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At least 883 records · Page 49Linked to original sources
USGS2004

Risk assessment and screening for potentially invasive fishes

Preventing the introduction of potentially invasive species is becoming more important as this worldwide problem continues to grow. The ability to predict the identity or range of potential invaders could influence regulatory decisions and help to optimally allocate resources to deal with ongoing invasions. One screening tool presented here, using species life history and environmental tolerances to identify potential invaders similar to past invaders, can be used to predict potential invading species. Another screening tool, genetic algorithms, can be used to predict the potential range of an invading species. Use and further development of tools such as these, that are quantitative and relatively transparent, would give managers and other decision makers more information for making better-informed decisions.

New Zealand Journal of Marine and Freshwater Resea↗
USGS2011

A case study of green tree frog population size estimation by repeated capture-mark-recapture method with individual tagging: A parametric bootstrap method vs. Jolly-Seber method

This paper deals with estimation of a green tree frog population in an urban setting using repeated capture–mark–recapture (CMR) method over several weeks with an individual tagging system which gives rise to a complicated generalization of the hypergeometric distribution. Based on the maximum likelihood estimation, a parametric bootstrap approach is adopted to obtain interval estimates of the weekly population size which is the main objective of our work. The method is computation-based; and programming intensive to implement the algorithm for re-sampling. This method can be applied to estimate the population size of any species based on repeated CMR method at multiple time points. Further, it has been pointed out that the well-known Jolly–Seber method, which is based on some strong assumptions, produces either unrealistic estimates, or may have situations where its assumptions are not valid for our observed data set.

Journal of Statistical Computation and Simulation↗
USGS2010

Water vapour correction of the daily 1 km AVHRR global land dataset: Part I validation and use of the Water Vapour input field

An atmospheric correction algorithm developed for the 1 km Advanced Very High Resolution Radiometer (AVHRR) global land dataset was modified to include a near real-time total column water vapour data input field to account for the natural variability of atmospheric water vapour. The real-time data input field used for this study is the Television and Infrared Observational Satellite (TIROS) Operational Vertical Sounder (TOVS) Pathfinder A global total column water vapour dataset. It was validated prior to its use in the AVHRR atmospheric correction process using two North American AVHRR scenes, namely 13 June and 28 November 1996. The validation results are consistent with those reported by others and entail a comparison between TOVS, radiosonde, experimental sounding, microwave radiometer, and data from a hand-held sunphotometer. The use of this data layer as input to the AVHRR atmospheric correction process is discussed.

International Journal of Remote Sensing↗
USGS2010

The use of multi-temporal Landsat Normalized Difference Vegetation Index (NDVI) data for mapping fuels in Yosemite National Park, USA

The objective of this study was to test the applicability of using Normalized Difference Vegetation Index (NDVI) values derived from a temporal sequence of six Landsat Thematic Mapper (TM) scenes to map fuel models for Yosemite National Park, USA. An unsupervised classification algorithm was used to define 30 unique spectral-temporal classes of NDVI values. A combination of graphical, statistical and visual techniques was used to characterize the 30 classes and identify those that responded similarly and could be combined into fuel models. The final classification of fuel models included six different types: short annual and perennial grasses, tall perennial grasses, medium brush and evergreen hardwoods, short-needled conifers with no heavy fuels, long-needled conifers and deciduous hardwoods, and short-needled conifers with a component of heavy fuels. The NDVI, when analysed over a season of phenologically distinct periods along with ancillary data, can elicit information necessary to distinguish fuel model types. Fuels information derived from remote sensors has proven to be useful for initial classification of fuels and has been applied to fire management situations on the ground.

International Journal of Remote Sensing↗
USGS2007

A multi-scale segmentation approach to filling gaps in Landsat ETM+ SLC-off images

On 31 May 2003, the Landsat Enhanced Thematic Plus (ETM+) Scan Line Corrector (SLC) failed, causing the scanning pattern to exhibit wedge-shaped scan-to-scan gaps. We developed a method that uses coincident spectral data to fill the image gaps. This method uses a multi-scale segment model, derived from a previous Landsat SLC-on image (image acquired prior to the SLC failure), to guide the spectral interpolation across the gaps in SLC-off images (images acquired after the SLC failure). This paper describes the process used to generate the segment model, provides details of the gap-fill algorithm used in deriving the segment-based gap-fill product, and presents the results of the gap-fill process applied to grassland, cropland, and forest landscapes. Our results indicate this product will be useful for a wide variety of applications, including regional-scale studies, general land cover mapping (e.g. forest, urban, and grass), crop-specific mapping and monitoring, and visual assessments. Applications that need to be cautious when using pixels in the gap areas include any applications that require per-pixel accuracy, such as urban characterization or impervious surface mapping, applications that use texture to characterize landscape features, and applications that require accurate measurements of small or narrow landscape features such as roads, farmsteads, and riparian areas.

