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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↗

USGS1978

Low-cost computer classification of land cover in the Portland area, Oregon, by signature extension techniques

Computer-aided techniques for interpreting multispectral data acquired by Landsat offer economies in the mapping of land cover. Even so, the actual establishment of the statistical classes, or "signatures," is one of the relatively more costly operations involved. Analysts have therefore been seeking cost-saving signature extension techniques that would accept training data acquired for one time or place and apply them to another. Opportunities to extend signatures occur in preprocessing steps and in the classification steps that follow. In the present example, land cover classes were derived by the simplest and most direct form of signature extension: Classes statistically derived from a Landsat scene for the Puget Sound area, Wash., were applied to the Portland area, Oreg., using data for the next Landsat scene acquired less than 25 seconds down orbit. Many features can be recognized on the reduced-scale version of the Portland land cover map shown in this report, although no statistical assessment of its accuracy is available. The cost of classifying 5,607 square kilometers (2,165 sq. mi.) in the Portland area was less than 8 cents per square kilometer ($0.0788, or $0.2041 per square mile). Besides saving in costs, this and other signature extension techniques may be useful in completing land use and land cover mapping in other large areas where multispectral and multitemporal Landsat data are available in digital form but other source materials are generally lacking.

Oregon, Washington↗

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.

Hawaii↗

USGS2013

Residential and service-population exposure to multiple natural hazards in the Mount Hood region of Clackamas County, Oregon

The objective of this research is to document residential and service-population exposure to natural hazards in the rural communities of Clackamas County, Oregon, near Mount Hood. The Mount Hood region of Clackamas County has a long history of natural events that have impacted its small, tourism-based communities. To support preparedness and emergency-management planning in the region, a geospatial analysis of population exposure was used to determine the number and type of residents and service populations in flood-, wildfire-, and volcano-related hazard zones. Service populations are a mix of residents and tourists temporarily benefitting from local services, such as retail, education, or recreation. In this study, service population includes day-use visitors at recreational sites, overnight visitors at hotels and resorts, children at schools, and community-center visitors. Although the heavily-forested, rural landscape suggests few people are in the area, there are seasonal peaks of thousands of visitors to the region. “Intelligent” dasymetric mapping efforts using 30-meter resolution land-cover imagery and U.S. Census Bureau data proved ineffective at adequately capturing either the spatial distribution or magnitude of population at risk. Consequently, an address-point-based hybrid dasymetric methodology of assigning population to the physical location of buildings mapped with a global positioning system was employed. The resulting maps of the population (1) provide more precise spatial distributions for hazard-vulnerability assessments, (2) depict appropriate clustering due to higher density structures, such as apartment complexes and multi-unit commercial buildings, and (3) provide new information on the spatial distribution and temporal variation of people utilizing services within the study area. Estimates of population exposure to flooding, wildfire, and volcanic hazards were determined by using overlay analysis in a geographic information system. Population exposure to flood hazards is low (less than 10 percent of residents) and does not vary substantially between 100-year and 500-year flood-hazard scenarios. Moderate, moderate-to-high, and high wildfire-risk areas within the study region account for 72 percent of residents, 62 percent of employees, and 60 percent of daytime visitors to recreation sites. Fifteen percent of businesses in the study area are in moderate-to-high or high wildfire-risk areas but these businesses represent 51 percent of the local workforce. A volcanic event at Mount Hood could directly impact up to 60 percent of residents in their homes and 87 percent of employees at their workplaces. The proximal volcanic-hazard zone alone includes 65 percent of employees, 80 percent of schools and community facilities, more than 60 percent of overnight visitors in peak seasons, and 82–100 percent of daytime visitors to recreation sites during the summer and winter months, respectively. The number of day-use visitors to recreation sites in the region is greatest during winter months (averaging 129,300 people per month), whereas overnight visitors are greatest during summer months (averaging 34,000 per month). This analysis of residential and service-population exposure to natural hazards supports the development of targeted risk-reduction efforts in the region, while also expanding the discourse on characterizing and assessing population dynamics in tourist-frequented areas.

