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At least 1,099 records · Page 61Linked to original sources
USGS2011

Where the wild things are: Predicting hotspots of seabird aggregations in the California Current System

Marine Protected Areas (MPAs) provide an important tool for conservation of marine ecosystems. To be most effective, these areas should be strategically located in a manner that supports ecosystem function. To inform marine spatial planning and support strategic establishment of MPAs within the California Current System, we identified areas predicted to support multispecies aggregations of seabirds (“hotspots”). We developed habitat‐association models for 16 species using information from at‐sea observations collected over an 11‐year period (1997–2008), bathymetric data, and remotely sensed oceanographic data for an area from north of Vancouver Island, Canada, to the USA/Mexico border and seaward 600 km from the coast. This approach enabled us to predict distribution and abundance of seabirds even in areas of few or no surveys. We developed single‐species predictive models using a machine‐learning algorithm: bagged decision trees. Single‐species predictions were then combined to identify potential hotspots of seabird aggregation, using three criteria: (1) overall abundance among species, (2) importance of specific areas (“core areas”) to individual species, and (3) predicted persistence of hotspots across years. Model predictions were applied to the entire California Current for four seasons (represented by February, May, July, and October) in each of 11 years. Overall, bathymetric variables were often important predictive variables, whereas oceanographic variables derived from remotely sensed data were generally less important. Predicted hotspots often aligned with currently protected areas (e.g., National Marine Sanctuaries), but we also identified potential hotspots in Northern California/Southern Oregon (from Cape Mendocino to Heceta Bank), Southern California (adjacent to the Channel Islands), and adjacent to Vancouver Island, British Columbia, that are not currently included in protected areas. Prioritization and identification of multispecies hotspots will depend on which group of species is of highest management priority. Modeling hotspots at a broad spatial scale can contribute to MPA site selection, particularly if complemented by fine‐scale information for focal areas.

California↗
USGS2003

Applying ecological criteria to marine reserve design: A case study from the California Channel Islands

Using ecological criteria as a theoretical framework, we describe the steps involved in designing a network of marine reserves for conservation and fisheries management. Although we describe the case study of the Channel Islands, the approach to marine reserve design may be effective in other regions where traditional management alone does not sustain marine resources. A group of agencies, organizations, and individuals established clear goals for marine reserves in the Channel Islands, including conservation of ecosystem biodiversity, sustainable fisheries, economic viability, natural and cultural heritage, and education. Given the constraints of risk management, experimental design, monitoring, and enforcement, scientists recommended at least one, but no more than four, reserves in each biogeographic region. In general, the percentage of an area to be included in a reserve network depends on the goals. In the Channel Islands, after consideration of both conservation goals and the risk from human threats and natural catastrophes, scientists recommended reserving an area of 30–50% of all representative habitats in each biogeographic region. For most species of concern, except pinnipeds and seabirds, information about distributions, dispersal, and population growth was limited. As an alternative to species distribution information, suitable habitats for species of concern were used to locate potential reserve sites. We used a simulated annealing algorithm to identify potential reserve network scenarios that would represent all habitats within the smallest area possible. The analysis produced an array of potential reserve network scenarios that all met the established goals.

California↗
USGS2003

Bayesian inference and decision theory - A framework for decision making in natural resource management

Bayesian inference and decision theory may be used in the solution of relatively complex problems of natural resource management, owing to recent advances in statistical theory and computing. In particular, Markov chain Monte Carlo algorithms provide a computational framework for fitting models of adequate complexity and for evaluating the expected consequences of alternative management actions. We illustrate these features using an example based on management of waterfowl habitat.

Ecological Applications↗
USGS2013

Mapping behavioral landscapes for animal movement: a finite mixture modeling approach

Because of its role in many ecological processes, movement of animals in response to landscape features is an important subject in ecology and conservation biology. In this paper, we develop models of animal movement in relation to objects or fields in a landscape. We take a finite mixture modeling approach in which the component densities are conceptually related to different choices for movement in response to a landscape feature, and the mixing proportions are related to the probability of selecting each response as a function of one or more covariates. We combine particle swarm optimization and an Expectation-Maximization (EM) algorithm to obtain maximum likelihood estimates of the model parameters. We use this approach to analyze data for movement of three bobcats in relation to urban areas in southern California, USA. A behavioral interpretation of the models revealed similarities and differences in bobcat movement response to urbanization. All three bobcats avoided urbanization by moving either parallel to urban boundaries or toward less urban areas as the proportion of urban land cover in the surrounding area increased. However, one bobcat, a male with a dispersal-like large-scale movement pattern, avoided urbanization at lower densities and responded strictly by moving parallel to the urban edge. The other two bobcats, which were both residents and occupied similar geographic areas, avoided urban areas using a combination of movements parallel to the urban edge and movement toward areas of less urbanization. However, the resident female appeared to exhibit greater repulsion at lower levels of urbanization than the resident male, consistent with empirical observations of bobcats in southern California. Using the parameterized finite mixture models, we mapped behavioral states to geographic space, creating a representation of a behavioral landscape. This approach can provide guidance for conservation planning based on analysis of animal movement data using statistical models, thereby linking connectivity evaluations to empirical data.

