Florida Land CoverFlorida Land Cover
Florida Cooperative Fish and Wildlife Research Unit
20000500
Florida Land Cover
2001
image
<http://www.wec.ufl.edu/coop/gap/lcmappi ng.htm>
Land Cover map for the state of Florida derived from the classification of
Landsat TM satellite imagery.
Land cover data layer for the state of Florida derived from the
classification of Landsat TM satellite imagery. The image is an 8-bit
color image which employs a color palette to ensure uniform colors
throughout a particular series. That is to say, if a color is defined as
having a particular RGB value, for all images within that series, the RGB
value is the same. The image is intended for use as an image backdrop,
planning purposes, and for feature identification for various conservation
or other purposes.
19930507
19940526
Observed
As funded
-82.663385
-82.040266
29.939313
29.422649
Land Cover
Land cover
Vegetation map
None
Florida
USA
None
Data represents 1993-1994
None
UNIX-ARC/INFO
Earth Observation Satellite Company
1993-1994
Landsat TM images
Image
THE DATA INCLUDED IN THIS APPLICATION ARE BASED ON INTERPRETATION OF
AVAILABLE INFORMATION AND SHOULD NOT BE CONSTRUED AS LEGALLY BINDING.
Scale is an important factor in data usage. Certain scale data sets are not
suitable for some projects, analysis, or modeling purposes. Please be sure
you are using the best available data.
1:24000 scale data sets are recommended for projects that are at the county
level. 1:24000 data should NOT be used for high accuracy base mapping such
as property parcel boundaries.
1:100000 scale data sets are recommended for projects that are at the
multi-county or regional level. 1:250000 scale data sets are recommended for
projects that are at the regional or state level or larger.
The Florida Gap Project is mandated by the National Gap Program, USGS/BRD to
use the recently enacted United States National Vegetation Classification
System (NVCS) as the classification for its vegetation map of the state of
Florida. The NVCS is based on a hierarchical structure with vegetation
physiognomic and floristic elements. The competing needs of producing a map
with a high classification resolution and the use of landcover data from the
LANDSAT satellite system has resulted in the development of a modified
vegetation classification for the state based on the NVCS classification.
Vegetation was classified to the Alliance level or to a higher aggregation
of Alliances when discrimination to the Alliance level was not reliable.
Various factors can limit the type and content of a classification. For
example, due to cloud cover and cost, the Florida Gap Project often used
LANDSAT data from a single date for each scene. Adjacent scenes often were
from seasonally different dates, resulting in spring/summer phenology for
one scene and fall/winter phenology for another. Thus, reliable
classification of deciduous versus evergreen dominated vegetation types was
reduced.
See Logical_Consistency_Report and Completeness_Report. Attribute specific
information is pending.
Earth Observation Satellite Company
1993-1994
Landsat TM images
State of Florida
Cartridge Tape
19930000
19940000
Ground Condition
EOSAT
Landsat Thematic Mapper Images
Each TM image to be used in the classification process was reviewed for
database consistency with land use/land cover maps and corrected for
atmospheric haze effects as needed. The images were normalized and the
first three rotations (brightness, greenness, wetness) of the tasseled
cap algorithm were applied. The tasseled-cap transformation which
provides a mechanism for data reduction and enhanced image
interpretation is computed for Natural areas were isolated from the high
reflectance values of urban and agricultural areas prior to
classification. Using the 'Urban and Ag' masks with the tasseled cap
image, the unsupervised classification routine (explained below) was
used to create a 10 to 25 Class image of the masked area. The classes
were visually compared to the tasseled cap imagery and those identified
as natural areas were included with the areas defined by the 'Natural
Areas Mask.' ERDAS Imagine's iterative unsupervised classification
algorithm (ISODATA) was applied to the tasseled cap plus raw bands
2,3,4, and 5, under the 'Natural Areas Mask' to create spectral
signatures. These signatures were then used with the
minimum-distance-to-mean classifier to create a classified image. The
classified image was stratified and the classes were combined or split
further using a knowledge based "cluster busting" method. Refer to the
Landcover Mapping chapter of the FLGap Final Report for detailed
description.
20000500
The GeoPlan Center obtained this data in image format (*.img) in May
2000. When received, the image was in the following projection: UTM Zone
17, Datum: NAD27, Spheroid: Clarke 1866. Image pixel size was 30.0
meters x 30.0 meters. The image was reprojected to Albers HPGN (see
"Spatial Reference Information") using ERDAS Imagine. The image was then
subset to each of the 67 Florida counties, and each county image was
exported to a grid (using ERDAS Imagine).
The land cover mapping technique developed by the Florida Fish and Wildlife
Cooperative Unit synergizes existing geospatial information with current
Landsat imagery. The primary data used in this method are:
Landsat TM imagery from 1992/1993 (92/92) and/or from 1993/1994 (93/94);
Updated Florida water management district land use/land cover maps
Videography ground truth information;
Third party ground-truth information;
National Wetlands Inventory (NWI) maps;
Soil Conservation Service Soils Maps.
For each Landsat scene location, the dates of the imagery are reviewed to
determine if two images are available that are approximately one year apart
and in different seasons. If this criteria is meet, the multi-date approach
is used. This approach capitalizes on the seasonal variation of vegetation
that can be detected using Landsat TM imagery. Each scene is classified
independently and subsequently, the classified images are merged to create a
seamless mosaic. Following is a description of the multi-date technique. The
method outlined below is divided into a pre-processing and post-processing
phase. In the pre-processing phase, the Landsat imagery is made usable for
classification by: checking for database consistency with the land use/ land
cover maps, correcting for atmospheric affects as needed, and computing the
first three spectral rotations of the tasseled cap algorithm (brightness,
greenness and wetness). During the processing phase, an iterative
unsupervised classification algorithm is used in a knowledge based cluster
busting method.
Methods:
Multi-date Land Cover Mapping Technique: When adequate information is
available, multi-temporal image classification procedures are used. Many
different techniques and band combinations can be used to classify Landsat
imagery. However, Hill and Megier (1988) found multi-temporal image
classification using the Tasseled-Cap algorithm resulted in improved land
cover mapping. Using similar procedures, each scene was classified as
follows.
Pre-processing: In the pre-processing phase, the data used in the
classification methodology is first checked for database constancy and
co-registration. The images are normalized and the tasseled cap
transformation for each image is computed. A discussion of each component is
presented below.
Database consistency: One component of the classification methodology uses
land use/land cover maps. These maps were obtained from Florida's water
management districts and revised with Landsat imagery from 1992 and 1993.
However, imagery from 1993 and 1994 were also used in the classification.
These images are overlayed with the land use maps and checked for database
consistency. If consistent positional errors are present, an affine
transformation and nearest-neighbor resampling is used to co-register the
image to the land use/land cover maps. Additionally, poor co-registration
between multi-date image bands will confuse the classification and
therefore, a poor classification will result. Each image, potentially having
been geo-rectified by different people and using different ground control,
is co-registered as needed.
Normalization: Prior to any multi-date image analysis, it is necessary to
correct for differences in sensor offset and gain and also scene
illumination caused by different seasons and atmospheric conditions. The
difference in overall brightness between the images was normalized using
image regression. This method is well suited for multi-temporal analysis
where care must be taken not to adjust the image for the seasonal variation
of vegetation. A regression model to account for these differences is
obtained by first identifying about 15 to 20 bright and dark objects in each
scene and, for each band, recording the digital number (DN). An example of a
dark object is uniform non turbid man-made lakes. Good bright object are:
airport runways, large roads, beaches, dense urban areas, and exposed soils.
Once these values are compiled, a linear regression model is computed with
the darker of the two images assigned to the X variable. This insures that
positive corrections are made such that when applied no negative numbers
resulted in the output image however, compression of values near 255 can
occur. For each band a linear regression model and an associated scatter
plot are computed. If the model has a correlation coefficient (r) higher
than 95% and the scatter plot does not have significant outliers, the linear
model is used. When outliers are detected, they are removed and the
regression model is recomputed.
