the island and the resultant groundwater-recharge rates, and a set of metrics indicating the changes to groundwater recharge for relevant areas to present a set of easily interpretable outcomes based on user-defined scenarios. Users have varying degrees of control over the granularity of data input and output, allowing for the quick production of a roughly defined scenario, or more precise land-cover definitions. These modifications can be exported for further analysis. Heuristics are used to provide a responsive user interface and performant integration with the database containing the full set of simulation data. This tool is designed to provide user-friendly access to the information on the impacts of land-cover and precipitation changes on groundwater-recharge rates needed to assist in making data-driven decisions.

Hawai'i↗

USGS2003

Forest Types in the Lower Suwannee River Floodplain, Florida?-A Report and Interactive Map

A map of forest types in the lower Suwannee River floodplain, Florida, was created during a study conducted from 1996 to 2000 by the U.S. Geological Survey in cooperation with the Suwannee River Water Management District. The map is presented with this report on a compact disc with interactive viewing software. The forest map can be used by scientists for ecological studies in the floodplain based on land cover types and by landowners and management personnel making land use decisions. The study area is the 10-year floodplain of the lower Suwannee River from its confluence with the Santa Fe River to the lower limit of forests near the Gulf of Mexico. The floodplain is divided into three reaches: riverine (non-tidal), upper tidal, and lower tidal, due to changes in hydrology, vegetation, and soils with proximity to the coast. The 10-year floodplain covers about 21,170 hectares; nearly 88 percent of this area (18,580 hectares) is mapped as 14 major forest types. Approximately 29 percent (5,319 hectares) of these forests have been altered by agriculture or development. About 75 percent of the area of major forest types (13,994 hectares) is wetland forests and about 25 percent (4,586 hectares) is upland forests. Tidal wetland forests (8,955 hectares) cover a much greater area than riverine wetland forests (5,039 hectares). Oak/pine upland forests are present in the riverine and upper tidal reaches of the floodplain on elevations that are inundated only briefly during the highest floods. High bottomland hardwoods are present on the higher levees, ridges, and flats of the riverine reach where soils are usually sandy. Low bottomland hardwood forests are present in the riverine reach on swamp margins and low levees and flats that are flooded continuously for several weeks or longer every 1 to 3 years. Riverine swamps are present in the lowest and wettest areas of the non-tidal floodplain that are either inundated or saturated most of the time. Upper tidal bottomland hardwood forests are present on sandy soils on high flats and in transitional areas between upland forests and swamps. Upper tidal mixed forests are found on low levees or between swamps and higher forest types. Upper tidal swamps are present at elevations below median monthly high stage and usually have surface soils that are permanently saturated mucks. Lower tidal hammocks are found on higher elevations that do not receive regular tidal inundation but have a high water table and are briefly inundated by storm surges several times a decade. Lower tidal mixed forests include swamps with numerous small hummocks or less common larger hummocks. Lower tidal swamps are found on deep muck soils that are below the elevation of the median daily or monthly high stage. Seven additional land cover types (2,590 hectares) are mapped. Water in the main channel of the lower Suwannee River (1,767 hectares) was mapped separately from open water in the floodplain (239 hectares). Other land cover types are: seepage slopes (70 hectares), isolated forested wetlands (19 hectares), marshes upstream of the tree line (505 hectares), beds of emergent aquatic vegetation (21 hectares), and floodplain glades (46 hectares)

Water-Resources Investigations Report↗

USGS2022

Deep and machine learning image classification of coastal wetlands using unpiloted aircraft system multispectral images and lidar datasets