Oregon↗

USGS2014

National Assessment of Oil and Gas Project: geologic assessment of undiscovered gas hydrate resources on the North Slope, Alaska

Scientists with the U.S. Geological Survey have completed the first assessment of the undiscovered, technically recoverable gas hydrate resources beneath the North Slope of Alaska. This assessment indicates the existence of technically recoverable gas hydrate resources—that is, resources that can be discovered, developed, and produced using current technology. The approach used in this assessment followed standard geology-based USGS methodologies developed to assess conventional oil and gas resources. In order to use the USGS conventional assessment approach on gas hydrate resources, three-dimensional industry-acquired seismic data were analyzed. The analyses indicated that the gas hydrates on the North Slope occupy limited, discrete volumes of rock bounded by faults and downdip water contacts. This assessment approach also assumes that the resource can be produced by existing conventional technology, on the basis of limited field testing and numerical production models of gas hydrate-bearing reservoirs. The area assessed in northern Alaska extends from the National Petroleum Reserve in Alaska on the west through the Arctic National Wildlife Refuge on the east and from the Brooks Range northward to the State-Federal offshore boundary (located 3 miles north of the coastline). This area consists mostly of Federal, State, and Native lands covering 55,894 square miles. Using the standard geology-based assessment methodology, the USGS estimated that the total undiscovered technically recoverable natural-gas resources in gas hydrates in northern Alaska range between 25.2 and 157.8 trillion cubic feet, representing 95 percent and 5 percent probabilities of greater than these amounts, respectively, with a mean estimate of 85.4 trillion cubic feet.

Alaska↗

USGS2009

Using LiDAR and quickbird data to model plant production and quantify uncertainties associated with wetland detection and land cover generalizations

Spatiotemporal data from satellite remote sensing and surface meteorology networks have made it possible to continuously monitor global plant production, and to identify global trends associated with land cover/use and climate change. Gross primary production (GPP) and net primary production (NPP) are routinely derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) onboard satellites Terra and Aqua, and estimates generally agree with independent measurements at validation sites across the globe. However, the accuracy of GPP and NPP estimates in some regions may be limited by the quality of model input variables and heterogeneity at fine spatial scales. We developed new methods for deriving model inputs (i.e., land cover, leaf area, and photosynthetically active radiation absorbed by plant canopies) from airborne laser altimetry (LiDAR) and Quickbird multispectral data at resolutions ranging from about 30??m to 1??km. In addition, LiDAR-derived biomass was used as a means for computing carbon-use efficiency. Spatial variables were used with temporal data from ground-based monitoring stations to compute a six-year GPP and NPP time series for a 3600??ha study site in the Great Lakes region of North America. Model results compared favorably with independent observations from a 400??m flux tower and a process-based ecosystem model (BIOME-BGC), but only after removing vapor pressure deficit as a constraint on photosynthesis from the MODIS global algorithm. Fine-resolution inputs captured more of the spatial variability, but estimates were similar to coarse-resolution data when integrated across the entire landscape. Failure to account for wetlands had little impact on landscape-scale estimates, because vegetation structure, composition, and conversion efficiencies were similar to upland plant communities. Plant productivity estimates were noticeably improved using LiDAR-derived variables, while uncertainties associated with land cover generalizations and wetlands in this largely forested landscape were considered less important.

Remote Sensing of Environment↗

USGS2014

Comparing cropland net primary production estimates from inventory, a satellite-based model, and a process-based model in the Midwest of the United States

Accurately quantifying the spatial and temporal variability of net primary production (NPP) for croplands is essential to understand regional cropland carbon dynamics. We compared three NPP estimates for croplands in the Midwestern United States: inventory-based estimates using crop yield data from the U.S. Department of Agriculture (USDA) National Agricultural Statistics Service (NASS); estimates from the satellite-based Moderate Resolution Imaging Spectroradiometer (MODIS) NPP product; and estimates from the General Ensemble biogeochemical Modeling System (GEMS) process-based model. The three methods estimated mean NPP in the range of 469–687 g C m −2 yr −1 and total NPP in the range of 318–490 Tg C yr −1 for croplands in the Midwest in 2007 and 2008. The NPP estimates from crop yield data and the GEMS model showed the mean NPP for croplands was over 650 g C m −2 yr −1 while the MODIS NPP product estimated the mean NPP was less than 500 g C m −2 yr −1 . MODIS NPP also showed very different spatial variability of the cropland NPP from the other two methods. We found these differences were mainly caused by the difference in the land cover data and the crop specific information used in the methods. Our study demonstrated that the detailed mapping of the temporal and spatial change of crop species is critical for estimating the spatial and temporal variability of cropland NPP. We suggest that high resolution land cover data with species–specific crop information should be used in satellite-based and process-based models to improve carbon estimates for croplands.

