Warning: This raster is a grid of a floating-point values; not a surface. To derive an accurate height transformation value, this raster grid must be downloaded in terms of NZGD2000 and then converted into a surface using bilinear interpolation.

The One Tree Point 1964 to NZVD2016 Conversion Raster provides users with a two arc-minute (approximately 3.6 kilometres) raster image of the conversion of normal-orthometric heights from the One Tree Point 1964 local vertical datum to the New Zealand Vertical Datum 2016 (NZVD2016).

The conversion value is represented by the attribute “O”, in metres. This conversion and NZVD2016 are formally defined in the LINZ standard LINZS25009.

The height conversion grid models the difference between the One Tree Point 1964 vertical datum and NZVD2016 using the LINZ GPS-levelling marks. From the GPS-levelling marks the expected accuracy is better than 2 centimetres (95% Confidence interval).

More information on converting heights between vertical datums can be found on the LINZ website.

Warning: This raster is a grid of a floating-point values; not a surface. To derive an accurate height transformation value, this raster grid must be downloaded in terms of NZGD2000 and then converted into a surface using bilinear interpolation.

The Wellington 1953 to NZVD2016 Conversion Raster provides users with a two arc-minute (approximately 3.6 kilometres) raster image of the conversion of normal-orthometric heights from the Wellington 1953 local vertical datum to the New Zealand Vertical Datum 2016 (NZVD2016).

The conversion value is represented by the attribute “O”, in metres. This conversion and NZVD2016 are formally defined in the LINZ standard LINZS25009.

The height conversion grid models the difference between the Wellington 1953 vertical datum and NZVD2016 using the LINZ GPS-levelling marks. From the GPS-levelling marks the expected accuracy is better than 2 centimetres (95% Confidence interval).

More information on converting heights between vertical datums can be found on the LINZ website.

Wave power is a major environmental forcing mechanism in Hawaii that influences a number of marine ecosystem processes including coral reef community development, structure, and persistence. By driving mixing of the upper water column, wave forcing can also play a role in nutrient availability and ocean temperature reduction during warming events. Wave forcing in Hawaii is highly seasonal, with winter months typically experiencing far greater wave power than that experienced during the summer months. This layer represents the annual average frequency of anomalies of wave power (kW/m) from 2000-2013, with values presented as fraction of a year. Data were obtained from the University of Hawaii at Manoa (UH) School of Ocean and Earth Science and Technology (SOEST) SWAN model (Simulating WAves Nearshore) following Li et al. (2016). Hourly 500-m SWAN model runs of wave power were converted to maximum daily wave power from 1979-2013 and then averaged over each month from 1979-2013, creating a monthly time series from which monthly climatologies were made. Time series of anomalies were calculated by quantifying the number and magnitude of events from the maximum daily data set that exceeded the maximum climatological monthly mean during 2000-2013. Pixels were removed directly adjacent to coastlines owing to the model being too coarse to handle extreme refraction and dissipation. Nearshore map pixels with no data were filled with values from the nearest neighboring valid offshore pixel by using a grid of points and the Near Analysis tool in ArcGIS then converting points to raster. The average annual frequency of wave power anomalies was calculated by taking the average number of days that exceeded the maximum monthly climatological wave power from 2000-2013 for each 500-m grid cell. Values are represented as a fraction of a year.

Warning: This raster is a grid of a floating-point values; not a surface. To derive an accurate height transformation value, this raster grid must be downloaded in terms of NZGD2000 and then converted into a surface using bilinear interpolation.

The Napier 1962 to NZVD2016 Conversion Raster provides users with a two arc-minute (approximately 3.6 kilometres) raster image of the conversion of normal-orthometric heights from the Napier 1962 local vertical datum to the New Zealand Vertical Datum 2016 (NZVD2016).

The conversion value is represented by the attribute “O”, in metres. This conversion and NZVD2016 are formally defined in the LINZ standard LINZS25009.

The height conversion grid models the difference between the Napier 1962 vertical datum and NZVD2016 using the LINZ GPS-levelling marks. From the GPS-levelling marks the expected accuracy is better than 2 centimetres (95% Confidence interval).

