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At least 1,045 records · Page 58Linked to original sources
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↗
USGS2018

Dynamic minimum set problem for reserve design: Heuristic solutions for large problems

Conversion of wild habitats to human dominated landscape is a major cause of biodiversity loss. An approach to mitigate the impact of habitat loss consists of designating reserves where habitat is preserved and managed. Determining the most valuable areas to preserve in a landscape is called the reserve design problem. There exists several possible formulations of the reserve design problem, depending on the objectives and the constraints. In this article, we considered the dynamic problem of designing a reserve that contains a desired area of several key habitats. The dynamic case implies that the reserve cannot be designed in one time step, due to budget constraints, and that habitats can be lost before they are reserved, due for example to climate change or human development. We proposed two heuristics strategies that can be used to select sites to reserve each year for large reserve design problem. The first heuristic is a combination of the Marxan and site-ordering algorithms and the second heuristic is an augmented version of the common naive myopic heuristic. We evaluated the strategies on several simulated examples and showed that the augmented greedy heuristic is particularly interesting when some of the habitats to protect are particularly threatened and/or the compactness of the network is accounted for.

PLoS ONE↗
USGS2019

Morphology and genetics of Lythrum salicaria from latitudinal gradients of the Northern Hemisphere grown in cold and hot common gardens

The aim of this project was to compare the phenotypic responses of global populations of Lythrum salicaria in cold/dry and hot/humid environments to determine if phenotypic plasticity varied between the native and invasive ranges, and secondarily if this variation was linked to genetic diversity. Common garden studies were conducted in Třeboň, Czech Republic, and Lafayette, Louisiana, USA (cold/dry vs. hot/humid garden, respectively), using populations from latitudinal gradients in Eurasia and North America. Lythrum salicaria seeds collected from the same maternal plants across these latitudinal gradients were germinated and grown in Třeboň and Lafayette. Tissue masses (above-, below-ground, inflorescence and total) of these individuals were assessed at the end of each growing season (2006–2008). Worldwide field measurements of L . salicaria height were made by volunteers from 2004–2016. Biomass and height data were analyzed using the General Linear Model framework and multivariate techniques. Molecular markers (amplified fragment length polymorphisms) of individuals used in the common garden study were analyzed using traditional genetic diversity metrics and Bayesian clustering algorithms in STRUCTURE. Reaction norms were developed from differences in maternal plant responses in Třeboň versus Lafayette. In the common garden studies, stem/leaf, root and total biomass generally were highest for individuals grown from seeds collected in the southern part of the range in the cold garden, particularly by the third year of the study. In contrast, inflorescence biomass in the cold garden was higher by the third year in individuals from mid-latitude populations. As measured by volunteers, plants were taller in Eurasia than in North America moving from north to south with the pattern switching southward of 40°N latitude. Genetic diversity was similar between native and non-native invasive populations regardless of geographical origin of the seed and was not significantly different in the GLM Select model (p > 0.05). Reaction norm slopes showed that Eurasia had larger values than North America for reaction norms for above-ground and total biomass. Plants from the seeds of mother plants from Turkey had wide variation in total biomass when grown in Třeboň versus Lafayette; this variation in response within certain populations may have contributed to the lack of population-level differences in plasticity. These results indicate no loss of genetic diversity for L . salicaria during its North American invasion, nor reduction in plastic tissue allocation responses to a varying environment, which may help explain some of its invasive qualities and which could be of adaptive value under changing future environments.

PLoS ONE↗
USGS2020

Molecular sequencing and morphological identification reveal similar patterns in native bee communities across public and private grasslands of eastern North Dakota

Bees play a key role in the functioning of human-modified and natural ecosystems by pollinating agricultural crops and wild plant communities. Global pollinator conservation efforts need large-scale and long-term monitoring to detect changes in species’ demographic patterns and shifts in bee community structure. The objective of this project was to test a molecular sequencing pipeline that would utilize a commonly used locus, produce accurate and precise identifications consistent with morphological identifications, and generate data that are both qualitative and quantitative. We applied this amplicon sequencing pipeline to native bee communities sampled across Conservation Reserve Program (CRP) lands and native grasslands in eastern North Dakota. We found the 28S LSU locus to be more capable of discriminating between species than the 18S SSU rRNA locus, and in some cases even resolved instances of cryptic species or morphologically ambiguous species complexes. Overall, we found the amplicon sequencing method to be a qualitatively accurate representation of the sampled bee community richness and species identity, especially when a well-curated database of known 28S LSU sequences is available. Both morphological identification and molecular sequencing revealed similar patterns in native bee community structure across CRP lands and native prairie. Additionally, a genetic algorithm approach to compute taxon-specific correction factors using a small subset of the most concordant samples demonstrated that a high level of quantitative accuracy could be possible if the specimens are fresh and processed soon after collection. Here we provide a first step to a molecular pipeline for identifying insect pollinator communities. This tool should prove useful for future national monitoring efforts as use of molecular tools becomes more affordable and as numbers of 28S LSU sequences for pollinator species increase in publicly-available databases.