The recent developments of new deep learning architectures create opportunities to accurately classify high-resolution unoccupied aerial system (UAS) images of natural coastal systems and mandate continuous evaluation of algorithm performance. We evaluated the performance of the U-Net and DeepLabv3 deep convolutional network architectures and two traditional machine learning techniques (support vector machine (SVM) and random forest (RF)) applied to seventeen coastal land cover types in west Florida using UAS multispectral aerial imagery and canopy height models (CHM). Twelve combinations of spectral bands and CHMs were used. Our results using the spectral bands showed that the U-Net (83.80–85.27% overall accuracy) and the DeepLabV3 (75.20–83.50% overall accuracy) deep learning techniques outperformed the SVM (60.50–71.10% overall accuracy) and the RF (57.40–71.0%) machine learning algorithms. The addition of the CHM to the spectral bands slightly increased the overall accuracy as a whole in the deep learning models, while the addition of a CHM notably improved the SVM and RF results. Similarly, using bands outside the three spectral bands, namely, near-infrared and red edge, increased the performance of the machine learning classifiers but had minimal impact on the deep learning classification results. The difference in the overall accuracies produced by using UAS-based lidar and SfM point clouds, as supplementary geometrical information, in the classification process was minimal across all classification techniques. Our results highlight the advantage of using deep learning networks to classify high-resolution UAS images in highly diverse coastal landscapes. We also found that low-cost, three-visible-band imagery produces results comparable to multispectral imagery that do not risk a significant reduction in classification accuracy when adopting deep learning models.

Florida↗

USGS2015

Effects of climate and land cover on hydrology in the southeastern U.S.: Potential impacts on watershed planning

The hydrologic response to statistically downscaled general circulation model simulations of daily surface climate and land cover through 2099 was assessed for the Apalachicola-Chattahoochee-Flint River Basin located in the southeastern United States. Projections of climate, urbanization, vegetation, and surface-depression storage capacity were used as inputs to the Precipitation-Runoff Modeling System to simulate projected impacts on hydrologic response. Surface runoff substantially increased when land cover change was applied. However, once the surface depression storage was added to mitigate the land cover change and increases of surface runoff (due to urbanization), the groundwater flow component then increased. For hydrologic studies that include projections of land cover change (urbanization in particular), any analysis of runoff beyond the change in total runoff should include effects of stormwater management practices as these features affect flow timing and magnitude and may be useful in mitigating land cover change impacts on streamflow. Potential changes in water availability and how biota may respond to changes in flow regime in response to climate and land cover change may prove challenging for managers attempting to balance the needs of future development and the environment. However, these models are still useful for assessing the relative impacts of climate and land cover change and for evaluating tradeoffs when managing to mitigate different stressors.

Alabama, Florida, Georgia↗

USGS2011

A water-budget model and assessment of groundwater recharge for the Island of Hawai'i