Ecological Applications↗
USGS2013

A spatial mark–resight model augmented with telemetry data

Abundance and population density are fundamental pieces of information for population ecology and species conservation, but they are difficult to estimate for rare and elusive species. Mark-resight models are popular for estimating population abundance because they are less invasive and expensive than traditional mark-recapture. However, density estimation using mark-resight is difficult because the area sampled must be explicitly defined, historically using ad-hoc approaches. We develop a spatial mark-resight model for estimating population density that combines spatial resighting data and telemetry data. Incorporating telemetry data allows us to inform model parameters related to movement and individual location. Our model also allows <100% individual identification of marked individuals. We implemented the model in a Bayesian framework, using a custom-made Metropolis-within-Gibbs Markov chain Monte Carlo algorithm. As an example, we applied this model to a mark-resight study of raccoons on South Core Banks, a barrier island in Cape Lookout National Seashore, North Carolina. We estimated a population of 186.71 ± 14.81 individuals, which translated to a density of 8.29 ± 0.66 individuals per km 2 . The model presented here will have widespread utility in future applications, especially for species that are not naturally marked.

Ecology↗
USGS2026

Cyanobacteria Assessment Network: Pilot study with Sentinel-2 derived chlorophyll data

Harmful algal blooms (HABs) affect global water quality, limiting uses like recreation and consumption because of excessive algal biomass and toxin production. These blooms cause surface scums, taste and odor issues, hypoxia, and negative health and socioeconomic impacts. The US Army Corps of Engineers (USACE) manages over 400 lakes and reservoirs and seeks to improve monitoring of HABs through Sentinel-2 (S2) satellite imagery. The purpose of this project is to develop workflows for a national chlorophyll-a product with 20 m spatial resolution, enabling HAB monitoring for over 270,000 lakes and reservoirs. The 1-year pilot study established a federal partnership to conduct preliminary S2 processing steps, using datasets from Florida, Ohio, and Oregon. Key steps included in situ data aggregation, data assimilation, S2 algorithm evaluation, spatial and temporal compositing, satellite sensor cross-validation, and web hosting of an example prototype product. The study covers 29 USACE reservoirs, with cross-validation using Sentinel-3 (S3) data from 212 lakes. The multiagency initiative aligns with federal agency missions to protect health and the environment, supports acts like the 2017 Harmful Algal Bloom and Hypoxia Research and Control Act, and has annual potential avoided costs of $42 million.

contiguous United States↗
USGS2006

Remote sensing for grassland management in the arid Southwest

We surveyed a group of rangeland managers in the Southwest about vegetation monitoring needs on grassland. Based on their responses, the objective of the RANGES (Rangeland Analysis Utilizing Geospatial Information Science) project was defined to be the accurate conversion of remotely sensed data (satellite imagery) to quantitative estimates of total (green and senescent) standing cover and biomass on grasslands and semidesert grasslands. Although remote sensing has been used to estimate green vegetation cover, in arid grasslands herbaceous vegetation is senescent much of the year and is not detected by current remote sensing techniques. We developed a ground truth protocol compatible with both range management requirements and Landsat's 30 m resolution imagery. The resulting ground-truth data were then used to develop image processing algorithms that quantified total herbaceous vegetation cover, height, and biomass. Cover was calculated based on a newly developed Soil Adjusted Total Vegetation Index (SATVI), and height and biomass were estimated based on reflectance in the near infrared (NIR) band. Comparison of the remotely sensed estimates with independent ground measurements produced r2 values of 0.80, 0.85, and 0.77 and Nash Sutcliffe values of 0.78, 0.70, and 0.77 for the cover, plant height, and biomass, respectively. The approach for estimating plant height and biomass did not work for sites where forbs comprised more than 30% of total vegetative cover. The ground reconnaissance protocol and image processing techniques together offer land managers accurate and timely methods for monitoring extensive grasslands. The time-consuming requirement to collect concurrent data in the field for each image implies a need to share the high fixed costs of processing an image across multiple users to reduce the costs for individual rangeland managers.