More information on converting heights between vertical datums can be found on the LINZ website.

Warning: This raster is a grid of a floating-point values; not a surface. To derive an accurate height transformation value, this raster grid must be downloaded in terms of NZGD2000 and then converted into a surface using bilinear interpolation.

The Gisborne 1926 to NZVD2016 Conversion Raster provides users with a two arc-minute (approximately 3.6 kilometres) raster image of the conversion of normal-orthometric heights from the Gisborne 1926 local vertical datum to the New Zealand Vertical Datum 2016 (NZVD2016).

The conversion value is represented by the attribute “O”, in metres. This conversion and NZVD2016 are formally defined in the LINZ standard LINZS25009.

The height conversion grid models the difference between the Gisborne 1926 vertical datum and NZVD2016 using the LINZ GPS-levelling marks. From the GPS-levelling marks the expected accuracy is better than 2 centimetres (95% Confidence interval).

More information on converting heights between vertical datums can be found on the LINZ website.

Wave power is a major environmental forcing mechanism in Hawaii that influences a number of marine ecosystem processes including coral reef community development, structure, and persistence. By driving mixing of the upper water column, wave forcing can also play a role in nutrient availability and ocean temperature reduction during warming events. Wave forcing in Hawaii is highly seasonal, with winter months typically experiencing far greater wave power than that experienced during the summer months. This layer represents the standard deviation of maximum daily wave power (kW/m) from 2000-2013. Data were obtained from the University of Hawaii at Manoa (UH) School of Ocean and Earth Science and Technology (SOEST) SWAN model (Simulating WAves Nearshore) following Li et al. (2016). Hourly 500-m SWAN model runs of wave power were converted to maximum daily wave power from 1979-2013 and then averaged over each month from 1979-2013, creating a monthly time series from which monthly climatologies were made. Pixels were removed directly adjacent to coastlines owing to the model being too coarse to handle extreme refraction and dissipation. Nearshore map pixels with no data were filled with values from the nearest neighboring valid offshore pixel by using a grid of points and the Near Analysis tool in ArcGIS then converting points to raster. The standard deviation of the long-term mean wave power was calculated by taking the standard deviation of the maximum daily time series of wave power data from 2000-2013 for each 500-m grid cell.

Warning: This raster is a grid of a floating-point values; not a surface. To derive an accurate height transformation value, this raster grid must be downloaded in terms of NZGD2000 and then converted into a surface using bilinear interpolation.

The Stewart Island 1977 to NZVD2016 Conversion Raster provides users with a two arc-minute (approximately 3.6 kilometres) raster image of the conversion of normal-orthometric heights from the Stewart Island 1977 local vertical datum to the New Zealand Vertical Datum 2016 (NZVD2016).

The conversion value is represented by the attribute “O”, in metres. This conversion and NZVD2016 are formally defined in the LINZ standard LINZS25009.

The height conversion grid models the difference between the Stewart Island 1977 vertical datum and NZVD2016 using the LINZ GPS-levelling marks. From the GPS-levelling marks the expected accuracy is better than 2 centimetres (95% Confidence interval).

More information on converting heights between vertical datums can be found on the LINZ website.

Warning: This raster is a grid of a floating-point values; not a surface. To derive an accurate height transformation value, this raster grid must be downloaded in terms of NZGD2000 and then converted into a surface using bilinear interpolation.

The Moturiki 1953 to NZVD2016 Conversion Raster provides users with a two arc-minute (approximately 3.6 kilometres) raster image of the conversion of normal-orthometric heights from the Moturiki 1953 local vertical datum to the New Zealand Vertical Datum 2016 (NZVD2016).

The conversion value is represented by the attribute “O”, in metres. This conversion and NZVD2016 are formally defined in the LINZ standard LINZS25009.

The height conversion grid models the difference between the Moturiki 1953 vertical datum and NZVD2016 using the LINZ GPS-levelling marks. From the GPS-levelling marks the expected accuracy is better than 2 centimetres (95% Confidence interval).

More information on converting heights between vertical datums can be found on the LINZ website.

