Elevation surface for portions of the Hawaiian islands based upon lidar data collected by State of Hawaii and its data partners from 1999-2017. Elevation values are in meters.

This map shows the extent of flooding of low-lying inland coastal areas around Hawaii Island (Big Island) in the State of Hawaii due to 2 feet (0.610 m) of sea level rise above mean higher high water (MHHW), created by subtracting the NOAA VDATUM MHHW surface from a digital elevation model (DEM). These low-lying areas are not hydrologically connected to the ocean but have the potential for flooding based on their elevation and require more detailed analysis. The resolution of the DEM is 3 meters and was derived from the best available LiDAR data sets known to exist at the time of creation. Water levels are shown as they would appear during the highest high tides (excluding wind-driven tides).Data produced in 2014 by NOAA Office for Coastal Management (OCM). These data do not consider future changes in coastal geomorphology and natural processes such as erosion, subsidence, or future construction. These data do not specify timing of inundation and are not appropriate for conducting detailed spatial analysis. The entire risk associated with the results and performance of these data is assumed by the user. These data should be used strictly as a planning reference and not for navigation, permitting, or other legal purposes.This map shows the extent of flooding of low-lying inland coastal areas around Hawaii Island (Big Island) in the State of Hawaii due to 2 feet (0.610 m) of sea level rise above mean higher high water (MHHW), created by subtracting the NOAA VDATUM MHHW surface from a digital elevation model (DEM). These low-lying areas are not hydrologically connected to the ocean but have the potential for flooding based on their elevation and require more detailed analysis. The resolution of the DEM is 3 meters and was derived from the best available LiDAR data sets known to exist at the time of creation. Water levels are shown as they would appear during the highest high tides (excluding wind-driven tides).Data produced in 2014 by NOAA Office for Coastal Management (OCM). These data do not consider future changes in coastal geomorphology and natural processes such as erosion, subsidence, or future construction. These data do not specify timing of inundation and are not appropriate for conducting detailed spatial analysis. The entire risk associated with the results and performance of these data is assumed by the user. These data should be used strictly as a planning reference and not for navigation, permitting, or other legal purposes.This map shows the extent of flooding of low-lying inland coastal areas around Hawaii Island (Big Island) in the State of Hawaii due to 2 feet (0.610 m) of sea level rise above mean higher high water (MHHW), created by subtracting the NOAA VDATUM MHHW surface from a digital elevation model (DEM). These low-lying areas are not hydrologically connected to the ocean but have the potential for flooding based on their elevation and require more detailed analysis. The resolution of the DEM is 3 meters and was derived from the best available LiDAR data sets known to exist at the time of creation. Water levels are shown as they would appear during the highest high tides (excluding wind-driven tides).Data produced in 2014 by NOAA Office for Coastal Management (OCM). These data do not consider future changes in coastal geomorphology and natural processes such as erosion, subsidence, or future construction. These data do not specify timing of inundation and are not appropriate for conducting detailed spatial analysis. The entire risk associated with the results and performance of these data is assumed by the user. These data should be used strictly as a planning reference and not for navigation, permitting, or other legal purposes.

This dataset combines the work of several different projects to create a seamless data set for the contiguous United States. Data from four regional Gap Analysis Projects and the LANDFIRE project were combined to make this dataset. In the northwestern United States (Idaho, Oregon, Montana, Washington and Wyoming) data in this map came from the Northwest Gap Analysis Project. In the southwestern United States (Colorado, Arizona, Nevada, New Mexico, and Utah) data used in this map came from the Southwest Gap Analysis Project. The data for Alabama, Florida, Georgia, Kentucky, North Carolina, South Carolina, Mississippi, Tennessee, and Virginia came from the Southeast Gap Analysis Project and the California data was generated by the updated California Gap land cover project. The Hawaii Gap Analysis project provided the data for Hawaii. In areas of the county (central U.S., Northeast, Alaska) that have not yet been covered by a regional Gap Analysis Project, data from the Landfire project was used. Similarities in the methods used by these projects made possible the combining of the data they derived into one seamless coverage. They all used multi-season satellite imagery (Landsat ETM+) from 1999-2001 in conjunction with digital elevation model (DEM) derived datasets (e.g. elevation, landform) to model natural and semi-natural vegetation. Vegetation classes were drawn from NatureServe's Ecological System Classification (Comer et al. 2003) or classes developed by the Hawaii Gap project. Additionally, all of the projects included land use classes that were employed to describe areas where natural vegetation has been altered. In many areas of the country these classes were derived from the National Land Cover Dataset (NLCD). For the majority of classes and, in most areas of the country, a decision tree classifier was used to discriminate ecological system types. In some areas of the country, more manual techniques were used to discriminate small patch systems and systems not distinguishable through topography. The data contains multiple levels of thematic detail. At the most detailed level natural vegetation is represented by NatureServe's Ecological System classification (or in Hawaii the Hawaii GAP classification). These most detailed classifications have been crosswalked to the five highest levels of the National Vegetation Classification (NVC), Class, Subclass, Formation, Division and Macrogroup. This crosswalk allows users to display and analyze the data at different levels of thematic resolution. Developed areas, or areas dominated by introduced species, timber harvest, or water are represented by other classes, collectively refered to as land use classes; these land use classes occur at each of the thematic levels. Raster data in both ArcGIS Grid and ERDAS Imagine format is available for download at http://gis1.usgs.gov/csas/gap/viewer/land_cover/Map.aspx Six layer files are included in the download packages to assist the user in displaying the data at each of the Thematic levels in ArcGIS. In adition to the raster datasets the data is available in Web Mapping Services (WMS) format for each of the six NVC classification levels (Class, Subclass, Formation, Division, Macrogroup, Ecological System) at the following links. http://gis1.usgs.gov/arcgis/rest/services/gap/GAP_Land_Cover_NVC_Class_Landuse/MapServer http://gis1.usgs.gov/arcgis/rest/services/gap/GAP_Land_Cover_NVC_Subclass_Landuse/MapServer http://gis1.usgs.gov/arcgis/rest/services/gap/GAP_Land_Cover_NVC_Formation_Landuse/MapServer http://gis1.usgs.gov/arcgis/rest/services/gap/GAP_Land_Cover_NVC_Division_Landuse/MapServer http://gis1.usgs.gov/arcgis/rest/services/gap/GAP_Land_Cover_NVC_Macrogroup_Landuse/MapServer http://gis1.usgs.gov/arcgis/rest/services/gap/GAP_Land_Cover_Ecological_Systems_Landuse/MapServer

