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At least 1,207 records · Page 67Linked to original sources

USGS2014

Influence of riparian and watershed alterations on sandbars in a Great Plains river

Anthropogenic alterations have caused sandbar habitats in rivers and the biota dependent on them to decline. Restoring large river sandbars may be needed as these habitats are important components of river ecosystems and provide essential habitat to terrestrial and aquatic organisms. We quantified factors within the riparian zone of the Kansas River, USA, and within its tributaries that influenced sandbar size and density using aerial photographs and land use/land cover (LULC) data. We developed, a priori , 16 linear regression models focused on LULC at the local, adjacent upstream river bend, and the segment (18–44 km upstream) scales and used an information theoretic approach to determine what alterations best predicted the size and density of sandbars. Variation in sandbar density was best explained by the LULC within contributing tributaries at the segment scale, which indicated reduced sandbar density with increased forest cover within tributary watersheds. Similarly, LULC within contributing tributary watersheds at the segment scale best explained variation in sandbar size. These models indicated that sandbar size increased with agriculture and forest and decreased with urban cover within tributary watersheds. Our findings suggest that sediment supply and delivery from upstream tributary watersheds may be influential on sandbars within the Kansas River and that preserving natural grassland and reducing woody encroachment within tributary watersheds in Great Plains rivers may help improve sediment delivery to help restore natural river function.

River Research and Applications↗

USGS2019

The ‘Ike Wai Hawai‘i groundwater recharge tool

This paper discusses the design and implementation of the ‘Ike Wai Hawai‘i Groundwater Recharge Tool, an application for providing data and analyses of the impacts of land-cover and climate modifications 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 two rainfall scenarios to provide users with real-time recharge calculations for interactively defined land-cover modifications. 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 of the map are provided to present a set of easily interpreted outcomes based on the user-defined simulations. Tools are provided to give users varying degrees of control over the granularity of data input and output, allowing for the quick production of a roughly defined simulation, or more precise land-cover models that 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 climate changes on groundwater recharge rates needed to make data-driven decisions.

Hawaii↗

USGS2009

Sample project: establishing a global forest monitoring capability using multi-resolution and multi-temporal remotely sensed data sets

Quantifying rates of forest-cover change is important for improved carbon accounting and climate change modeling, management of forestry and agricultural resources, and biodiversity monitoring. A practical solution to examining trends in forest cover change at global scale is to employ remotely sensed data. Satellite-based monitoring of forest cover can be implemented consistently across large regions at annual and inter-annual intervals. This research extends previous research on global forest-cover dynamics and land-cover change estimation to establish a robust, operational forest monitoring and assessment system. The approach integrates both MODIS and Landsat data to provide timely biome-scale forest change estimation. This is achieved by using annual MODIS change indicator maps to stratify biomes into low, medium and high change categories. Landsat image pairs can then be sampled within these strata and analyzed for estimating area of forest cleared.

The NASA LCLUC Program: an interdisciplinary appro↗

USGS2013

A comprehensive evaluation of two MODIS evapotranspiration products over the conterminous United States: using point and gridded FLUXNET and water balance ET

Remote sensing datasets are increasingly being used to provide spatially explicit large scale evapotranspiration (ET) estimates. Extensive evaluation of such large scale estimates is necessary before they can be used in various applications. In this study, two monthly MODIS 1 km ET products, MODIS global ET (MOD16) and Operational Simplified Surface Energy Balance (SSEBop) ET, are validated over the conterminous United States at both point and basin scales. Point scale validation was performed using eddy covariance FLUXNET ET (FLET) data (2001–2007) aggregated by year, land cover, elevation and climate zone. Basin scale validation was performed using annual gridded FLUXNET ET (GFET) and annual basin water balance ET (WBET) data aggregated by various hydrologic unit code (HUC) levels. Point scale validation using monthly data aggregated by years revealed that the MOD16 ET and SSEBop ET products showed overall comparable annual accuracies. For most land cover types, both ET products showed comparable results. However, SSEBop showed higher performance for Grassland and Forest classes; MOD16 showed improved performance in the Woody Savanna class. Accuracy of both the ET products was also found to be comparable over different climate zones. However, SSEBop data showed higher skill score across the climate zones covering the western United States. Validation results at different HUC levels over 2000–2011 using GFET as a reference indicate higher accuracies for MOD16 ET data. MOD16, SSEBop and GFET data were validated against WBET (2000–2009), and results indicate that both MOD16 and SSEBop ET matched the accuracies of the global GFET dataset at different HUC levels. Our results indicate that both MODIS ET products effectively reproduced basin scale ET response (up to 25% uncertainty) compared to CONUS-wide point-based ET response (up to 50–60% uncertainty) illustrating the reliability of MODIS ET products for basin-scale ET estimation. Results from this research would guide the additional parameter refinement required for the MOD16 and SSEBop algorithms in order to further improve their accuracy and performance for agro-hydrologic applications.