North Dakota↗
USGS2020

A modeling workflow that balances automation and human intervention to inform invasive plant management decisions at multiple spatial scales

Predictions of habitat suitability for invasive plant species can guide risk assessments at regional and national scales and inform early detection and rapid-response strategies at local scales. We present a general approach to invasive species modeling and mapping that meets objectives at multiple scales. Our methodology is designed to balance trade-offs between developing highly customized models for few species versus fitting non-specific and generic models for numerous species. We developed a national library of environmental variables known to physiologically limit plant distributions and relied on human input based on natural history knowledge to further narrow the variable set for each species before developing habitat suitability models. To ensure efficiency, we used largely automated modeling approaches and human input only at key junctures. We explore and present uncertainty by using two alternative sources of background samples, including five statistical algorithms, and constructing model ensembles. We demonstrate the use and efficiency of the Software for Assisted Habitat Modeling [SAHM 2.1.2], a package in VisTrails, which performs the majority of the modeling analyses. Our workflow includes solicitation of expert feedback on model outputs such as spatial prediction results and variable response curves, and iterative improvement based on new data availability and directed field validation of initial model results. We highlight the utility of the models for decision-making at regional and local scales with case studies of two plant species that invade natural areas: fountain grass ( Pennisetum setaceum ) and goutweed ( Aegopodium podagraria ). By balancing model automation with human intervention, we can efficiently provide land managers with mapped predicted distributions for multiple invasive species to inform decisions across spatial scales.

PLoS ONE↗
USGS2024

A multi-objective approach for timber harvest scheduling to include management of at-risk species and spatial configuration objectives

Sustainable forestry typically involves integration of several economic and ecological objectives which, at times, may not be compatible with one another. Multi-objective prioritization via harvest scheduling programs can be used to elucidate these relationships and explore solutions. One such program is a spatially explicit harvest scheduler that adopts the Metropolis-Hastings algorithm to iteratively find management solutions to achieve multiple objectives (Habplan). Although this program has been used to address forest management scheduling and simulation-based tasks, its utility is constrained by time-intensive data preparation and challenges with incorporating spatial configuration objectives. To address these shortcomings, we introduce an open-source software package, HabplanR, streamlines data preparation, sets parameters, visualizes results, and assesses spatial components of ecological objectives. We developed four example objectives to incorporate into a multi-objective management problem: habitat quality indices for three species “types” (open, closed, and intermediate-canopy-associated species), and harvested pine pulpwood (revenue). We demonstrate the utility of this package to find management schedules that can accommodate potentially conflicting habitat needs of species, while achieving economic targets. We produced 100 software runs and prioritized individual objectives to select four management schedules for further comparisons. We compared outcome differences of the four schedules, including a spatial comparison of two high performing schedules. The software package makes costs and benefits of different schedules explicit and allows for consideration of the spatial configuration of management outcomes in decision-making.

southeastern United States↗
USGS2024

Machine learning and new-generation spaceborne hyperspectral data advance crop type mapping

Hyperspectral sensors provide near-continuous spectral data that can facilitate advancements in agricultural crop classification and characterization, which are important for addressing global food and water security issues. We investigated two new-generation hyperspectral sensors, Germany’s Deutsches Zentrum für Luft‐ und Raumfahrt Earth Sensing Imaging Spectrometer (DESIS) and Italy’s PRecursore IperSpettrale della Missione Applicativa (PRISMA), within California's Central Valley in August 2021 focusing on five irrigated agricultural crops (alfalfa, almonds, corn, grapes, and pistachios). With reference data from the U.S. Department of Agriculture Cropland Data Layer, we developed a spectral library of the crops and classified them using three machine learning algorithms (support vector machines [SVM], random forest [RF], and spectral angle mapper [SAM]) and two philosophies: 1. Full spectral analysis (FSA) and 2. Optimal hyperspectral narrowband (OHNB) analysis. For FSA, we used 59 DESIS four-bin product bands and 207 of 238 PRISMA bands. For OHNB analysis, 9 DESIS and 16 PRISMA nonredundant OHNBs for studying crops were selected. FSA achieved only 1% to 3% higher accuracies relative to OHNB analysis in most cases. SVM provided the best results, closely followed by RF. Using both DESIS and PRISMA image OHNBs in SVM for classification led to higher accuracy than using either image alone, with an overall accuracy of 99%, producer’s accuracies of 94% to 100%, and user's accuracies of 95% to 100%.