Concern surrounding increasing demand for groundwater on the Island of Hawaiʻi, caused by a growing population and an increasing reliance on groundwater as a source for municipal and private water systems, has prompted a study of groundwater recharge on the island using the most current data and accepted methods. For this study, a daily water-budget model for the entire Island of Hawaiʻi was developed and used to estimate mean recharge for various land-cover and rainfall conditions, and a submodel for the Kona area was developed and used to estimate historical groundwater recharge in the Kona area during the period 1984–2008. Estimated mean annual recharge on the Island of Hawaiʻi is 6,594 million gallons per day, which is about 49 percent of mean annual rainfall. Recharge is highest on the windward slopes of Mauna Loa, below the tradewind inversion, and lowest on the leeward slopes of Kohala and Mauna Kea. Local recharge maxima also occur on (1) windward Kohala, with the exception of the northern tip, (2) windward Mauna Kea below the tradewind inversion, (3) windward Kīlauea, (4) the middle elevations of southeastern Mauna Loa, and (5) the lower-middle elevations of leeward Mauna Loa and southwestern Hualālai, in the Kona area. Local recharge minima also occur on (1) Mauna Kea and Mauna Loa, above the tradewind inversion, (2) the northern tip of Kohala, (3) leeward Kīlauea, (4) the southern tip of Mauna Loa, and (5) the northwestern slopes of Mauna Loa and Hualālai. In 18 of the 24 aquifer systems on the island, estimated mean annual recharge for baseline conditions was higher than the recharge estimates used in the 2008 State of Hawaiʻi Water Resource Protection Plan (2008 WRPP). Baseline conditions for this study were 2008 land cover and mean annual rainfall from the period 1916–1983. Estimates of recharge for the Māhukona, Waimea, and Hāwī aquifer systems, however, were between 29 and 38 percent lower than the 2008 WRPP estimates, mainly because of much higher evapotranspiration estimates in this study compared to the 2008 WRPP. For the drought simulation (1991–95 rainfall), the estimates of recharge for these three aquifer systems were only 15 to 33 percent of the sustainable yields (maximum allowable pumping rates) set by the 2008 WRPP. This may be cause for concern, as these areas are experiencing a rapid growth in development and a related growth in water demand. Recent projections of change in rainfall owing to effects of ongoing climate change generally indicate a slight increase in islandwide rainfall, and estimates of annual recharge in the late 21st century are higher than baseline estimates for every aquifer system, except ʻAnaehoʻomalu. On average, these aquifer-system recharge estimates are higher by about 8 percent compared to baseline estimates. In the Kona area, estimated groundwater recharge during the period 1984–2008 was highest during 2004–8 and lowest during 1999–2003, with the 1999–2003 recharge being about 50 percent of the 2004–8 recharge. These extremes in recharge coincided with the periods of lowest and highest mean rainfall, respectively. No seasonal pattern in recharge is discernible. Spatially, the highest recharge occurred in a belt about 4 miles wide running parallel to the coast about 2 miles inland. The sensitivity of recharge estimates to input parameters is related to the climate and land-cover conditions of the particular area of study. For the wet, forested areas characteristic of the windward side of the island, recharge was most sensitive to the ratio of runoff to rainfall. For the dry, grassland areas characteristic of the northwestern leeward side of the island, recharge was most sensitive to root depth. For the Kona area, characterized by moderate rainfall and a wide variety of land cover, recharge was most sensitive to the pan coefficient and canopy-evaporation rates in

Hawai'i↗

USGS2015

Combining NLCD and MODIS to create a land cover-albedo database for the continental United States

Land surface albedo is an essential climate variable that is tightly linked to land cover, such that specific land cover classes (e.g., deciduous broadleaf forest, cropland) have characteristic albedos. Despite the normative of land-cover class specific albedos, there is considerable variability in albedo within a land cover class. The National Land Cover Database (NLCD) and the Moderate Resolution Imaging Spectroradiometer (MODIS) albedo product were combined to produce a long-term (14 years) integrated land cover-albedo database for the continental United States that can be used to examine the temporal behavior of albedo as a function of land cover. The integration identifies areas of homogeneous land cover at the nominal spatial resolution of the MODIS (MCD43A) albedo product (500 m × 500 m) from the NLCD product (30 m × 30 m), and provides an albedo data record per 500 m × 500 m pixel for 14 of the 16 NLCD land cover classes. Individual homogeneous land cover pixels have up to 605 albedo observations, and 75% of the pixels have at least 319 MODIS albedo observations (≥ 50% of the maximum possible number of observations) for the study period (2000–2013). We demonstrated the utility of the database by conducting a multivariate analysis of variance of albedo for each NLCD land cover class, showing that locational (pixel-to-pixel) and inter-annual variability were significant factors in addition to expected seasonal (intra-annual) and geographic (latitudinal) effects.

