Coastal Change Analysis Program Research Articles

Use of High-Resolution Land Cover Maps to Support the Maintenance of the NWI Geospatial Dataset: A Case Study in a Coastal New Orleans Region

The National Wetlands Inventory (NWI) is the most comprehensive wetland geospatial dataset in the United States. However, it can be time-consuming and costly to maintain. This study introduces automated algorithms and methods to support NWI maintenance. Through a wall-to-wall comparison between NWI and Coastal Change Analysis Program (C-CAP) datasets, a pixel-level difference product was generated at 1 m resolution. Building upon this, supplementary attributes describing wetland changes were incorporated into each NWI polygon. Additionally, new water polygons were extracted from C-CAP data, and regional statistics regarding wetland changes were computed for HUC12 watersheds. The 1 m difference product can indicate specific wetland change locations, such as wetland loss to impervious surfaces, the gain of open water bodies from uplands, and the conversion of drier vegetated wetlands to open water. The supplementary attributes can indicate the amount and percentage of wetland loss or water regime change for NWI polygons. Extracted new water polygons can serve as preliminary materials for generating NWI standard-compliant products, expediating NWI maintenance processes while reducing costs. Regional statistics of wetland change can help target watersheds with the most significant changes for maintenance, thereby reducing work areas. The approaches we present hold significant value in supporting NWI maintenance.

A phenology- and trend-based approach for accurate mapping of sea-level driven coastal forest retreat

The rapid replacement of upland forest by encroaching marshland is a striking manifestation of global sea-level rise (SLR). Timely and high-resolution information on the location and extent of transition forest (the ecotone between upland forest and marsh where tree mortality due to seawater intrusion begins) is fundamental to understanding the processes and patterns of SLR-driven landscape reorganization. Despite its significance, accurate characterization of salt-impacted transition forest remains challenging due to the complexity of coastal environments, scarcity of ground-truth data, and the lack of effective mapping algorithms. Here we use the full archive of Landsat images between 1984 and 2021 to investigate the spectral, temporal, and phenological characteristics of transition forest, and develop a robust framework for monitoring coastal vegetation shifts in the mid-Atlantic U.S., a global SLR hotspot. We found that transition forest exhibits strong negative NDVI trends and a deviation of land surface phenology from marsh and upland forest that distinguishes itself from surrounding vegetation. By integrating temporal trends and land surface phenology, our results demonstrate superior discrimination between marsh and coastal forests to existing map products (e.g. NOAA Coastal Change Analysis Program, National Land Cover Database) that allows a reliable identification of the coastal treeline. We applied the approach to map regional land cover in 1985, 2000 and 2020 (overall classification accuracy >92%) and found that the area of coastal forest decreased by 22.0% from 1985 to 2020, the majority of which transitioned to marshland (92.3%, 5.3 × 103 ha). Based upon fine-scale patterns of coastal transgression, we created a practical workflow for spatially explicit quantification of forest retreat rates. Concurrent with rising sea level, coastal forests migrated upslope from 0.63 (± 0.27) m above sea level in 1985 to 0.78 (± 0.32) m above sea level in 2020, and horizontal forest retreat rates accelerated from 3.1 (range of 0–36) m yr−1 during 1985–2000 to 4.7 (0–55) m yr−1 during 2001–2020. As SLR continues to accelerate, our study may serve as a scalable solution for consistent tracking of coastal landscape evolution that is urgently needed for sustainable forest and wetland management.

A New Algorithm for Land-Cover Classification Using PolSAR and InSAR Data and Its Application to Surface Roughness Mapping Along the Gulf Coast

