Impervious Surfaces Oahu, Hawaii 2006

Metadata also available as - [Outline] - [Parseable text] - [XML]

Frequently anticipated questions:

What does this data set describe?

Title: Impervious Surfaces Oahu, Hawaii 2006
This is a final impervious surface layer ready for distribution through NOAA CSC. The data set is an inventory of impervious surfaces for the island of Oahu, Hawaii for the year 2006. Impervious surfaces prevent infiltration of precipitation into the soil, disrupting the water cycle and affecting both the quantity and quality of water resources. Impervious surfaces include manmade features such as building rooftops, parking lots and roads consisting of asphalt, concrete and/or compacted dirt. This data set utilized 29 full or partial Quickbird multispectral scenes which were processed to detect impervious features on the island of Oahu.
  1. How should this data set be cited?

    Department of Commerce (DOC), National Oceanic and Atmospheric Administration (NOAA), National Ocean Service (NOS), Coastal Services Center (CSC), 20070512, Impervious Surfaces Oahu, Hawaii 2006: NOAA's Ocean Service, Coastal Services Center (CSC), Charleston, SC.

    Online Links:

  2. What geographic area does the data set cover?

    West_Bounding_Coordinate: -158.287540
    East_Bounding_Coordinate: -157.625001
    North_Bounding_Coordinate: 21.714256
    South_Bounding_Coordinate: 21.243775

  3. What does it look like?

  4. Does the data set describe conditions during a particular time period?

    Calendar_Date: 31-Dec-2005
    Currentness_Reference: Acquisition date of the Quickbird Scenes

  5. What is the general form of this data set?

    Geospatial_Data_Presentation_Form: remote-sensing image

  6. How does the data set represent geographic features?

    1. How are geographic features stored in the data set?

      This is a Raster data set. It contains the following raster data types:

      • Dimensions 21511 x 28459 x 1, type Pixel

    2. What coordinate system is used to represent geographic features?

      Grid_Coordinate_System_Name: Universal Transverse Mercator
      UTM_Zone_Number: 4
      Scale_Factor_at_Central_Meridian: 0.999600
      Longitude_of_Central_Meridian: -159.000000
      Latitude_of_Projection_Origin: 0.000000
      False_Easting: 500000.000000
      False_Northing: 0.000000

      Planar coordinates are encoded using Row and Column
      Abscissae (x-coordinates) are specified to the nearest 2.400000
      Ordinates (y-coordinates) are specified to the nearest 2.400000
      Planar coordinates are specified in meters

      The horizontal datum used is D_WGS_1984.
      The ellipsoid used is WGS_1984.
      The semi-major axis of the ellipsoid used is 6378137.000000.
      The flattening of the ellipsoid used is 1/298.257224.

  7. How does the data set describe geographic features?

    Island of Oahu delineated by Quickbird Scene(s) collected on December 31, 2005 (Source: unknown)

    0 or 1 indicates whether pixel is impervious or not. (Source: NOAA Coastal Services Center High-Resolution Land Cover Project)

    0 PerviousFeatures which allow infiltration from precipitation.
    1 ImperviousAnthropogenic features such as buildings, parking lots and roads developed from asphalt, concrete or other constructed surfaces which do not allow infiltration from precipitation.

Who produced the data set?

  1. Who are the originators of the data set? (may include formal authors, digital compilers, and editors)

  2. Who also contributed to the data set?

  3. To whom should users address questions about the data?

Why was the data set created?

To create a baseline inventory of impervious surfaces which can be utilized by state and local resource managers to assess the impacts precipitation events on water quality and flooding.

How was the data set created?

  1. From what previous works were the data drawn?

    NOAA CSC (source 1 of 1), Sanborn., 20070515, C-CAP Oahu, Hawaii. Land Cover Project: NOAA's Ocean Service, Coastal Services Center (CSC), Charleston SC.