California↗
USGS2003

Urban land-cover change detection through sub-pixel imperviousness mapping using remotely sensed data

We developed a Sub-pixel Imperviousness Change Detection (SICD) approach to detect urban land-cover changes using Landsat and high-resolution imagery. The sub-pixel percent imperviousness was mapped for two dates (09 March 1993 and 11 March 2001) over western Georgia using a regression tree algorithm. The accuracy of the predicted imperviousness was reasonable based on a comparison using independent reference data. The average absolute error between predicted and reference data was 16.4 percent for 1993 and 15.3 percent for 2001. The correlation coefficient (r) was 0.73 for 1993 and 0.78 for 2001, respectively. Areas with a significant increase (greater than 20 percent) in impervious surface from 1993 to 2001 were mostly related to known land-cover/land-use changes that occurred in this area, suggesting that the spatial change of an impervious surface is a useful indicator for identifying spatial extent, intensity, and, potentially, type of urban land-cover/land-use changes. Compared to other pixel-based change-detection methods (band differencing, rationing, change vector, post-classification), information on changes in sub-pixel percent imperviousness allow users to quantify and interpret urban land-cover/land-use changes based on their own definition. Such information is considered complementary to products generated using other change-detection methods. In addition, the procedure for mapping imperviousness is objective and repeatable, hence, can be used for monitoring urban land-cover/land-use change over a large geographic area. Potential applications and limitations of the products developed through this study in urban environmental studies are also discussed.

Photogrammetric Engineering and Remote Sensing↗
USGS2006

A 16-year time series of 1 km AVHRR satellite data of the conterminous United States and Alaska

The U.S. Geological Survey (USGS) has developed a 16-year time series of vegetation condition information for the conterminous United States and Alaska using 1 km Advanced Very High Resolution Radiometer (AVHRR) data. The AVHRR data have been processed using consistent methods that account for radiometric variability due to calibration uncertainty, the effects of the atmosphere on surface radiometric measurements obtained from wide field-of-view observations, and the geometric registration accuracy. The conterminous United States and Alaska data sets have an atmospheric correction for water vapor, ozone, and Rayleigh scattering and include a cloud mask derived using the Clouds from AVHRR (CLAVR) algorithm. In comparison with other AVHRR time series data sets, the conterminous United States and Alaska data are processed using similar techniques. The primary difference is that the conterminous United States and Alaska data are at 1 km resolution, while others are at 8 km resolution. The time series consists of weekly and biweekly maximum normalized difference vegetation index (NDVI) composites.

Photogrammetric Engineering and Remote Sensing↗
USGS2015

A landsat data tiling and compositing approach optimized for change detection in the conterminous United States

Annual disturbance maps are produced by the LANDFIRE program across the conterminous United States (CONUS). Existing LANDFIRE disturbance data from 1999 to 2010 are available and current efforts will produce disturbance data through 2012. A tiling and compositing approach was developed to produce bi-annual images optimized for change detection. A tiled grid of 10,000 × 10,000 30 m pixels was defined for CONUS and adjusted to consolidate smaller tiles along national borders, resulting in 98 non-overlapping tiles. Data from Landsat-5,-7, and -8 were re-projected to the tile extents, masked to remove clouds, shadows, water, and snow/ice, then composited using a cosine similarity approach. The resultant images were used in a change detection algorithm to determine areas of vegetation change. This approach enabled more efficient processing compared to using single Landsat scenes, by taking advantage of overlap between adjacent paths, and allowed an automated system to be developed for the entire process.

