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At least 1,279 records · Page 71Linked to original sources
USGS2009

An integrated approach to benthic habitat mapping using remote sensing and GIS: An example from the Hawaiian Islands

This chapter documents our effort to map benthic habitats within the KalokoHonokohau National Historic Park, Hawai`i, USA. We produce detailed benthichabitat maps by using a combination of color aerial photography, high-resolution bathymetry, and georeferenced underwater video and still photography. We classify individual habitat polygons using five basic attributes and additional information regarding geology, morphology, and coral species. Derivative data sets including isobaths, hillshades, and slope maps are also generated. The mapping shows that benthic habitat and seafloor morphology varies greatly throughout the study area. Nearly 73% of the study area consists of a hardbottom structure that is potentially available for coral habitation; the remaining 27% includes unconsolidated sediment and artificial or historical features. Coral cover is generally low and increases with water depth. The offshore geology is predominantly composed of smooth to undulating pahoehoe-type basalt flows that form flat to gently sloping benches, vertical walls, and steep escarpments. In some locations the basalt surface is irregular and mounded into ridges, pinnacles, and arches. Large rounded basaltic boulders and smaller scattered rocks are common throughout the marine portions of the park. Coral or accreted carbonate reef obscures the underlying volcanic surface in only a few areas. The underlying geologic framework and morphology of the submerged volcanic flows within Kaloko-Honokohau National Historical Park provide the primary control on benthic habitats within the park. The habitat maps and associated data can be used as a stand-alone product or in a GIS to provide useful baseline information to scientists, managers and the general public.

Hawaii↗
USGS1992

Multispectral thermal infrared mapping of the 1 October 1988 Kupaianaha flow field, Kilauea volcano, Hawaii

Multispectral thermal infrared radiance measurements of the Kupaianaha flow field were acquired with the NASA airborne Thermal Infrared Multispectral Scanner (TIMS) on the morning of 1 October 1988. The TIMS data were used to map both the temperature and emissivity of the surface of the flow field. The temperature map depicted the underground storage and transport of lava. The presence of molten lava in a tube or tumulus resulted in surface temperatures that were at least 10?? C above ambient. The temperature map also clearly defined the boundaries of hydrothermal plumes which resulted from the entry of lava into the ocean. The emissivity map revealed the boundaries between individual flow units within the Kupaianaha field. In general, the emissivity of the flows varied systematically with age but the relationship between age and emissivity was not unique. Distinct spectral anomalies, indicative of silica-rich surface materials, were mapped near fumaroles and ocean entry sites. This apparent enrichment in silica may have resulted from an acid-induced leaching of cations from the surfaces of glassy flows. Such incipient alteration may have been the cause for virtually all of the emissivity variations observed on the flow field, the spectral anomalies representing areas where the acid attack was most intense. ?? 1992 Springer-Verlag.

Bulletin of Volcanology↗
USGS2025

Avian navigation: Comparing the olfactory navigational “map” and the infrasound direction-finding hypotheses to aeronautics

Animal navigation has long been a fascinating but bewildering subject. Humans and animals might well share similar navigational strategies because they developed within the same physical environments. A “map-and-compass” model has been proposed to explain the two-step avian navigational process, but the “map” step has remained elusive. Although scalar values from bicoordinate geomagnetic or atmospheric olfactory gradients have been considered foundational to the avian map, neither has proved convincing engendering decades of controversy. The olfactory map, and an alternative infrasound direction-finding (IDF) hypothesis, are discussed in this review. The olfactory map hypothesis currently requires extensive stable gradients of trace-odor ratios, but such gradients are highly unlikely within a turbulent and rapidly mixed lower atmosphere. The IDF hypothesis, on the other hand, postulates a two-step navigational model analogous to the maritime and aeronautical radio direction-finding technique. This review was also written to encourage further investigation, and direct testing, of the acoustic navigational process. The IDF hypothesis, at present, appears the better explanation of observed avian navigational behavior and accuracy within the atmosphere’s physical environment.

