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At least 1,351 records · Page 75Linked to original sources
USGS2023

National Land Cover Database 2019: A comprehensive strategy for creating the 1986-2019 forest disturbance product

The National Land Cover Database (NLCD) 2016 products show that, between 2001 and 2016, nearly half of the land cover change in the conterminous United States (CONUS) involved forested areas. To ensure the quality of NLCD land cover and land cover change products, it is important to accurately detect the location and time of forest disturbance. We designed a comprehensive strategy to integrate a continuous time series forest change detection method and a discrete 2-date forest change detection method to produce the NLCD 1986–2019 forest disturbance product, which shows the most recent forest disturbance date between the years 1986 and 2019 for every 2- to 3-year interval. This method, the Time-Series method Using Normalized Spectral Distance (NSD) index (TSUN), uses NSD to detect multi-date forest land cover changes and was shown to be easily extended to a new date even when new images were processed in a different way than previous date images. The discrete 2-date method uses the Multi-Index Integrated Change Analysis (MIICA) method to detect changes between 2-date images. A method based on confidence and object grouping was designed to combine the multiple MIICA outputs to improve change detection accuracy. Finally, an aggregation scheme was implemented to combine the TSUN output, the integrated MIICA results, and ancillary data to produce the NLCD 2019 forest disturbance 1986–2019 product. The initial accuracy assessments from 1,600 samples over 4 Landsat path/rows show that the producer’s and user’s accuracies of the 2001–2019 forest disturbance map are 76% and 74%, respectively. The final CONUS-wide forest disturbance product is provided at https://www.mrlc.gov/nlcd-2019-science-research-products .

conterminous United States↗
USGS2007

Influences of watershed, riparian-corridor, and reach-scale characteristics on aquatic biota in agricultural watersheds

Multivariate analyses and correlations revealed strong relations between watershed and riparian-corridor land cover, and reach-scale habitat versus fish and macroinvertebrate assemblages in 38 warmwater streams in eastern Wisconsin. Watersheds were dominated by agricultural use, and ranged in size from 9 to 71 km 2 Watershed land cover was summarized from satellite-derived data for the area outside a 30-m buffer. Riparian land cover was interpreted from digital orthophotos within 10-, 10-to 20-, and 20-to 30-m buffers. Reach-scale habitat, fish, and macroinvertebrates were collected in 1998 and biotic indices calculated. Correlations between land cover, habitat, and stream-quality indicators revealed significant relations at the watershed, riparian-corridor, and reach scales. At the watershed scale, fish diversity, intolerant fish and EPT species increased, and Hilsenhoff biotic index (HBI) decreased as percent forest increased. At the riparian-corridor scale, EPT species decreased and HBI increased as riparian vegetation became more fragmented. For the reach, EPT species decreased with embeddedness. Multivariate analyses further indicated that riparian (percent agriculture, grassland, urban and forest, and fragmentation of vegetation), watershed (percent forest) and reach-scale characteristics (embeddedness) were the most important variables influencing fish (IBI, density, diversity, number, and percent tolerant and insectivorous species) and macroinvertebrate (HBI and EPT) communities.

Wisconsin↗
USGS2011

Predicting future changes in Muskegon River Watershed game fish distributions under future land cover alteration and climate change scenarios