Warning: This raster is a grid of a floating-point values; not a surface. To derive an accurate height transformation value, this raster grid must be downloaded in terms of NZGD2000 and then converted into a surface using bilinear interpolation.

The Bluff 1955 to NZVD2016 Conversion Raster provides users with a two arc-minute (approximately 3.6 kilometres) raster image of the conversion of normal-orthometric heights from the Bluff 1955 local vertical datum to the New Zealand Vertical Datum 2016 (NZVD2016).

The conversion value is represented by the attribute “O”, in metres. This conversion and NZVD2016 are formally defined in the LINZ standard LINZS25009.

The height conversion grid models the difference between the Bluff 1955 vertical datum and NZVD2016 using the LINZ GPS-levelling marks. From the GPS-levelling marks the expected accuracy is better than 2 centimetres (95% Confidence interval).

More information on converting heights between vertical datums can be found on the LINZ website.

The Raster Based GIS Coverage of Mexican Population is a gridded coverage (1 x 1 km) of Mexican population. The data were converted from vector into raster. The population figures were derived based on available point data (the population of known localities - 30,000 in all). Cell values were derived using a weighted moving average function (Burrough, 1986), and then calculated based on known population by state. The result from this conversion is a coverage whose population data is based on square grid cells rather than a series of vectors. This data set is produced by the Columbia University Center for International Earth Science Information Network (CIESIN) in collaboration with the Instituto Nacional de Estadistica Geografia e Informatica (INEGI).

HUDen is a raster of housing-unit density measured in housing units per square kilometer. The HUDen raster was generated using population and housing-unit count and data from the U.S. Census Bureau, building footprint data from Microsoft, and land cover data from LANDFIRE. Generate the HUDen raster from the building points. We first converted the building points to a 30-m raster where the raster value is the sum of the housing-units-per-centroid attribute of all building centroids within each raster grid cell. We then generated a smoothed density raster using a three-step process: 1) calculate a 200-m radius moving-window sum of the 30-m housing-unit count raster; 2) calculate a 200-m radius moving- window sum of habitable land cover (in sq km), where habitable land cover is all land covers except open water and permanent-snow/ice; and 3) divide the smoothed housing-unit count raster by the smoothed habitable land cover raster to generate housing unit density in housing units/sq km. To produce the final integer version of the HUDen raster, we set values less than 0.1 HU/sq km to zero, values between 0.1 and 1.5 to a value of 1 HU/sq km, and rounded all other values to the nearest integer.