During a flooding event, the ability of the terrain to dissipate water flow energy depends on its land-cover type and the associated surface roughness. In this study, we developed a new land-cover classification algorithm using repeat-pass polarimetric synthetic aperture radar (PolSAR) and interferometric synthetic aperture radar (InSAR) data. Through a two-level hierarchical approach, we classified nine land-cover types with distinct surface roughness coefficients (Manning’s <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula> ). We demonstrated the performance of this algorithm using available L-band ALOS PALSAR scenes acquired between April 2007 and April 2011 over the Houston area. The radar-based surface roughness estimates show a good agreement with those independently derived from NOAA’s 22-class Coastal Change Analysis Program (C-CAP) 2010 land-cover classification data. Our algorithm is robust, and the randomly selected training sets only account for 0.3% of the total multilooked radar pixels (30-m spacing). Furthermore, we were able to accurately map surface roughness over the New Orleans area using available ALOS PALSAR scenes without selecting any new training sets. We note that NOAA’s C-CAP data are currently used for estimating surface roughness in the operational storm-surge models, and a new version is typically released every five to six years. With the launch of the L-band NASA-ISRO Synthetic Aperture Radar (NISAR) mission in the near future, our algorithm can be used to fill the temporal gaps of the existing C-CAP-based surface roughness database and improve the accuracy of near real-time hydrodynamic modeling.

Mapping salt marsh along coastal South Carolina using U-Net

Coastal wetland mapping is often difficult because of the heterogeneous vegetation compositions and associated tidal effects. In this study, we employed the U-Net and developed an adaptive deep learning approach to map statewide salt marshes in estuarine emergent wetlands of South Carolina (SC), USA, from 20 Sentinel-2A&B images. Considering the spatial heterogeneity of the coastal environment, two NOAA National Estuarine Research Reserves (NERRs) in SC were examined, the North Inlet-Winyah Bay (NIWB) NERR for model training and the ACE Basin NERR for testing. A high-resolution land cover map in the NIWB was downloaded for the training process. Ground reference points recorded by the NERR, as well as Google Earth were utilized during the accuracy assessment. The highest overall accuracy (90%) was achieved when all scenes with 10 Sentinel bands and the Normalized Difference Vegetation Index (NDVI) were included. The time used to train the model was 5 h, while then the statewide classification was performed in 20 min. Low marsh and high marsh distributions were successfully delineated. Compared to the national marsh maps from the NOAA Coastal Change Analysis Program (C-CAP), this study refined the land cover details concerning low marsh and high marsh distributions on the SC coast. Owing to the computational power of the U-Net, the seasonality and tide influence on marsh classification were mitigated by using multi-temporal images. With images available, the deep learning approach developed in this study could be easily adopted in other coastal areas.

Using Geospatial Analysis to Map Forest Change in New Hampshire: 1996–Present

AbstractNew Hampshire’s forests are vitally important to the state’s economy; however, there are indications that the state is experiencing a continuous loss in forest cover. We sought to investigate forest cover trends in New Hampshire. A baseline trend in forest cover between 1996 and 2010 was established using National Oceanic and Atmospheric Administration Coastal Change Analysis Program land cover data. A land cover map was then generated from Landsat imagery to extend the baseline trend to 2018. Results show that the state has experienced a continual decline in forest cover with the annual net loss steadily increasing from 0.14% between 1996 and 2001 to 0.27% between 2010 and 2018. Additionally, the more urbanized counties in southern New Hampshire are experiencing some of the greatest rates of net forest loss, most likely because of urbanization and agricultural expansion. This study demonstrated an effective methodology for tracking forest cover change and will hopefully inform future forest use policies.