Future alterations in land cover and climate are likely to cause substantial changes in the ranges of fish species. Predictive distribution models are an important tool for assessing the probability that these changes will cause increases or decreases in or the extirpation of species. Classification tree models that predict the probability of game fish presence were applied to the streams of the Muskegon River watershed, Michigan. The models were used to study three potential future scenarios: (1) land cover change only, (2) land cover change and a 3°C increase in air temperature by 2100, and (3) land cover change and a 5°C increase in air temperature by 2100. The analysis indicated that the expected change in air temperature and subsequent change in water temperatures would result in the decline of coldwater fish in the Muskegon watershed by the end of the 21st century while cool- and warmwater species would significantly increase their ranges. The greatest decline detected was a 90% reduction in the probability that brook trout Salvelinus fontinalis would occur in Bigelow Creek. The greatest increase was a 276% increase in the probability that northern pike Esox lucius would occur in the Middle Branch River. Changes in land cover are expected to cause large changes in a few fish species, such as walleye Sander vitreus and Chinook salmon Oncorhynchus tshawytscha, but not to drive major changes in species composition. Managers can alter stream environmental conditions to maximize the probability that species will reside in particular stream reaches through application of the classification tree models. Such models represent a good way to predict future changes, as they give quantitative estimates of the n-dimensional niches for particular species.

Michigan↗
USGS2018

Assessing cropland area in west Africa for agricultural yield analysis

Accurate estimates of cultivated area and crop yield are critical to our understanding of agricultural production and food security, particularly for semi-arid regions like the Sahel of West Africa, where crop production is mainly rain-fed and food security is closely correlated with the inter-annual variations in rainfall. Several global and regional land cover products, based on satellite remotely-sensed data, provide estimates of the agricultural land use intensity, but the initial comparisons indicate considerable differences among them, relating to differences in the satellite data quality, classification approaches, and spatial and temporal resolutions. Here, we quantify the accuracy of available cropland products across Sahelian West Africa using an independent, high-resolution, visually interpreted sample dataset that classifies all points across West Africa using a 2-km sample grid (~500,000 points for the study area). We estimate the “quantity” and “allocation” disagreements for the cropland class of eight land cover products in five Western Sahel countries (Burkina Faso, Mali, Mauritania, Niger, and Senegal). The results confirm that coarse spatial resolution (300 m, 500 m, and 1000 m) land cover products have higher disagreements in mapping the fragmented agricultural landscape of the Western Sahel. Earlier products (e.g., GLC2000) are less accurate than recent products (e.g., ESA CCI 2013, MODIS 2013 and GlobCover 2009). We also show that two of the finer spatial resolution maps (GFSAD30, and GlobeLand30) using advanced classification approaches (random forest, decision trees, and pixel-object combined) are currently the best available products for cropland identification. However, none of the eight land cover databases examined is consistent in reaching the targeted 75% accuracy threshold in the five Sahelian countries. The majority of currently available land cover products overestimate cultivated areas by an average of 170% relative to the cropland area in the reference data.

Remote Sensing↗
USGS2020

The relationship between biodiversity and wetland cover varies across regions of the conterminous United States

Identifying the factors that determine the spatial distribution of biodiversity is a major focus of ecological research. These factors vary with scale from interspecific interactions to global climatic cycles. Wetlands are important biodiversity hotspots and contributors of ecosystem services, but the association between proportional wetland cover and species richness has shown mixed results. It is not well known as to what extent there is a relationship between proportional wetland cover and species richness, especially at the sub-continental scale. We used the National Wetlands Inventory to model wetland cover for the conterminous United States and the National Land Cover Database to estimate wetland change between 2001 and 2011. We used a Bayesian spatial Poisson model to estimate a spatially varying coefficient surface describing the effect of proportional wetland cover on the distribution of amphibians, birds, mammals, and reptiles and the cumulative distribution of terrestrial endemic species. Species richness and wetland cover were significantly correlated, and this relationship varied both spatially and by taxonomic group. Rather than a continental-scale association, however, we found that this relationship changed more closely among ecoregions. The species richness of each of the five groups was positively associated with wetland cover in some or all of the Great Plains; additionally, a positive association was found for mammals in the Southeastern Plains and Piedmont of the eastern U.S. Model results indicated negative association especially in the Cold Deserts and Northern Lakes & Forests of Minnesota and Wisconsin, though these varied greatly between groups. Our results highlight the need for wetland conservation initiatives that focus efforts at the level II and III ecoregional scale rather than along political boundaries.