Photogrammetric Engineering and Remote Sensing↗
USGS1994

Identification of tundra land cover near Teschekpuk Lake, Alaska using SPOT satellite data

Tundra vegetation in the Teshekpuk Lake area of the Alaskan Arctic Coastal Plain was mapped to assess distribution and abundance of waterfowl habitats. Three SPOT satellite scenes were acquired and registered to a 20 m Universal Transverse Mercator grid. Two clustering techniques were used to develop statistical parameters by which the SPOT data were spectrally classified. A maximum likelihood algorithm that correlated spectral classes with land cover types was applied to the SPOT data. Field data were used to assist in spectral class labeling and vegetation descriptions. Twelve cover classes were mapped. The most common type was moist sedge meadow tundra (13.5%); the least common was moss/peat shoreline (0.2%). The moss/peat shoreline type, important to moulting geese and other waterfowl, was spectrally identified using supervised clustering techniques. All other land cover types were identified using unsupervised clustering techniques. Cover classes were described, and a tundra landscape profile produced.

Alaska↗
USGS2009

A framework for implementing biodiversity offsets: selecting sites and determining scale

Biodiversity offsets provide a mechanism for maintaining or enhancing environmental values in situations where development is sought despite detrimental environmental impacts. They seek to ensure that unavoidable negative environmental impacts of development are balanced by environmental gains, with the overall aim of achieving a net neutral or positive outcome. Once the decision has been made to offset, multiple issues arise regarding how to do so in practice. A key concern is site selection. In light of the general aim to locate offsets close to the affected sites to ensure that benefits accrue in the same area, what is the appropriate spatial scale for identifying potential offset sites (e.g., local, ecoregional)? We use the Marxan site-selection algorithm to address conceptual and methodological challenges associated with identifying a set of potential offset sites and determining an appropriate spatial scale for them. To demonstrate this process, we examined the design of offsets for impacts from development on the Jonah natural gas field in Wyoming.

Wyoming↗
USGS2012

Wetland selection by breeding and foraging black terns in the Prairie Pothole Region of the United States

We examined wetland selection by the Black Tern (Chlidonias niger), a species that breeds primarily in the prairie pothole region, has experienced population declines, and is difficult to manage because of low site fidelity. To characterize its selection of wetlands in this region, we surveyed 589 wetlands throughout North and South Dakota. We documented breeding at 5% and foraging at 17% of wetlands. We created predictive habitat models with a machine-learning algorithm, Random Forests, to explore the relative role of local wetland characteristics and those of the surrounding landscape and to evaluate which characteristics were important to predicting breeding versus foraging. We also examined area-dependent wetland selection while addressing the passive sampling bias by replacing occurrence of terns in the models with an index of density. Local wetland variables were more important than landscape variables in predictions of occurrence of breeding and foraging. Wetland size was more important to prediction of foraging than of breeding locations, while floating matted vegetation was more important to prediction of breeding than of foraging locations. The amount of seasonal wetland in the landscape was the only landscape variable important to prediction of both foraging and breeding. Models based on a density index indicated that wetland selection by foraging terns may be more area dependent than that by breeding terns. Our study provides some of the first evidence for differential breeding and foraging wetland selection by Black Terns and for a more limited role of landscape effects and area sensitivity than has been previously shown.

Prairie Pothole Region↗
USGS2018

Development of Raman spectroscopy as a thermal maturity proxy in unconventional resource assessment

The objective of this study was to correlate shale hydrous pyrolysis with thermal maturity measurements based on solid bitumen reflectance (BRo) at the U.S. Geological Survey (USGS) and Raman microscopy (RM) at WellDog. In semi-blind Phase I, BRo values of the initial set of 8 samples were withheld prior to RM analysis. As reported previously, a strong correlation was observed between BRo and Raman parameters. For Phase-II, BRo values for the second set of 8 samples were shared before RM. Observations from Phase-II are reported here as well as the ability of RM to correctly order the semi-blind Phase I samples. Immature shale samples from the Bakken (Phase-I) and Duvernay (Phase-II) formations were subjected to hydrous pyrolysis for 72 hours at temperatures from 280°C to 360°C. Rock residues from both series were mounted and polished (ASTM D2797) for analysis of BRo (ASTM D7708) and confocal laser-scanning Raman microscopy. For RM, multiple hyperspectral maps were collected from each sample, resulting in tens of thousands of spectra per sample. Map areas were ~5,000 μm2, with a spectrum collected from every square micrometer. The organic carbon G- (Graphitic-) and D- (Disordered) bands in each Raman spectrum were fit algorithmically to a multi-peak model, yielding a number of diagnostic parameters that correlate with changes occurring in samples as a result of thermal maturation and pyrolysis. Parameters extracted from analysis of Raman spectra were plotted against the previously determined BRo values to determine which Raman parameters best correlate with thermal maturity. Plotting two of the sample-averaged anonymized spectral parameters versus BRo in the Bakken series indicated an exponential trend with strong correlations (R2>0.8) as reported at URTeC in 2017 (MS-2671253). Similar strong relationships occurred in the Duvernay samples with respect to increasing maturity when using Partial Least-Squares analysis.