Tasseled-Cap: The last step in the pre-processing phase is to compute the
tasseled cap algorithms. The tasseled-cap transformation provides a
mechanism for data reduction and enhanced image interpretation by
emphasizing the structures in the spectral data which arise as a result of
particular physical characteristics of scene classes (Crist 1985). The
equations for this spectral index have been supplied by ERDAS, Inc. Atlanta,
Georgia. Brightness, greenness, and wetness are computed for each image and
combined into one multi-temporal data set.
Processing: In the processing phase, for each Landsat scene location two
classified images are created, a classified image of natural areas, and a
classified image of some urban and agricultural areas. This segmentation
assistes with reducing the overall spectral variability of the image and
hence results in an improved unsupervised classification. Using Florida's
modified land use/land cover classification system (FLUCCS), codes that
represent natural areas are used to isolate natural areas in the imagery.
Similarly, FLUCCS codes are used to isolate urban and agricultural areas
which may contain natural areas (e.g. FLUCCS code for institution lands fall
under the urban codes however, these lands many times are natural).
Classification of Natural Areas: For the natural areas, Imagine's interative
unsupervised classification routine (ISODATA) is used to create 6
signatures. These signatures are then used with the minimum distance to mean
classifier to classify natural areas to 6 classes. Next, for each class of
the classified natural areas image, 3 to 5 new classes are created using
ISODATA and the minimum distance to mean classifier. At this point, up-to 30
classes could exist. These classes are then summarized against ground truth
information obtained from videography. This summary is then used in a
knowledge based class combining or class "busting" method.Using this
approach, classes with multiple labels are identified and "busted"
(classified into more classes). Conversely, multiple classes with the same
class name are combined. When possible, post-classification sorting is used
to refine the classification. In some instances certain classes can be
separated based on ancillary information such as NWI or soils information.
For example, this refinement allowes for the separation of some class that
could be differentiated and reclassified based on fresh water or salt water
NWI classes. At a pixel level, this could easily be done however, treating
contiguous pixels as a group and reclassifying all pixels in that group
based on a majority NWI class value will eliminate potential "salt and
pepper" and it will create a more natural split of the classes. Splitting
the classes involved: isolating and recoding the classes of interest,
clumping the classes, summarizing these clumps against the NWI coverage, and
splitting the classes through crosstabulation.
Classification of Urban Areas: The classification of urban areas for the GAP
Anaylsis project is being performed by the Coastal Service Center (C-CAP)
Program. However, an unsupervised classification of these areas was
performed. For urban/agricultural areas, 15 to 30 classes were created using
ISODATA and the minimum-distance-to-mean classifier. Classes that
represented natural areas were isolated and labeled. Finally, the classified
images, with class names, were merge to create a seamless mosaic. Classes
from land use/land cover maps were used to populate excluded lands in the
mosaic. The descriptive form of the FLUCCS codes were used as labels.
References
Crist, E. P. (1985). "A TM Tasseled Cap Equivalent Transformation for
Reflectance Factor Data." Paper, Elsevier Science Publishing Co., Inc., New
York, New York.
Hill, J. and J. Megier (1988). "The Use of Multi-Temporal TM Tasseled Cap
Features for Land Use Mapping in European Marginal Areas An operational
Approach." International Geoscience and Remote Sensing Symposium (IGARSS),
v2, pp.798-801.
The Florida Gap Project is mandated by the National Gap Program, USGS/BRD to
use the recently enacted United States National Vegetation Classification
System (NVCS) as the classification for its vegetation map of the state of
Florida (Anderson et al. 1998, Grossman et al. 1998). Please refer to the
following works for more information regarding the classifications used in
this data layer:
Anderson, M., P. Bourgeron, M. T. Bryer, R. Crawford, L. Engelking, D.
Faber-Langendoen, M. Gallyoun, K. Goodin, D. H. Grossman, S. Landaal, K.
Metzler, K. D. Patterson, M. Pyne, M. Reid, L. Sneddon, and A. S. Weakley.
1998. International Classification of Ecological Communities: TERRESTRIAL
VEGETATION of the UNITED STATES VOLUME II The National Vegetation
Classification System: List of Types. The Nature Conservancy, Arlington,
Virginia, USA.
Grossman, D.H., D. Faber-Langendoen, A.S. Weakley, M.Anderson, P.B ourgeron,
K. Goodin, D. H. Grossman, S. Landaal, K. Metzler, K. D. Patterson, M. Pyne,
M. Reid, L. Sneddon, and A. S. Weakley. 1998. International Classification
of Ecological Communities: TERRESTRIAL VEGETATION of the UNITED STATES
VOLUME I The National Vegetation Classification System: Development, Status,
and Applications. The Nature Conservancy, Arlington, Virginia, USA.
Leitman, H. M., J. E. Sohm, and M. A. Franklin. 1983. Wetland hydrology and
tree distribution of the Apalachicola River flood plain, Florida. U.S. Dept.
of the Interior, Geological Survey ; Alexandria, Va.
Loftin, C. S. 1998. Assessing patterns and processes of landscape change in
Okefenokee Swamp, Georgia. Ph.D. Dissertation, University of Florida,
Gainesville, Florida. 835pp.
Pearlstine, L., A. McKerrow, M. Pyne, S. Williams, and S McNulty. 1998.
Compositional Groups and Ecological Complexes: A Method for Alliance-Based
Vegetation Mapping. In: Gap Analysis Bulletin #7 National Gap Analysis
Program, USGS-BRD, <http://www.gap.uidaho.edu/Bulletins/7/>
State of Florida, USA
Raster
Pixel
26616
27463
Albers Conical Equal Area
24.000000
31.500000
-84.000000
24.000000
400000.00000
0.00000
row and column
30.000000
30.000000
METERS
Value
0
Background: This class represents marine areas and land outside of the
classification.
Value
1
Open water: All fresh water bodies without vegetation or with submerged
aquatic species and no emergents.
Value
2
Tropical Hardwood Hammock Formation: This class represents the species
rich hardwood hammocks of south Florida. Two major vegetation alliances,
coastal and interior hardwood hammocks, are included in this formation.
Value
3
Semi-deciduous Tropical/Subtropical Swamp Forest: This class represents
semi-deciduous forested swamps of south Florida. In large strand swamps,
such as, Fakahatchee Strand dominant canopy species include baldcypress
(Taxodium distichum), royal palm (Roystonea elata), laurel oak (Quercus
laurifolia), and red maple (Acer rubrum). Included within this class are
communities known as South Florida Bayhead Forest. These low stature
swamps are also referred to as bayhead forest and tree island. They
contain an assemblage of temperate and tropical species including:
Annona glabra, Magnolia virginiana, and Persea palustris.
Value
4
Xeric-Mesic Live Oak Ecological Complex: This complex is predominantly
live oak (Quercus virginiana) and sand live oak (Quercus geminata) found
in areas with hydrologic conditions varying from mesic to xeric.
Value
5
Mesic-Hydric Live Oak/ Sabal Palm Ecological Complex: This class is
generally a coastal live oak (Quercus virginiana) and sabal palm (Sabal
palmetto). It generally is found on mesic to hydric sites. The hydric
sites may be analogous to hydric hammocks
Value
6
Bay/Gum/Cypress Ecological Complex: This class represents forested
communities containing combinations of bay (Gordonia lasianthus,
Magnolia virginiana, Persea palustris), gum (Nyssa spp.), and cypress
(Taxodium spp.). Due the difficulty of spectral differentiation of
communities containing these species a broad more general class was
created. The order of species in the class name does not represent the
order of dominance. (Leitman et al. 1983, Loftin 1998).
Value
7
Loblolly Bay Forest: This class is dominated by Gordonia lasianthus
Value
8
Cajeput Forest Compositional Group: This class represents both forest
and woodland Melaleuca quinquenervia community types.
Value
9
Mixed Mangrove Forest Formation: This formation is a catch all for
mangrove forest types containing the three mangrove species in varying
levels of dominance. The class generally represents mangrove forest
found inland of the fringe. Dominance is generally shared by white and
black mangrove with occasional red mangrove.
