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At least 433 records · Page 24Linked to original sources

USGS2021

Understanding collaborative governance from a communication network perspective: A case study of the Atlantic Salmon recovery framework

Atlantic salmon populations in Maine remain critically low despite extensive hatchery supplementation and habitat improvement efforts. In 2000, the Gulf of Maine Distinct Population Segment was listed as Endangered under the ESA with joint listing authority shared by the National Oceanic and Atmospheric Administration (NOAA) and the United States Fish and Wildlife Service (USFWS). Because, regulators, and tribal, federal and state managers operate with independent authorities, recovery decisions depend upon effective communication and coordination among groups. Using a mixed-methods approach, we surveyed (n = 41) and interviewed (n = 28) members of the Atlantic Salmon Recovery Framework (ASRF), the joint governance structure responsible for Atlantic salmon management in Maine. We used survey results to examine the communication between members of ASRF through communication network analysis. While there is a relatively high network density for individual communication (56 %), connections are decentralized, a characteristic that can be incompatible with some organizational structures. Challenges reported by members fit into three general categories; i. slow and ineffective decision-making, ii. confusion surrounding leadership and accountability, and iii. low adaptive capacity. Despite these challenges, participants reported a commitment to maintaining a collaborative governance structure and a long history of inter-organizational relationships. This introspective effort has led to a reorganization effort to optimize communication pathways.

Maine↗

USGS2019

Natural resource management decision-making under climate uncertainty: Building social-ecological resilience in southwestern Colorado

The goal of this project was to facilitate climate change adaptation that contributes to social-ecological resilience, ecosystem and species conservation, and sustainable human communities in southwestern Colorado. The team developed and piloted integrated adaptation planning tools and principles that merge the strengths of the iterative scenario process, the Adaptation for Conservation Targets (ACT) planning framework, institutional analysis, and climate modeling. These tools and principles generated practical strategies and scientific knowledge to advance climate change adaptation in both the Gunnison River Basin and San Juan River Basin and, potentially, other landscapes. A key objective of this project was to work with decision-makers to develop social-ecological adaptation strategies and coordinate actions to reduce the impacts of a changing climate on nature and people. To accomplish this, the project blended science (biophysical and social) and participatory approaches to integrate expert knowledge, land management decision making, and local needs. These adaptation strategies and their creation process were documented by the project team to assist communities and scientists elsewhere who are grappling with the challenges posed by climate change.

Colorado↗

USGS2018

Hierarchical multi-population viability analysis

Population viability analysis (PVA) uses concepts from theoretical ecology to provide a powerful tool for quantitative estimates of population dynamics and extinction risks. However, conventional statistical PVA requires long-term data from every population of interest, whereas many species of concern exist in multiple isolated populations that are only monitored occasionally. We present a hierarchical multi-population viability analysis model that increases inference power from sparse data by sharing information among populations to assess extinction risks while accounting for incomplete detection and sampling biases with explicit observation and sampling sub-models. We present a case study in which we customized this model for historical population monitoring data (1985–2015) from federally threatened Lahontan cutthroat trout populations in the Great Basin, USA. Data were counts of fish captured during backpack electrofishing surveys from locations associated with 155 isolated populations. Some surveys (25%) included multi-pass removal sampling, which provided valuable information about capture efficiency. GIS and remote sensing were used to estimate August stream temperatures, peak flows, and riparian vegetation condition in each population each year. Field data were used to derive an annual index of nonnative trout densities. Results indicated that population growth rates were higher in colder streams and that nonnative trout reduced carrying capacities of native trout. Extinction risks increased with more environmental stochasticity and were also related to population extent, water temperatures, and nonnative densities. We developed a graphical user interface to interact with the fitted model results and to simulate future habitat scenarios and management actions to assess their influence on extinction risks in each population. Hierarchical multi-population viability analysis bridges the gap between site-level field observations and population-level processes, making effective use of existing datasets to support management decisions with robust estimates of population dynamics, extinction risks, and uncertainties.