International Journal of Remote Sensing↗
USGS2009

Global irrigated area map (GIAM), derived from remote sensing, for the end of the last millennium

A Global Irrigated Area Map (GIAM) has been produced for the end of the last millennium using multiple satellite sensor, secondary, Google Earth and groundtruth data. The data included: (a) Advanced Very High Resolution Radiometer (AVHRR) 3-band and Normalized Difference Vegetation Index (NDVI) 10 km monthly time-series for 1997-1999, (b) Syste me pour l'Observation de la Terre Vegetation (SPOT VGT) NDVI 1 km monthly time series for 1999, (c) East Anglia University Climate Research Unit (CRU) rainfall 50km monthly time series for 1961-2000, (d) Global 30 Arc-Second Elevation Data Set (GTOPO30) 1 km digital elevation data of the World, (e) Japanese Earth Resources Satellite-1 Synthetic Aperture Radar (JERS-1 SAR) data for the rain forests during two seasons in 1996 and (f) University of Maryland Global Tree Cover 1 km data for 1992-1993. A single mega-file data-cube (MFDC) of the World with 159 layers, akin to hyperspectral data, was composed by re-sampling different data types into a common 1 km resolution. The MFDC was segmented based on elevation, temperature and precipitation zones. Classification was performed on the segments. Quantitative spectral matching techniques (SMTs) used in hyperspectral data analysis were adopted to group class spectra derived from unsupervised classification and match them with ideal or target spectra. A rigorous class identification and labelling process involved the use of: (a) space-time spiral curve (ST-SC) plots, (b) brightness-greenness-wetness (BGW) plots, (c) time series NDVI plots, (d) Google Earth very-high-resolution imagery (VHRI) 'zoom-in views' in over 11 000 locations, (e) groundtruth data broadly sourced from the degree confluence project (3 864 sample locations) and from the GIAM project (1 790 sample locations), (f) high-resolution Landsat-ETM+ Geocover 150m mosaic of the World and (g) secondary data (e.g. national and global land use and land cover data). Mixed classes were resolved based on decision tree algorithms and spatial modelling, and when that did not work, the problem class was used to mask and re-classify the MDFC, and the class identification and labelling protocol repeated. The sub-pixel area (SPA) calculations were performed by multiplying full-pixel areas (FPAs) with irrigated area fractions (IAFs) for every class. A 28 class GIAM was produced and the area statistics reported as: (a) annualized irrigated areas (AIAs), which consider intensity of irrigation (i.e. sum of irrigated areas from different seasons in a year plus continuous year-round irrigation or gross irrigated areas), and (b) total area available for irrigation (TAAI), which does not consider intensity of irrigation (i.e. irrigated areas at any given point of time plus the areas left fallow but 'equipped for irrigation' at the same point of time or net irrigated areas). The AIA of the World at the end of the last millennium was 467 million hectares (Mha), which is sum of the non-overlapping areas of: (a) 252 Mha from season one, (b) 174 Mha from season two and (c) 41 Mha from continuous year-round crops. The TAAI at the end of the last millennium was 399 Mha. The distribution of irrigated areas is highly skewed amongst continents and countries. Asia accounts for 79% (370 Mha) of all AIAs, followed by Europe (7%) and North America (7%). Three continents, South America (4%), Africa (2%) and Australia (1%), have a very low proportion of the global irrigation. The GIAM had an accuracy of 79-91%, with errors of omission not exceeding 21%, and the errors of commission not exceeding 23%. The GIAM statistics were also compared with: (a) the United Nations Food and Agricultural Organization (FAO) and University of Frankfurt (UF) derived irrigated areas and (b) national census data for India. The relationships and causes of differences are discussed in detail. The GIAM products are made available through a web portal (http://www.iwmigiam.org). ?? 2009 Taylor & Francis.