Remote Sensing of Environment↗

USGS2013

Actual evapotranspiration (water use) assessment of the Colorado River Basin at the Landsat resolution using the operational simplified surface energy balance model

Accurately estimating consumptive water use in the Colorado River Basin (CRB) is important for assessing and managing limited water resources in the basin. Increasing water demand from various sectors may threaten long-term sustainability of the water supply in the arid southwestern United States. We have developed a first-ever basin-wide actual evapotranspiration (ET a ) map of the CRB at the Landsat scale for water use assessment at the field level. We used the operational Simplified Surface Energy Balance (SSEBop) model for estimating ET a using 328 cloud-free Landsat images acquired during 2010. Our results show that cropland had the highest ET a among all land cover classes except for water. Validation using eddy covariance measured ET a showed that the SSEBop model nicely captured the variability in annual ET a with an overall R 2 of 0.78 and a mean bias error of about 10%. Comparison with water balance-based ETa showed good agreement (R 2 = 0.85) at the sub-basin level. Though there was good correlation (R 2 = 0.79) between Moderate Resolution Imaging Spectroradiometer (MODIS)-based ETa (1 km spatial resolution) and Landsat-based ET a (30 m spatial resolution), the spatial distribution of MODIS-based ET a was not suitable for water use assessment at the field level. In contrast, Landsat-based ET a has good potential to be used at the field level for water management. With further validation using multiple years and sites, our methodology can be applied for regular production of ET a maps of larger areas such as the conterminous United States.

Arizona, California, Colorado, Nevada, New Mexico,↗

USGS2010

Building hierarchical models of avian distributions for the State of Georgia

To predict the distributions of breeding birds in the state of Georgia, USA, we built hierarchical models consisting of 4 levels of nested mapping units of decreasing area: 90,000 ha, 3,600 ha, 144 ha, and 5.76 ha. We used the Partners in Flight database of point counts to generate presence and absence data at locations across the state of Georgia for 9 avian species: Acadian flycatcher (Empidonax virescens), brownheaded nuthatch (Sitta pusilla), Carolina wren (Thryothorus ludovicianus), indigo bunting (Passerina cyanea), northern cardinal (Cardinalis cardinalis), prairie warbler (Dendroica discolor), yellow-billed cuckoo (Coccyxus americanus), white-eyed vireo (Vireo griseus), and wood thrush (Hylocichla mustelina). At each location, we estimated hierarchical-level-specific habitat measurements using the Georgia GAP Analysis18 class land cover and other Geographic Information System sources. We created candidate, species-specific occupancy models based on previously reported relationships, and fit these using Markov chain Monte Carlo procedures implemented in OpenBugs. We then created a confidence model set for each species based on Akaike's Information Criterion. We found hierarchical habitat relationships for all species. Three-fold cross-validation estimates of model accuracy indicated an average overall correct classification rate of 60.5%. Comparisons with existing Georgia GAP Analysis models indicated that our models were more accurate overall. Our results provide guidance to wildlife scientists and managers seeking predict avian occurrence as a function of local and landscape-level habitat attributes.

Journal of Wildlife Management↗

USGS2017

UAS-SfM for coastal research: Geomorphic feature extraction and land cover classification from high-resolution elevation and optical imagery

The vulnerability of coastal systems to hazards such as storms and sea-level rise is typically characterized using a combination of ground and manned airborne systems that have limited spatial or temporal scales. Structure-from-motion (SfM) photogrammetry applied to imagery acquired by unmanned aerial systems (UAS) offers a rapid and inexpensive means to produce high-resolution topographic and visual reflectance datasets that rival existing lidar and imagery standards. Here, we use SfM to produce an elevation point cloud, an orthomosaic, and a digital elevation model (DEM) from data collected by UAS at a beach and wetland site in Massachusetts, USA. We apply existing methods to (a) determine the position of shorelines and foredunes using a feature extraction routine developed for lidar point clouds and (b) map land cover from the rasterized surfaces using a supervised classification routine. In both analyses, we experimentally vary the input datasets to understand the benefits and limitations of UAS-SfM for coastal vulnerability assessment. We find that (a) geomorphic features are extracted from the SfM point cloud with near-continuous coverage and sub-meter precision, better than was possible from a recent lidar dataset covering the same area; and (b) land cover classification is greatly improved by including topographic data with visual reflectance, but changes to resolution (when <50 cm) have little influence on the classification accuracy.