Value
10
Black Mangrove Forest: This forest is generally pure black mangrove.
Value
11
Red Mangrove Forest: This forest tends to found as patches embedded in
Mixed Mangrove Forest Formation, higher energy islands, and forest
fringes greater than 30 m wide.
Value
12
Casuarina Compositional Complex: Casuarina forest and woodland were
combined in this class.
Value
13
South Florida Slash Pine Forest: This is an exclusively south Florida
pine forest type. The forest is dominated by Pinus elliottii var. densa
and tends to be found on sand in the northern part of it’s range and
limestone rock in the south part. This forest tends to have reduced
canopy coverage compared to north Florida slash pine (Pinus elliottii
var. elliottii)
Value
14
Sand Pine Forest: Forest dominated by sand pine (Pinus clausa). No
attempt was made to differentiate between Pinus clausa var. clausa and
Pinus clausa var. immuginata. These forests are found on dry, sand
ridges in the interior and along the coast.
Value
15
Mesic-Xeric Mixed Pine/Oak/Hickory Forest Ecological Complex: This
complex represents mesic to xeric mixed pine/oak/hickory forest. The
dominant species may include varying levels of Pinus elliottii, P.
palustris, P. taeda, Quercus falcata, Q. hemisphaerica, Q. virginiana,
Carya glabra, and C. tomentosa. These species are not exclusive
dominants for this class, but they were observed frequently during
ground-truthing
Value
16
Mesic-Hydric Pine Forest Compositional Group: This class represents
multiple pine forest types. The variation found among forest types is
dependent on slightly varying moisture conditions. The dominant pine
type in the class tends to be slash pine (Pinus elliottii var.
elliottii) flatwoods. Classes were combined because of the difficulty in
differentiating pine types from satellite data.
Value
17
Swamp Forest Compositional Group: This class represents deciduous and
evergreen swamp forests of north and central Florida. Classes were
consolidated because LANDSAT satellite data from phenologically varying
times (leaf on vs. leaf off) was not available. Leaf on or leaf off data
were commonly available for adjacent scenes. The resulting
classifications tended to consistently detect broad-leaved dominated
swamp forest, but not differentiate deciduous from evergreen. This class
may contain measurable, but not dominant amounts of cypress (Taxodium
spp.). The class may contain some of the same species and species
combinations as class 6 (Bay/Gum/Cypress Forest Ecological Complex). The
Bay/Gum/Cypress forest was treated as a separate class because it is
common to north Florida and was detectable using LANDSAT data and our
classification techniques. Contrast with class 7 Loblolly Bay forest, in
which this type of evergreen swamp was separable.
Value
18
Cypress Forest Compositional Group: This class represents cypress
communities dominated by Taxodium ascendens and T. distichum. These
communities include cypress domes (T. ascendens), and river and lake
fringes (T. distichum). Confusion associated with this class may include
overlap with pines and cypress/gum ponds within the pine flatwoods in
which they all occur.
Value
19
Mixed Evergreen-Cold-deciduous Hardwood Forest: The mixed
evergreen/cold-deciduous forest varies in species composition across
northern Florida. The eastern component is dominated by various oaks and
hickory, including Quercus hemispherica, Q. virginiana and Carya glabra.
The western component is dominated by beech (Fagus grandifolia) and
southern magnolia (Magnolia grandiflora). The community is known by
various names including, southern mesic hardwood forest and upland
hardwood forest.
Value
20
Buttonwood Woodland: This class represents buttonwood (Conocarpus
erectus) woodland of south Florida. These communities are usually found
inland and adjacent to the mangrove zone over marl soils or on exposed
limestone rock.
Value
21
Mixed Mangrove Woodland: The mixed mangrove woodlands in our map are
generally the result of hurricane Andrew in August 1992. The forest
species are the same as the mixed mangrove forest, but canopy coverage
has been reduced to 25-60%.
Value
22
Black Mangrove Woodland: Black mangrove (Avicennia germinans) with
canopy coverage 25-60%.
Value
23
Red Mangrove Woodland: Red mangrove (Rhizophora mangle) with canopy
coverage 25-60%.
Value
24
Live Oak Woodland: Live oak (Quercus virginiana) woodlands are usually
found along the coast on sand or shell deposits. In our map they can
also occur as isolated patches within pasture areas.
Value
25
South Florida Slash Pine Woodland: This class represents open, generally
low stature south Florida slash pine (Pinus elliottii var. densa) stands
on marl, sand or rock. Understory usually is graminoid and occasional
dwarf cypress (Taxodium ascendens) may be present.
Value
26
Sandhill Ecological Complex: Sandhill ecosystems are characterized by
longleaf pine (Pinus palustris), a few xeriphytic oaks (Quercus incana,
Q. geminata, Q. laevis), and a wiregrass/sporobolus understory on sand.
Tree cover is generally 25-60%.
Value
27
Broad-leaved Evergreen and Mixed Evergeen/Cold-deciduous Shrubland
Compositional Group: This class serves as a catch-all for many evergreen
and mixed evergreen/cold-deciduous shrub communities that were obviously
present, but difficult or impossible to differentiate. As it is used in
this map this class tends to be mesic to hydric. More specific classes
(e.g. Flooded/Saturated Broad-leaved Evergreen Shrubland Ecological
Complex, Dry Prairie, Gallberry/Saw Palmetto Shrubland, Dwarf Mangrove)
have been identified for this map and are treated as subsets of this
class within the vegetation classification system.
Value
28
Flooded/Saturated Broad-leaved Evergreen/Mixed Evergreen-Cold deciduous
Shrubland Compositional Group: This class represents communities
dominated by broad-leaved evergreen species. Representative species
include fetterbush (Lyonia lucida) in north Florida and cocoplum
(Chrysobalanus icaco) in south Florida. This class also includes a
freshwater variant of the red mangrove dwarf shrubland. In freshwater
areas red mangrove (Rhizophora mangle) and cocoplum (C. icaco) are often
found together.
Value
29
Dry Prairie Ecological Complex: In Florida dry prairies are sparsely
wooded savannas with dominance by a mosaic of saw palmetto (Serenoa
repens) and grasses (Aristida spp., Sporobolus spp., and Andropogon
spp.)
Value
30
Gallberry/Saw Palmetto Compositional Group: This class represents shrub
and graminoid communities found in association with wet flatwoods. While
similar to the dry prairie class it tends to be wetter and have a
greater dominance by shrubs. Gallberry (Ilex glabra and I. coriacea),
fetterbush (Lyonia lucida), sweet pepperbush (Clethra alnifolia), and
titi (Cyrilla racemosa and Cliftonia monophylla) are representative
species. This community may be an early phase of pine regeneration or it
may have a more permanent status (see Apalachicola National Forest for
examples).
Value
31
Brazilian Pepper Shrubland: The exotic shrub Schinus terebinthifolius
dominates this community in dense, monospecific stands. This community
is generally found in south Florida and along both coasts further north
to central Florida.
Value
32
Dwarf Mangrove Ecological Complex: This complex represents shrub
mangroves, regardless of dominance by the three mangrove species. The
largest stands are found in south Florida in areas with marl dominated
soils and in areas with standing freshwater near the coast. The
community is also found in the Indian River Lagoon.
Value
33
Coastal Strand: This is a coastal dune, shrub dominated community.
Dominance in north Florida by saw palmetto (Serenoa repens) and yaupon
holly (Ilex vomitoria) is common. In southern Florida, saw palmetto
(Serenoa repens) remains common and sea grape (Coccoloba uvifera)
becomes a more prominent community member.
Value
34
Groundsel-tree/Marsh Elder Tidal Shrubland: The groundsel-tree
(Baccharis halimifolia)/Marsh-Elder (Borrichia frutescens) is an open,
coastal community found at slightly higher elevation than the high salt
marsh. It is often transitional to upland communities, such as, Live
Oak/Sabal Palm forest.