Rangeland Ecology and Management↗
USGS2009

A multiscale analysis of coral reef topographic complexity using lidar-derived bathymetry

Coral reefs represent one of the most irregular substrates in the marine environment. This roughness or topographic complexity is an important structural characteristic of reef habitats that affects a number of ecological and environmental attributes, including species diversity and water circulation. Little is known about the range of topographic complexity exhibited within a reef or between different reef systems. The objective of this study was to quantify topographic complexity for a 5-km x 5-km reefscape along the northern Florida Keys reef tract, over spatial scales ranging from meters to hundreds of meters. The underlying dataset was a 1-m spatial resolution, digital elevation model constructed from lidar measurements. Topographic complexity was quantified using a fractal algorithm, which provided a multi-scale characterization of reef roughness. The computed fractal dimensions ( D ) are a measure of substrate irregularity and are bounded between values of 2 and 3. Spatial patterns in D were positively correlated with known reef zonation in the area. Landward regions of the study site contain relatively smooth ( D ≈ 2.35) flat-topped patch reefs, which give way to rougher ( D ≈ 2.5), deep, knoll-shaped patch reefs. The seaward boundary contains a mixture of substrate features, including discontinuous shelf-edge reefs, and exhibits a corresponding range of roughness values (2.28 ≤ D ≤ 2.61).

Journal of Coastal Research↗
USGS2009

Extraction of lidar-based dune-crest elevations for use in examining the vulnerability of beaches to inundation during hurricanes

The morphology of coastal sand dunes plays an important role in determining how a beach will respond to a hurricane. Accurate measurements of dune height and position are essential for assessing the vulnerability of beaches to extreme coastal change during future landfalls. Lidar topographic surveys provide rapid, accurate, high-resolution datasets for identifying the location, position, and morphology of coastal sand dunes over large stretches of coast. An algorithm has been developed for identification of the crest of the most seaward sand dune that defines the landward limit of the beach system. Based on changes in beach slope along cross-shore transects of lidar data, dune elevation and location can automatically be extracted every few meters along the coastline. Dune elevations in conjunction with storm-induced water levels can be used to predict the type of coastal response (e.g., beach erosion, dune erosion, overwash, or inundation) that may be expected during hurricane landfall. The vulnerability of the beach system at Fire Island National Seashore in New York to the most extreme of these changes, inundation, is assessed by comparing lidar-derived dune elevations to modeled wave setup and storm surge height. The vulnerability of the beach system to inundation during landfall of a Category 3 hurricane is shown to be spatially variable because of longshore variations in dune height (mean elevation = 5.44 m, standard deviation = 1.32 m). Hurricane-induced mean water levels exceed dune elevations along 70% of the coastal park, making these locations more vulnerable to inundation during a Category 3 storm.

Journal of Coastal Research↗
USGS1981

Economics and petroleum resource appraisal: The case of the Permian basin:

Estimates of oil and gas resources typically are presented in terms of proved and undiscovered resources. This paper presents a methodology for incorporating economic considerations into resource appraisals for petroleum basins. A cost algorithm is used to calculate estimates of the costs of finding and developing undiscovered oil and gas fields in the Permian basin. The sensitivity of the resource estimates to variations in values of the variables in the costing model was investigated, and the results of this analysis are presented. The model indicates that at prices up to $40/bbl, the total reserves of oil and gas in barrels-of-oil equivalent (BOE's) from future discoveries will be less than 15% of the estimated ultimate recovery from fields discovered before Jan. 1 1975. Only discoveries to a depth of 20,000 ft were included.