Ecology↗

USGS2022

Quantifying and visualizing 32 years of agricultural land use change in Kabul, Afghanistan

Agriculture is a key element of Afghanistan’s economy and plays an essential role supporting the expanding population and urban development of Kabul, the country’s capital. Over the past decades the urban landscape has changed substantially and agricultural land use has shifted in its extent, location, and density. Identifying trends in the amount of agricultural area, as an indication of food production, is important for city planning and humanitarian efforts. While many studies have investigated Afghanistan's agriculture, most are conducted at scales that preclude their use for local-scale decision-making. This study quantifies agricultural extent across 32 years from 1988 to 2020 at local scale using simple and repeatable Landsat multispectral image analysis. The volume of data in time-series analysis complicatesvisualization of key findings and long-term trends. This study also explored visualization methods such as zonal mapping, animations, and the isolation of key themes in a 2D static map.

Kabul↗

USGS2001

Assessment method for epithermal gold deposits in northeast Washington State using weights-of-evidence GIS modeling

The weights-of-evidence analysis, a quantitative mineral resource mapping tool, is used to delineate favorable areas for epithermal gold deposits and to predict future exploration activity of the mineral industry for similar deposits in a four-county area (222 x 277 km), including the Okanogan and Colville National Forests of northeastern Washington. Modeling is applied in six steps: (1) building a spatial digital database, (2) extracting predictive evidence for a particular deposit, based on an exploration model, (3) calculating relative weights for each predictive map, (4) combining the geologic evidence maps to predict the location of undiscovered mineral resources and (5) measuring the intensity of recent exploration activity by use of mining claims on federal lands, and (6) combining mineral resource and exploration activity into an assessment model of future mining activity. The analysis is accomplished on a personal computer using ArcView GIS platform with Spatial Analyst and Weights-of-Evidence software. In accord with the descriptive model for epithermal gold deposits, digital geologic evidential themes assembled include lithologic map units, thrust faults, normal faults, and igneous dikes. Similarly, geochemical evidential themes include placer gold deposits and gold and silver analyses from stream sediment (silt) samples from National Forest lands. Fifty mines, prospects, or occurrences of epithermal gold deposits, the training set, define the appropriate a really-associated terrane. The areal (or spatial) correlation of each evidential theme with the training set yield predictor theme maps for lithology, placer sites and normal faults. The weights-of-evidence analysis disqualified the thrust fault, dike, and gold and silver silt analyses evidential themes because they lacked spatial correlation with the training set. The decision to accept or reject evidential themes as predictors is assisted by considering probabilistic data consisting of weights and contrast values calculated for themes according to areal correlation with the training sites. Predictor themes having acceptable weights and contrast values are combined into a preliminary model to predict the locations of undiscovered epithermal gold deposits. This model facilitates ranking of tracts as non-permissive, permissive or favorable categories based on exclusionary, passive, and active criteria through evaluation of probabilistic data provided by interaction of predictor themes. The method is very similar to the visual inspection method of drawing conclusions from anomalies on a manually overlain system of maps. This method serves as a model for future mineral assessment procedures because of its objective nature. To develop a model to predict future exploration activity, the locations of lode mining claims were summarized for 1980, 1985, 1990, and 1996. Land parcels containing historic claims were identified either as those with mining claims present in 1980 or valid claims present in 1985. Current claim parcels were identified as those containing valid lode claims in either 1990 or 1996. A consistent parcel contains both historic and current claims. The epithermal gold and mining claim activity models were combined into an assessment (or mineral resource-activity) model to assist in land use decisions by providing a prediction of mineral exploration activity on federal land in the next decade. Ranks in the assessment model are: (1) no activity, (2) low activity, (3) low to moderate activity, (4) moderate activity and (5) high activity.

