for Horry County, South Carolina
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| 1990 to 1995 C-CAP Change Analysis Data for Horry County, South Carolina |
The Coastal Change Analysis Program (C-CAP) monitors change in terrestrial land cover and nearshore submerged habitats.
One of the goals of C-CAP is to develop a nationally standardized database of land cover and change analysis in the coastal regions of the U.S. This is accomplished using remotely sensed data, primarily satellite imagery and aerial photography. With this information, coastal resource managers can correlate changes in terrestrial regions with those in coastal aquatic habitats.
The program is managed through the National Oceanic and Atmospheric Administration (NOAA) Coastal Services Center, in Charleston, South Carolina in coordination with the National Ocean Service, Center for Coastal Fisheries and Habitat Research in Beaufort, North Carolina.
C-CAP processes remotely sensed imagery for land cover classification. A remotely sensed image is composed of rows and columns of pixels. Each pixel stores a brightness value that provides information about the earth's surface. Classification is the process of sorting these pixels into a definitive number of groups or classes of similar pixels. Pixels are sorted by their brightness values and/or other criteria prescribed by the image analyst. After like pixel values have been grouped into land cover classes they are re-coded to a specific color representative of that class. The final result is a classified land cover map representing forests, wetlands, developed or cultivated lands, and unconsolidated shore.
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| 1990 Land Cover Data for Dorchester County |
1995 Land Cover Data for Dorchester County |
1990 to 1995 Change Analysis Data for Dorchester County |
There are two common methods of classification, supervised and unsupervised. In a supervised classification, the image analyst "trains" the computer-clustering software to recognize patterns or spectral signatures. Typical signatures that could be used for training a classification system include bare land, grassland, and evergreen forests.
In an unsupervised classification, the image analyst allows the computer-clustering software to recognize patterns in the data. Like pixel values will be sorted into a number of clusters defined by the image analyst. The analyst will then use data collected in the field, personal knowledge, and ancillary data to group these clusters into meaningful classes. Therefore, the image analyst must convert or label the clusters the computer chose into classes such as those found in the NOAA Coastal Change Analysis Program (C-CAP): Guidance for Regional Implementation. This document defines the protocol C-CAP uses to process the land cover and change analysis data.
Federal Geographic Data Committee (FGDC)-compliant metadata are available for the land cover and change analysis data on this CD-ROM. To view the metadata, click here.
To develop the land cover and change analysis data for the coastal South Carolina, Landsat Thematic Mapper (TM) imagery was acquired for 1990 and 1995. Each scene was geo-rectified, which means the image coordinates were converted to map coordinates. Using the 1995 imagery, a hybrid of supervised and unsupervised classification techniques were used to define the 15 land cover classifications as prescribed by the C-CAP protocol. Subtracting the 1995 from the 1990 data identified pixels where change potentially occurred. Once identified, these pixels in the 1990 imagery could be re-classified to reflect the change.
To view the extent of the data and change analysis statistics, click here.
Many hours of an image analyst's time are spent out in the field collecting training and accuracy assessment data. An image analyst uses training data to aid in identifying the spectral signature of a feature. During acquisition of field data, an image analyst would gather information at several ground points in various locations such as cultivated land and deciduous forests. At the lab, the training points would be used in the classification process to identify the features in other areas of the scene.
Accuracy assessment studies are conducted after the imagery has been classified. These studies determine whether an image analyst has correctly identified the land cover. Image analysts use a laptop computer based field station with commercial software. This system permits access to real-time Global Positioning System (GPS) data, which uses satellites to record the latitude and longitude position of the GPS receiver. Additionally, the system allows the visualization of raster data (satellite data and scanned aerial photographs) and vector data (road networks), as shown in the graphic below.
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| Example of the Portable Field Station Contained on a Color Laptop Computer |
Before an accuracy assessment study can be conducted, a set of random points is generated from the classified imagery. These coordinates are loaded onto the laptop computer. Using real-time GPS data and the vector road data, image analysts drive to the randomly selected points. Once at the location, the image analyst records whether the point was given the correct land cover classification.
The field station is an integral part of collecting the training information as well as conducting the accuracy assessment study. The system allows the image analysts to follow field movements directly on the image and map data. Additionally, the software allows for completion of field forms in real time while in the field. Prior to the advent of this technology, image analysts used hard copy maps and could only hope to visit 20 points in a day. Using the field station it is possible for the image analysts to check 200 points in a day, greatly improving the efficiency of all field data collection.
For more detailed information about C-CAP's accuracy assessment guidelines refer to the "Selection of Training and Verification Samples for Supervised and Unsupervised Classification" in the NOAA C-CAP: Guidance for Regional Implementation.