Assessment of Wetland Change on the Delmarva Peninsula from 1984 to 2010

Stubbs, Q.; Yeo, I.-Y.; Lang, M.; Townshend, J.; Sun, L.; Prestegaard, K., and Jantz, C., 2020. Assessment of wetland change on the Delmarva Peninsula from 1984 to 2010. Journal of Coastal Research, 36(3), 575–589. Coconut Creek (Florida), ISSN 0749-0208.The decline in wetland extent and condition emphasizes the need for sound wetland restoration and conservation policies, which require baseline information on wetland status, change, and change drivers. Multiple wetland maps are available, but they can be quite inconsistent bcause of different input and generation techniques, dates, and objectives. Moderate-resolution (30 m2) regional land-cover data sets (LCDs) were analyzed to (1) quantify historical wetland changes on the Delmarva Peninsula at multiple spatial scales between 1984 and 2010, (2) identify differences in the spatial area of wetland change and discuss the source of and implications for these differences, and (3) investigate the extent to which drivers of wetland change can be identified using existing LCDs. The following regional LCDs were considered: the National Oceanic and Atmospheric Administration Coastal Change Analysis Program (C-CAP), the U.S. Geological Survey (USGS) Chesapeake Bay Land Cover Data Series (CBLCD), and the USGS National Land Cover Database. The C-CAP and CBLCD had the highest spatial agreement at 97%, and an average of 76% spatial agreement with the U.S. Fish and Wildlife Service National Wetland Inventory. The highest percentages of net wetland loss occurred between 1992 and 2001, whereas net wetland gain occurred between 2001 and 2010. Wetlands were predominantly converted (e.g., lost) to croplands/grass/shrubs (67%) and water (11%), which could be linked to drivers such as agriculture and sea-level rise.

Quantifying Spatiotemporal Patterns and Major Explanatory Factors of Urban Expansion in Miami Metropolitan Area During 1992–2016

Urban expansion is one of the most dramatic forms of land transformation in the world and it is one of the greatest challenges in achieving sustainable development in the 21st century. Previous studies analyzed urbanization patterns in areas with rapid urban expansion while urban areas with low to moderate expansion have been overlooked, especially in developed countries. In this study, we examined the spatiotemporal dynamics of urban expansion patterns in South Florida, United States (US) over the last 25 years (1992–2016) using Remote Sensing and GIS techniques. The main goal of this paper was to investigate the degree and spatiotemporal patterns of urban expansion at different administrative level in the study area and how spatiotemporal variance in different explanatory factors influence urban expansion in this region. More specifically, this research quantifies the rates, types, intensity, and landscape metrics of urban expansion in Miami-Fort Lauderdale-Palm Beach, Florida Metropolitan Statistical Area (Miami MSA) which is the 7th largest MSA and 4th largest urbanized area in the US using remote sensing (satellite imageries) data from National Land Cover Datasets (NLCD) and Coastal Change Analysis Program (C-CAP) at 30 m spatial resolution. We further investigated the urban growth patterns at the county and city areas that are located within this MSA to portray the local ‘picture’ of urban growth in this region. Urban expansion in this region can be divided into two time periods: pre-2001 and post-2001 where the former experienced rapid urban expansion and the later had comparatively slow urban expansion. Results suggest that infilling was the dominant type of urban expansion followed by edge-expansion and outlying. Results from landscape metrics represent that newly developed urban lands became more aggregated and simplified in form as the time progressed in the study region. Also, new urban lands were generated away from the east coast and historic cities which eventually created new urban cores. We also used correlation analysis and multiple linear stepwise regression to address major explanatory factors of spatiotemporal change in urban expansion during the study period. Although the influence of factors on urban expansion varied temporally, Population and Distance to Coast were the strongest variables followed by Distance to Roads and Median Income that influence overall urban expansion in the study area.

Voluntary Restoration: Mitigation's Silent Partner in the Quest to Reverse Coastal Wetland Loss in the USA.