Washington↗

USGS2015

A conceptual model for site-level ecology of the giant gartersnake ( Thamnophis gigas ) in the Sacramento Valley, California

Giant gartersnakes (Thamnophis gigas) comprise a species of semi-aquatic snakes precinctive to marshes in the Central Valley of California (Hansen and Brode, 1980; Rossman and others, 1996). Because more than 90 percent of their historical wetland habitat has been converted to other uses (Frayer and others, 1989; Garone, 2007), giant gartersnakes have been listed as threatened by the State of California (California Department of Fish and Game Commission , 1971) and the United States (U.S. Fish and Wildlife Service, 1993). Giant gartersnakes currently occur in a highly modified landscape, with most extant populations occurring in the rice - growing regions of the Sacramento Valley, especially near areas that historically were tule marsh habitat (Halstead and others, 2010, 2014). In ricelands and managed marshes, many operational decisions likely affect the health and viability of giant gartersnake populations. Land-use decisions, including the management of water, aquatic vegetation, terrestrial vegetation, and co-occurring species, have the potential to affect giant gartersnakes. Little is known, however, about the effects of these types of decisions on the viability of giant gartersnake populations. Bayesian network models are a useful tool to help guide decisions with uncertain outcomes. These models require the articulation of what experts think they know about a system, and facilitate learning about the hypothesized relations (Marcot and others, 2001; Uusitalo , 2007). Bayesian networks further provide a clear visual display of the model that facilitates understanding among various stakeholders (Marcot and others, 2001; Uusitalo , 2007). Empirical data and expert judgment can be combined, as continuous or categorical variables, to update knowledge about the system (Marcot and others, 2001; Uusitalo , 2007). Importantly, Bayesian network models allow inference from causes to consequences, but also from consequences to causes, so that data can inform the states of nodes (values of different random variables) in either direction (Marcot and others, 2001; Uusitalo , 2007). Because they can incorporate both decision nodes that represent management actions and utility nodes that quantify the costs and benefits of outcomes, Bayesian networks are ideally suited to risk analysis and adaptive management (Nyberg and others, 2006; Howes and others, 2010). Thus, Bayesian network models are useful in situations where empirical data are not available, such as questions concerning the responses of giant gartersnakes to management.

California↗

USGS2017

National Park Service Vegetation Mapping Inventory Program: Jean Lafitte National Historical Park and Preserve Vegetation Mapping Project

The National Park Service, Natural Resource Stewardship and Science office in Fort Collins, Colorado, publishes a range of reports that address natural resource topics. These reports are of interest and applicability to a broad audience in the National Park Service and others in natural resource management, including scientists, conservation and environmental constituencies, and the public. The Natural Resource Report Series is used to disseminate comprehensive information and analysis about natural resources and related topics concerning lands managed by the National Park Service. The series supports the advancement of science, informed decision-making, and the achievement of the National Park Service mission. The series also provides a forum for presenting lengthier results that may not be accepted by publications with page limitations. All manuscripts in the series receive the appropriate level of peer review to ensure that the information is scientifically credible, technically accurate, appropriately written for the intended audience, and designed and published in a professional manner. This report received formal peer review by subject-matter experts whose background and expertise put them on par technically and scientifically with the authors of the information. The peer review was led according to the Fundamental Science Practices of the U.S. Geological Survey. Views, statements, findings, conclusions, recommendations, and data in this report do not necessarily reflect views and policies of the National Park Service, U.S. Department of the Interior. Mention of trade names or commercial products does not constitute endorsement or recommendation for use by the U.S. Government. This report is available in digital format from the Gulf Coast Network website and the Natural Resource Publications Management website.