Journal of Comparative Physiology A↗
USGS2017

Advancements in hydrochemistry mapping: methods and application to groundwater arsenic and iron concentrations in Varanasi, Uttar Pradesh, India

The area east of Varanasi is one of numerous places along the watershed of the Ganges River with groundwater concentrations of arsenic surpassing the maximum value of 10 parts per billion (ppb) recommended by the World Health Organization in drinking water. Here we apply geostatistics and compositional data analysis for the mapping of arsenic and iron to help in understanding the conditions leading to the occurrence of elevated level of arsenic in groundwater. The methodology allows for displaying concentrations of arsenic and iron as maps consistent with the limited information from 95 water wells across an area of approximately 210 km 2 ; visualization of the uncertainty associated with the sampling; and summary of the findings in the form of probability maps. For thousands of years, Varanasi has been on the erosional side in a meander of the river that is free of arsenic values above 10 ppb. Maps reveal two anomalies of high arsenic concentrations on the depositional side of the valley, which has started seeing urban development. The methodology using geostatistics combined with compositional data analysis is completely general, so this study could be used as a prototype for hydrochemistry mapping in other areas.

Uttar Pradesh↗
USGS2015

Beat-the-wave evacuation mapping for tsunami hazards in Seaside, Oregon, USA

Previous pedestrian evacuation modeling for tsunamis has not considered variable wave arrival times or critical junctures (e.g., bridges), nor does it effectively communicate multiple evacuee travel speeds. We summarize an approach that identifies evacuation corridors, recognizes variable wave arrival times, and produces a map of minimum pedestrian travel speeds to reach safety, termed a “beat-the-wave” (BTW) evacuation analysis. We demonstrate the improved approach by evaluating difficulty of pedestrian evacuation of Seaside, Oregon, for a local tsunami generated by a Cascadia subduction zone earthquake. We establish evacuation paths by calculating the least cost distance (LCD) to safety for every grid cell in a tsunami-hazard zone using geospatial, anisotropic path distance algorithms. Minimum BTW speed to safety on LCD paths is calculated for every grid cell by dividing surface distance from that cell to safety by the tsunami arrival time at safety. We evaluated three scenarios of evacuation difficulty: (1) all bridges are intact with a 5-minute evacuation delay from the start of earthquake, (2) only retrofitted bridges are considered intact with a 5-minute delay, and (3) only retrofitted bridges are considered intact with a 10-minute delay. BTW maps also take into account critical evacuation points along complex shorelines (e.g., peninsulas, bridges over shore-parallel estuaries) where evacuees could be caught by tsunami waves. The BTW map is able to communicate multiple pedestrian travel speeds, which are typically visualized by multiple maps with current LCD-based mapping practices. Results demonstrate that evacuation of Seaside is problematic seaward of the shore-parallel waterways for those with any limitations on mobility. Tsunami vertical-evacuation refuges or additional pedestrian bridges may be effective ways of reducing loss of life seaward of these waterways.

Oregon↗
USGS1981

Computer-assisted photogrammetric mapping systems for geologic studies-A progress report

Photogrammetry has played an important role in geologic mapping for many years; however, only recently have attempts been made to automate mapping functions for geology. Computer-assisted photogrammetric mapping systems for geologic studies have been developed and are currently in use in offices of the Geological Survey of Greenland at Copenhagen, Denmark, and the U.S. Geological Survey at Denver, Colorado. Though differing somewhat, the systems are similar in that they integrate Kern PG-2 photogrammetric plotting instruments and small desk-top computers that are programmed to perform special geologic functions and operate flat-bed plotters by means of specially designed hardware and software. A z-drive capability, in which stepping motors control the z-motions of the PG-2 plotters, is an integral part of both systems. This feature enables the computer to automatically position the floating mark on computer-calculated, previously defined geologic planes, such as contacts or the base of coal beds, throughout the stereoscopic model in order to improve the mapping capabilities of the instrument and to aid in correlation and tracing of geologic units. The common goal is to enhance the capabilities of the PG-2 plotter and provide a means by which geologists can make conventional geologic maps more efficiently and explore ways to apply computer technology to geologic studies. ?? 1981.