PLoS ONE↗
USGS2011

Algal bioassessment metrics for wadeable streams and rivers of Maine, USA

Many state water-quality agencies use biological assessment methods based on lotic fish and macroinvertebrate communities, but relatively few states have incorporated algal multimetric indices into monitoring programs. Algae are good indicators for monitoring water quality because they are sensitive to many environmental stressors. We evaluated benthic algal community attributes along a landuse gradient affecting wadeable streams and rivers in Maine, USA, to identify potential bioassessment metrics. We collected epilithic algal samples from 193 locations across the state. We computed weighted-average optima for common taxa for total P, total N, specific conductance, % impervious cover, and % developed watershed, which included all land use that is no longer forest or wetland. We assigned Maine stream tolerance values and categories (sensitive, intermediate, tolerant) to taxa based on their optima and responses to watershed disturbance. We evaluated performance of algal community metrics used in multimetric indices from other regions and novel metrics based on Maine data. Metrics specific to Maine data, such as the relative richness of species characterized as being sensitive in Maine, were more correlated with % developed watershed than most metrics used in other regions. Few community-structure attributes (e.g., species richness) were useful metrics in Maine. Performance of algal bioassessment models would be improved if metrics were evaluated with attributes of local data before inclusion in multimetric indices or statistical models.

Maine↗
USGS2014

Integrating recent land cover mapping efforts to update the National Gap Analysis Program's species habitat map

Over the past decade, great progress has been made to develop national extent land cover mapping products to address natural resource issues. One of the core products of the GAP Program is range-wide species distribution models for nearly 2000 terrestrial vertebrate species in the U.S. We rely on deductive modeling of habitat affinities using these products to create models of habitat availability. That approach requires that we have a thematically rich and ecologically meaningful map legend to support the modeling effort. In this work, we tested the integration of the Multi-Resolution Landscape Characterization Consortium's National Land Cover Database 2011 and LANDFIRE's Disturbance Products to update the 2001 National GAP Vegetation Dataset to reflect 2011 conditions. The revised product can then be used to update the species models. We tested the update approach in three geographic areas (Northeast, Southeast, and Interior Northwest). We used the NLCD product to identify areas where the cover type mapped in 2011 was different from what was in the 2001 land cover map. We used Google Earth and ArcGIS base maps as reference imagery in order to label areas identified as "changed" to the appropriate class from our map legend. Areas mapped as urban or water in the 2011 NLCD map that were mapped differently in the 2001 GAP map were accepted without further validation and recoded to the corresponding GAP class. We used LANDFIRE's Disturbance products to identify changes that are the result of recent disturbance and to inform the reassignment of areas to their updated thematic label. We ran species habitat models for three species including Lewis's Woodpecker ( Melanerpes lewis ) and the White-tailed Jack Rabbit ( Lepus townsendii ) and Brown Headed nuthatch ( Sitta pusilla ). For each of three vertebrate species we found important differences in the amount and location of suitable habitat between the 2001 and 2011 habitat maps. Specifically, Brown headed nuthatch habitat in 2011 was −14% of the 2001 modeled habitat, whereas Lewis's Woodpecker increased by 4%. The white-tailed jack rabbit ( Lepus townsendii ) had a net change of −1% (11% decline, 10% gain). For that species we found the updates related to opening of forest due to burning and regenerating shrubs following harvest to be the locally important main transitions. In the Southeast updates related to timber management and urbanization are locally important.