Louisiana↗

USGS2019

Optimizing historic preservation under climate change: Decision support for cultural resource adaptation planning in national parks

Climate change poses great challenges for cultural resource management, particularly in coastal areas. Cultural resources, such as historic buildings, in coastal areas are vulnerable to climate impacts including inundation, deterioration , and destruction from sea-level rise and storm-related flooding and erosion. However, research that assesses the trade-offs between actions for protecting vulnerable and valuable cultural resources under budgetary constraints is limited. This study focused on developing a decision support model for managing historic buildings at Cape Lookout National Seashore. We designed the Optimal Preservation Decision Support (OptiPres) model to: (a) identify optimal, annual adaptation actions for historic buildings across a 30-year planning horizon, (b) quantify trade-offs between different actions and the timing of adaptation actions under constrained budgets, and (c) estimate the effectiveness of budget allocations on the resource value of historic buildings. Our analysis of the model suggests that: (1) funding allocation thresholds may exist for national parks to maintain the historical significance and use potential of historic buildings under climate change, (2) the quantitative assessment of trade-offs among alternative adaptation actions provides generalizable guidance for decision makers about the dynamics of their managed system, and (3) the OptiPres model can identify cost-efficient approaches to allocate funding to maintain the historical value of buildings vulnerable to the effects of climate change. Therefore, the OptiPres model, while not designed as a prescriptive decision tool, allows managers to understand the consequences of proposed adaptation actions. The OptiPres model can guide park managers to make cost-effective climate adaptation decisions for historic buildings more transparently and robustly.

Land Use Policy↗

USGS2020

Quantifying drought’s influence on moist soil seed vegetation in California’s Central Valley through time-series remote sensing

Californias Central Valley, USA is a critical component of the Pacific Flyway despite loss of more than 90% of its wetlands. Moist soil seed (MSS) wetland plants are now produced by mimicking seasonal flooding in managed wetlands to provide an essential food resource for waterfowl. Managers need MSS plant area and productivity estimates to support waterfowl conservation, yet this remains unknown at the landscape scale. Also the effects of recent drought on MSS plants have not been quantified. We generated Landsat-derived estimates of extents and productivity (seed yield or its proxy, the green chlorophyll index) of major MSS plants including watergrass (Echinochloa crusgalli) and smartweed (Polygonum spp.) (WGSW), and swamp timothy (Crypsis schoenoides) (ST) in all Central Valley managed wetlands from 20072017. We tested the effects of water year, land ownership and region on plant area and productivity with a multifactor nested analysis of variance. For the San Joaquin Valley we explored the association between water year and water supply, and we developed metrics to support management decisions. MSS plant area maps were based on a support vector machine classification of Landsat phenology metrics (2017 map overall accuracy: 89%). ST productivity maps were created with a linear regression model of seed yield (n=68, R2 = 0.53, normalized RMSE = 10.5%). The Central Valley-wide estimated area for ST in 2017 was 32,369 ha 2,524 ha (95% C.I.), and 13,012 ha 1,384 ha for WGSW. Mean ST seed yield ranged from 577 kg/ha in the Delta Basin to 365 kg/ha in the San Joaquin Basin. WGSW area and ST seed yield decreased while ST area increased in critical drought years compared to normal water years (Scheffes test, p<0.05). Greatest ST area increases occurred in the Sacramento Valley (~75%). Voluntary water deliveries increased in normal water years, and ST seed yield increased with water supply. Z-scores of ST seed yield can be used to evaluate wetland performance and aid resource allocation decisions. Updated maps will support habitat monitoring, conservation planning and water management in future years, which are likely to face greater uncertainty in water availability with climate change.

California↗

USGS2010

Multiobjective sampling design for parameter estimation and model discrimination in groundwater solute transport