Methods: This lidar derivative provides information about tree (and tall shrub) cover. The 3-foot resolution raster was produced from the 2020 Quality Level 1 classified lidar point cloud, which was provided by Sanborn Map Company, Inc. Tukman Geospatial developed the canopy cover raster from the classified point cloud using the following processing steps in LasTools:Create Tiles (lastile)Height Normalize the Point Cloud (lasheight)Set points classified as buildings to 0 heightThin the remaining points, taking the highest point in a 1.5 x 1.5 foot area (lasthin)Convert the thinned point cloud to a DEM (las2dem) Assign all pixels with values >= 15 feet to 1 (tree canopy), and all others to 0 (no tree canopy)The data was developed based on a horizontal projection/datum of NAD83 (2011).Lidar was collected in early 2020, while no snow was on the ground and rivers were at or below normal levels. To postprocess the lidar data to meet task order specifications and meet ASPRS vertical accuracy guidelines, Sanborn Map Company, Inc., utilized a total of 25 ground control points that were used to calibrate the lidar to known ground locations established throughout the project area.An additional 125 independent accuracy checkpoints, 70 in Bare Earth and Urban landcovers (70 NVA points), 55 in Tall Grass and Brushland/Low Trees categories (55 VVA points), were used to assess the vertical accuracy of the data. These check points were not used to calibrate or post process the data.Uses and Limitations:The canopy cover raster provides a raster of tree and shrub canopy greater than or equal to 15 feet in height. All pixels with any vegetation exceeding this height threshold have a pixel value of 1; all others have a 0. The layer is useful for myriad vegetation and forest-related analysis and is an important input to the automated processes used to develop the Santa Clara fine scale vegetation map. However, this data product was produced based on a rapid, fully automated point cloud classification and was not manually edited. As such, it may include some ‘false positives’ – pixels with a canopy height in the raster that aren’t vegetation. These false positives include noise from water aboveground non-vegetation returns from bridge decks, powerlines, and edges of buildings.Related Datasets:This dataset is part of a suite of lidar of derivatives for Santa Clara County. See table 1 for a list of all the derivatives. Table 1. lidar derivatives for Santa Clara CountyDatasetDescriptionLink to DataLink to DatasheetCanopy Height ModelPixel values represent the aboveground height of vegetation and trees.https://vegmap.press/clara_chmhttps://vegmap.press/clara_chm_datasheetCanopy Height Model – Veg Returns OnlySame as canopy height model, but does not include lidar returns labelled as ‘unclassified’ (uses only returns classified as vegetation)https://vegmap.press/clara_chm_veg_returnshttps://vegmap.press/clara_chm_veg_returns_datasheetCanopy CoverPixel values represent the presence or absence of tree canopy or vegetation greater than or equal to 15 feet tall.https://vegmap.press/clara_coverhttps://vegmap.press/clara_cover_datasheetCanopy Cover – Veg Returns OnlySame as canopy height model, but does not include lidar returns labelled as ‘unclassified’ (uses only returns classified as vegetation)https://vegmap.press/clara_cover_veg_returnshttps://vegmap.press/clara_cover_veg_returns_datasheet HillshadeThis depicts shaded relief based on the Hillshade. Hillshades are useful for visual reference when mapping features such as roads and drainages and for visualizing physical geography. https://vegmap.press/clara_hillshadehttps://vegmap.press/clara_hillshade_datasheetDigital Terrain ModelPixel values represent the elevation above sea level of the bare earth, with all above-ground features, such as trees and buildings, removed. The vertical datum is NAVD88 (GEOID18).https://vegmap.press/clara_dtmhttps://vegmap.press/clara_dtm_datasheetDigital Surface ModelPixel values represent the elevation above sea level of the highest surface, whether that surface for a given pixel is the bare earth, the top of vegetation, or the top of a building.https://vegmap.press/clara_dsmhttps://vegmap.press/clara_dsm_datasheet

Warning: This raster is a grid of a floating-point values; not a surface. To derive an accurate height transformation value, this raster grid must be downloaded in terms of NZGD2000 and then converted into a surface using bilinear interpolation.

New Zealand Quasigeoid 2016 Raster, provides users with a one arc-minute gridded (approximately 1.8 kilometres) raster image of the New Zealand Quasigeoid 2016 (NZGeoid2016).

The relationship between the GRS80 ellipsoid and the New Zealand Vertical Datum 2016 (NZVD2016) is modelled by [NZGeoid2016] and is represented by the attribute “N”, in metres.(http://data.linz.govt.nz/layer/3418). NZVD2016 is formally defined in the LINZ standard LINZS25009.

Users may also be interested in transforming heights to any of the 13 historic local vertical datums used in New Zealand using the appropriate datum relationship grid displayed in the NZ Height Conversion Index.

More information on these transformations is available on the LINZ website.

Solar irradiance is one of the most important factors influencing coral reefs. As the majority of their nutrients are obtained from symbiotic photosynthesizing organisms, reef-building corals need irradiance as a fundamental source of energy. Seasonally-low irradiance at high latitudes may be linked to reduced growth rates in corals and may limit reef calcification to shallower depths than that observed at lower latitudes. However, high levels of irradiance can lead to light-induced damage, production of free radicals, and in combination with increased temperatures, can exacerbate coral bleaching. This layer represents the standard deviation of the 8-day time series of irradiance (mol/m2/day) from July 2002 to December 31, 2013. Irradiance is here represented by PAR (photosynthetically active radiation), which is the spectrum of light that is important for photosynthesis. Monthly and 8-day 4-km (0.0417-degree) spatial resolution data were obtained from the MODIS (Moderate-resolution Imaging Spectroradiometer) Aqua satellite instrument from the NASA OceanColor website (http://oceancolor.gsfc.nasa.gov).