Illinois, Indiana, Iowa, Kansas, Michigan, Minneso↗

USGS2010

Macroinvertebrate-based assessment of biological condition at selected sites in the Eagle River watershed, Colorado, 2000-07

The U.S. Geological Survey (USGS), in cooperation with the Colorado River Water Conservation District, Eagle County, Eagle River Water and Sanitation District, Upper Eagle Regional Water Authority, Colorado Department of Transportation, City of Aurora, Town of Eagle, Town of Gypsum, Town of Minturn, Town of Vail, Vail Resorts, Colorado Springs Utilities, Denver Water, and the U.S. Department of Agriculture Forest Service (FS), compiled macroinvertebrate (73 sites, 124 samples) data previously collected in the Eagle River watershed from selected USGS and FS studies, 2000-07. These data were analyzed to assess the biological condition (that is, biologically ?degraded? or ?good?) at selected sites in the Eagle River watershed and determine if site class (for example, urban or undeveloped) described biological condition. An independently developed predictive model was applied to calculate a site-specific measure of taxonomic completeness for macroinvertebrate communities, where taxonomic completeness was expressed as the ratio of observed (O) taxa to those expected (E) to occur at each site. Macroinvertebrate communities were considered degraded at sites were O/E values were less than 0.80, indicating that at least 20 percent of expected taxa were not observed. Sites were classified into one of four classes (undeveloped, adjacent road or highway or both, mixed, urban) using a combination of riparian land-cover characteristics, examination of topographic maps and aerial imagery, screening for exceedances in water-quality standards, and best professional judgment. Analysis of variance was used to determine if site class accounted for variability in mean macroinvertebrate O/E values. Finally, macroinvertebrate taxa observed more or less frequently than expected at urban sites were indentified. This study represents the first standardized assessment of biological condition of selected sites distributed across the Eagle River watershed. Of the 73 sites evaluated, just over half (55 percent) were considered in good biological condition (O/E greater than 0.80). The remaining sites were either consistently biologically degraded (30 percent; O/E less than 0.80) or varied annually between good and degraded condition (15 percent; O/E is less than or greater than 0.80). Sites primarily affected by urbanization were among the most severely degraded (lowest O/E values) when compared to other site classes. Although most urban sites were among the most severely degraded (lowest O/E values), a few sites had nearly intact macroinvertebrate communities (O/E near 1.0). Similar observations were noted among sites classified as mixed. Thirteen macroinvertebrate taxa were indentified that occurred more or less frequently than expected at urban sites. Additionally, six other taxa were impartial (tolerant) to the same conditions. Combined, these 19 taxa provide an opportunity to enhance the interpretation of future studies in the Eagle River watershed, but will require better insight into the responses of these taxa to specific stressors. Understanding the sources of variability affecting biological condition along with why some sites expected to be degraded, but showed otherwise, will have clear implications for mitigation efforts. Integrating results of this study with field and laboratory investigations will greatly enhance the ability to identify causal factors affecting biological condition at degraded sites, the logical next step. Information generated from such integrative studies will be imperative for well targeted mitigation efforts in the Eagle River watershed.

Scientific Investigations Report↗

USGS2013

An approach for characterizing the distribution of shrubland ecosystem components as continuous fields as part of NLCD

Characterizing and quantifying distributions of shrubland ecosystem components is one of the major challenges for monitoring shrubland vegetation cover change across the United States. A new approach has been developed to quantify shrubland components as fractional products within National Land Cover Database (NLCD). This approach uses remote sensing data and regression tree models to estimate the fractional cover of shrubland ecosystem components. The approach consists of three major steps: field data collection, high resolution estimates of shrubland ecosystem components using WorldView-2 imagery, and coarse resolution estimates of these components across larger areas using Landsat imagery. This research seeks to explore this method to quantify shrubland ecosystem components as continuous fields in regions that contain wide-ranging shrubland ecosystems. Fractional cover of four shrubland ecosystem components, including bare ground, herbaceous, litter, and shrub, as well as shrub heights, were delineated in three ecological regions in Arizona, Florida, and Texas. Results show that estimates for most components have relatively small normalized root mean square errors and significant correlations with validation data in both Arizona and Texas. The distribution patterns of shrub height also show relatively high accuracies in these two areas. The fractional cover estimates of shrubland components, except for litter, are not well represented in the Florida site. The research results suggest that this method provides good potential to effectively characterize shrubland ecosystem conditions over perennial shrubland although it is less effective in transitional shrubland. The fractional cover of shrub components as continuous elements could offer valuable information to quantify biomass and help improve thematic land cover classification in arid and semiarid areas.

Arizona, Florida, Texas↗