Value
35
Xeric Scrubland: This class represents broad-leaved shrublands on inland
sand and coastal dune ridges. It is dominated by various scrubby oaks
and other xeriphytic species, such as, Quercus chapmanii, Q. geminata,
Q. inopina, Q. myrtifolia, Ceratiola ericoides, and Lyonia ferruginea.
Scattered sand pine (Pinus clausa), longleaf pine (P.palustris), and
slash pine (rarely P. elliottii var. elliottii in the north and commonly
P. elliottii var. densa in the south) may be found in the scrub.
Value
36
St. Johns Wort Shrubland Compositional Group: These are shrub
communities often found in isolated, small, acid wetlands. St. Johns
Wort may cover the entire wetland or only inhabit the fringe of deeper
water bodies.
Value
37
Flooded Cold-deciduous shrubland Ecological Complex: This class
represents shrub wetlands dominated by willow (Salix spp.), buttonbush
(Cephalanthus occidentalis), river birch (Betula nigra), and/or hazel
alder (Alnus serrulata). These species share the same habitat in some
but not all cases. River birch and hazel alder are northern species,
while willow and buttonbush are found throughout the state. In some
areas, especially in south Florida, willow and buttonbush may inhabit
areas with high proportions of cattail (Typha spp.).
Value
38
Saltwort/ Glasswort Ecological Complex: The Saltwort (Batis
maritima)/Glasswort (Salicornia spp.) complex represents saltwort and/or
glasswort. These communities vary geographically from pure stands of
either species to mixed stands. The communities are found in
association, but inland of salt marsh in northern Florida. In south
Florida they are found on marl and limestone near the coast in
association with mangroves and buttonwood.
Value
39
Graminiod Dry Prairie Ecological Complex: This class was generally used
to describe coastal graminoid communities found on the landward side of
dunes. Muhlenbergia spp., and Eragrostis spp. are representative
species.
Value
40
Sea Oats Dune Grassland: Vegetated coastal dunes near beaches are
generally dominated by a cover of sea oats (Uniola paniculata), other
grasses (Panicum spp., Sporobolus spp), forbs (Sesuvium portulacastrum),
and vines (Ipomoea pes-caprae).
Value
41
Wiregrass Grassland: Wiregrass (Aristida spp.) communities are
repesented here. These grasslands may also contain significant
proportions of Sporobolus spp. which are spectrally indistinguishable
from Aristida spp.
Value
42
Graminoid Emergent Marsh Compositional Group: This class represents
freshwater graminoid marshes that cannot be distinguished to the
specific level.
Value
43
Sawgrass Marsh: Freshwater marshes dominated by sawgrass (Cladium
mariscus var. jamaicense). This community is found throughout Florida.
It is found most extensively in the Everglades of south Florida. In the
remainder of Florida it is found in small isolated wetlands and at the
mouths of many rivers.
Value
44
Spikerush Marsh: Freshwater marshes dominated by spikerush (Eleocharis
spp.). This community is found throughout Florida. It is found most
extensively in the Everglades of south Florida, often in association
with more open areas known as wet prairies. In the remainder of Florida
it is found in small isolated wetlands.
Value
45
Muhly Grass Marsh: Muhly prairies in south Florida are dominated by
Muhlenbergia filipes and are generally found on marl soils with a
relatively short hydroperiod. Muhlenbergia spp. are also found on dry
coastal sands and shells and may be confused with marshes under dry
conditions.
Value
46
Cattail Marsh Compositional Group: This class represents southern
cattail (Typha domingensis) and common cattail (T. latifolia). Southern
cattail is found primarily in southern Florida and common cattail in
northern Florida. Both species can be found together anywhere in the
state.
Value
47
Salt Marsh Ecological Complex: This class represents salt water
graminoid marshes that cannot be distinguished to the specific level.
Value
48
Sand Cordgrass Grassland: Sand cordgrass (Spartina bakeri) tends to be
found along the coast in the interface between salt marsh and the
adjacent upland. It also is found in patches along rivers and in some
inland upland sites.
Value
49
Black needle Rush Marsh: This class represents black needle rush (Juncus
roemerianus). This is the most widespread of the salt marsh communities.
Value
50
Saltmarsh Cordgrass Marsh: This class represents saltmarsh cordgrass
marsh (Spartina alterniflora). This communities is found most
extensively in northern Florida.
Value
51
Saltmeadow Cordgrass/Salt Grass Salt Marsh: Saltmeadow Cordgrass
(Spartina patens)/Salt Grass (Distichlis spicata) Salt Marsh is a high
salt marsh often containing Baccharis halimifolia and Myrica cerifera
shrubs.
Value
52
Sparsely Wooded Wet Prairie Compositional Group: This represents
communities with a graminoid or forb wetland understory and a sparse
wooded overstory. The class may include dwarf or tree size cypress
(Taxodium ascendens), pine (Pinus spp.), or other wetland adapted trees.
Value
53
Dwarf Cypress Prairie: This class is prominent in south Florida. It is
dominated by graminoids (e.g. Muhlenbergia filipes, Rhynchospora spp.)
with a very sparse pond cypress (Taxodium ascendens) shrub overstory.
Value
54
Temperate Wet Prairie: These are wetland communties dominated by
graminoids, forbs and hydrophyllic species.
Value
55
Maidencane Marsh: Maidencane (Panicum hemitomon) marsh is represented by
this class. The community is found throughout Florida as a lake fringing
marsh and in south Florida in prominent large patches in the Everglades.
The community may not be detected when found around lakes when the marsh
is to narrow.
Value
56
Forb Emergent Marsh: This class represents emergent marsh containing
flag species, such as Pontederia cordata, Sagittaria lancifolia, and
Thalia geniculata.
Value
57
Water lily or Floating Leaved Vegetation: This class represents water
lily and floating leaves species such as, Eichhornia crassipes,
Hydrocotyle spp., Nuphar luteum, Nymphaea odorata, and Nymphoides
aquatica. While different ecologically, the water lilies (Nuphar luteum,
Nymphaea odorata, and Nymphoides aquatica) and floating leaved species
(Eichhornia crassipes and Hydrocotyle spp.) are difficult to distinguish
spectrally due to the high water content of their respective
environments. Nevertheless, large patches will tend to be water lily
dominated, while small patches and fringing communities will be
dominated by floating leaved species.
Value
58
Periphyton: This class represents periphyton, an aggregate of calcareous
algae. It covers the greatest area and is most obvious in south Florida.
Value
Sand, Beach: This class represents unvegetated
59
sand and beach.
Value
60
Bare soil/Clearcut: Disturbed sites and recent clearcuts generally have
a large proportion of area in exposed sand. They appear similar
spectrally and are difficult to differentiate. As a result, some
agricultural fields and recently developed residential sites may be
confused with clearcuts.
Value
61
Pavement, Roadside: As one might expect these are transportation
corridors including both the pavement and associated cultivated
roadside.
Value
62
Urban: This class represents predominantly commercial urban areas.
Value
Urban Residential: Urban residential is as it
63
seems.
Value
64
Urban Open/Others: This class represents the open areas and unknown
urban uses.
Value
65
Agriculture: Row crops, farm roads, and structures are found under this
class.
Value
66
Pasture/Grassland/Agriculture: This class represents pasture, grassland,
and some agriculture. The difficulty of differentiating grassland and
some forms of agriculture (e.g. hay) from pasture using spectral data
has resulted in this lumped class. The class appears to be primarily
pasture, although some overlap with sandhill and other open, graminoid
type communities may have occurred.
Value
67
Ag/Groves/Ornamental: This class represents orchards (e.g. pecan, peach,
pear) and groves (e.g. Citrus).
Value
68
Ag/Confined Feeding Operation/ Specialty Farms: This represents cattle
feetlots and dairy farms.
Value
69
Extractive: This class represents mined areas, including phosphate and
sand mines.
Value
Recreation
70
Value
71
Cloud: Yes, it happens clouds creep into a coverage and cannot be
removed.