Remote Sensing of Environment↗

USGS2002

Classification of irrigated land using satellite imagery, the High Plains aquifer, nominal date 1992

Satellite imagery from the Landsat Thematic Mapper (nominal date 1992) was used to classify and map the location of irrigated land across the High Plains aquifer. The High Plains aquifer underlies 174,000 square miles in parts of Colorado, Kansas, Nebraska, New Mexico, Oklahoma, South Dakota, Texas, and Wyoming. The U.S. Geological Survey is conducting a waterquality study of the High Plains aquifer as part of the National Water-Quality Assessment Program. To help interpret data and select sites for the study, it is helpful to know the location of irrigated land within the study area. To date, the only information available for the entire area is 20 years old. To update the data on irrigated land, 40 summer and 40 spring images (nominal date 1992) were acquired from the National Land Cover Data set and processed using a band-ratio method (Landsat Thematic Mapper band 4 divided by band 3) to enhance the vegetation signatures. The study area was divided into nine subregions with similar environmental characteristics, and a band-ratio threshold was selected from imagery in each subregion that differentiated the cutoff between irrigated and nonirrigated land. The classified images for each subregion were mosaicked to produce an irrigated land map for the study area. The total amount of irrigated land classified from the 1992 imagery was 13.1 million acres, or about 12 percent of the total land in the High Plains. This estimate is approximately 1.5 percent greater than the amount of irrigated land reported in the 1992 Census of Agriculture (12.8 millions acres). This information was also compared to a similar data set based on 1980 imagery. The 1980 data classified 13.7 million acres as irrigated. Although the change in the amount of irrigated land between the two times was not substantial, the location of the irrigated land did shift from areas where there were large ground-water-level declines to other areas where ground-water levels were static or rising.

Water-Resources Investigations Report↗

USGS2002

Location of irrigated land classified from satellite imagery - High Plains Area, nominal date 1992

Satellite imagery from the Landsat Thematic Mapper (nominal date 1992) was used to classify and map the location of irrigated land overlying the High Plains aquifer. The High Plains aquifer underlies 174,000 square miles in parts of Colorado, Kansas, Nebraska, New Mexico, Oklahoma, South Dakota, Texas, and Wyoming. The U.S. Geological Survey is conducting a water-quality study of the High Plains aquifer as part of the National Water-Quality Assessment Program. To help interpret data and select sites for the study, it is helpful to know the location of irrigated land within the study area. To date, the only information available for the entire area is 20 years old. To update the data on irrigated land, 40 summer and 40 spring images (nominal date 1992) were acquired from the National Land Cover Data set and processed using a band-ratio method (Landsat Thematic Mapper band 4 divided by band 3) to enhance the vegetation signatures. The study area was divided into nine subregions with similar environmental characteristics, and a band-ratio threshold was selected from imagery in each subregion that differentiated the cutoff between irrigated and nonirrigated land. The classified images for each subregion were mosaicked to produce an irrigated-land map for the study area. The total amount of irrigated land classified from the 1992 imagery was 13.1 million acres, or about 12 percent of the total land in the High Plains. This estimate is approximately 1.5 percent greater than the amount of irrigated land reported in the 1992 Census of Agriculture (12.8 millions acres).

Open-File Report↗

USGS2022

Implementation of the CCDC algorithm to produce the LCMAP Collection 1.0 annual land surface change product

The increasing availability of high-quality remote sensing data and advanced technologies have spurred land cover mapping to characterize land change from local to global scales. However, most land change datasets either span multiple decades at a local scale or cover limited time over a larger geographic extent. Here, we present a new land cover and land surface change dataset created by the Land Change Monitoring, Assessment, and Projection (LCMAP) program over the conterminous United States (CONUS). The LCMAP land cover change dataset consists of annual land cover and land cover change products over the period 1985-2017 at 30-meter resolution using Landsat and other ancillary data via the Continuous Change Detection and Classification (CCDC) algorithm. In this paper, we describe our novel approach to implement the CCDC algorithm to produce the LCMAP product suite composed of five land cover and five land surface change related products. The LCMAP land cover products were validated using a collection of ~ 25,000 reference samples collected independently across CONUS. The overall agreement for all years of the LCMAP primary land cover product reached 82.5%. The LCMAP products are produced through the LCMAP Information Warehouse and Data Store (IW+DS) and Shared Mesos Cluster systems that can process, store, and deliver all datasets for public access. To our knowledge, this is the first set of published 30m annual land cover and land cover change datasets that span from the 1980s to the present for the United States. The LCMAP product suite provides useful information for land resource management and facilitates studies to improve the understanding of terrestrial ecosystems and the complex dynamics of the Earth system. The LCMAP system could be implemented to produce global land change products in the future.

Earth System Science Data↗

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↗