Photogrammetria↗
USGS1995

Mapping the response of riparian vegetation to possible flow reductions in the Snake River, Idaho

This study was initiated to determine the general effects of potential flow reductions in the middle Snake River (Swan Falls Dam downstream to the Idaho-Oregon border) on its riparian vegetation. Considerable water from the river is currently used to irrigate the adjacent Snake River Plain, and increased demand for water in the future is likely. The problem was subdivided into several research components including: field investigation of the existing riparian vegetation and river environment, hydrological modeling to calculate the effects of one flow scenario on hydrological regime, and integration of vegetation and hydrological modeling results with a Geographic Information System (GIs) to map the riverbed, island, and bank conditions under the scenario flow. Field work was conducted in summer 1990. Riparian vegetation along 40 U.S. Geological Survey cross-sections was sampled at approximately 1.25 mile intervals within the 50 mile long study area. Cross-section and flow data were provided by the U.S. Geological. Survey. GIs mapping of land/water cover using ARC/INFO was based on 1987 aerial photographs. Riverbed contour maps were produced by linking cross-section data, topographic contouring software ( anudem ), and GIs. The maps were used to spatially display shallow areas in the channel likely to become vegetated under reduced flow conditions. The scenario would reduce flow by approximately 20% (160 MAF) and lower the river an average of 0.5 ft. The scenario flow could cause a drop in the elevation of the riparian zone comparable to the drop in mean river level and expansion of the lower riparian zone into shallow areas of the channel. The GIs maps showed that the shallow areas of the channel more likely to become vegetated under the scenario flow are located in wide reaches near islands. Some possible ecological consequences of the scenario flow include a greater area of riparian habitat, reduced flow velocity and sedimentation in shallow channels leading to channel deactivation, increased island visitation and nest predation by predatory mammals due to loss of a water barrier between some islands and banks, and larger populations of alien plant species in the new riparian vegetation.

Idaho, Oregon↗
USGS2025

Extracting data from maps: Lessons learned from the artificial intelligence for critical mineral assessment competition

The U.S. Geological Survey (USGS), Defense Advanced Projects Research Agency (DARPA), NASA Jet Propulsion Laboratory (JPL), and MITRE ran a 12-week machine learning competition aimed at accelerating development of AI tools for critical mineral assessments. The Artificial Intelligence for Critical Mineral Assessment Competition solicited innovative solutions for two challenges: 1) automated georeferencing of historical maps, and 2) automated feature extraction from historical maps. Competitors used a new dataset of historical map images to train, validate, and evaluate their models. Automated georeferencing pipelines attained a median root-mean square error of 1.1 km. Prompt-based extraction (i.e., with user input) of polygons, polylines, and points from geologic maps yielded median F1-scores of 0.77, 0.56, 0.35, respectively. Geologic maps pose numerous challenges for AI workflows because they vary significantly. However, despite its short duration, the competition yielded promising results that have since spurred further innovation in this area and led to the development of new AI tools to semi-automate key, time-consuming parts of the assessment workflow.

Applied Computing and Geosciences↗
USGS2004

An automated approach to mapping corn from Landsat imagery

Most land cover maps generated from Landsat imagery involve classification of a wide variety of land cover types, whereas some studies may only need spatial information on a single cover type. For example, we required a map of corn in order to estimate exposure to agricultural chemicals for an environmental epidemiology study. Traditional classification techniques, which require the collection and processing of costly ground reference data, were not feasible for our application because of the large number of images to be analyzed. We present a new method that has the potential to automate the classification of corn from Landsat satellite imagery, resulting in a more timely product for applications covering large geographical regions. Our approach uses readily available agricultural areal estimates to enable automation of the classification process resulting in a map identifying land cover as ‘highly likely corn,’ ‘likely corn’ or ‘unlikely corn.’ To demonstrate the feasibility of this approach, we produced a map consisting of the three corn likelihood classes using a Landsat image in south central Nebraska. Overall classification accuracy of the map was 92.2% when compared to ground reference data.