    Online Links:

    Type_of_Source_Media: DVD/CD-ROM
    Source_Contribution: NOAA CSC

  2. How were the data generated, processed, and modified?

    Date: 22-Nov-2006 (process 1 of 3)
    This dataset was created by the Sanborn Mapping Company. The impervious surface classification is based on Quickbird imagery. The study area for the project was the island of Oahu, U.S. Hawaiian Island Chain. Automated Classification: The first step in the classification methodology involved incorporating automated techniques. Sanborn analysts utilized the Feature Analyst software extension to extract impervious surfaces from the Quickbird imagery. This was performed by providing the software with photo-interpreted polygon examples which "train" the software's learning mechanism to recognize impervious surface features for extraction from the imagery. The software then provides the user with an automated output which can be accepted by the analyst or retrained for an improved result. This automated output captures a large percentage of the existing impervious surface features within the imagery but does not meet the required accuracy. Classes 0 - Pervious 1 - Impervious

    Person who carried out this activity:

    CRS (Coastal Remote Sensing) Program Manager
    NOAA Coastal Services Center Coastal Change Analysis Program (C-CAP)
    CRS Program Manager
    2234 S. Hobson Ave.
    Charleston, SC 29405

    843-740-1210 (voice)
    843-740-1224 (FAX)

    Hours_of_Service: 8:00 am to 5:00 p.m. EST. M-F
    Date: 11-Apr-2007 (process 2 of 3)
    Manual editing and modeling: Errors or areas that could not be consistently extracted from the imagery were resolved using ancillary data sets or through manual edits. In some areas of the island, impervious surface features are obscured by clouds, shadows or tree canopy. For these types of issues Sanborn employed used ancillary data sources. 2004 USGS Orthophotography was used to capture impervious surfaces in cloud covered areas while the USGS DLG data for Hawaii was used to fill in missing secondary and compacted dirt roads. Due to spectral confusion between impervious surfaces and other features such as bare land. Manual editing was performed in Erdas Imagine to correct comission errors within the map. The same technique was used to capture missing impervious features that could not be derived from ancillary data sources.

    Person who carried out this activity:

    NOAA Coastal Services Center Coastal Change Analysis Program (C-CAP)
    CRS Program Manager
    2234 S. Hobson Ave.
    Charleston, SC 29405

    843-740-1210 (voice)
    843-740-1224 (FAX)

    Hours_of_Service: Monday to Friday, 8 a.m. to 5 p.m., Eastern Standard Time
    Date: 11-Jun-2007 (process 3 of 3)
    Metadata imported

    Person who carried out this activity:

    NOAA Coastal Services Center Coastal Change Analysis Program (C-CAP)
    CRS Program Manager
    2234 S. Hobson Ave.
    Charleston, SC 29405

    843-740-1210 (voice)
    843-740-1224 (FAX)

    Hours_of_Service: Monday to Friday, 8 a.m. to 5 p.m., Eastern Standard Time
  3. What similar or related data should the user be aware of?

How reliable are the data; what problems remain in the data set?