Conference Paper↗
USGS2001

All equal-area map projections are created equal, but some are more equal than others

High-resolution regional and global raster databases are currently being generated for a variety of environmental and scientific modeling applications. The projection of these data from geographic coordinates to a plane coordinate system is subject to significant areal error. Sources of error include users selecting an inappropriate projection or incorrect parameters for a given projection, algorithmic errors in commercial geographic information system (GIS) software, and errors resulting from the projection of data in the raster format. To assess the latter type of errors, the accuracy of raster projection was analyzed by two methods. First, a set of 12 one-degree by one-degree quadrilaterals placed at various latitudes was projected at several raster resolutions and compared to the projection of a vector representation of the same quadrilaterals. Second, several different raster resolutions of land cover data for Asia were projected and the total areas of 21 land cover categories were tabulated and compared. While equal-area projections are designed to specifically preserve area, the comparison of the results of the one-degree by one-degree quadrilaterals with the common equal area projections (e.g., the Mollweide) indicates a considerable variance in the one-degree area after projection. Similarly, the empirical comparison of land cover areas for Asia among various projections shows that total areas of land cover vary with projection type, raster resolution, and latitude. No single projection is best for all resolutions and all latitudes. While any of the equal-area projections tested are reasonably accurate for most applications with resolutions of eight-kilometer pixels or smaller, significant variances in accuracies appear at larger pixel sizes.

Cartography and Geographic Information Science↗
USGS2005

Cubic map algebra functions for spatio-temporal analysis

We propose an extension of map algebra to three dimensions for spatio-temporal data handling. This approach yields a new class of map algebra functions that we call "cube functions." Whereas conventional map algebra functions operate on data layers representing two-dimensional space, cube functions operate on data cubes representing two-dimensional space over a third-dimensional period of time. We describe the prototype implementation of a spatio-temporal data structure and selected cube function versions of conventional local, focal, and zonal map algebra functions. The utility of cube functions is demonstrated through a case study analyzing the spatio-temporal variability of remotely sensed, southeastern U.S. vegetation character over various land covers and during different El Nin??o/Southern Oscillation (ENSO) phases. Like conventional map algebra, the application of cube functions may demand significant data preprocessing when integrating diverse data sets, and are subject to limitations related to data storage and algorithm performance. Solutions to these issues include extending data compression and computing strategies for calculations on very large data volumes to spatio-temporal data handling.

Cartography and Geographic Information Science↗
USGS2011

Adapting generalization tools to physiographic diversity for the united states national hydrography dataset

This paper reports on generalization and data modeling to create reduced scale versions of the National Hydrographic Dataset (NHD) for dissemination through The National Map, the primary data delivery portal for USGS. Our approach distinguishes local differences in physiographic factors, to demonstrate that knowledge about varying terrain (mountainous, hilly or flat) and varying climate (dry or humid) can support decisions about algorithms, parameters, and processing sequences to create generalized, smaller scale data versions which preserve distinct hydrographic patterns in these regions. We work with multiple subbasins of the NHD that provide a range of terrain and climate characteristics. Specifically tailored generalization sequences are used to create simplified versions of the high resolution data, which was compiled for 1:24,000 scale mapping. Results are evaluated cartographically and metrically against a medium resolution benchmark version compiled for 1:100,000, developing coefficients of linear and areal correspondence.

Cartography and Geographic Information Science↗
USGS2013

Preparation and Presentation of Digital Maps in Raster Format

A set of algorithms has been developed at USGS Flagstaff for displaying digital map data in raster format. The set includes: FILLIN, which assigns a specified attribute code to units of a map which have been outlined on a digitizer and converted to raster format; FILBND, which removes the outlines; ZIP, which adds patterns to the map units; and COLOR, which provides a simplified process for creating color separation plates for either photographic or lithographic reproduction. - Authors

American Cartographer↗