The International Archives of the Photogrammetry, ↗
USGS2010

Application of AFINCH as a tool for evaluating the effects of streamflow-gaging-network size and composition on the accuracy and precision of streamflow estimates at ungaged locations in the southeast Lake Michigan hydrologic subregion

Bootstrapping techniques employing random subsampling were used with the AFINCH (Analysis of Flows In Networks of CHannels) model to gain insights into the effects of variation in streamflow-gaging-network size and composition on the accuracy and precision of streamflow estimates at ungaged locations in the 0405 (Southeast Lake Michigan) hydrologic subregion. AFINCH uses stepwise-regression techniques to estimate monthly water yields from catchments based on geospatial-climate and land-cover data in combination with available streamflow and water-use data. Calculations are performed on a hydrologic-subregion scale for each catchment and stream reach contained in a National Hydrography Dataset Plus (NHDPlus) subregion. Water yields from contributing catchments are multiplied by catchment areas and resulting flow values are accumulated to compute streamflows in stream reaches which are referred to as flow lines. AFINCH imposes constraints on water yields to ensure that observed streamflows are conserved at gaged locations. Data from the 0405 hydrologic subregion (referred to as Southeast Lake Michigan) were used for the analyses. Daily streamflow data were measured in the subregion for 1 or more years at a total of 75 streamflow-gaging stations during the analysis period which spanned water years 1971–2003. The number of streamflow gages in operation each year during the analysis period ranged from 42 to 56 and averaged 47. Six sets (one set for each censoring level), each composed of 30 random subsets of the 75 streamflow gages, were created by censoring (removing) approximately 10, 20, 30, 40, 50, and 75 percent of the streamflow gages (the actual percentage of operating streamflow gages censored for each set varied from year to year, and within the year from subset to subset, but averaged approximately the indicated percentages). Streamflow estimates for six flow lines each were aggregated by censoring level, and results were analyzed to assess (a) how the size and composition of the streamflow-gaging network affected the average apparent errors and variability of the estimated flows and (b) whether results for certain months were more variable than for others. The six flow lines were categorized into one of three types depending upon their network topology and position relative to operating streamflow-gaging stations. Statistical analysis of the model results indicates that (1) less precise (that is, more variable) estimates resulted from smaller streamflow-gaging networks as compared to larger streamflow-gaging networks, (2) precision of AFINCH flow estimates at an ungaged flow line is improved by operation of one or more streamflow gages upstream and (or) downstream in the enclosing basin, (3) no consistent seasonal trend in estimate variability was evident, and (4) flow lines from ungaged basins appeared to exhibit the smallest absolute apparent percent errors (APEs) and smallest changes in average APE as a function of increasing censoring level. The counterintuitive results described in item (4) above likely reflect both the nature of the base-streamflow estimate from which the errors were computed and insensitivity in the average model-derived estimates to changes in the streamflow-gaging-network size and composition. Another analysis demonstrated that errors for flow lines in ungaged basins have the potential to be much larger than indicated by their APEs if measured relative to their true (but unknown) flows. “Missing gage” analyses, based on examination of censoring subset results where the streamflow gage of interest was omitted from the calibration data set, were done to better understand the true error characteristics for ungaged flow lines as a function of network size. Results examined for 2 water years indicated that the probability of computing a monthly streamflow estimate within 10 percent of the true value with AFINCH decreased from greater than 0.9 at about a 10-percent network-censoring level to less than 0.6 as the censoring level approached 75 percent. In addition, estimates for typically dry months tended to be characterized by larger percent errors than typically wetter months.

Indiana, Michigan↗
USGS2009

A case study of carbon fluxes from land change in the Southwest Brazilian Amazon

Worldwide, land change is responsible for one-fifth of anthropogenic carbon emissions. In Brazil, three-quarters of carbon emissions originate from land change. This study represents a municipal-scale study of carbon fluxes from vegetation in Rio Branco, Brazil. Land-cover maps of pasture, forest, and secondary growth from 1993, 1996, 1999, and 2003 were produced using an unsupervised classification method (overall accuracy = 89%). Carbon fluxes from land change over the decade of imagery were estimated from transitions between land-cover categories for each time interval. This article presents new methods for estimating emissions reductions from carbon stored in the vegetation that replaces forests (e.g., pasture) and sequestration by new (>10–15 years) forests, which reduced gross emissions by 16, 15, and 22% for the period of 1993–1996, 1996–1999, and 1999–2003, respectively. The methods used in the analysis are broadly applicable and provide a comprehensive characterization of regional-scale carbon fluxes from land change.