This map shows levels of confidence of coastal flooding (inundation) around Hawaii Island (Big Island) in the State of Hawaii due to 0 feet of sea level rise above mean higher high water (MHHW). Blue areas denote a high confidence of inundation, orange areas denote a low confidence of inundation, and unshaded areas denote a high confidence that these areas will be dry at this water level. In this application, 80% is considered a high degree of confidence such that, for example, the blue areas denote locations that may be correctly mapped as 'inundated 'more than 8 out of 10 times. Areas with a low degree of confidence represent locations that may be mapped correctly (either as inundated or dry) less than 8 out of 10 times.Confidence mapping is a fairly complicated procedure that is explained in detail in 'Mapping and Portraying Inundation Uncertainty of Bathtub-Type Models 'available at 'http://www.jcronline.org/doi/abs/10.2112/JCOASTRES-D-13-00118.1 '. In short, the method includes the uncertainty in the LiDAR-derived elevation data (root mean square error, or RMSE) and the uncertainty in the modeled tidal surface from the NOAA VDATUM MHHW (RMSE). This uncertainty is combined and mapped to show that inundation extent is not really a hard line, but rather a zone with greater and lesser chances of getting wet.Data produced in 2014 by NOAA Office for Coastal Management (OCM). These data do not consider future changes in coastal geomorphology and natural processes such as erosion, subsidence, or future construction. These data do not specify timing of inundation and are not appropriate for conducting detailed spatial analysis. The entire risk associated with the results and performance of these data is assumed by the user. These data should be used strictly as a planning reference and not for navigation, permitting, or other legal purposes.This map shows levels of confidence of coastal flooding (inundation) around Hawaii Island (Big Island) in the State of Hawaii due to 0 feet of sea level rise above mean higher high water (MHHW). Blue areas denote a high confidence of inundation, orange areas denote a low confidence of inundation, and unshaded areas denote a high confidence that these areas will be dry at this water level. In this application, 80% is considered a high degree of confidence such that, for example, the blue areas denote locations that may be correctly mapped as 'inundated 'more than 8 out of 10 times. Areas with a low degree of confidence represent locations that may be mapped correctly (either as inundated or dry) less than 8 out of 10 times.Confidence mapping is a fairly complicated procedure that is explained in detail in 'Mapping and Portraying Inundation Uncertainty of Bathtub-Type Models 'available at 'http://www.jcronline.org/doi/abs/10.2112/JCOASTRES-D-13-00118.1 '. In short, the method includes the uncertainty in the LiDAR-derived elevation data (root mean square error, or RMSE) and the uncertainty in the modeled tidal surface from the NOAA VDATUM MHHW (RMSE). This uncertainty is combined and mapped to show that inundation extent is not really a hard line, but rather a zone with greater and lesser chances of getting wet.Data produced in 2014 by NOAA Office for Coastal Management (OCM). These data do not consider future changes in coastal geomorphology and natural processes such as erosion, subsidence, or future construction. These data do not specify timing of inundation and are not appropriate for conducting detailed spatial analysis. The entire risk associated with the results and performance of these data is assumed by the user. These data should be used strictly as a planning reference and not for navigation, permitting, or other legal purposes.This map shows levels of confidence of coastal flooding (inundation) around Hawaii Island (Big Island) in the State of Hawaii due to 0 feet of sea level rise above mean higher high water (MHHW). Blue areas denote a high confidence of inundation, orange areas denote a low confidence of inundation, and unshaded areas denote a high confidence that these areas will be dry at this water level. In this application, 80% is considered a high degree of confidence such that, for example, the blue areas denote locations that may be correctly mapped as 'inundated 'more than 8 out of 10 times. Areas with a low degree of confidence represent locations that may be mapped correctly (either as inundated or dry) less than 8 out of 10 times.Confidence mapping is a fairly complicated procedure that is explained in detail in 'Mapping and Portraying Inundation Uncertainty of Bathtub-Type Models 'available at 'http://www.jcronline.org/doi/abs/10.2112/JCOASTRES-D-13-00118.1 '. In short, the method includes the uncertainty in the LiDAR-derived elevation data (root mean square error, or RMSE) and the uncertainty in the modeled tidal surface from the NOAA VDATUM MHHW (RMSE). This uncertainty is combined and mapped to show that inundation extent is not really a hard line, but rather a zone with greater and lesser chances of getting wet.Data produced in 2014 by NOAA Office for Coastal Management (OCM). These data do not consider future changes in coastal geomorphology and natural processes such as erosion, subsidence, or future construction. These data do not specify timing of inundation and are not appropriate for conducting detailed spatial analysis. The entire risk associated with the results and performance of these data is assumed by the user. These data should be used strictly as a planning reference and not for navigation, permitting, or other legal purposes.