Coastal ecosystems are under pressure from a vast array of anthropogenic stressors, including development and climate change, resulting in significant habitat losses globally Conservation policies are often implemented with the intent of reducing habitat loss. However, losses already incurred will require restoration if ecosystem functions and services are to be recovered. The United States has a long history of wetland loss and recognizes that averting loss requires a multi-pronged approach including mitigation for regulated activities and non-mitigation (voluntary herein) restoration. The 1989 “No Net Loss” (NNL) policy stated the Federal government’s intent that losses of wetlands would be offset by at least as many gains of wetlands. However, coastal wetlands losses result from both regulated and non-regulated activities. We examined the effectiveness of Federally funded, voluntary restoration efforts in helping avert losses of coastal wetlands by assessing: (1) What are the current and past trends in coastal wetland change in the U.S.?; and (2) How much and where are voluntary restoration efforts occurring? First, we calculated palustrine and estuarine wetland change in U.S. coastal shoreline counties using data from NOAA’s Coastal Change Analysis Program, which integrates both types of potential losses and gains. We then synthesized available data on Federally funded, voluntary restoration of coastal wetlands. We found that from 1996 to 2010, the U.S. lost 139,552 acres (~565 km2) of estuarine wetlands (2.5% of 1996 area) and 336,922 acres (~1,363 km2) of palustrine wetlands (1.4%). From 2006 to 2015, restoration of 145,442 acres (~589 km2) of estuarine wetlands and 154,772 acres (~626 km2) of palustrine wetlands occurred. Further, wetland losses and restoration were not always geographically aligned, resulting in local and regional “winners” and “losers.” While these restoration efforts have been considerable, restoration and mitigation collectively have not been able to keep pace with wetland losses; thus, reversing this trend will likely require greater investment in coastal habitat conservation and restoration efforts. We further conclude that “area restored,” the most prevalent metric used to assess progress, is inadequate, as it does not necessarily equate to restoration of functions. Assessing the effectiveness of wetland restoration not just in the U.S., but globally, will require allocation of sufficient funding for long-term monitoring of restored wetland functions, as well as implementation of standardized methods for monitoring data collection, synthesis, interpretation, and application.

A remote sensing-based model of tidal marsh aboveground carbon stocks for the conterminous United States

Remote sensing based maps of tidal marshes, both of their extents and carbon stocks, have the potential to play a key role in conducting greenhouse gas inventories and implementing climate mitigation policies. Our objective was to generate a single remote sensing model of tidal marsh aboveground biomass and carbon that represents nationally diverse tidal marshes within the conterminous United States (CONUS). We developed the first calibration-grade, national-scale dataset of aboveground tidal marsh biomass, species composition, and aboveground plant carbon content (%C) from six CONUS regions: Cape Cod, MA, Chesapeake Bay, MD, Everglades, FL, Mississippi Delta, LA, San Francisco Bay, CA, and Puget Sound, WA. Using the random forest machine learning algorithm, we tested whether imagery from multiple sensors, Sentinel-1 C-band synthetic aperture radar, Landsat, and the National Agriculture Imagery Program (NAIP), can improve model performance. The final model, driven by six Landsat vegetation indices and with the soil adjusted vegetation index as the most important (n = 409, RMSE = 310 g/m2, 10.3% normalized RMSE), successfully predicted biomass for a range of marsh plant functional types defined by height, leaf angle and growth form. Model results were improved by scaling field-measured biomass calibration data by NAIP-derived 30 m fraction green vegetation. With a mean plant carbon content of 44.1% (n = 1384, 95% C.I. = 43.99%–44.37%), we generated regional 30 m aboveground carbon density maps for estuarine and palustrine emergent tidal marshes as indicated by a modified NOAA Coastal Change Analysis Program map. We applied a multivariate delta method to calculate uncertainties in regional carbon densities and stocks that considered standard error in map area, mean biomass and mean %C. Louisiana palustrine emergent marshes had the highest C density (2.67 ± 0.004 Mg/ha) of all regions, while San Francisco Bay brackish/saline marshes had the highest C density of all estuarine emergent marshes (2.03 ± 0.004 Mg/ha). Estimated C stocks for predefined jurisdictional areas ranged from 1023 ± 39 Mg in the Nisqually National Wildlife Refuge in Washington to 507,761 ± 14,822 Mg in the Terrebonne and St. Mary Parishes in Louisiana. This modeling and data synthesis effort will allow for aboveground C stocks in tidal marshes to be included in the coastal wetland section of the U.S. National Greenhouse Gas Inventory. With the increased availability of free post-processed satellite data, we provide a tractable means of modeling tidal marsh aboveground biomass and carbon at the global extent as well.