Southwest Brazilian Amazon Rainforest↗
USGS2015

Mapping forest functional type in a forest-shrubland ecotone using SPOT imagery and predictive habitat distribution modelling

The availability of land cover data at local scales is an important component in forest management and monitoring efforts. Regional land cover data seldom provide detailed information needed to support local management needs. Here we present a transferable framework to model forest cover by major plant functional type using aerial photos, multi-date Système Pour l’Observation de la Terre (SPOT) imagery, and topographic variables. We developed probability of occurrence models for deciduous broad-leaved forest and needle-leaved evergreen forest using logistic regression in the southern portion of the Wyoming Basin Ecoregion. The model outputs were combined into a synthesis map depicting deciduous and coniferous forest cover type. We evaluated the models and synthesis map using a field-validated, independent data source. Results showed strong relationships between forest cover and model variables, and the synthesis map was accurate with an overall correct classification rate of 0.87 and Cohen’s kappa value of 0.81. The results suggest our method adequately captures the functional type, size, and distribution pattern of forest cover in a spatially heterogeneous landscape.

Colorado, Utah, Wyoming↗
USGS2010

The IGBP-DIS global 1 km land cover data set, DISCover: First results

The International Geosphere-Biosphere Programme Data and Information System (IGBP-DIS) is co-ordinating the development of global land data sets from Advanced Very High Resolution Radiometer (AVHRR) data. The first is a 1 km spatial resolution land cover product 'DISCover', based on monthly Normalized Difference Vegetation Index composites from 1992 and 1993. DISCover is a 17 class land cover dataset based on the science requirements of IGBP elements. Mapping uses unsupervised classification with post-classification refinement using ancillary data. Draft Africa, North America and South America products are now available for peer review.

International Journal of Remote Sensing↗
USGS2024

An ensemble mean method for remote sensing of actual evapotranspiration to estimate water budget response across a restoration landscape

Estimates of actual evapotranspiration (ETa) are valuable for effective monitoring and management of water resources. In areas that lack ground-based monitoring networks, remote sensing allows for accurate and consistent estimates of ETa across a broad scale—though each algorithm has limitations (i.e., ground-based validation, temporal consistency, spatial resolution). We developed an ensemble mean ETa (EMET) product to incorporate advancements and reduce uncertainty among algorithms (e.g., energy-balance, optical-only), which we use to estimate vegetative water use in response to restoration practices being implemented on the ground using management interventions (i.e., fencing pastures, erosion control structures) on a private ranch in Baja California Sur, Mexico. This paper describes the development of a monthly EMET product, the assessment of changes using EMET over time and across multiple land use/land cover types, and the evaluation of differences in vegetation and water distribution between watersheds treated by restoration and their controls. We found that in the absence of a ground-based monitoring network, the EMET product is more robust than using a single ETa data product and can augment the efficacy of ETa-based studies. We then found increased ETa within the restored watershed when compared to the control sites, which we attribute to increased plant water availability.

Baja California Sur↗
USGS2017

Assessment of forest degradation in Vietnam using Landsat time series data

Landsat time series data were used to characterize forest degradation in Lam Dong Province, Vietnam. We conducted three types of image change analyses using Landsat time series data to characterize the land cover changes. Our analyses concentrated on the timeframe of 1973–2014, with much emphasis on the latter part of that range. We conducted a field trip through Lam Dong Province to develop a better understanding of the ground conditions of the region, during which we obtained many photographs of representative forest sites with Global Positioning System locations to assist us in our image interpretations. High-resolution Google Earth imagery and Landsat data of the region were used to validate results. In general, our analyses indicated that many land-use changes have occurred throughout Lam Dong Province, including gradual forest to non-forest transitions. Recent changes are most marked along the relatively narrow interfaces between agricultural and forest areas that occur towards the boundaries of the province. One important observation is that the most highly protected national reserves in the region have not changed much over the entire Landsat timeframe (1972–present). Spectral changes within these regions have not occurred at the same levels as those areas adjacent to the reserves.