The standard deviation of the long-term mean of PAR was calculated by taking the standard deviation over all 8-day data from 2002-2013 for each pixel. A quality control mask was applied to remove spurious data associated with shallow water, following Gove et al., 2013. Nearshore map pixels with no data were filled with values from the nearest neighboring valid offshore pixel by using a grid of points and the Near Analysis tool in ArcGIS then converting points to raster.

Chlorophyll-a, is a widely used proxy for phytoplankton biomass and an indicator for changes in phytoplankton production. As an essential source of energy in the marine environment, the extent and availability of phytoplankton biomass can be highly influential for fisheries production and dictate trophic structure in marine ecosystems. Changes in phytoplankton biomass are predominantly effected by changes in nutrient availability, through either natural (e.g., turbulent ocean mixing) or anthropogenic (e.g., agricultural runoff) processes. This layer represents the standard deviation of the 8-day time series of chlorophyll-a (mg/m3) from 1998-2018.

Data products generated by the Ocean Colour component of the European Space Agency (ESA) Climate Change Initiative (CCI) project. These files are 8-day 4-km composites of merged sensor products: Global Area Coverage (GAC), Local Area Coverage (LAC), MEdium Resolution Imaging Spectrometer (MERIS), Moderate Resolution Imaging Spectroradiometer (MODIS) Aqua, Ocean and Land Colour Instrument (OLCI), Sea-viewing Wide Field-of-view Sensor (SeaWiFS), and Visible Infrared Imaging Radiometer Suite (VIIRS).

The standard deviation was calculated over all 8-day chlorophyll-a data from 1998-2018 for each pixel. A quality control mask was applied to remove spurious data associated with shallow water, following Gove et al., 2013. Nearshore map pixels with no data were filled with values from the nearest neighboring valid offshore pixel by using a grid of points and the Near Analysis tool in ArcGIS then converting points to raster.

Data source: https://oceanwatch.pifsc.noaa.gov/erddap/griddap/esa-cci-chla-8d-v5-0.graph

Solar irradiance is one of the most important factors influencing coral reefs. As a majority of their nutrients are obtained from symbiotic photosynthesizing organisms, reef-building corals need sunlight as a fundamental source of energy. Seasonally low irradiance at high latitudes may be linked to reduced growth rates in corals and may limit reef calcification to shallower depths than that observed at lower latitudes. However, high levels of irradiance can lead to light-induced damage, production of free radicals, and in combination with increased temperatures, can exacerbate coral bleaching. Irradiance is here represented by PAR (photosynthetically active radiation), which is the spectrum of light that is important for photosynthesis. This layer represents the mean of 8-day time series of PAR (mol/m2/day) from 2003-2018.

Data for PAR for the time period 2003-2018 were obtained from the Moderate Resolution Imaging Spectroradiometer (MODIS) Aqua satellite instrument from the NASA OceanColor website as 8-day 4-km composites.

The PAR long-term mean was calculated by taking the average of all 8-day data from 2003-2018 for each pixel. A quality control mask was applied to remove spurious data associated with shallow water, following Gove et al., 2013. Nearshore map pixels with no data were filled with values from the nearest neighboring valid offshore pixel by using a grid of points and the Near Analysis tool in ArcGIS then converting points to raster.

Data source: https://oceanwatch.pifsc.noaa.gov/erddap/griddap/aqua_par_8d_2018_0.graph

Methods: This lidar derivative provides information about vegetation height. The 3-foot resolution raster was produced from the 2020 Quality Level 1 classified lidar point cloud, which was provided by Sanborn Map Company, Inc. Tukman Geospatial developed the CHM from the classified point cloud using the following processing steps in LasTools:

Create Tiles (lastile) Height Normalize the Point Cloud (lasheight) Set points classified as buildings and unclassified to 0 height Thin the remaining points, taking the highest point in a 1.5 x 1.5 foot area (lasthin) Convert the thinned point cloud to a DEM (las2dem)