Journal of Petroleum Technology↗
USGS2018

Soil property and class maps of the conterminous United States at 100-meter spatial resolution

With growing concern for the depletion of soil resources, conventional soil maps need to be updated and provided at finer and finer resolutions to be able to support spatially explicit human–landscape models. Three US soil point datasets—the National Cooperative Soil Survey Characterization Database, the National Soil Information System, and the Rapid Carbon Assessment dataset—were combined with a stack of over 200 environmental datasets and gSSURGO polygon maps to generate complete coverage gridded predictions at 100-m spatial resolution of six soil properties (percentage of organic C, total N, bulk density, pH, and percentage of sand and clay) and two US soil taxonomic classes (291 great groups [GGs] and 78 modified particle size classes [mPSCs]) for the conterminous United States. Models were built using parallelized random forest and gradient boosting algorithms as implemented in the ranger and xgboost packages for R. Soil property predictions were generated at seven standard soil depths (0, 5, 15, 30, 60, 100, and 200 cm). Prediction probability maps for US soil taxonomic classifications were also generated. Cross validation results indicated an out-of-bag classification accuracy of 60% for GGs and 66% for mPSCs; for soil properties, RMSE for leave-location-out cross-validation was 0.74 ( R 2 = 0.68), 17.8 wt% ( R 2 = 0.57), 12 wt% ( R 2 = 0.46), 3.63 wt% ( R 2 = 0.41), 0.2 g cm −3 ( R 2 = 0.42), and 0.27 wt% ( R 2 = 0.39) for pH, percent sand and clay, weight percentage of organic C, bulk density, and weight percentage of total N, respectively. Nine independent validation datasets were used to assess prediction accuracies for soil class models, and results ranged between 24 and 58% and between 24 and 93% for GG and mPSC prediction accuracies, respectively. Although mapping accuracies were variable and likely lower than gSSURGO in some areas, this modeling approach can enable easier integration of soil information with spatially explicit models compared with multicomponent map units.

Soil Science Society of America Journal↗
USGS2019

Digital mapping of ecological land units using a nationally scalable modeling framework

Ecological site descriptions (ESDs) and associated state-and-transition models (STMs) provide a nationally consistent classification and information system for defining ecological land units for management applications in the United States. Current spatial representations of ESDs, however, occur via soil mapping and are therefore confined to the spatial resolution used to map soils within a survey area. Land management decisions occur across a range of spatial scales and therefore require ecological information that spans similar scales. Digital mapping provides an approach for optimizing the spatial scale of modeling products to best serve decision makers and have the greatest impact in addressing land management concerns. Here, we present a spatial modeling framework for mapping ecological sites using machine learning algorithms, soil survey field observations, soil survey geographic databases, ecological site data, and a suite of remote sensing-based spatial covariates (e.g., hyper-temporal remote sensing, terrain attributes, climate data, land-cover, lithology). Based on the theoretical association between ecological sites and landscape biophysical properties, we hypothesized that the spatial distribution of ecological sites could be predicted using readily available geospatial data. This modeling approach was tested at two study areas within the western United States, representing 6.1 million ha on the Colorado Plateau and 7.5 million ha within the Chihuahuan Desert. Results show our approach was effective in mapping grouped ecological site classes (ESGs), with 10-fold cross-validation accuracies of 70% in the Colorado Plateau based on 1405 point observations across eight expertly-defined ESG classes and 79% in the Chihuahuan Desert based on 2589 point observations across nine expertly-defined ESG classes. Model accuracies were also evaluated using external-validation datasets; resulting in 56 and 44% correct classification for the Colorado Plateau and Chihuahuan Desert, respectively. National coverage of the training and covariate data used in this study provides opportunities for a consistent national-scale mapping effort of ecological sites.

Chihuahuan Desert, Colorado Plateau↗
USGS2011

Improved electron probe microanalysis of trace elements in quartz

Quartz occurs in a wide range of geologic environments throughout the Earth's crust. The concentration and distribution of trace elements in quartz provide information such as temperature and other physical conditions of formation. Trace element analyses with modern electron-probe microanalysis (EPMA) instruments can achieve 99% confidence detection of ~100 ppm with fairly minimal effort for many elements in samples of low to moderate average atomic number such as many common oxides and silicates. However, trace element measurements below 100 ppm in many materials are limited, not only by the precision of the background measurement, but also by the accuracy with which background levels are determined. A new "blank" correction algorithm has been developed and tested on both Cameca and JEOL instruments, which applies a quantitative correction to the emitted X-ray intensities during the iteration of the sample matrix correction based on a zero level (or known trace) abundance calibration standard. This iterated blank correction, when combined with improved background fit models, and an "aggregate" intensity calculation utilizing multiple spectrometer intensities in software for greater geometric efficiency, yields a detection limit of 2 to 3 ppm for Ti and 6 to 7 ppm for Al in quartz at 99% t-test confidence with similar levels for absolute accuracy.