Land cover, lightning frequency, and turbulent fluxes over Southern Louisiana

Lightning is one of the most impactful weather phenomena but little precise and accurate information is known about how its frequency is impacted by changes in land use/land cover (LULC). Similarly, little is known about the spatial and temporal variability of the convective (i.e., turbulent) fluxes of sensible and latent heat that fuel the updrafts associated with lightning under changing LULC. This research applies NOAA's gridded annual lightning data from the National Lightning Detection Network (NLDN), gridded annual mean convective flux data from the North American Regional Reanalysis database (NARR), and LULC classification data from the NOAA Coastal Change Analysis Program (C-CAP) to analyze frequency changes in lightning and the turbulent fluxes across a swath of Louisiana, U.S.A., over the years 1996–2011. Results suggest that urban areas have the highest means of CG lightning with a consistent mean of approximately 152 flashes per km−2. However, urban areas support less convective flux of sensible (latent) heat than five (four) of six other generalized land classes tested. Lightning frequency tends to be positively related to latent heat fluctuations but less directly related to sensible heat, at least at the annual time scale, as we see positive relationships in all land classes except unconsolidated shore (p < 0.05). Collectively, these results suggest that land use change should be considered carefully in models of future projections of severe weather events and climate.

Measuring and Modeling Flow Rates in Tidal Creeks: A Case Study from the Central Coast of South Carolina

The purpose of this study was to collect site- and condition-specific hydrology data to better understand the water flow dynamics of tidal creeks and terrestrial runoff from surrounding watersheds. In this paper, we developed mathematical models of tidal creek flow (discharge) in relation to time during a tidal cycle and also estimated terrestrial runoff volume from design storms to compare to tidal creek volumes. Currently, limited data are available about how discharge in tidal creeks behaves as a function of stage or the time of tide (i.e., rising or falling tide) for estuaries in the southeastern United States, so this information fills an existing knowledge gap. Ultimately, findings from this study will be used to inform managers about numeric nutrient criteria (nitrogen-N and phosphorus-P) when it is combined with biological response (e.g., phytoplankton assemblages) data from a concurrent study. We studied four tidal creek sites, two in the Ashepoo-Combahee-Edisto (ACE) Basin and two in the Charleston Harbor system. We used ArcGIS to delineate two different watersheds for each study site, to classify the surrounding land cover using the NOAA Coastal Change Analysis Program (C-CAP) data, and to analyze the soils using the NRCS Soil Survey Geographic database (SSURGO). The size of the U.S. Geological Survey’s Elevation Derivatives for National Application (EDNA) watersheds varied from 778 to 2,582 ha; smaller geographic watersheds were delineated for all sites (except Wimbee) for stormwater modeling purposes. The two sites in Charleston Harbor were within the first-order Horlbeck Creek and the second-order Bulls Creek areas. The ACE Basin sites were within the third-order Big Bay Creek and the fourth-order Wimbee Creek areas. We measured the stage and discharge in each creek with an acoustic Doppler current profiler (ADCP) unit for multiple tide conditions over a 2-year period (2015–2016) with the goal of encompassing as large of a range of tide stage and discharge data measurements as possible. The Stormwater Runoff Modeling System (SWARM) was also used to estimate the potential water entering the creeks from the land surface; this volume was very small relative to the tide water volume except for the more-developed Bulls Creek watershed. The results show that the peak discharge occurred on the ebb tide and that the duration of the flood tide spanned a longer period of time; both of these observations are consistent with traits associated with an ebb-dominated tidal creek system. The tidal inflow and outflow (flood and ebb tides, respectively) showed an asymmetrical pattern with respect to stage and discharge; peak discharge during the flood (rising) tide occurred at a higher stage than for the peak discharge during the ebb (falling) tide. This is not an unexpected result, as the water on an ebb tide is moving down gradient funneled through the creek channel toward the coast. Furthermore, water moving with the rising flood tide must overcome frictional losses due to the marsh bank and vegetation; i.e., the peak discharge can only happen when the water has risen above these impediments. We infer from the flow dynamics data that faster water velocities during ebb tide imply that more erosive energy could transport a larger mass of suspended solids and associated nutrients (e.g., orthophosphate) from the estuary to the coastal ocean. However, the discharge and runoff modeling indicate that land-based flux was important in the developed Bulls Creek watershed, but not at the larger and less-developed Big Bay Creek watershed. At Big Bay Creek, the relatively large tidal discharge volume compared to the smaller potential runoff generated within the watershed indicates that the creek could potentially dilute terrestrial runoff contaminants. Smaller, more-urbanized tidal wetland systems may not benefit from such dilution effects and thus are vulnerable to increased runoff from adjacent developed landscapes.