International Journal of Remote Sensing↗
USGS2018

An initial validation of Landsat 5 and 7 derived surface water temperature for U.S. lakes, reservoirs, and estuaries

The United States Harmful Algal Bloom and Hypoxia Research Control Act of 2014 identified the need for forecasting and monitoring harmful algal blooms (HAB) in lakes, reservoirs, and estuaries across the nation. Temperature is a driver in HAB forecasting models that affects both HAB growth rates and toxin production. Therefore, temperature data derived from the U.S. Geological Survey Landsat 5 Thematic Mapper and Landsat 7 Enhanced Thematic Mapper Plus thermal band products were validated across 35 lakes and reservoirs, and 24 estuaries. In situ data from the Water Quality Portal (WQP) were used for validation. The WQP serves data collected by state, federal, and tribal groups. Discrete in situ temperature data included measurements at 11,910 U.S. lakes and reservoirs from 1980 through 2015. Landsat temperature measurements could include 170,240 lakes and reservoirs once an operational product is achieved. The Landsat-derived temperature mean absolute error was 1.34°C in lake pixels >180 m from land, 4.89°C at the land-water boundary, and 1.11°C in estuaries based on comparison against discrete surface in situ measurements. This is the first study to quantify Landsat resolvable U.S. lakes and reservoirs, and large-scale validation of an operational satellite provisional temperature climate data record algorithm. Due to the high performance of open water pixels, Landsat satellite data may supplement traditional in situ sampling by providing data for most U.S. lakes, reservoirs, and estuaries over consistent seasonal intervals (even with cloud cover) for an extended period of record of more than 35 years.

International Journal of Remote Sensing↗
USGS2019

The potential role of very high-resolution imagery to characterise lake, wetland and stream systems across the Prairie Pothole Region, United States

Aquatic features critical to watershed hydrology range widely in size from narrow, shallow streams to large, deep lakes. In this study we evaluated wetland, lake, and river systems across the Prairie Pothole Region to explore where pan-sharpened high-resolution (PSHR) imagery, relative to Landsat imagery, could provide additional data on surface water distribution and movement, missed by Landsat. We used the monthly Global Surface Water (GSW) Landsat product as well as surface water derived from Landsat imagery using a matched filtering algorithm (MF Landsat) to help consider how including partially inundated Landsat pixels as water influenced our findings. The PSHR outputs (and MF Landsat) were able to identify ~60–90% more surface water interactions between waterbodies, relative to the GSW Landsat product. However, regardless of Landsat source, by documenting many smaller (<0.2 ha), inundated wetlands, the PSHR outputs modified our interpretation of wetland size distribution across the Prairie Pothole Region.

Prairie Pothole Region↗
USGS1989

Problems of snowmelt runoff modelling for a variety of physiographic and climatic conditions

Problems include: a) definition of the spatial and temporal distribution of model input; b) measurement or estimation of snow accumulation, snowmelt, and runoff process parameters for a range of applications and scales; and c) development of accurate short term and long term snowmelt runoff forecasts. Procedures being investigated to solve these problems include: a) integrating conventional and remote-sensing data to improve estimates of input data; b) developing snowmelt process algorithms which have parameters that are closely related to measurable basin and climatic characteristics; and c) updating model paramters and components using measured data or knowledge of past uncertainty. -from Author

Hydrological Sciences Journal↗
USGS2011

Estimating the hatchery fraction of a natural population: a Bayesian approach

There is strong and growing interest in estimating the proportion of hatchery fish that are in a natural population (the hatchery fraction). In a sample of fish from the relevant population, some are observed to be marked, indicating their origin as hatchery fish. The observed proportion of marked fish is usually less than the actual hatchery fraction, since the observed proportion is determined by the proportion originally marked, differential survival (usually lower) of marked fish relative to unmarked hatchery fish, and rates of mark retention and detection. Bayesian methods can work well in a setting such as this, in which empirical data are limited but for which there may be considerable expert judgment regarding these values. We explored a Bayesian estimation of the hatchery fraction using Monte Carlo–Markov chain methods. Based on our findings, we created an interactive Excel tool to implement the algorithm, which we have made available for free.

North American Journal of Fisheries Management↗
USGS2014

Landsat TM memory effect characterization and correction

Before radiometric calibration of Landsat Thematic Mapper (TM) data can be done accurately, it is necessary to minimize the effects of artifacts present in the data that originate in the instrument's signal processing path. These artifacts have been observed in downlinked image data since shortly after launch of Landsat 4 and 5. However, no comprehensive work has been done to characterize all the artifacts and develop methods for their correction. In this paper, the most problematic artifact is discussed: memory effect (ME). Characterization of this artifact is presented, including the parameters necessary for its correction. In addition, a correction algorithm is described that removes the artifact from TM imagery. It will be shown that this artifact causes significant radiometry errors, but the effect can be removed in a straightforward manner.