Forests↗
USGS2014

Long-term trends in alkalinity in large rivers of the conterminous US in relation to acidification, agriculture, and hydrologic modification

Alkalinity increases in large rivers of the conterminous US are well known, but less is understood about the processes leading to these trends as compared with headwater systems more intensively examined in conjunction with acid deposition studies. Nevertheless, large rivers are important conduits of inorganic carbon and other solutes to coastal areas and may have substantial influence on coastal calcium carbonate saturation dynamics. We examined long-term (mid-20th to early 21st century) trends in alkalinity and other weathering products in 23 rivers of the conterminous US. We used a rigorous flow-weighting technique which allowed greater focus on solute trends occurring independently of changes in flow. Increasing alkalinity concentrations and yield were widespread, occurring at 14 and 13 stations, respectively. Analysis of trends in other weathering products suggested that the causes of alkalinity trends were diverse, but at many stations alkalinity increases coincided with decreasing nitrate + sulfate and decreasing cation:alkalinity ratios, which is consistent with recovery from acidification. A positive correlation between the Sen–Thiel slopes of alkalinity increases and agricultural lime usage indicated that agricultural lime contributed to increasing solute concentration in some areas. However, several stations including the Altamaha, Upper Mississippi, and San Joaquin Rivers exhibited solute trends, such as increasing cation:alkalinity ratios and increasing nitrate + sulfate, more consistent with increasing acidity, emphasizing that multiple processes affect alkalinity trends in large rivers. This study was unique in its examination of alkalinity trends in large rivers covering a wide range of climate and land use types, but more detailed analyses will help to better elucidate temporal changes to river solutes and especially the effects they may have on coastal calcium carbonate saturation state.

Science of the Total Environment↗
USGS2018

Vegetative and geomorphic complexity at tributary junctions on the Colorado and Dolores Rivers: a blueprint for riparian restoration

Context Habitat complexity in rivers is linked to dynamic fluvial conditions acting at various spatial scales. On regulated rivers in the western United States, tributaries are regions of high energy and disturbance, providing important resource inputs for riparian ecosystems. Objectives This study investigated spatial patterns and extents of tributary influence on riparian habitat complexity in the near channel zone along regulated reaches of the Colorado (> 200 km) and Dolores Rivers (~ 300 km) in the western United States. Because tributary confluences are regions of increased dynamism, we hypothesized that: (1) geomorphic and land cover complexity would be greatest close to tributary junctions and decrease with distance from tributaries; and (2) patterns in complexity would vary across different sized spatial units. Methods Using a combination of remote sensing and spatial analysis, we classified fluvial features and land cover classes to investigate patterns longitudinally at 10-, 25-, and 100-m spatial units in the near channel zone of two regulated rivers. Results Using change point analysis and randomization tests, we detected shifts in riparian habitat complexity closer to tributary junctions. Patterns varied across 10-, 25-, and 100-m spatial units in the near channel zone, with significance (p ≤ 0.05) recorded for 10- and 25-m spatial units. Conclusions Tributary junctions deliver critical resource inputs on regulated systems, providing for increased geomorphic and land cover diversity upstream and downstream of tributaries. We found that patterns of response were non-linear and discontinuous, varying across spatial units and potentially influenced by the degree of mainstem flow regulation.