  1. How well have the observations been checked?

    Treating the map as a binary or single class scheme, impervious/pervious, allows for a simpler sampling technique than a traditional thematic land cover assessment. For this product, Sanborn developed an efficient procedure to assess accuracy of the impervious classification. The desired accuracy is at least 85% for the impervious/pervious map of Oahu. The approach described here tested the map for an accuracy of 90% for a MMU of 0.06 acre. Since Sanborn was working with a two-case situation they only needed to know whether the classification was right or wrong, a binomial distribution was used to calculate the sample size. Using that assumption, Sanborn used Ginevan (1979) who illustrated a sampling method using stratified random points that satisfies three criteria: 1. The scheme should have a low probability of accepting a map of low accuracy. 2. It should have a high probability of accepting a map of high accuracy. 3. It should require a minimum number of ground truth samples. Using the look-up table given in the paper that presents the required sample size for a given minimum error and a desired level of confidence, Sanborn determined the specific number of points that are required to meet the accuracy specification. To validate a map to determine if it meets a thematic accuracy of 90%, with a 95% confidence level (a 95% confidence interval means the chance of 1 in 20 of rejecting a map that is actually correct), the minimum number of sites required was 298; with the map being rejected if more than 21 are misclassified. In order to compensate for the rarity of impervious surfaces over a large portion of the Oahu landscape, the approach was to create 3 strata layers in order to limit the selection process to areas of likely impervious, and produce a map that accurately assesses the usefulness of the map to the user. Strata 1: Impervious Features This strata layer is based on the actual classification of impervious features. Strata 2: Distance buffer from impervious map The presence of impervious surfaces are correlated with other impervious features, and are likely to occur near other impervious features. A distance map based on a buffer of 250 m will be used to sample from. Strata 3: Remaining un-sampled area. Each of the strata was sampled to build an assessment database that exceeds the required minimum of 298 assessment points. The goal for the division of accuracy assessment points was an approximate 50:50 (impervious:pervious) stratification. The break down is as follows. Table 1: Sampling breakdown based on strata layer. Strata Layer Percentage of Total Samples Target Number of Samples Impervious Classification 30 >149 Distance From Impervious feature Mask 25 >75 Remaining Un-sampled Area 25 >74 Areas of impervious or where impervious surfaces are expected were biased in the sampling structure. Once the samples are collected, labeling and quality control procedures were completed on the samples to create a final accuracy database. The result was a simplified confusion matrix where only two classes are categorized (Figure 2). An overall accuracy statement can be made by summing the correct accuracy locations and deriving a percentage relative to the total number of accuracy points. References Congalton, R.G., Kass Green 1999. Assessing the accuracy of remotely sensed data: principles and practices. CRC/Lewis Press, Boca Raton, FL. (In Press) Congalton, R. G. and R. D. Macleod. 1998. A quantitative comparison of change-detection algorithms for monitoring eelgrass from remotely sensed data. Photogrammetric Engineering and Remote Sensing. Vol. 64, No. 3, March 1998, pp. 207-216. Ginevan, M.E. 1979. Testing land-use map accuracy: another look. Photogrammetric Engineering and Remote Sensing, 45(10): 1371-1377.

  2. How accurate are the geographic locations?

    12 meters CE90

  3. How accurate are the heights or depths?

    There was no terrain correction in the geo-referencing procedure.

  4. Where are the gaps in the data? What is missing?

    Data set is complete.

  5. How consistent are the relationships among the observations, including topology?

    Tests for logical consistency indicate that all row and column positions in the selected latitude/longitude window contain data. Conversion and integration with vector files indicates that all positions are consistent with earth coordinates covering the same area. Attribute files are logically consistent.

How can someone get a copy of the data set?

Are there legal restrictions on access or use of the data?

Access_Constraints: None
Data set is not for use in litigation. While efforts have been made to ensure that these data are accurate and reliable within the state of the art, NOAA, cannot assume liability for any damages, or misrepresentations, caused by any inaccuracies in the data, or as a result of the data to be used on a particular system. NOAA makes no warranty, expressed or implied, nor does the fact of distribution constitute such a warranty.

  1. Who distributes the data set? (Distributor 1 of 1)

    NOAA Coastal Services Center
    Attn: Clearinghouse Manager
    Clearinghouse Manager
    2234 South Hobson Avenue
    Charleston, SC 29405-2413

    (843)740-1210 (voice)
    (843)740-1224 (FAX)

    Hours_of_Service: Monday-Friday, 8-5 EST
  2. What's the catalog number I need to order this data set?

    Downloadable Data

  3. What legal disclaimers am I supposed to read?

    Users must assume responsibility to determine the usability of these data.

  4. How can I download or order the data?

Who wrote the metadata?

Last modified: 12-Jun-2013
Metadata author:
NOAA Coastal Services Center
Attn: Metadata Specialist
Metadata Specialist
2234 S Hobson Ave.
Charleston, SC 29405

843-740-1210 (voice)
843-740-1224 (FAX)

Hours_of_Service: 8:00 am to 5:00 pm EST.
Metadata standard:
FGDC Content Standards for Digital Geospatial Metadata (FGDC-STD-001-1998)

Generated by mp version 2.9.13 on Wed Nov 26 10:44:16 2014