COUNT
Number of cells corresponding to value
COUNT
DESCRIPT
Based on Value
None
Operator
FLORIDA GEOGRAPHIC DATA LIBRARY
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20010000
Florida Cooperative Fish & Wildlife Research Unit, Leonard Pearlstine
Physical
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PO Box 110450
University of Florida
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FGDC Content Standards for Digital Geospatial
FGDC-STD-001-1998
Generated by mp version 2.7.3 on Thu Mar 22 18:07:28 2001
{3803D533-AAD1-4B98-907E-00F15E722C93}2002120515110800FALSE20021205151345002002120612050400cartridge tapePublication dateSpatial and Attribute InformationEarth Observation Satellite Company 1993-1994 Landsat TM images n/aEOSATEach TM image to be used in the classification process was reviewed for database consistency with land use/land cover maps and corrected for atmospheric haze effects as needed. The images were normalized and the first three rotations (brightness, greenness, wetness) of the tasseled cap algorithm were applied. The tasseled-cap transformation which provides a mechanism for data reduction and enhanced image interpretation is computed for Natural areas were isolated from the high reflectance values of urban and agricultural areas prior to classification. Using the 'Urban and Ag' masks with the tasseled cap image, the unsupervised classification routine (explained below) was used to create a 10 to 25 Class image of the masked area. The classes were visually compared to the tasseled cap imagery and those identified as natural areas were included with the areas defined by the 'Natural Areas Mask.' ERDAS Imagine's iterative unsupervised classification algorithm (ISODATA) was applied to the tasseled cap plus raw bands 2,3,4, and 5, under the 'Natural Areas Mask' to create spectral signatures. These signatures were then used with the minimum-distance-to-mean classifier to create a classified image. The classified image was stratified and the classes were combined or split further using a knowledge based "cluster busting" method. Refer to the Landcover Mapping chapter of the FLGap Final Report for detailed description.
EOSAT 1994The GeoPlan Center obtained this data in image format (*.img) in May 2000. When received, the image was in the following projection: UTM Zone 17, Datum: NAD27, Spheroid: Clarke 1866. Image pixel size was 30.0 meters x 30.0 meters. The image was reprojected to Albers HPGN (see "Spatial Reference Information") using ERDAS Imagine. The image was then subset to each of the 67 Florida counties, and each county image was exported to a grid (using ERDAS Imagine).
GeoPlan Center20000500Relied on the integrity of the attribute information within the original data layer.Scale is an important factor in data usage. Certain scale datasets are not suitable for some project, analysis, or modelling purposes. Please be sure you are using the best available data.
1:24000 scale datasets are recommended for projects that are at the county level. 1:24000 data should NOT be used for high accuracy base mapping such as property parcel boundaries.
1:100000 scale datasets are recommended for projects that are at the multi-county or regional level. 1:125000 scale datasets are recommended for projects that are at the regional or state level or larger.
Vector datasets with no defined scale or accuracy should be considered suspect. Make sure you are familiar with your data before using it for projects or analysis. Every effort has been made to supply the user with data documentation. For additional information, see the References section and the Data Source Contact section of this documentation. For more information regarding scale and accuracy, see our webpage at: http://geoplan.ufl.edu/education.html. These data are based on interpretation of available information and should not be construed as legally binding.The land cover mapping technique developed by the Florida Fish and Wildlife Cooperative Unit synergizes existing geospatial information with current Landsat imagery. The primary data used in this method are:
Landsat TM imagery from 1992/1993 (92/92) and/or from 1993/1994 (93/94);
Updated Florida water management district land use/land cover maps
Videography ground truth information;
Third party ground-truth information;
National Wetlands Inventory (NWI) maps;
Soil Conservation Service Soils Maps.
For each Landsat scene location, the dates of the imagery are reviewed to determine if two images are available that are approximately one year apart and in different seasons. If this criteria is meet, the multi-date approach is used. This approach capitalizes on the seasonal variation of vegetation that can be detected using Landsat TM imagery. Each scene is classified independently and subsequently, the classified images are merged to create a seamless mosaic. Following is a description of the multi-date technique. The method outlined below is divided into a pre-processing and post-processing phase. In the pre-processing phase, the Landsat imagery is made usable for classification by: checking for database consistency with the land use/ land cover maps, correcting for atmospheric affects as needed, and computing the first three spectral rotations of the tasseled cap algorithm (brightness, greenness and wetness). During the processing phase, an iterative unsupervised classification algorithm is used in a knowledge based cluster busting method.
Methods:
Multi-date Land Cover Mapping Technique: When adequate information is available, multi-temporal image classification procedures are used. Many different techniques and band combinations can be used to classify Landsat imagery. However, Hill and Megier (1988) found multi-temporal image classification using the Tasseled-Cap algorithm resulted in improved land cover mapping. Using similar procedures, each scene was classified as follows.
Pre-processing: In the pre-processing phase, the data used in the classification methodology is first checked for database constancy and co-registration. The images are normalized and the tasseled cap transformation for each image is computed. A discussion of each component is presented below.
Database consistency: One component of the classification methodology uses land use/land cover maps. These maps were obtained from Florida's water management districts and revised with Landsat imagery from 1992 and 1993. However, imagery from 1993 and 1994 were also used in the classification. These images are overlayed with the land use maps and checked for database consistency. If consistent positional errors are present, an affine transformation and nearest-neighbor resampling is used to co-register the image to the land use/land cover maps. Additionally, poor co-registration between multi-date image bands will confuse the classification and therefore, a poor classification will result. Each image, potentially having been geo-rectified by different people and using different ground control, is co-registered as needed.
Normalization: Prior to any multi-date image analysis, it is necessary to correct for differences in sensor offset and gain and also scene illumination caused by different seasons and atmospheric conditions. The difference in overall brightness between the images was normalized using image regression. This method is well suited for multi-temporal analysis where care must be taken not to adjust the image for the seasonal variation of vegetation. A regression model to account for these differences is obtained by first identifying about 15 to 20 bright and dark objects in each scene and, for each band, recording the digital number (DN). An example of a dark object is uniform non turbid man-made lakes. Good bright object are: airport runways, large roads, beaches, dense urban areas, and exposed soils. Once these values are compiled, a linear regression model is computed with the darker of the two images assigned to the X variable. This insures that positive corrections are made such that when applied no negative numbers resulted in the output image however, compression of values near 255 can occur. For each band a linear regression model and an associated scatter plot are computed. If the model has a correlation coefficient (r) higher than 95% and the scatter plot does not have significant outliers, the linear model is used. When outliers are detected, they are removed and the regression model is recomputed.
Tasseled-Cap: The last step in the pre-processing phase is to compute the tasseled cap algorithms. The tasseled-cap transformation provides a mechanism for data reduction and enhanced image interpretation by emphasizing the structures in the spectral data which arise as a result of particular physical characteristics of scene classes (Crist 1985). The equations for this spectral index have been supplied by ERDAS, Inc. Atlanta, Georgia. Brightness, greenness, and wetness are computed for each image and combined into one multi-temporal data set.
Processing: In the processing phase, for each Landsat scene location two classified images are created, a classified image of natural areas, and a classified image of some urban and agricultural areas. This segmentation assistes with reducing the overall spectral variability of the image and hence results in an improved unsupervised classification. Using Florida's modified land use/land cover classification system (FLUCCS), codes that represent natural areas are used to isolate natural areas in the imagery. Similarly, FLUCCS codes are used to isolate urban and agricultural areas which may contain natural areas (e.g. FLUCCS code for institution lands fall under the urban codes however, these lands many times are natural).
Classification of Natural Areas: For the natural areas, Imagine's interative unsupervised classification routine (ISODATA) is used to create 6 signatures. These signatures are then used with the minimum distance to mean classifier to classify natural areas to 6 classes. Next, for each class of the classified natural areas image, 3 to 5 new classes are created using ISODATA and the minimum distance to mean classifier. At this point, up-to 30 classes could exist. These classes are then summarized against ground truth information obtained from videography. This summary is then used in a knowledge based class combining or class "busting" method.Using this approach, classes with multiple labels are identified and "busted" (classified into more classes). Conversely, multiple classes with the same class name are combined. When possible, post-classification sorting is used to refine the classification. In some instances certain classes can be separated based on ancillary information such as NWI or soils information. For example, this refinement allowes for the separation of some class that could be differentiated and reclassified based on fresh water or salt water NWI classes. At a pixel level, this could easily be done however, treating contiguous pixels as a group and reclassifying all pixels in that group based on a majority NWI class value will eliminate potential "salt and pepper" and it will create a more natural split of the classes. Splitting the classes involved: isolating and recoding the classes of interest, clumping the classes, summarizing these clumps against the NWI coverage, and splitting the classes through crosstabulation.