Sampling design for site characterization studies of solute transport in porous media is formulated as a multiobjective problem. Optimal design of a sampling network is a sequential process in which the next phase of sampling is designed on the basis of all available physical knowledge of the system. Three objectives are considered: model discrimination, parameter estimation, and cost minimization. For the first two objectives, physically based measures of the value of information obtained from a set of observations are specified. In model discrimination, value of information of an observation point is measured in terms of the difference in solute concentration predicted by hypothesized models of transport. Points of greatest difference in predictions can contribute the most information to the discriminatory power of a sampling design. Sensitivity of solute concentration to a change in a parameter contributes information on the relative variance of a parameter estimate. Inclusion of points in a sampling design with high sensitivities to parameters tends to reduce variance in parameter estimates. Cost minimization accounts for both the capital cost of well installation and the operating costs of collection and analysis of field samples. Sensitivities, discrimination information, and well installation and sampling costs are used to form coefficients in the multiobjective problem in which the decision variables are binary (zero/one), each corresponding to the selection of an observation point in time and space. The solution to the multiobjective problem is a noninferior set of designs. To gain insight into effective design strategies, a one-dimensional solute transport problem is hypothesized. Then, an approximation of the noninferior set is found by enumerating 120 designs and evaluating objective functions for each of the designs. Trade-offs between pairs of objectives are demonstrated among the models. The value of an objective function for a given design is shown to correspond to the ability of a design to actually meet an objective.

Water Resources Research↗

USGS2021

A roadmap for sampling and scaling biological nitrogen fixation in terrestrial ecosystems

Accurately quantifying rates and patterns of biological nitrogen fixation (BNF) in terrestrial ecosystems is essential to characterize ecological and biogeochemical interactions, identify mechanistic controls, improve BNF representation in conceptual and numerical modelling, and forecast nitrogen limitation constraints on future carbon (C) cycling. While many resources address the technical advantages and limitations of different methods for measuring BNF, less systematic consideration has been given to the broader decisions involved in planning studies, interpreting data, and extrapolating results. Here, we present a conceptual and practical road map to study design, study execution, data analysis and scaling, outlining key considerations at each step. We address issues including defining N‐fixing niches of interest, identifying important sources of temporal and spatial heterogeneity, designing a sampling scheme (including method selection, measurement conditions, replication, and consideration of hotspots and hot moments), and approaches to analysing, scaling and reporting BNF. We also review the comparability of estimates derived using different approaches in the literature, and provide sample R code for simulating symbiotic BNF data frames and upscaling. Improving and standardizing study design at each of these stages will improve the accuracy and interpretability of data, define limits of extrapolation, and facilitate broader use of BNF data for downstream applications. We highlight aspects—such as quantifying scales of heterogeneity, statistical approaches for dealing with non‐normality, and consideration of rates versus ecological significance—that are ripe for further development.

Methods in Ecology and Evolution↗

USGS2011

The Lake Tahoe Basin Land Use Simulation Model

This U.S. Geological Survey Open-File Report describes the final modeling product for the Tahoe Decision Support System project for the Lake Tahoe Basin funded by the Southern Nevada Public Land Management Act and the U.S. Geological Survey's Geographic Analysis and Monitoring Program. This research was conducted by the U.S. Geological Survey Western Geographic Science Center. The purpose of this report is to describe the basic elements of the novel Lake Tahoe Basin Land Use Simulation Model, publish samples of the data inputs, basic outputs of the model, and the details of the Python code. The results of this report include a basic description of the Land Use Simulation Model, descriptions and summary statistics of model inputs, two figures showing the graphical user interface from the web-based tool, samples of the two input files, seven tables of basic output results from the web-based tool and descriptions of their parameters, and the fully functional Python code.

Open-File Report↗

USGS2008

A regression model for computing index flows describing the median flow for the summer month of lowest flow in Michigan