Massachusetts↗

USGS2010

Bird migration patterns in the arid southwest-Final report

To ensure full life-cycle conservation, we need to understand migrant behavior en route and how migrating species use stopover and migration aerohabitats. In the Southwest, birds traverse arid and mountainous landscapes in migration. Migrants are known to use riparian stopover habitats; we know less about how migrant density varies across the Southwest seasonally and annually, and how migrants use other habitat types during migratory stopover. Furthermore, we lack information about migrant flight altitudes, speeds, and directions of travel, and how these patterns vary seasonally and annually across the Southwest. Using weather surveillance radar data, we identified targets likely dominated by nocturnally migrating birds and determined their flight altitudes, speeds, directions over ground, and variations in abundance. Migrating or foraging bats likely are present across the region in some of these data, particularly in central Texas. We found that migrants flew at significantly lower altitudes and significantly higher speeds in spring than in fall. In all seasons migrants maintained seasonally appropriate directions of movement. We detected significant differences in vertical structure of migrant densities that varied both geographically within seasons and seasonally within sites. We also found that in fall there was a greater and more variable passage of migrants through the central part of the borderlands (New Mexico and west Texas); in spring there was some suggestion of greater and more variable passage of migrants in the eastern borderlands (central and south Texas). Such patterns are consistent with the existence of at least two migration systems through western North America and the use of different migration routes in spring and fall for at least some species. Using radar data and satellite land cover data, we determined the habitats with which migrants are associated during migration stopover. There were significant differences in bird densities among habitat types at all sites in at least one season. Upland forest habitat in parts of Arizona and New Mexico supported high migrant densities, especially in fall. Developed habitats in areas with little upland forest habitat also supported high migrant densities. Scrub/shrub and grassland habitats supported low to intermediate migrant densities, but because these habitat types dominate the region, they may support large numbers of migratory birds. This may be especially true for species that do not use forested habitats during migration. Target identity remains a challenge for radar-based studies. Presence of bats in the data complicates interpretation of some observations, particularly from central Texas. Based on our results it is simplistic to: (1) consider the arid west as a largely inhospitable landscape in which there are only relatively small oases of habitat that provide the resources needed by all migrants; (2) think of western riparian and upland forest habitat as supporting the majority of migrants in all cases; or (3) consider a particular habitat type unimportant migrant stopover habitat based solely on migrant densities.

Open-File Report↗

USGS2021

The Boreal-Arctic Wetland and Lake Dataset (BAWLD)

Methane emissions from boreal and arctic wetlands, lakes, and rivers are expected to increase in response to warming and associated permafrost thaw. However, the lack of appropriate land cover datasets for scaling field-measured methane emissions to circumpolar scales has contributed to a large uncertainty for our understanding of present-day and future methane emissions. Here we present the Boreal–Arctic Wetland and Lake Dataset (BAWLD), a land cover dataset based on an expert assessment, extrapolated using random forest modelling from available spatial datasets of climate, topography, soils, permafrost conditions, vegetation, wetlands, and surface water extents and dynamics. In BAWLD, we estimate the fractional coverage of five wetland, seven lake, and three river classes within 0.5 × 0.5∘ grid cells that cover the northern boreal and tundra biomes (17 % of the global land surface). Land cover classes were defined using criteria that ensured distinct methane emissions among classes, as indicated by a co-developed comprehensive dataset of methane flux observations. In BAWLD, wetlands occupied 3.2 × 106 km2 (14 % of domain) with a 95 % confidence interval between 2.8 and 3.8 × 106 km2. Bog, fen, and permafrost bog were the most abundant wetland classes, covering ∼ 28 % each of the total wetland area, while the highest-methane-emitting marsh and tundra wetland classes occupied 5 % and 12 %, respectively. Lakes, defined to include all lentic open-water ecosystems regardless of size, covered 1.4 × 106 km2 (6 % of domain). Low-methane-emitting large lakes (>10 km2) and glacial lakes jointly represented 78 % of the total lake area, while high-emitting peatland and yedoma lakes covered 18 % and 4 %, respectively. Small (<0.1 km2) glacial, peatland, and yedoma lakes combined covered 17 % of the total lake area but contributed disproportionally to the overall spatial uncertainty in lake area with a 95 % confidence interval between 0.15 and 0.38 × 106 km2. Rivers and streams were estimated to cover 0.12 × 106 km2 (0.5 % of domain), of which 8 % was associated with high-methane-emitting headwaters that drain organic-rich landscapes. Distinct combinations of spatially co-occurring wetland and lake classes were identified across the BAWLD domain, allowing for the mapping of “wetscapes” that have characteristic methane emission magnitudes and sensitivities to climate change at regional scales. With BAWLD, we provide a dataset which avoids double-accounting of wetland, lake, and river extents and which includes confidence intervals for each land cover class. As such, BAWLD will be suitable for many hydrological and biogeochemical modelling and upscaling efforts for the northern boreal and arctic region, in particular those aimed at improving assessments of current and future methane emissions. Data are freely available at https://doi.org/10.18739/A2C824F9X (Olefeldt et al., 2021).