Classification of Urban Areas: The classification of urban areas for the GAP Anaylsis project is being performed by the Coastal Service Center (C-CAP) Program. However, an unsupervised classification of these areas was performed. For urban/agricultural areas, 15 to 30 classes were created using ISODATA and the minimum-distance-to-mean classifier. Classes that represented natural areas were isolated and labeled. Finally, the classified images, with class names, were merge to create a seamless mosaic. Classes from land use/land cover maps were used to populate excluded lands in the mosaic. The descriptive form of the FLUCCS codes were used as labels.
References
Crist, E. P. (1985). "A TM Tasseled Cap Equivalent Transformation for Reflectance Factor Data." Paper, Elsevier Science Publishing Co., Inc., New York, New York.
Hill, J. and J. Megier (1988). "The Use of Multi-Temporal TM Tasseled Cap Features for Land Use Mapping in European Marginal Areas An operational Approach." International Geoscience and Remote Sensing Symposium (IGARSS), v2, pp.798-801.
The Florida Gap Project is mandated by the National Gap Program, USGS/BRD to use the recently enacted United States National Vegetation Classification System (NVCS) as the classification for its vegetation map of the state of Florida (Anderson et al. 1998, Grossman et al. 1998). Please refer to the following works for more information regarding the classifications used in this data layer:
Anderson, M., P. Bourgeron, M. T. Bryer, R. Crawford, L. Engelking, D. Faber-Langendoen, M. Gallyoun, K. Goodin, D. H. Grossman, S. Landaal, K. Metzler, K. D. Patterson, M. Pyne, M. Reid, L. Sneddon, and A. S. Weakley. 1998. International Classification of Ecological Communities: TERRESTRIAL VEGETATION of the UNITED STATES VOLUME II The National Vegetation Classification System: List of Types. The Nature Conservancy, Arlington, Virginia, USA.
Grossman, D.H., D. Faber-Langendoen, A.S. Weakley, M.Anderson, P.B ourgeron, K. Goodin, D. H. Grossman, S. Landaal, K. Metzler, K. D. Patterson, M. Pyne, M. Reid, L. Sneddon, and A. S. Weakley. 1998. International Classification of Ecological Communities: TERRESTRIAL VEGETATION of the UNITED STATES VOLUME I The National Vegetation Classification System: Development, Status, and Applications. The Nature Conservancy, Arlington, Virginia, USA.
Leitman, H. M., J. E. Sohm, and M. A. Franklin. 1983. Wetland hydrology and tree distribution of the Apalachicola River flood plain, Florida. U.S. Dept. of the Interior, Geological Survey ; Alexandria, Va.
Loftin, C. S. 1998. Assessing patterns and processes of landscape change in Okefenokee Swamp, Georgia. Ph.D. Dissertation, University of Florida, Gainesville, Florida. 835pp.
Pearlstine, L., A. McKerrow, M. Pyne, S. Williams, and S McNulty. 1998. Compositional Groups and Ecological Complexes: A Method for Alliance-Based Vegetation Mapping. In: Gap Analysis Bulletin #7 National Gap Analysis Program, USGS-BRD, <http://www.gap.uidaho.edu/Bulletins/7/>
enLand Cover map for the state of Florida derived from the classification of Landsat TM satellite imagery. Land cover data layer for the state of Florida derived from the classification of Landsat TM satellite imagery. The image is an 8-bit color image which employs a color palette to ensure uniform colors throughout a particular series. That is to say, if a color is defined as having a particular RGB value, for all images within that series, the RGB value is the same. The image is intended for use as an image backdrop, planning purposes, and for feature identification for various conservation or other purposes.
The Florida Gap Project is mandated by the National Gap Program, USGS/BRD to use the recently enacted United States National Vegetation Classification System (NVCS) as the classification for its vegetation map of the state of Florida. The NVCS is based on a hierarchical structure with vegetation physiognomic and floristic elements. The competing needs of producing a map with a high classification resolution and the use of landcover data from the LANDSAT satellite system has resulted in the development of a modified vegetation classification for the state based on the NVCS classification. Vegetation was classified to the Alliance level or to a higher aggregation of Alliances when discrimination to the Alliance level was not reliable. Various factors can limit the type and content of a classification. For example, due to cloud cover and cost, the Florida Gap Project often used LANDSAT data from a single date for each scene. Adjacent scenes often were from seasonally different dates, resulting in spring/summer phenology for one scene and fall/winter phenology for another. Thus, reliable classification of deciduous versus evergreen dominated vegetation types was reduced.
Florida Cooperative Fish and Wildlife Research Unit 20000500Florida Land Covergap_lcovraster digital dataground condition1993050719940526As needed-82.663385-82.04026629.93931329.422649529185.000000588945.000000602834.000000660284.000000NONELand CoverVegetation mapFloridaNONENONERaster DatasetWeb site:
http://www.fgdl.orgTechnical Support:
http://www.fgdl.org/fgdlfeed.htmlFor FGDL Software:
http://www.fgdl.org/software.htmlFGDL Frequently Asked Questions:
http://www.fgdl.org/fgdlfaq.htmlMailing list for FGDL:
http://www.fgdl.org/fgdl-l.htmlmailing address431 Architecture PO Box 115706GainesvilleFlorida32611-5706For FGDL Software: http://www.fgdl.org/software.htmlFlorida Geographic Data Library (FGDL)Web site: http://www.fgdl.orgUNIX-ARC/INFOgap_lcov5291855889456602846028341-82.666367-82.040129.94325729.4186621enFGDC Content Standards for Digital Geospatial MetadataFGDC-STD-001-1998local timeLeonard Pearlstine Florida Cooperative Fish & Wildlife Research Unitphysical addressGainesville FL 32611 117 Newins-Ziegler Hall PO Box 110450 University of Florida 352-846-0630 2001http://www.esri.com/metadata/esriprof80.htmlESRI Metadata ProfileISO 19115 Geographic Information - MetadataDIS_ESRI1.0datasetDownloadable Data or CD ROM.2.1892.189 THE FGDL DATA AS PROVIDED BY CONTRIBUTING ORGANIZATIONS AND ANY PROGRAMMING SOFTWARE CREATED BY THE UNIVERSITY OF FLORIDA GEOPLAN CENTER (COLLECTIVELY THE 'MATERIALS') ARE COPYRIGHTED BY THE UNIVERSITY OF FLORIDA GEOPLAN CENTER FOR THE FGDL CONTRIBUTING AGENCIES AND ORGANIZATIONS (THE 'DATA PROVIDERS'). DO NOT REPRODUCE, REDISTRIBUTE OR RESELL THE MATERIALS, OR PROVIDE THE MATERIALS FOR FREE TO CUSTOMERS OR CLIENTS, OR PLACE THE MATERIALS FOR DOWNLOAD ON A WEBSITE. ADDITIONALLY, WHEN USING FGDL DATA OR SOFTWARE IN PROJECTS, MAPS, ETC.; YOU AGREE TO ACKNOWLEDGE THE FGDL AS A DATA SOURCE. THE MATERIALS ARE PROVIDED AS IS.THE UNIVERSITY OF FLORIDA GEOPLAN CENTER MAKES NO REPRESENTATIONS OR WARRANTIES ABOUT THE QUALITY OR SUITABILITY OF THE MATERIALS, EITHER EXPRESSLY OR IMPLIED, INCLUDING BUT NOT LIMITED TO ANY IMPLIED WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE, OR NON-INFRINGEMENT.THE UNIVERSITY OF FLORIDA GEOPLAN CENTER MAKES NO WARRANTIES, GUARANTIES OR REPRESENTATIONS AS TO THE TRUTH, ACCURACY OR COMPLETENESS OF THE DATA PROVIDED BY THE FGDL CONTRIBUTING ORGANIZATIONS. THE UNIVERSITY OF FLORIDA GEOPLAN CENTER SHALL NOT BE LIABLE FOR ANY DAMAGES SUFFERED AS A RESULT OF USING, MODIFYING, CONTRIBUTING OR DISTRIBUTING THE MATERIALS.Florida Geographic Data Library (FGDL)mailing address431 Architecture PO Box 115706GainesvilleFlorida32611-5706United StatesWeb site:
http://www.fgdl.orgTechnical Support:
http://www.fgdl.org/fgdlfeed.htmlFor FGDL Software:
http://www.fgdl.org/software.htmlFGDL Frequently Asked Questions:
http://www.fgdl.org/fgdlfaq.htmlMailing list for FGDL:
http://www.fgdl.org/fgdl-l.html002Local Area Network2.189Raster DatasetRasterGrid Cell1915199230.00000030.00000081Upper LeftTRUEDefault1matrix codedTRUEESRI GRIDGCS_North_American_1983_HARNAlbers Conical Equal Areacoordinate pairmeters0.0020480.002048Albers Conical Equal Area24.00000031.500000-84.00000024.000000400000.0000000.000000D_North_American_1983_HARNGeodetic Reference System 806378137.000000298.257222NAD_1983_HARN_Albers21992301915301gap_lcov01Table33Value Attribute TableFGDLObjectIDObjectIDOID400Internal feature number.ESRISequential unique whole numbers that are automatically generated.ValueValueInteger0000Background: This class represents marine areas and land outside of the classification. 1Open water: All fresh water bodies without vegetation or with submerged aquatic species and no emergents. 2Tropical Hardwood Hammock Formation: This class represents the species rich hardwood hammocks of south Florida. Two major vegetation alliances, coastal and interior hardwood hammocks, are included in this formation. 3Semi-deciduous Tropical/Subtropical Swamp Forest: This class represents semi-deciduous forested swamps of south Florida. In large strand swamps, such as, Fakahatchee Strand dominant canopy species include baldcypress (Taxodium distichum), royal palm (Roystonea elata), laurel oak (Quercus laurifolia), and red maple (Acer rubrum). Included within this class are communities known as South Florida Bayhead Forest. These low stature swamps are also referred to as bayhead forest and tree island. They contain an assemblage of temperate and tropical species including: Annona glabra, Magnolia virginiana, and Persea palustris. 4Xeric-Mesic Live Oak Ecological Complex: This complex is predominantly live oak (Quercus virginiana) and sand live oak (Quercus geminata) found in areas with hydrologic conditions varying from mesic to xeric. 5Mesic-Hydric Live Oak/ Sabal Palm Ecological Complex: This class is generally a coastal live oak (Quercus virginiana) and sabal palm (Sabal palmetto). It generally is found on mesic to hydric sites. The hydric sites may be analogous to hydric hammocks 6Bay/Gum/Cypress Ecological Complex: This class represents forested communities containing combinations of bay (Gordonia lasianthus, Magnolia virginiana, Persea palustris), gum (Nyssa spp.), and cypress (Taxodium spp.). Due the difficulty of spectral differentiation of communities containing these species a broad more general class was created. The order of species in the class name does not represent the order of dominance. (Leitman et al. 1983, Loftin 1998). 7Loblolly Bay Forest: This class is dominated by Gordonia lasianthus 8Cajeput Forest Compositional Group: This class represents both forest and woodland Melaleuca quinquenervia community types. 9Mixed Mangrove Forest Formation: This formation is a catch all for mangrove forest types containing the three mangrove species in varying levels of dominance. The class generally represents mangrove forest found inland of the fringe. Dominance is generally shared by white and black mangrove with occasional red mangrove. 10Black Mangrove Forest: This forest is generally pure black mangrove. 11Red Mangrove Forest: This forest tends to found as patches embedded in Mixed Mangrove Forest Formation, higher energy islands, and forest fringes greater than 30 m wide. 12Casuarina Compositional Complex: Casuarina forest and woodland were combined in this class. 13South Florida Slash Pine Forest: This is an exclusively south Florida pine forest type. The forest is dominated by Pinus elliottii var. densa and tends to be found on sand in the northern part of it's range and limestone rock in the south part. This forest tends to have reduced canopy coverage compared to north Florida slash pine (Pinus elliottii var. elliottii) 14Sand Pine Forest: Forest dominated by sand pine (Pinus clausa). No attempt was made to differentiate between Pinus clausa var. clausa and Pinus clausa var. immuginata. These forests are found on dry, sand ridges in the interior and along the coast. 15Mesic-Xeric Mixed Pine/Oak/Hickory Forest Ecological Complex: This complex represents mesic to xeric mixed pine/oak/hickory forest. The dominant species may include varying levels of Pinus elliottii, P. palustris, P. taeda, Quercus falcata, Q. hemisphaerica, Q. virginiana, Carya glabra, and C. tomentosa. These species are not exclusive dominants for this class, but they were observed frequently during ground-truthing 16Mesic-Hydric Pine Forest Compositional Group: This class represents multiple pine forest types. The variation found among forest types is dependent on slightly varying moisture conditions. The dominant pine type in the class tends to be slash pine (Pinus elliottii var. elliottii) flatwoods. Classes were combined because of the difficulty in differentiating pine types from satellite data. 17Swamp Forest Compositional Group: This class represents deciduous and evergreen swamp forests of north and central Florida. Classes were consolidated because LANDSAT satellite data from phenologically varying times (leaf on vs. leaf off) was not available. Leaf on or leaf off data were commonly available for adjacent scenes. The resulting classifications tended to consistently detect broad-leaved dominated swamp forest, but not differentiate deciduous from evergreen. This class may contain measurable, but not dominant amounts of cypress (Taxodium spp.). The class may contain some of the same species and species combinations as class 6 (Bay/Gum/Cypress Forest Ecological Complex). The Bay/Gum/Cypress forest was treated as a separate class because it is common to north Florida and was detectable using LANDSAT data and our classification techniques. Contrast with class 7 Loblolly Bay forest, in which this type of evergreen swamp was separable. 18Cypress Forest Compositional Group: This class represents cypress communities dominated by Taxodium ascendens and T. distichum. These communities include cypress domes (T. ascendens), and river and lake fringes (T. distichum). Confusion associated with this class may include overlap with pines and cypress/gum ponds within the pine flatwoods in which they all occur. 19Mixed Evergreen-Cold-deciduous Hardwood Forest: The mixed evergreen/cold-deciduous forest varies in species composition across northern Florida. The eastern component is dominated by various oaks and hickory, including Quercus hemispherica, Q. virginiana and Carya glabra. The western component is dominated by beech (Fagus grandifolia) and southern magnolia (Magnolia grandiflora). The community is known by various names including, southern mesic hardwood forest and upland hardwood forest. 20Buttonwood Woodland: This class represents buttonwood (Conocarpus erectus) woodland of south Florida. These communities are usually found inland and adjacent to the mangrove zone over marl soils or on exposed limestone rock. 21Mixed Mangrove Woodland: The mixed mangrove woodlands in our map are generally the result of hurricane Andrew in August 1992. The forest species are the same as the mixed mangrove forest, but canopy coverage has been reduced to 25-60%. 22Black Mangrove Woodland: Black mangrove (Avicennia germinans) with canopy coverage 25-60%. 23Red Mangrove Woodland: Red mangrove (Rhizophora mangle) with canopy coverage 25-60%. 24Live Oak Woodland: Live oak (Quercus virginiana) woodlands are usually found along the coast on sand or shell deposits. In our map they can also occur as isolated patches within pasture areas. 25South Florida Slash Pine Woodland: This class represents open, generally low stature south Florida slash pine (Pinus elliottii var. densa) stands on marl, sand or rock. Understory usually is graminoid and occasional dwarf cypress (Taxodium ascendens) may be present. 26Sandhill Ecological Complex: Sandhill ecosystems are characterized by longleaf pine (Pinus palustris), a few xeriphytic oaks (Quercus incana, Q. geminata, Q. laevis), and a wiregrass/sporobolus understory on sand. Tree cover is generally 25-60%. 