Accuracy Assessment of NOAA Coastal Change Analysis Program 2006-2010 Land Cover and Land Cover Change Data

A new approach to locating accuracy assessment sample units was used to quantify 2010 land cover accuracy, in addition to being able to make statements about 2006-2010 land cover change mapping accuracy for National Oceanic and Atmospheric Administration (noaa) Coastal Change Analysis Program (c-cap) data. Three customized tiers of sampling strata were created, as discussed, to meet these goals. Stratified random sampling was employed in each stratum with a six out of nine pixel-homogeneity criteria (different from the final minimum mapping unit) required for each sampling unit. Accuracy was assessed for nine regions in the coastal United States with overall accuracy ranging from 82.3 percent to 85.6 percent. Binary change was mapped with 88.7 percent accuracy, with the largest error being errors of commission (71.2 percent user accuracy). This sampling design also allowed for the identification of 137 locations where true change was not mapped, allowing for statements to be made about missed change.

Accuracy Assessment of NOAA Coastal Change Analysis Program 2006-2010 Land Cover and Land Cover Change Data

A new approach to locating accuracy assessment sample units was used to quantify 2010 land cover accuracy, in addition to being able to make statements about 2006-2010 land cover change mapping accuracy for National Oceanic and Atmospheric Administration (&lt;small&gt;NOAA&lt;/small&gt;) Coastal Change Analysis Program (&lt;small&gt;C-CAP&lt;/small&gt;) data. Three customized tiers of sampling strata were created, as discussed, to meet these goals. Stratified random sampling was employed in each stratum with a six out of nine pixel-homogeneity criteria (different from the final minimum mapping unit) required for each sampling unit. Accuracy was assessed for nine regions in the coastal United States with overall accuracy ranging from 82.3 percent to 85.6 percent. Binary change was mapped with 88.7 percent accuracy, with the largest error being errors of commission (71.2 percent user accuracy). This sampling design also allowed for the identification of 137 locations where true change was not mapped, allowing for statements to be made about missed change.

Assessing the relevance of wetlands for storm surge protection: a coupled hydrodynamic and geospatial framework

The expectation that wetlands can protect coastal communities has been a major topic in the effort to evaluate innovative methods of mitigating coastal impacts from storm surge. Recent investigations have shown that there is a potential flood mitigation benefit to be gained from the presence of marshes. Though the extent of that benefit is not yet clearly defined, prioritizing wetland systems for coastal protection requires a consideration of the interactions between communities at risk of storm surge damage and wetland areas of sufficient spatial scales to reliably attenuate storm surge. Here, a framework is proposed for geospatial characterization of these interactions based on numerical model results and is applied to Virginia’s Chesapeake Bay region. Spatial identification of Chesapeake Bay wetlands was derived from four nationally available datasets (National Wetland Inventory, National Land Cover Dataset, Coastal Change Analysis Program, and NOAA’s Wetland Potential database). Maps of maximum storm tides for four historical storms were generated based on a coupled hydrodynamic wave model (ADCIRC–SWAN), validated for those storms with a mean root mean square error of 0.44 m. Population information was extracted from US Census block data in FEMA’s HAZUS Multi-Hazard geodatabase. Results from geospatial analysis of the relationships between wetland land cover, inundation, and population were used to identify where interactions with coastal populations are relevant for the study area when spatial limitations are considered. Approximately 1160 sq. km of wetlands were inundated by all four storms. Total population present in a range of proximities (200, 400, and 600 m) to flooded wetlands was used as a metric to evaluate the effect of a range of limitations on wetland size (5–50 sq. km) on potential population protected. A high level of uncertainty was seen due to wetland dataset selection (number of standard deviations from the mean ranged from 0.04 to 1.37), owing to high variability in spatial delineation/configuration of wetlands. Applying the framework to the Virginia Chesapeake Bay region yields an index of potential protection based on spatial scale limitations. For example, such an evaluation for wetlands meeting the 5 sq. km limitation (disregarding wetlands <5 sq. km) indicates that the presence of wetlands offers a potential protection for 3.4 % of the population. An application of the index on a more local scale indicates that the interaction of the flooded wetlands with more populated localities is such that the coastal marshes in Hampton Roads and Gloucester may offer protection to nearby coastal communities. This framework demonstrates that effective surge protection from wetlands and marshes is related to a combination of the incidence of storm surge inundation, proximity to coastal communities, and the relationship of sufficient spatial scales for surge attenuation.