Canadian Journal of Remote Sensing↗
USGS1994

Overview of South‐east Asia land cover using a NOAA AVHRR one kilometer composite

A cloud free AVHRR composite of South‐East Asia at one kilometer resolution has been produced from 38 selected daily NOAA‐11 AVHRR images. Geometric accuracy of about 1 pixel is achieved using a two‐step rectification algorithm (orbital model and transformation by ground control points). A spatial and spectral enhancement has been performed, the sea masked out and political boundaries included in the final product. This AVHRR composite is particularly useful for a comprehensive overview of land cover at a regional scale. Qualitative comparison between a monthly composite and the existing forest maps highlights the forest cover change and points out the hot spots where the maps have to be updated.

Geocarto International↗
USGS2024

Comparison of imaging flow cytometry and microscopy for freshwater algal bloom detection

Imaging flow cytometry (IFC) is an emerging tool that allows for rapid identification and enumeration of phytoplankton in freshwater systems. However, few studies have assessed the effects of preservation on IFC results or compared live IFC and microscopy results in freshwater systems. Understanding the effects of preservation and differences between IFC and microscopy will improve interpretation of these data and inform strategies to use IFC-based approaches in freshwater systems. Our study objectives were to compare IFC and phase contrast with epifluorescence microscopy as techniques for phytoplankton identification and enumeration, and the effects of sample preservation with an emphasis on taxa forming harmful cyanobacterial blooms (HCBs). During June through October 2020, samples were collected from 2 lakes in the Finger Lakes region of New York. Live and preserved samples were analyzed by laboratory-based IFC, and preserved samples were analyzed by microscopy. The IFC approach captured community dynamics while detecting potential cyanobacterial bloom-forming taxa earlier and at lower abundances than microscopy. Laboratory-based IFC allowed for an intermediate level of taxonomic information when compared to microscopy, gross techniques, such as extracted chlorophyll a or fluorescence sensors, and field-based operation of IFC approaches. The laboratory-based application of IFC in this study allowed receipt of results in 5 d or less, a substantial improvement over microscopy, which can be time-consuming to conduct. However, the laboratory-based IFC approach had some limitations. Imaging flow cytometry-estimated biovolume may be less accurate than microscopy for some taxa because of the algorithms used to calculate biovolume, particularly for chrysophytes and coccoid cyanobacteria. Colonial dissociation during preservation appeared to affect detection of Microcystis by IFC less than for other fragile bloom-forming taxa like chrysophytes. Our study results advance understanding of how IFC may translate to field-based approaches for early harmful algal bloom indicators in freshwater.

Lake and Reservoir Management↗
USGS2020

A random forest approach for bounded outcome variables

Random forests have become an established tool for classication and regres- sion, in particular in high-dimensional settings and in the presence of non-additive predictor-response relationships. For bounded outcome variables restricted to the unit interval, however, classical modeling approaches based on mean squared error loss may severely suer as they do not account for heteroscedasticity in the data. To address this issue, we propose a random forest approach for relating a beta dis- tributed outcome to a set of explanatory variables. Our approach explicitly makes use of the likelihood function of the beta distribution for the selection of splits dur- ing the tree-building procedure. In each iteration of the tree-building algorithm it chooses one explanatory variable in combination with a split point that maximizes the log-likelihood function of the beta distribution with the parameter estimates de- rived from the nodes of the currently built tree. Results of several simulation studies and an application using data from the U.S.A. National Lakes Assessment Survey demonstrate the properties and usefulness of the method, in particular when com- pared to random forest approaches based on mean squared error loss and parametric regression models.

Journal of Computational and Graphical Statistics↗
USGS2022

Multiple agricultural cropland products of South Asia developed using Landsat-8 30 m and MODIS 250 m data using machine learning on the Google Earth Engine (GEE) cloud and spectral matching techniques (SMTs) in support of food and water security