The data was developed based on a horizontal projection/datum of NAD83 (2011). Lidar was collected in early 2020, while no snow was on the ground and rivers were at or below normal levels. To postprocess the lidar data to meet task order specifications and meet ASPRS vertical accuracy guidelines, Sanborn Map Company, Inc., utilized a total of 25 ground control points that were used to calibrate the lidar to known ground locations established throughout the project area. An additional 125 independent accuracy checkpoints, 70 in Bare Earth and Urban landcovers (70 NVA points), 55 in Tall Grass and Brushland/Low Trees categories (55 VVA points), were used to assess the vertical accuracy of the data. These check points were not used to calibrate or post process the data. Uses and Limitations: The CHM provides a raster depiction of the highest vegetation returns for each 3x3 foot raster cell across Santa Clara County. The layer is useful for myriad vegetation and forest-related analysis and is an important input to the automated processes used to develop the Santa Clara fine scale vegetation map. This CHM was derived from the point cloud using only returns classified as vegetation. See the ‘Santa Clara County Canopy Height Model’ for a CHM that also includes points labelled as unclassified. Related Datasets: This dataset is part of a suite of lidar of derivatives for Santa Clara County. See table 1 for a list of all the derivatives. Table 1. lidar derivatives for Santa Clara County

  Dataset


  Description


  Link to Data


  Link to Datasheet




  Canopy Height Model


  Pixel values represent the aboveground height of vegetation and trees.


  https://vegmap.press/clara_chm


  https://vegmap.press/clara_chm_datasheet




  Canopy Height Model – Veg Returns Only


  Same as canopy height model, but does not include lidar returns labelled as ‘unclassified’ (uses only returns classified as vegetation)


  https://vegmap.press/clara_chm_veg_returns


  https://vegmap.press/clara_chm_veg_returns_datasheet




  Canopy Cover


  Pixel values represent the presence or absence of tree canopy or vegetation greater than or equal to 15 feet tall.


  https://vegmap.press/clara_cover


  https://vegmap.press/clara_cover_datasheet




  Canopy Cover – Veg Returns Only


  Same as canopy height model, but does not include lidar returns labelled as ‘unclassified’ (uses only returns classified as vegetation)


  https://vegmap.press/clara_cover_veg_returns


  https://vegmap.press/clara_cover_veg_returns_datasheet





  Hillshade


  This depicts shaded relief based on the Hillshade. Hillshades are useful for visual reference when mapping features such as roads and drainages and for visualizing physical geography. 


  https://vegmap.press/clara_hillshade


  https://vegmap.press/clara_hillshade_datasheet




  Digital Terrain Model


  Pixel values represent the elevation above sea level of the bare earth, with all above-ground features, such as trees and buildings, removed. The vertical datum is NAVD88 (GEOID18).


  https://vegmap.press/clara_dtm


  https://vegmap.press/clara_dtm_datasheet




  Digital Surface Model


  Pixel values represent the elevation above sea level of the highest surface, whether that surface for a given pixel is the bare earth, the top of vegetation, or the top of a building.