In 2006, Michigan enacted laws to prevent new large capacity withdrawals from decreasing flows to the extent that they would functionally impair a stream's ability to support characteristic fish populations. The median streamflow for the summer month of lowest flow was specified by state decision makers as the index flow on which likely impacts of withdrawals would be assessed. At sites near long-term streamflow-gaging stations, analysis of streamflow records during July, August, and September was used to determine the index flow. At ungaged sites, an alternate method for computing the index flow was needed. This report documents the development of a method for computing index flows at ungaged stream sites in Michigan. The method is based on a regression model that computes the index water yield, which is the index flow divided by the drainage area. To develop the regression model, index flows were determined on the basis of daily flows measured during July, August, and September at 147 streamflow-gaging stations having 10 or more years of record (considered long-term stations) in Michigan. The corresponding index water yields were statistically related to climatic and basin characteristics upstream from the stations in the regression model. Climatic and basin characteristics selected as explanatory variables in the regression model include two aquifer-transmissivity and hydrologic-soil groups, forest land cover, and normal annual precipitation. Regression model estimates of water yield explain about 70.8 percent of the variability in index water yields indicated by streamflow-gaging station records. Index flows computed on the basis of regression-model estimates of water yield and corresponding drainage areas explain about 94.0 percent of the variability in index flows indicated by streamflow-gaging station records. No regional bias was detected in the regression-based estimates of water yield within seven hydrologic subregions spanning Michigan. Thus, the single regression model developed in this report can be used to produce unbiased estimates of index water yield and flow statewide. In addition, a technique is presented for computing prediction intervals about the index flow estimates.

Michigan↗

USGS2019

Trade-offs relating to grassland and forest mine reclamation approaches in the central Appalachian region and its implications for the songbird community

Surface mining in the Appalachian region, USA, converts large areas of mature forest to early-successional habitat. This shift in landscape structure has the potential to reduce habitat availability and suitability for forest-dwelling songbirds by reducing and fragmenting mature forest, but also to increase habitat availability for grassland and shrubland-associated songbirds. We examined the influence of mountaintop mining/valley fill (MTMVF) reclamation habitats (grassland, shrubland, and remnant forest) on songbird community composition and abundance at three former MTMVF mines in southwestern West Virginia, relative to intact forest. We quantified the songbird community in 1999 and 2000 using point counts arranged throughout the mine complexes to assess landscape composition of the songbird community. Community analysis showed songbirds had strong associations with their respective guild based on species’ habitat preferences. Although remnant and intact forest treatments had similar species compositions, the forest interior guild had greater richness in intact than remnant forest. Total species richness was greatest in the reclaimed shrubland treatment. Focal species analysis followed similar trends as community assessments, as species’ abundances within treatment types were strongly associated with species’ habitat preferences. Our study indicates reclamation habitat decisions (i.e. grasslands versus forests) can have large effects on avian community composition. Determining appropriate mine restoration actions depends on the suite of species desired for long-term occupancy and their conservation priority.

Appalachian Basin↗

USGS2014

A mapping and monitoring assessment of the Philippines' mangrove forests from 1990 to 2010

Information on the present condition and spatiotemporal dynamics of mangrove forests is needed for land-change studies and integrated natural resources planning and management. Although several national mangrove estimates for the Philippines exist, information is unavailable at sufficient spatial and thematic detail for change analysis. Historical and contemporary mangrove distribution maps of the Philippines for 1990 and 2010 were prepared at nominal 30-m spatial resolution using Landsat satellite data. Image classification was performed using a supervised decision tree classification approach. Additionally, decadal land-cover change maps from 1990 to 2010 were prepared to depict changes in mangrove area. Total mangrove area decreased 10.5% from 1990 to 2010. Comparison of estimates produced from this study with selected historical mangrove area estimates revealed that total mangrove area decreased by approximately half (51.8%) from 1918 to 2010. This study provides the most current and reliable data regarding the Philippines mangrove area and spatial distribution and delineates where and when mangrove change has occurred in recent decades. The results from this study are useful for developing conservation strategies, biodiversity loss mitigation efforts, and future monitoring and analysis.