American Mineralogist↗
USGS2025

Case study of deep learning image segmentation for the purposes of rapid 2D petrographic analysis in volcanic rocks

Automation using deep learning methods is a useful alternative to manual methods of petrographic segmentation, but often requires user familiarity with coding and/or algorithms. We examine the Dragonfly TM program's deep learning tools for application by users with a variety of skill levels as a method for petrographic image segmentation. An image processing methodology, bimodal image stacking, was created for low-input-data, high-efficacy training of models which can then be applied to varied samples. Using backscatter electron images we show that the resulting model segmentations agree with manual segmentation total and modal crystallinity values within 5%, and calculated plagioclase crystal size distribution (CSD) values within 2σ, despite limitations in discriminating mafic phases. Model creation and training takes <24 hours, 1–3 hours of which are supervised, and the resultant model can then be applied to new uncharacterized samples in <15 minutes per image. This allows for non-experts to create and utilize deep learning models to segment images of variable brightness and texture, at low user-time cost and resulting in size and shape data which are within uncertainty of manual segmentation. While some limitations are noted (for example, sieve-textured phases may need manual correction, and different minerals with similar BSE intensity may not be resolved as separate phases), this methodology can be utilized for general application of models to wide ranges of volcanic crystalline and bubble textures, and to create a library of models for rapid petrological analysis during volcanic eruptions.

Alaska↗
USGS2019

National earthquake information center strategic plan, 2019–23

Executive Summary Damaging earthquakes occur regularly around the world; since the turn of the 20th century, hundreds of earthquakes have caused significant loss of life and (or) millions of dollars or more in economic losses. While most of these did not directly affect the United States and its Territories, by studying worldwide seismicity we can better understand how to mitigate the effects of earthquakes when they do occur within U.S. borders. Within the U.S. Government, this mandate falls on the U.S. Geological Survey (USGS) National Earthquake Information Center (NEIC), which has the statutory responsibility for monitoring and reporting on earthquakes domestically and globally. The NEIC has been operating since 1966, and throughout its history has been recognized as a world leader for earthquake information. For much of this time, NEIC has been cooperating with a number of regional seismic networks (RSNs) which operate in areas of heightened seismicity in the United States. In 2000, the Advanced National Seismic System (ANSS) was founded as a cooperative umbrella for earthquake-related data collection, analysis, and dissemination in the United States, thereby promoting advanced interoperability between the NEIC and RSN partners. The NEIC also cooperates and coordinates with dozens of global seismic networks. At present (2019), NEIC acquires real-time waveform data from more than 2,000 seismic stations worldwide, contributed from more than 130 seismic networks. Since 2006, the NEIC has operated on a 24-hour, 7-days per week (24/7) basis, and reports on about 30,000 earthquakes per year. Soon after the occurrence of a significant global earthquake, notifications are issued to government representatives, aid agencies, the press, and members of the general public by the Earthquake Notification Service (ENS), electronic feeds, and through the USGS Earthquake Hazards Program (EHP) website. Event-specific web pages provide detailed source parameter information outlining the location and magnitude of the earthquake, including more detailed source characteristics like moment magnitude and focal mechanisms and finite fault solutions. Further, NEIC produces a suite of real-time situational awareness products, including ShakeMap, ShakeCast, Did-You-Feel-It? (DYFI?), and Prompt Assessment of Global Earthquakes for Response (PAGER), to characterize the shaking resulting from the earthquake and the impact it is likely to have on nearby populations and infrastructure. All of these products are ultimately archived in the ANSS Comprehensive Catalog (ComCat), hosted and served by the NEIC. The NEIC also pursues an active research program to improve its ability to characterize earthquakes and understand their hazards. These efforts are all aimed at mitigating the risks of earthquakes to humankind. To maintain its prominent position in earthquake monitoring, the NEIC must continue to evolve, concurrently improving its operations and 24/7 robustness, streamlining services and infrastructure, and keeping pace with research and innovation in the field of seismology. This document outlines how the NEIC might best achieve such goals, by describing specific avenues and opportunities for development in the next five years (2019–23). Several key areas of operational and research focus are identified in this plan as being of the highest importance. First, NEIC must finalize improvements to its regional monitoring capabilities, including the implementation of a variety of improved earthquake detection and association algorithms. One of the most exciting avenues of recent research expansion in earthquake monitoring has involved the use of machine learning; NEIC must explore the benefits of machine learning for improved earthquake detection and source characterization. NEIC also needs to address issues related to the timeliness of earthquake information, exploring the benefits of distributing information as it becomes available, rather than when certain quality criteria are met. To that end, the incorporation of real-time Global Positioning System (GPS) data into the NEIC operational workflow will help improve the speed and accuracy of information for moderate-to-large earthquakes. Finally, NEIC should explore how to further expand and improve the quality and content of the products served during earthquake response efforts, including the generation of new earthquake sequence-specific products, adding an evolutionary component to earthquake information, and continued improvements to earthquake impact products.