Computers and Electronics in Agriculture↗
USGS2020

Machine-learning models to map pH and redox conditions in groundwater in a layered aquifer system, Northern Atlantic Coastal Plain, eastern USA

Study region The study was conducted in the Northern Atlantic Coastal Plain aquifer system, in the eastern USA. Study focus Groundwater pH and redox conditions are fundamental chemical characteristics controlling the distribution of many contaminants of concern for drinking water or the ecological health of receiving waters. In this study, pH and redox conditions were modeled and mapped in a complex, layered aquifer system. Machine-learning methods (boosted regression trees) were applied to data from 3000 to 5000 wells. Predicted pH and the probability of anoxic conditions, defined by three thresholds of dissolved oxygen (0.5, 1, and 2 mg/L), were mapped at the 1-km 2 scale for each of 10 regional aquifer layers. New Hydrological Insights for the Region Maps depict the extent of acidic groundwater and oxic conditions in the shallow, unconfined surficial aquifer and in unconfined, recharge-proximal areas of underlying aquifers, in contrast to alkaline and anoxic groundwater elsewhere. Geographic patterns and influential predictors–including elevation, overlying confining-units thickness, and simulated groundwater age and flux–are consistent with prior understanding of the processes controlling pH and redox in the aquifer system. The model-based maps support robust estimates of aquifer proportions, either areal or volumetric, likely to contain groundwater of a specified quality or be vulnerable to specific pH- or redox-sensitive contaminants. The machine-learning methods were an effective tool to map groundwater quality at the regional scale.

New Jersey, Maryland, Delaware, Virginia↗
USGS2020

An assessment of the representation of ecosystems in global protected areas using new maps of World Climate Regions and World Ecosystems

Representation of ecosystems in protected area networks and conservation strategies is a core principle of global conservation priority setting approaches and a commitment in Aichi Target 11 of the Convention on Biological Diversity. The 2030 Sustainable Development Goals (SDGs) explicitly call for the conservation of terrestrial, freshwater, and marine ecosystems. Accurate ecosystem distribution maps are required to assess representation of ecosystems in protected areas, but standardized, high spatial resolution, and globally comprehensive ecosystem maps have heretofore been lacking. While macroscale global ecoregions maps have been used in global conservation priority setting exercises, they do not identify distinct localized ecosystems at the occurrence (patch) level, and instead describe large ecologically meaningful areas within which additional conservation planning and management are necessary. We describe a new set of maps of globally consistent climate regions and ecosystems at a much finer spatial resolution (250 m) than existing ecological regionalizations. We then describe a global gap analysis of the representation of these ecosystems in protected areas. The new map of terrestrial World Ecosystems was derived from the objective development and integration of 1) global temperature domains, 2) global moisture domains, 3) global landforms, and 4) 2015 global vegetation and land use. These new terrestrial World Ecosystems do not include either freshwater or marine ecosystems, but analog products for the freshwater and marine domains are in development. A total of 431 World Ecosystems were identified, and of these a total of 278 units were natural or semi-natural vegetation/environment combinations, including different kinds of forestlands, shrublands, grasslands, bare areas, and ice/snow regions. The remaining classes were different kinds of croplands and settlements. Of the 278 natural and semi-natural classes, 9 were not represented in global protected areas with a strict biodiversity conservation management objective (IUCN management categories I-IV), and an additional 206 were less than 8.5% protected (half way to the 17% Aichi Target 11 goal). Forty four classes were between 8.5% and 17% protected (more than half way towards the Aichi 17% target), and only 19 classes exceeded the 17% Aichi target. However, when all protected areas (IUCN management categories I-VI plus protected areas with no IUCN designation) were included in a separate global gap analysis, representation of ecosystems increases substantially, with a third of the ecosystems exceeding the 17% Aichi target, and another third between 8.5% and 17%. The overall protection (representation) of global ecosystems in protected areas is considerably less when assessed using only strictly conserved protected areas, and more if all protected areas are included in the analysis. Protected area effectiveness should be included in further evaluations of global ecosystem protection. The ecosystems with the highest representation in protected areas were often bare or sparsely vegetated and found in inhospitable environments (e.g. cold mountains, deserts), and the eight most protected ecosystems were all snow and ice ecosystems. In addition to the global gap analysis of World Ecosystems in protected areas, we report on the representation results for the ecosystems in each biogeographic realm (Neotropical, Nearctic, Afrotropical, Palearctic, Indomalayan, Australasian, and Oceania).