Journal of Coastal Research↗

USGS2025

HarvestStat Africa – Harmonized subnational crop statistics for sub-Saharan Africa

Sub-Saharan Africa (SSA) faces severe agricultural data scarcity amidst high food insecurity and a large agricultural yield gap, making crop production data crucial for understanding and enhancing food systems. To address this gap, HarvestStat Africa presents the largest compilation of open-access subnational crop statistics and time-series across SSA. Based on agricultural statistics collated by USAID’s Famine Early Warning Systems Network, the subnational crop statistics are standardized and calibrated across changing administrative units to produce consistent and continuous time-series. The dataset includes 546,605 records, primarily spanning from 1980 to 2022, detailing crop production, harvested areas, and yields for 33 countries and 90 crop types, including key cereals in SSA such as wheat, maize, rice, sorghum, barley, millet, and fonio. This new dataset enhances our understanding of how climate variability and change influence agricultural production, supports subnational food system analysis, and aids in operational yield forecasting. As an open-source resource, it sets an important precedent for sharing subnational crop statistics to inform decision-making and modeling efforts.

Scientific Data - Nature↗

USGS2006

Bedrock Geologic Map of New Hampshire, a Digital Representation of Lyons and Others 1997 Map and Ancillary Files

The New Hampshire Geological Survey collects data and performs research on the land, mineral, and water resources of the State, and disseminates the findings of such research to the public through maps, reports, and other publications. The Bedrock Geologic Map of New Hampshire, by John B. Lyons, Wallace A. Bothner, Robert H. Moench, and James B. Thompson, was published in paper format by the U.S. Geological Survey (USGS) in 1997. The online version of this CD contains digital datasets of the State map that are intended to assist the professional geologist, land-use planners, water resource professionals, and engineers and to inform the interested layperson. In addition to the bedrock geology, the datasets include geopolitical and hydrologic information, such as political boundaries, quadrangle boundaries, hydrologic units, and water-well data. A more thorough explanation for each of these datasets may be found in the accompanying metadata files. The data are spatially referenced and may be used in a geographic information system (GIS). ArcExplorer, the Environmental Systems Research Institute's (ESRI) free GIS data viewer, is available at http://www.esri.com/software/arcexplorer. ArcExplorer provides basic functions that are needed to harness the power and versatility of the spatial datasets. Additional information on the viewer and other ESRI products may be found on the ArcExplorer website. Although extensive review and revisions of the data have been performed by the USGS and the New Hampshire Geological Survey, these data represent interpretations made by professional geologists using the best available data, and are intended to provide general geologic information. Use of these data at scales larger than 1:250,000 will not provide greater accuracy. The data are not intended to replace site-specific or specific-use investigations. The U.S. Geological Survey, New Hampshire Geological Survey, and State of New Hampshire make no representation or warranty, expressed or implied, regarding the use, accuracy, or completeness of the data presented herein, or from a map printed from these data; nor shall the act of distribution constitute any such warranty. The New Hampshire Geological Survey disclaims any legal responsibility or liability for interpretations made from the map, or decisions based thereon. For more information on New Hampshire Geological Survey programs please visit the State's website at http://des.nh.gov/Geology/. New Hampshire Geographically Referenced Analysis and Information Transfer System (NH GRANIT) provides access to statewide GIS (http://www.granit.unh.edu/). Questions about this CD or about other datasets should be directed to the New Hampshire Department of Environmental Services.

Data Series↗

USGS2024

The 3D Elevation Program—Supporting Florida's economy

Introduction Florida has the longest coastline of any State in the contiguous United States, and its coastal resources are one of the main drivers of its economic growth. High-quality elevation data are beneficial for use in emergency management, especially for hurricane response, recovery, and mitigation, as well as for coastal zone management, flood risk management, infrastructure planning, agriculture, forestry, and natural resources management. Having regional or statewide elevation data coverage that was collected at about the same time allows for improved results from in-depth modeling and more meaningful analysis to support the State’s Chief Science Officer, Geographic Information Officer, State agencies, water management districts, and local governments that will use these data for decision making. Critical applications that meet the State’s management needs depend on light detection and ranging (lidar) data that provide a highly detailed three-dimensional (3D) model of the Earth’s surface and aboveground features.

Florida↗