Colorado, Utah↗
USGS2006

Land-cover trends in the Mojave basin and range ecoregion

The U.S. Geological Survey's Land-Cover Trends Project aims to estimate the rates of contemporary land-cover change within the conterminous United States between 1972 and 2000. A random sampling approach was used to select a representative sample of 10-km by 10-km sample blocks and to estimate change within +/- 1 percent at an 85-percent confidence interval. Landsat Multispectral Scanner, Thematic Mapper, and Enhanced Thematic Mapper Plus data were used, and each 60-m pixel was assigned to one of 11 distinct land-cover classes based upon a modified Anderson classification system. Upon completion of land-cover change mapping for five dates, land-cover change statistics were generated and analyzed. This paper presents estimates for the Mojave Basin and Range ecoregion located in the southwestern United States. Our research suggests land-cover change within the Mojave to be relatively rare and highly localized. The primary shift in land cover is unidirectional, with natural desert grass/shrubland being converted to development. We estimate that more than 1,300 km2 have been converted since 1973 and that the conversion is being largely driven by economic and recreational opportunities provided by the Mojave ecoregion. The time interval with the highest rate of change was 1986 to 1992, in which the rate was 0.21 percent (321.9 km2) per year total change.

Scientific Investigations Report↗
USGS2009

Application guide for AFINCH (Analysis of Flows in Networks of Channels) described by NHDPlus

AFINCH (Analysis of Flows in Networks of CHannels) is a computer application that can be used to generate a time series of monthly flows at stream segments (flowlines) and water yields for catchments defined in the National Hydrography Dataset Plus (NHDPlus) value-added attribute system. AFINCH provides a basis for integrating monthly flow data from streamgages, water-use data, monthly climatic data, and land-cover characteristics to estimate natural monthly water yields from catchments by user-defined regression equations. Images of monthly water yields for active streamgages are generated in AFINCH and provide a basis for detecting anomalies in water yields, which may be associated with undocumented flow diversions or augmentations. Water yields are multiplied by the drainage areas of the corresponding catchments to estimate monthly flows. Flows from catchments are accumulated downstream through the streamflow network described by the stream segments. For stream segments where streamgages are active, ratios of measured to accumulated flows are computed. These ratios are applied to upstream water yields to proportionally adjust estimated flows to match measured flows. Flow is conserved through the NHDPlus network. A time series of monthly flows can be generated for stream segments that average about 1-mile long, or monthly water yields from catchments that average about 1 square mile. Estimated monthly flows can be displayed within AFINCH, examined for nonstationarity, and tested for monotonic trends. Monthly flows also can be used to estimate flow-duration characteristics at stream segments. AFINCH generates output files of monthly flows and water yields that are compatible with ArcMap, a geographical information system analysis and display environment. Chloropleth maps of monthly water yield and flow can be generated and analyzed within ArcMap by joining NHDPlus data structures with AFINCH output. Matlab code for the AFINCH application is presented.

Scientific Investigations Report↗
USGS2008

Use of Light Detection and Ranging (LiDAR) to Obtain High-Resolution Elevation Data for Sussex County, Delaware

Sussex County, Delaware, occupies a 938-square-mile area of low relief near sea level in the Atlantic Coastal Plain. The county is bounded on the east by the Delaware Bay and the Atlantic Ocean, including a barrier-island system, and inland bays that provide habitat for valuable living resources. Eastern Sussex County is an area of rapid population growth with a long-established beach-resort community, where land elevation is a key factor in determining areas that are appropriate for development. Of concern to State and local planners are evacuation routes inland to escape flooding from severe coastal storms, as most major transportation routes traverse areas of low elevation that are subject to inundation. The western half of the county is typically rural in character, and land use is largely agricultural with some scattered forest land cover. Western Sussex County has several low-relief river flood-prone areas, where accurate high-resolution elevation data are needed for Federal Emergency Management Agency (FEMA) Digital Flood Insurance Rate Map (DFIRM) studies. This fact sheet describes the methods and techniques used to collect and process LiDAR elevation data, the generation of the digital elevation model (DEM) and the 2-foot contours, and the quality-assurance procedures and results. It indicates where to view metadata on the data sets and where to acquire bare-earth mass points, DEM data, and contour data.

Fact Sheet↗