Global Ecology and Conservation↗
USGS2019

Relative prediction intervals reveal larger uncertainty in 3D approaches to predictive digital soil mapping of soil properties with legacy data

Fine scale maps of soil properties enable efficient land management and inform earth system models. Recent efforts to create soil property maps from field observations tend to use similar tree-based machine learning interpolation approaches, but often deal with depth of predictions, validation, and uncertainty differently. One of the main differences in approaches is whether to model individual depths of interest separately as ‘2D’ models, or to create models that incorporate depth as a predictor variable creating a ‘3D’ model that can make pre-dictions for all depths. It is unclear how choice of 2D or 3D approach influences model accuracy and uncertainty due to lack of direct comparison and inconsistent presentation of results in past studies. This study compares 2D and 3D methods for mapping soil electrical conductivity (salinity), pH, sum of fine and very fine sands, and organic carbon at 30 m resolution for the upper 432,000 km 2 of the Colorado River Watershed of the United States of America. A new, simple, model-agnostic relative prediction interval (RPI) approach to report un-certainty is presented that scales prediction interval width to the 95% interquantile width of the original training sample distribution. The RPI approach enables direct comparison of uncertainty between properties and depths and is easily interpretable by end users. Results indicate that 3D mapping of soil properties with strong variation with depth can result in substantial areas with much higher uncertainty that coincide with unrealistic predictions relative to 2D models, even though 3D models had slightly better global cross-validation scores. Maps and global model summaries of RPI proved helpful in identifying these issues with 3D models. These results suggest that the use of RPI or similar approaches to evaluate models can identify accuracy problems not evident in global va-lidation diagnostics.

Geoderma↗
USGS2018

Mapping of compositional properties of coal using isometric log-ratio transformation and sequential Gaussian simulation – A comparative study for spatial ultimate analyses data

Chemical properties of coal largely determine coal handling, processing, beneficiation methods, and design of coal-fired power plants. Furthermore, these properties impact coal strength, coal blending during mining, as well as coal's gas content, which is important for mining safety. In order for these processes and quantitative predictions to be successful, safer, and economically feasible, it is important to determine and map chemical properties of coals accurately in order to infer these properties prior to mining. Ultimate analysis quantifies principal chemical elements in coal. These elements are C, H, N, S, O, and, depending on the basis, ash, and/or moisture. The basis for the data is determined by the condition of the sample at the time of analysis, with an “as-received” basis being the closest to sampling conditions and thus to the in-situ conditions of the coal. The parts determined or calculated as the result of ultimate analyses are compositions, reported in weight percent, and pose the challenges of statistical analyses of compositional data. The treatment of parts using proper compositional methods may be even more important in mapping them, as most mapping methods carry uncertainty due to partial sampling as well. In this work, we map the ultimate analyses parts of the Springfield coal from an Indiana section of the Illinois basin, USA, using sequential Gaussian simulation of isometric log-ratio transformed compositions. We compare the results with those of direct simulations of compositional parts. We also compare the implications of these approaches in calculating other properties using correlations to identify the differences and consequences. Although the study here is for coal, the methods described in the paper are applicable to any situation involving compositional data and its mapping.