  https://vegmap.press/clara_dsm


  https://vegmap.press/clara_dsm_datasheet

This dataset contains impervious landbase features updated using digital orthoimagery acquired in 2015 by the Delaware Valley Regional Regional Planning Commission (DVRPC) and its partners. This is an update of existing features originally captured in 2013 using 2010 orthoimagery. Additionally, estimated building heights were derived from high resolution normalized digital surface elevation data models generated from NIR LiDAR data using the highest hit method. Digital surface elevation models were derived from LiDAR data collected by Quantum Spatial and other vendors and compiled for delivery to USGS and its partners. The horizontal datum for this dataset is North American Datum, 1983, Geoid GRS 1980, and the data is projected in Lambert Conformal Conic StatePlane Pennsylvania South (FIPS 3702). Units are US Foot. The minimum size for building features is 200 square feet. Please note that since this data is an update to data originally created using 2010 aerial imagery, many of the features may not appear to be positionally accurate when used with newer imagery. Heights provided for 'Building' class impervious surface features are estimates of 90th and 50th percentile statistical distribution and should be considered approximate. Process Steps for Calculating Building Height Statistics: Normalized digital surface models (nDSM) and slope rasters were generated from 0.5-meter LiDAR data. Geodesic area was calculated (in square feet) for all features classified as ‘Building’. A negative 1 meter buffer was applied to all building features with an area greater than 200 square meters - applied in an effort to ensure nDSM input values were those corresponding to the building roof and not the adjacent ground. Zonal statistics (Spatial Analyst extension) were calculated on the buffered features for each nDSM raster (first multiplying each raster by 10 to maintain precision and then converting from floating point to integer raster) and slope raster (generated from each integerized nDSM raster). Zonal percentile statistics were also calculated on buffered features for each raster to obtain the 90th percentile building heights (HEIGHT_ESTIMATE field). All output statistics fields were joined to the original input feature class (with unit conversions applied, where necessary). A complete dataset, which includes the following fields that are populated with various statistical outputs generated during the building height estimation process, is available upon request: HEIGHT_PCT90 – 90th percentile value of all cells in the nDSM raster located within the building footprint. HEIGHT_PCT50 – median value of all cells in the nDSM raster located within the building footprint. HEIGHT_COUNT – total number of cells with tabulated values in the nDSM raster located within the building footprint. SLOPE_MEAN – average of all cells in the slope raster located within the building footprint. SLOPE_STD – standard deviation of all cells in the slope raster located within the building footprint. SLOPE_COUNT - total number of cells with tabulated values in the slope raster located within the building footprint.

Chlorophyll-a is a widely used proxy for phytoplankton biomass and an indicator for changes in phytoplankton production. As an essential source of energy in the marine environment, the extent and availability of phytoplankton biomass can be highly influential for fisheries production and dictate trophic structure in marine ecosystems. Changes in phytoplankton biomass are predominantly effected by changes in nutrient availability, through either natural (e.g., turbulent ocean mixing) or anthropogenic (e.g., agricultural runoff) processes. This layer represents the annual average number of anomalies of chlorophyll-a (mg/m3) from 2002-2013, with values presented as fraction of a year. Monthly and 8-day 4-km (0.0417-degree) spatial resolution data were obtained from the MODIS (Moderate-resolution Imaging Spectroradiometer) Aqua satellite instrument from the NASA OceanColor website (http://oceancolor.gsfc.nasa.gov). The chlorophyll-a average annual frequency of anomalies was calculated by taking the average number of times that the 8-day time series exceeded the maximum monthly climatological chlorophyll-a value from 2002-2013 for each pixel. A quality control mask was applied to remove spurious data associated with shallow water, following Gove et al., 2013. Monthly climatologies were calculated from monthly time series using only full years over the Ocean Tipping Points (OTP) project time frame of interest (2002-2013). Time series of anomalies were calculated by quantifying the number and magnitude of events from the 8-day time series that exceed the maximum climatological monthly mean. Nearshore map pixels with no data were filled with values from the nearest neighboring valid offshore pixel by using a grid of points and the Near Analysis tool in ArcGIS then converting points to raster.

Chlorophyll-a is a widely used proxy for phytoplankton biomass and an indicator for changes in phytoplankton production. As an essential source of energy in the marine environment, the extent and availability of phytoplankton biomass can be highly influential for fisheries production and dictate trophic structure in marine ecosystems. Changes in phytoplankton biomass are predominantly effected by changes in nutrient availability, through either natural (e.g., turbulent ocean mixing) or anthropogenic (e.g., agricultural runoff) processes. This layer represents the mean of the 8-day time series of chlorophyll-a (mg/m3) from 2002-2013. Monthly and 8-day 4-km (0.0417-degree) spatial resolution data were obtained from the MODIS (Moderate-resolution Imaging Spectroradiometer) Aqua satellite instrument from the NASA OceanColor website (http://oceancolor.gsfc.nasa.gov). The long-term mean was calculated by taking the average of all 8-day data from 2002-2013 for each pixel. A quality control mask was applied to remove spurious data associated with shallow water, following Gove et al., 2013. Nearshore map pixels with no data were filled with values from the nearest neighboring valid offshore pixel by using a grid of points and the Near Analysis tool in ArcGIS then converting points to raster.