The Multi-Resolution Land Characteristics (MRLC) Consortium — 20 Years of Development and Integration of USA National Land Cover Data

The Multi-Resolution Land Characteristics (MRLC) Consortium demonstrates the national benefits of USA Federal collaboration. Starting in the mid-1990s as a small group with the straightforward goal of compiling a comprehensive national Landsat dataset that could be used to meet agencies’ needs, MRLC has grown into a group of 10 USA Federal Agencies that coordinate the production of five different products, including the National Land Cover Database (NLCD), the Coastal Change Analysis Program (C-CAP), the Cropland Data Layer (CDL), the Gap Analysis Program (GAP), and the Landscape Fire and Resource Management Planning Tools (LANDFIRE). As a set, the products include almost every aspect of land cover from impervious surface to detailed crop and vegetation types to fire fuel classes. Some products can be used for land cover change assessments because they cover multiple time periods. The MRLC Consortium has become a collaborative forum, where members share research, methodological approaches, and data to produce products using established protocols, and we believe it is a model for the production of integrated land cover products at national to continental scales. We provide a brief overview of each of the main products produced by MRLC and examples of how each product has been used. We follow that with a discussion of the impact of the MRLC program and a brief overview of future plans.

Uncertainty in hurricane surge simulation due to land cover specification

Hurricane storm surge is one of the most costly natural hazards in the United States. Numerical modeling to predict and estimate hurricane surge flooding is currently widely used for research, planning, decision making, and emergency response. Land cover plays an important role in hurricane surge numerical modeling because of its impacts on the forcing (changes in wind momentum transfer to water column) and dissipation (bottom friction) mechanisms of storm surge. In this study, the hydrodynamic model ADCIRC was used to investigate predicted surge response in bays on the central and lower Texas coast using different land cover data sets: (1) Coastal Change Analysis Program for 1996, 2001, and 2006; (2) the National Land Cover Dataset for 1992, 2001, and 2006; and (3) the National Wetlands Inventory for 1993. Hypothetical storms were simulated with varying the storm track, forward speed, central pressure, and radius to maximum wind, totaling 140 simulations. Data set choice impacts the mean of maximum surges throughout the study area, and variability in the surge prediction due to land cover data set choice strongly depends on storm characteristics and geographical location of the bay in relation to storm track. Errors in surge estimation due to land cover choice are approximately 7% of the surge value, with change in surge prediction varying by as much as 1 m, depending on location and storm condition. Finally, the impact of land cover choice on the accuracy of simulating surges for Hurricane Bret in 1999 is evaluated.

A multi-scale occupancy model for the grasshopper sparrow in the Mid-Atlantic

Identifying features of breeding habitat that influence occupancy and modeling the distribution of grassland birds is needed to direct conservation efforts to reduce population declines associated with habitat loss and fragmentation. Many recent studies on grassland bird habitat use incorporate both local and landscape attributes. However, few studies have determined the appropriate spatial scales at which to measure these relationships. We conducted roadside point counts within Delaware, USA, to determine the presence of grasshopper sparrows (Ammodramus savannarum). We quantified both land cover composition and configuration at local and landscape scales at our survey sites using data from the National Oceanic and Atmospheric Administration Coastal Change Analysis Program. We determined the spatial scales at which grasshopper sparrow presence was most strongly related to landscape metrics and modeled grasshopper sparrow habitat occupancy at multiple scales, while accounting for variation in detection. At the site scale, occupancy was negatively related to forest and shrub composition. At the landscape scale, grasshopper sparrow occupancy was positively related to the amount of grasslands and pastures, and negatively related to mean inter-patch distance of grasslands and amount of low-intensity development. Our model had good predictive accuracy (area under the receiver operating characteristics curve = 0.717). We present our predictive model applied to the Delmarva Peninsula, USA. © 2013 The Wildlife Society.