Indiana↗
USGS2016

Automated mapping of persistent ice and snow cover across the western U.S. with Landsat

We implemented an automated approach for mapping persistent ice and snow cover (PISC) across the conterminous western U.S. using all available Landsat TM and ETM+ scenes acquired during the late summer/early fall period between 2010 and 2014. Two separate validation approaches indicate this dataset provides a more accurate representation of glacial ice and perennial snow cover for the region than either the U.S. glacier database derived from US Geological Survey (USGS) Digital Raster Graphics (DRG) maps (based on aerial photography primarily from the 1960s–1980s) or the National Land Cover Database 2011 perennial ice and snow cover class. Our 2010–2014 Landsat-derived dataset indicates 28% less glacier and perennial snow cover than the USGS DRG dataset. There are larger differences between the datasets in some regions, such as the Rocky Mountains of Northwest Wyoming and Southwest Montana, where the Landsat dataset indicates 54% less PISC area. Analysis of Landsat scenes from 1987–1988 and 2008–2010 for three regions using a more conventional, semi-automated approach indicates substantial decreases in glaciers and perennial snow cover that correlate with differences between PISC mapped by the USGS DRG dataset and the automated Landsat-derived dataset. This suggests that most of the differences in PISC between the USGS DRG and the Landsat-derived dataset can be attributed to decreases in PISC, as opposed to differences between mapping techniques. While the dataset produced by the automated Landsat mapping approach is not designed to serve as a conventional glacier inventory that provides glacier outlines and attribute information, it allows for an updated estimate of PISC for the conterminous U.S. as well as for smaller regions. Additionally, the new dataset highlights areas where decreases in PISC have been most significant over the past 25–50 years.

ISPRS Journal of Photogrammetry and Remote Sensing↗
USGS2019

High-Resolution mapping of biomass and distribution of marsh and forested wetlands in southeastern coastal Louisiana

This study estimates herbaceous and forested wetland coverage and aboveground biomass (AGB) within the Atchafalaya and Terrebonne coastal basins representing sediment rich and sediment poor coastal regions of southern Louisiana. Louisiana coastal wetlands account for approximately one third (37%) of the estuarine wetland area in the conterminous United States, yet the spatial distribution of their extent and aboveground biomass estimates are not well defined. Despite the importance of Louisiana’s coastal wetlands, existing maps on their extent are often outdated yet repeatedly used in national greenhouse gas (GHG) inventories and within regional aboveground biomass and carbon content estimates. Sentinel-2 optical satellite data was used within an object-oriented machine learning approach to classify wetland extent, mapping the spatial distribution of the coastal wetlands at high-resolution. A total wetland extent of 2950 km2 was mapped during a period of peak biomass in September 2017, comprised of forested and herbaceous wetlands (accuracy >90%) and demonstrated considerable differences (793.7 km2) from currently available estimates of wetland area. The distribution of aboveground biomass (AGB) was mapped using class averaged values derived from field data collected during May and September 2015. Total herbaceous biomass at the study site increased by 108.9% from May (668,684.5 Mg) to September (1,396,969.2 Mg) whilst the total woody vegetation biomass (27,667,232.1 Mg) was sampled in May only. This method provides a tractable means of mapping wetland extent and biomass, in a region threatened with wetland loss under projections of increasing sea-level rise and local subsidence.

Louisiana↗
USGS2025

Reliability of satellite-based vegetation maps for planning wildfire-fuel treatments in shrub steppe: Inferences from two contrasting national parks

Protecting habitat threatened by increasing wildfire size and frequency requires identifying the spatial intersection of wildfire behavior and ecological conditions that favor positive management outcomes. In the perennial sagebrush steppe of Western North America, invasions by fire-prone annual grasses are a key concern, and management of them requires reliable maps of vegetation cover, fuels, and wildfire behavior. We compared commonly used, publicly available vegetation cover and fuels maps, specifically the Rangeland Analysis Platform (RAP) and LANDFIRE, with field-based assessments at two U.S. National Parks dominated by sagebrush steppe: City of Rocks National Reserve and Craters of the Moon National Monument and Preserve. Plant-community composition and fuels measured at ∼1700 field locations spanning ∼300,000 ha revealed that 1) RAP generally underestimated each vegetation cover type where the cover was actually abundant, and conversely overestimated cover types where they were actually scarce, and 2) there was considerable disagreement in fuel-bed maps derived from LANDFIRE compared to field observations. As a result, there were substantial discrepancies in the spatial patterning of wildfire behavior estimated from the fire-spread model FLAMMAP when parameterized with LANDFIRE compared to field-based fuel-bed maps created from Random Forests models. Reliable maps of vegetation cover and fuel conditions are needed to help guide fuels and invasive species management, especially given recent increases in pre- and post-fire treatments in arid and semiarid landscapes. The costs associated with poorly informed fuel reduction may greatly exceed the costs of field-based vegetation and fuels inventory to inform effective design of vegetative fuels treatments.