Cropland products are of great importance in water and food security assessments, especially in South Asia, which is home to nearly 2 billion people and 230 million hectares of net cropland area. In South Asia, croplands account for about 90% of all human water use. Cropland extent, cropping intensity, crop watering methods, and crop types are important factors that have a bearing on the quantity, quality, and location of production. Currently, cropland products are produced using mainly coarse-resolution (250–1000 m) remote sensing data. As multiple cropland products are needed to address food and water security challenges, our study was aimed at producing three distinct products that would be useful overall in South Asia. The first of these, Product 1, was meant to assess irrigated versus rainfed croplands in South Asia using Landsat 30 m data on the Google Earth Engine (GEE) platform. The second, Product 2, was tailored for major crop types using Moderate Resolution Imaging Spectroradiometer (MODIS) 250 m data. The third, Product 3, was designed for cropping intensity (single, double, and triple cropping) using MODIS 250 m data. For the kharif season (the main cropping season in South Asia, Jun–Oct), 10 major crops (5 irrigated crops: rice, soybean, maize, sugarcane, cotton; and 5 rainfed crops: pulses, rice, sorghum, millet, groundnut) were mapped. For the rabi season (post-rainy season, Nov–Feb), five major crops (three irrigated crops: rice, wheat, maize; and two rainfed crops: chickpea, pulses) were mapped. The irrigated versus rainfed 30 m product showed an overall accuracy of 79.8% with the irrigated cropland class providing a producer’s accuracy of 79% and the rainfed cropland class 74%. The overall accuracy demonstrated by the cropping intensity product was 85.3% with the producer’s accuracies of 88%, 85%, and 67% for single, double, and triple cropping, respectively. Crop types were mapped to accuracy levels ranging from 72% to 97%. A comparison of the crop-type area statistics with national statistics explained 63–98% variability. The study produced multiple-cropland products that are crucial for food and water security assessments, modeling, mapping, and monitoring using multiple-satellite sensor big-data, and Random Forest (RF) machine learning algorithms by coding, processing, and computing on the GEE cloud.

GIScience & Remote Sensing↗
USGS2020

Riverbank stability assessment of lower Osage River during hydro-peak flow events: The lower Osage River case (Missouri, USA)

The fluctuation of water level downstream from dams due to hydropower flow releases can negatively affect riverbank stability. This research aims to investigate riverbank instability resulting from the outflow variation of hydropower plants, using Bagnell Dam and the lower Osage River (Missouri, USA) as the basis of analysis. The effects of the water releases from the Bagnell Dam were investigated by computing a series of safety factors in relation to outflow events for 78 cross sections along the 130-km stretch of the lower Osage River using the BSTEM algorithm integrated into the HEC-RAS model package. The results showed that the rate of change in the flow magnitude at each cross section impacts the calculated safety factor. The flow release fluctuations will change the value of the safety factor along the river over time, as the safety factor value closely follows the variation in the flow rate. Also, the results show that the cross sections closer to Bagnell Dam are subjected to be more unstable than those farther downstream of Osage River.

Missouri↗
USGS2018

Classifying physiographic regimes on terrain and hydrologic factors for adaptive generalization of stream networks

Automated generalization software must accommodate multi-scale representations of hydrographic networks across a variety of geographic landscapes, because scale-related hydrography differences are known to vary in different physical conditions. While generalization algorithms have been tailored to specific regions and landscape conditions by several researchers in recent years, the selection and characterization of regional conditions have not been formally defined nor statistically validated. This paper undertakes a systematic classification of landscape types in the conterminous United States to spatially subset the country into workable units, in preparation for systematic tailoring of generalization workflows that preserve hydrographic characteristics. The classification is based upon elevation, standard deviation of elevation, slope, runoff, drainage and bedrock density, soil and bedrock permeability, area of inland surface water, infiltration-excess of overland flow, and a base flow index. A seven class solution shows low misclassification rates except in areas of high landscape diversity such as the Appalachians, Rocky Mountains, and Western coastal regions.

International Journal of Cartography↗
USGS2019

Remote sensing of tracer dye concentrations to support dispersion studies in river channels

In river channels the flow field influences the dispersion of biota, contaminants, and other suspended or dissolved materials. Insight on patterns and rates of dispersion can be gained by injecting a pulse of visible dye and observing spatial and temporal variations in dye concentration as the pulse moves downstream. We evaluated the potential of passive optical remote sensing to enhance such tracer experiments by providing spatially distributed concentration information. During tests performed in both an experimental flume facility and a large natural channel, we made field measurements of Rhodamine WT dye concentration and above-water spectral reflectance. At Korea's River Experiment Center, a small unmanned aircraft system (sUAS) was used to acquire hyperspectral images of a sinuous outdoor flume. On the Kootenai River in northern Idaho, USA, field spectra were collected from a boat and hyperspectral image data and high resolution aerial photographs were obtained from manned aircraft. We modified an Optimal Band Ratio Analysis (OBRA) algorithm to identify wavelength combinations that yielded strong correlations between a spectrally based quantity X and dye concentration C. For both the flume and field tests, we obtained very strong (R^2 from 0.94 to 0.99) relationships between X and C across a broad range of visible wavelengths. On the Kootenai, we found that X vs. C relations derived from field spectra could be applied to airborne hyperspectral images and that dye concentrations could be estimated nearly as reliably from relatively simple three-band images as from hyperspectral data. These results imply that remote sensing could become a powerful tool for mapping dye patterns. Such a capability would advance our understanding of dispersion processes by enabling more rigorous testing of numerical flow models.

Journal of Ecohydraulics↗