Earth System Science Data↗

USGS2003

Exploration of satellite-measured vegetation seasonality for Landfire land cover

The purpose of this study is to explore the use of satellite data and other sources of spatial data for large area classification in the western United States to support research on potential fire hazards. Extensive field information was made available to this project from two sources: Forest Inventory and Assessment (FIA) and Utah State University. Seasonal spectral patterns of reflectance generated for select vegetation communities indicated that substantial spectral changes occurred through the growing season for most land cover types. In many cases, pronounced spectral differences characterized different types of vegetation, indicating a high probability that classification will accurately separate these particular types of land cover. However, spectral similarities between other types of land cover, such as Douglas fir and white fir, indicate potential classification challenges. Results from this study also show that decision tree analysis is highly effective for assessing quality of input field data and for generating large area land cover classification data sets. It was found that a 5-7% improvement in classification results could be achieved simply by not using those field plots that appeared to be sub-optimal for classification purposes based on image interpretation.

Conference Paper↗

USGS2017

Evaluating the role of land cover and climate uncertainties in computing gross primary production in Hawaiian Island ecosystems

Gross primary production (GPP) is the Earth’s largest carbon flux into the terrestrial biosphere and plays a critical role in regulating atmospheric chemistry and global climate. The Moderate Resolution Imaging Spectrometer (MODIS)-MOD17 data product is a widely used remote sensing-based model that provides global estimates of spatiotemporal trends in GPP. When the MOD17 algorithm is applied to regional scale heterogeneous landscapes, input data from coarse resolution land cover and climate products may increase uncertainty in GPP estimates, especially in high productivity tropical ecosystems. We examined the influence of using locally specific land cover and high-resolution local climate input data on MOD17 estimates of GPP for the State of Hawaii, a heterogeneous and discontinuous tropical landscape. Replacing the global land cover data input product (MOD12Q1) with Hawaii-specific land cover data reduced statewide GPP estimates by ~8%, primarily because the Hawaii-specific land cover map had less vegetated land area compared to the global land cover product. Replacing coarse resolution GMAO climate data with Hawaii-specific high-resolution climate data also reduced statewide GPP estimates by ~8% because of the higher spatial variability of photosynthetically active radiation (PAR) in the Hawaii-specific climate data. The combined use of both Hawaii-specific land cover and high-resolution Hawaii climate data inputs reduced statewide GPP by ~16%, suggesting equal and independent influence on MOD17 GPP estimates. Our sensitivity analyses within a heterogeneous tropical landscape suggest that refined global land cover and climate data sets may contribute to an enhanced MOD17 product at a variety of spatial scales.

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