Idaho↗
USGS2013

Mapping invasive Phragmites australis in the coastal Great Lakes with ALOS PALSAR satellite imagery for decision support

The invasive variety of Phragmites australis (common reed) forms dense stands that can cause negative impacts on coastal Great Lakes wetlands including habitat degradation and reduced biological diversity. Early treatment is key to controlling Phragmites, therefore a map of the current distribution is needed. ALOS PALSAR imagery was used to produce the first basin-wide distribution map showing the extent of large, dense invasive Phragmites-dominated habitats in wetlands and other coastal ecosystems along the U.S. shore of the Great Lakes. PALSAR is a satellite imaging radar sensor that is sensitive to differences in plant biomass and inundation patterns, allowing for the detection and delineation of these tall (up to 5 m), high density, high biomass invasive Phragmites stands. Classification was based on multi-season ALOS PALSAR L-band (23 cm wavelength) HH and HV polarization data. Seasonal (spring, summer, and fall) datasets were used to improve discrimination of Phragmites by taking advantage of phenological changes in vegetation and inundation patterns over the seasons. Extensive field collections of training and randomly selected validation data were conducted in 2010–2011 to aid in mapping and for accuracy assessments. Overall basin-wide map accuracy was 87%, with 86% producer's accuracy and 43% user's accuracy for invasive Phragmites. The invasive Phragmites maps are being used to identify major environmental drivers of this invader's distribution, to assess areas vulnerable to new invasion, and to provide information to regional stakeholders through a decision support tool.

Lake Erie;Lake Huron;Lake Michigan;Lake Ontario;La↗
USGS2022

Rock alteration mapping in and around fossil shallow intrusions at Mt. Ruapehu New Zealand with laboratory and aerial hyperspectral imaging

Diagnostic absorption features in hyperspectral data can be used to identify a specific mineral or mineral associations. However, it is unknown how accurate hyperspectral mapping can be for identifying alteration mineral compositions at the resolution required to describe structures such as fossil intrusions, or whether it can accurately quantify the alteration present. This study compared petrographic observation with visible, near-infrared (VNIR), and shortwave infrared (SWIR) hyperspectral remote sensing at laboratory- (centimetre-scale) and aerial- (metre-scale) scales to characterise the abundance of surface hydrothermal rock alteration in and around a shallow fossil intrusion on Pinnacle Ridge, Mt. Ruapehu, New Zealand. We classified a high-resolution aerial hyperspectral image to develop a new surface alteration map using Spectral Angle Mapper (SAM) algorithm. The petrographic thin-section and the laboratory and aerial hyperspectral imaging revealed a spectrum of hydrous alteration phases as indicated by the presence of an absorption feature at 2207 nm. Moderate correlation exists between the depth of the absorption feature at 2207 nm and the point counting-derived alteration percent values, indicating reliability of laboratory-based hyperspectral analytical methods. In contrast, aerial hyperspectral data failed to provide any clear correlations to field-mapped alteration using a band-depth approach, and we interpret this due to ‘oversampling’ of surface (supergene) alteration, spectral mixing, and sensor limitations (e.g., bandwidth, signal-to-noise ratio). The hyperspectral image-derived alteration map, created using supervised image classification, can loosely be translated to a geotechnical map where porosity and permeability play a major role in localizing hydrothermal fluid flow and the formation of alteration mineral associations.

Mt. Ruapehu↗