Evaluating the use of publicly available remotely sensed land cover data for areal interpolation

Areal interpolation is used to transfer attribute data between geographically incongruous zonal systems. Remotely sensed land cover data are widely used in intelligent areal interpolation methods to solve this problem. This article examines the usefulness of different publicly available remotely sensed land cover data sets as ancillary data used in conjunction with different areal interpolation methods. Two land cover data sets were compiled at the national scale; one by the Multi-Resolution Land Characteristics Consortium (the National Land Cover Dataset or NLCD) and one by the Coastal Change Analysis Program. A third land cover data set was compiled at a regional scale for the state of Connecticut by the Center for Land Use Education and Research. Results show that for areal interpolation, greater detail in the classification of developed areas was important whether the data were developed for use at a national or a regional scale. Even more important is the further enhancement of remotely sensed land use categories by incorporating local road or parcel data layers. The worst performing interpolation method using enhanced remote sensing-derived land cover data produced more accurate results than the best performing method using only the original land cover data. The results also show that parcels produce better enhancements than road buffers because they remove the areas of the roads themselves from population consideration.

Temporal land cover analysis for net ecosystem improvement

Although ecological processes at landscape scales can drive ecosystem structure and function at local sites, the monitoring and assessment of restoration projects and programs rarely incorporates changes in landscapes. Riverscapes naturally have a high level of hydrologic connectivity so when floodplain habitats are reconnected to the main stem river to restore function, it is especially important to consider the potential effects of watershed processes on outcomes. This study demonstrates a method for selecting relevant indicators, identifying appropriate spatial scales, and measuring trends to inform assessment of a large-river restoration program. We identified eight major watersheds contributing to river reaches within the 1468km2 historical floodplain of the lower Columbia River and estuary. We assessed land-cover change at the watershed, reach, and restoration site scales by reclassifying remote-sensing data from the NOAA Coastal Change Analysis Program's land cover/change product into forest, wetland, and urban categories. The watershed analysis showed a 361.6km2 total and 198.3km2 net forest loss during the first six years of the Columbia Estuary Ecosystem Restoration Program, 2001–2006. Total urbanization in the watersheds during 1996–2006 was 48.4km2. Trends in forest gain/loss and urbanization differed between watersheds. Wetland gains and losses were within the margin of error of the satellite imagery analysis. These findings are important to restoration planning and assessment, e.g., floodplain restoration sites in reaches downstream of watersheds with decreasing forest cover may be subject to increased sediment loads and those downstream of urbanization may experience altered hydrological processes caused by increased impervious surface.

위성영상을 이용한 개발과 미개발 지역의 구분을 위한 탐색적 방법

This research attempted to find possibilities of the practical use utilizing geospatial methods for the balanced promotion of sustainable coastal development and preservation through a case study of Jekyll Island, one of Georgia`s barrier islands. In response, this research provided ways for practical use in sustainable development and preservation plans. First this research thoroughly investigated the 1996 master plan of Jekyll Island and tried to recalculate developed and undeveloped areas. Second, new estimations for developed areas were investigated through field survey. Third, this research proposed the use of the satellite images with different levels of spatial resolutions and tested different classification schemes to find possibilities for practical use. For these purposes, first, we classified developed and undeveloped features by manual digitization using an aerial photo image with 0.5m spatial resolution. Second, a Landsat 7 ETM+ and a QuickBird satellite images with mid- and high-levels of spatial resolutions were applied to identify developed and undeveloped areas using both the National Land Cover Data (NLCD) and the Coastal Change Analysis Program (CCAP) classification schemes. Also, GEOBIA (Geographic Object-Based Image Analysis) was conducted to accurately identify developed and undeveloped areas.