Circular↗
USGS1986

Principles of logic and the use of digital geographic information systems

Digital geographic information systems allow many different types of data to be spatially and statistically analyzed. Logical operations can be performed on individual or multiple data planes by algorithms that can be implemented in computer systems. Users and creators of the systems should fully understand these operations. This paper describes the relationships of layers and features in geographic data bases and the principles of logic that can be applied by geographic information systems and suggests that a thorough knowledge of the data that are entered into a geographic data base and of the logical operations will produce results that are most satisfactory to the user. Methods of spatial analysis are reduced to their primitive logical operations and explained to further such understanding.

Circular↗
USGS2009

ATM Coastal Topography-Alabama 2001

These remotely sensed, geographically referenced elevation measurements of Lidar-derived first surface (FS) topography were produced collaboratively by the U.S. Geological Survey (USGS), Florida Integrated Science Center (FISC), St. Petersburg, FL, and the National Aeronautics and Space Administration (NASA), Wallops Flight Facility, VA. This project provides highly detailed and accurate datasets of the Alabama coastline, acquired October 3-4, 2001. The datasets are made available for use as a management tool to research scientists and natural resource managers. An innovative scanning Lidar instrument originally developed by NASA, and known as the Airborne Topographic Mapper (ATM), was used during data acquisition. The ATM system is a scanning Lidar system that measures high-resolution topography of the land surface, and incorporates a green-wavelength laser operating at pulse rates of 2 to 10 kilohertz. Measurements from the laser ranging device are coupled with data acquired from inertial navigation system (INS) attitude sensors and differentially corrected global positioning system (GPS) receivers to measure topography of the surface at accuracies of +/-15 centimeters. The nominal ATM platform is a Twin Otter or P-3 Orion aircraft, but the instrument may be deployed on a range of light aircraft. Elevation measurements were collected over the survey area using the ATM system, and the resulting data were then processed using the Airborne Lidar Processing System (ALPS), a custom-built processing system developed in a NASA-USGS collaboration. ALPS supports the exploration and processing of Lidar data in an interactive or batch mode. Modules for pre-survey flight line definition, flight path plotting, Lidar raster and waveform investigation, and digital camera image playback have been developed. Processing algorithms have been developed to extract the range to the first and last significant return within each waveform. ALPS is routinely used to create maps that represent submerged or first surface topography.

Alabama↗
USGS2009

ATM coastal topography-Florida 2001: Western Panhandle

These remotely sensed, geographically referenced elevation measurements of Lidar-derived first surface (FS) topography were produced collaboratively by the U.S. Geological Survey (USGS), Florida Integrated Science Center (FISC), St. Petersburg, FL, and the National Aeronautics and Space Administration (NASA), Wallops Flight Facility, VA. This project provides highly detailed and accurate datasets of the western Florida panhandle coastline, acquired October 2-4 and 7-10, 2001. The datasets are made available for use as a management tool to research scientists and natural resource managers. An innovative scanning Lidar instrument originally developed by NASA, and known as the Airborne Topographic Mapper (ATM), was used during data acquisition. The ATM system is a scanning Lidar system that measures high-resolution topography of the land surface and incorporates a green-wavelength laser operating at pulse rates of 2 to 10 kilohertz. Measurements from the laser-ranging device are coupled with data acquired from inertial navigation system (INS) attitude sensors and differentially corrected global positioning system (GPS) receivers to measure topography of the surface at accuracies of +/-15 centimeters. The nominal ATM platform is a Twin Otter or P-3 Orion aircraft, but the instrument may be deployed on a range of light aircraft. Elevation measurements were collected over the survey area using the ATM system, and the resulting data were then processed using the Airborne Lidar Processing System (ALPS), a custom-built processing system developed in a NASA-USGS collaboration. ALPS supports the exploration and processing of Lidar data in an interactive or batch mode. Modules for presurvey flight line definition, flight path plotting, Lidar raster and waveform investigation, and digital camera image playback have been developed. Processing algorithms have been developed to extract the range to the first and last significant return within each waveform. ALPS is routinely used to create maps that represent submerged or first surface topography.

Florida↗