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Many people may not be familiar with Landsat TM imagery or with
land cover and change analysis data. To the untrained eye, common features such as
roads, airports, and golf courses may be difficult to distinguish. The
example to the left demonstrates how displaying a
Landsat TM scene next to land cover data can make
interpretation easier.
The golf courses on the Landsat TM scene appear as bright red lines. Now look below at the land cover data. Here the golf courses appear a peach color, defined as grassland. Without using the Landsat TM scene one might have missed identifying the golf courses. |
When using a C-CAP product, as with any remotely sensed product, it is necessary for the user to understand the inherent limitations of the data. C-CAP products are meant to give state, county, or regional users a method by which land cover change can be determined in their area of interest. However, while the resolution of the TM sensor is a 30 meter by 30 meter pixel, it does not mean that users should consider the data on a pixel by pixel basis. C-CAP considers its minimum measurement unit to be one acre, or in TM imagery, an area of approximately two pixels by two pixels. This means that for areas smaller than one acre, users may encounter errors that are not consistent with C-CAP’s overall intended map accuracy of 85 percent.
The following text contains a listing of the C-CAP land cover classes and a brief description of what types of land cover occur within each category. This data was compiled from the NOAA Coastal Change Analysis Program (C-CAP): Guidance for Regional Implementation, which summarizes C-CAP protocols and procedures that are used by scientists throughout the U.S. to develop consistent and reliable coastal change information for input to the C-CAP nationwide database. This document also provides useful guidelines for contributors working on related projects. It is considered to be a working document subject to periodic review and revision.
Developed - High Intensity
Contains little or no vegetation. This subclass includes heavily built-up
urban centers as well as large constructed surfaces in suburban and rural
areas. Large buildings (such as multiple family housing, hangars, and large
barns), interstate highways, and runways typically fall into this subclass.
Developed - Low Intensity
Contains substantial amounts of constructed surface mixed with substantial
amounts of vegetated surface. Small buildings (such as single family
housing, farm outbuildings, and sheds), streets, roads, and cemeteries with
associated grasses and trees typically fall into this subclass.
Cultivated Land
Includes herbaceous (cropland) and woody (e.g., orchards, nurseries,
vineyards) cultivated lands.
Grassland
Dominated by naturally occurring grasses and non-grasses (forbs) that are not fertilized,
cut, tilled, or planted regularly.
Deciduous Forest
Includes areas dominated by single stemmed, woody vegetation unbranched 0.6 to 1 meter (2
to 3 feet) above the ground and having a height greater than 6 meters (20 feet).
Evergreen Forest
Includes areas in which more than 67 percent of the trees remain green
throughout the year. Both coniferous and broad-leaved evergreens are
included in this category.
Mixed Forest
Contains all forested areas in which both evergreen and deciduous trees are
growing and neither predominate.
Scrub/Shrub
Areas dominated by woody vegetation less than 6 meters in height. This
class includes true shrubs, young trees, and trees or shrubs that are small
or stunted because of environmental conditions.
Palustrine Forest
Includes all nontidal wetlands dominated by woody vegetation greater than
or equal to 6 meters in height, and all such wetlands that occur in tidal
areas in which salinity due to ocean-derived salts is below 0.5 parts per thousand (ppt).
Palustrine Scrub/Shrub
Includes all nontidal wetlands dominated by woody vegetation less than or
equal to 6 meters in height, and all such wetlands that occur in tidal
areas in which salinity due to ocean-derived salts is below 0.5 ppt.
Palustrine Emergent Wetland
Includes all nontidal wetlands dominated by trees, shrubs, persistent
emergents, emergent mosses, or lichens, and all such wetlands that occur in
tidal areas in which salinity due to ocean-derived salts is below 0.5
ppt.
Estuarine Emergent Wetland
Characterized by erect, rooted, herbaceous hydrophytes (excluding mosses
and lichens) that are present for most of the growing season in most years.
Perennial plants usually dominate these wetlands. All water regimes
are included except those that are subtidal and irregularly exposed.
Unconsolidated Shore
Characterized by substrates lacking vegetation except for pioneering plants
that become established during brief periods when growing conditions are
favorable. Erosion and deposition by waves and currents produce a number of
landforms, such as beaches, bars, and flats, all of which are included in
this class.
Bare Land
Composed of bare soil, rock, sand, silt, gravel, or other earthen material
with little or no vegetation.
Water
Includes all areas of open water with less than 30 percent cover of trees,
shrubs, persistent emergent plants, emergent mosses, or lichens.
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