27Broad-leaved Evergreen and Mixed Evergeen/Cold-deciduous Shrubland Compositional Group: This class serves as a catch-all for many evergreen and mixed evergreen/cold-deciduous shrub communities that were obviously present, but difficult or impossible to differentiate. As it is used in this map this class tends to be mesic to hydric. More specific classes (e.g. Flooded/Saturated Broad-leaved Evergreen Shrubland Ecological Complex, Dry Prairie, Gallberry/Saw Palmetto Shrubland, Dwarf Mangrove) have been identified for this map and are treated as subsets of this class within the vegetation classification system. 28Flooded/Saturated Broad-leaved Evergreen/Mixed Evergreen-Cold deciduous Shrubland Compositional Group: This class represents communities dominated by broad-leaved evergreen species. Representative species include fetterbush (Lyonia lucida) in north Florida and cocoplum (Chrysobalanus icaco) in south Florida. This class also includes a freshwater variant of the red mangrove dwarf shrubland. In freshwater areas red mangrove (Rhizophora mangle) and cocoplum (C. icaco) are often found together. 29Dry Prairie Ecological Complex: In Florida dry prairies are sparsely wooded savannas with dominance by a mosaic of saw palmetto (Serenoa repens) and grasses (Aristida spp., Sporobolus spp., and Andropogon spp.) 30Gallberry/Saw Palmetto Compositional Group: This class represents shrub and graminoid communities found in association with wet flatwoods. While similar to the dry prairie class it tends to be wetter and have a greater dominance by shrubs. Gallberry (Ilex glabra and I. coriacea), fetterbush (Lyonia lucida), sweet pepperbush (Clethra alnifolia), and titi (Cyrilla racemosa and Cliftonia monophylla) are representative species. This community may be an early phase of pine regeneration or it may have a more permanent status (see Apalachicola National Forest for examples). 31Brazilian Pepper Shrubland: The exotic shrub Schinus terebinthifolius dominates this community in dense, monospecific stands. This community is generally found in south Florida and along both coasts further north to central Florida. 32Dwarf Mangrove Ecological Complex: This complex represents shrub mangroves, regardless of dominance by the three mangrove species. The largest stands are found in south Florida in areas with marl dominated soils and in areas with standing freshwater near the coast. The community is also found in the Indian River Lagoon. 33Coastal Strand: This is a coastal dune, shrub dominated community. Dominance in north Florida by saw palmetto (Serenoa repens) and yaupon holly (Ilex vomitoria) is common. In southern Florida, saw palmetto (Serenoa repens) remains common and sea grape (Coccoloba uvifera) becomes a more prominent community member. 34Groundsel-tree/Marsh Elder Tidal Shrubland: The groundsel-tree (Baccharis halimifolia)/Marsh-Elder (Borrichia frutescens) is an open, coastal community found at slightly higher elevation than the high salt marsh. It is often transitional to upland communities, such as, Live Oak/Sabal Palm forest. 35Xeric Scrubland: This class represents broad-leaved shrublands on inland sand and coastal dune ridges. It is dominated by various scrubby oaks and other xeriphytic species, such as, Quercus chapmanii, Q. geminata, Q. inopina, Q. myrtifolia, Ceratiola ericoides, and Lyonia ferruginea. Scattered sand pine (Pinus clausa), longleaf pine (P.palustris), and slash pine (rarely P. elliottii var. elliottii in the north and commonly P. elliottii var. densa in the south) may be found in the scrub. 36St. Johns Wort Shrubland Compositional Group: These are shrub communities often found in isolated, small, acid wetlands. St. Johns Wort may cover the entire wetland or only inhabit the fringe of deeper water bodies. 37Flooded Cold-deciduous shrubland Ecological Complex: This class represents shrub wetlands dominated by willow (Salix spp.), buttonbush (Cephalanthus occidentalis), river birch (Betula nigra), and/or hazel alder (Alnus serrulata). These species share the same habitat in some but not all cases. River birch and hazel alder are northern species, while willow and buttonbush are found throughout the state. In some areas, especially in south Florida, willow and buttonbush may inhabit areas with high proportions of cattail (Typha spp.). 38Saltwort/ Glasswort Ecological Complex: The Saltwort (Batis maritima)/Glasswort (Salicornia spp.) complex represents saltwort and/or glasswort. These communities vary geographically from pure stands of either species to mixed stands. The communities are found in association, but inland of salt marsh in northern Florida. In south Florida they are found on marl and limestone near the coast in association with mangroves and buttonwood. 39Graminiod Dry Prairie Ecological Complex: This class was generally used to describe coastal graminoid communities found on the landward side of dunes. Muhlenbergia spp., and Eragrostis spp. are representative species. 40Sea Oats Dune Grassland: Vegetated coastal dunes near beaches are generally dominated by a cover of sea oats (Uniola paniculata), other grasses (Panicum spp., Sporobolus spp), forbs (Sesuvium portulacastrum), and vines (Ipomoea pes-caprae). 41Wiregrass Grassland: Wiregrass (Aristida spp.) communities are repesented here. These grasslands may also contain significant proportions of Sporobolus spp. which are spectrally indistinguishable from Aristida spp. 42Graminoid Emergent Marsh Compositional Group: This class represents freshwater graminoid marshes that cannot be distinguished to the specific level. 43Sawgrass Marsh: Freshwater marshes dominated by sawgrass (Cladium mariscus var. jamaicense). This community is found throughout Florida. It is found most extensively in the Everglades of south Florida. In the remainder of Florida it is found in small isolated wetlands and at the mouths of many rivers. 44Spikerush Marsh: Freshwater marshes dominated by spikerush (Eleocharis spp.). This community is found throughout Florida. It is found most extensively in the Everglades of south Florida, often in association with more open areas known as wet prairies. In the remainder of Florida it is found in small isolated wetlands. 45Muhly Grass Marsh: Muhly prairies in south Florida are dominated by Muhlenbergia filipes and are generally found on marl soils with a relatively short hydroperiod. Muhlenbergia spp. are also found on dry coastal sands and shells and may be confused with marshes under dry conditions. 46Cattail Marsh Compositional Group: This class represents southern cattail (Typha domingensis) and common cattail (T. latifolia). Southern cattail is found primarily in southern Florida and common cattail in northern Florida. Both species can be found together anywhere in the state. 47Salt Marsh Ecological Complex: This class represents salt water graminoid marshes that cannot be distinguished to the specific level. 48Sand Cordgrass Grassland: Sand cordgrass (Spartina bakeri) tends to be found along the coast in the interface between salt marsh and the adjacent upland. It also is found in patches along rivers and in some inland upland sites. 49Black needle Rush Marsh: This class represents black needle rush (Juncus roemerianus). This is the most widespread of the salt marsh communities. 50Saltmarsh Cordgrass Marsh: This class represents saltmarsh cordgrass marsh (Spartina alterniflora). This communities is found most extensively in northern Florida. 51Saltmeadow Cordgrass/Salt Grass Salt Marsh: Saltmeadow Cordgrass (Spartina patens)/Salt Grass (Distichlis spicata) Salt Marsh is a high salt marsh often containing Baccharis halimifolia and Myrica cerifera shrubs. 52Sparsely Wooded Wet Prairie Compositional Group: This represents communities with a graminoid or forb wetland understory and a sparse wooded overstory. The class may include dwarf or tree size cypress (Taxodium ascendens), pine (Pinus spp.), or other wetland adapted trees. 53Dwarf Cypress Prairie: This class is prominent in south Florida. It is dominated by graminoids (e.g. Muhlenbergia filipes, Rhynchospora spp.) with a very sparse pond cypress (Taxodium ascendens) shrub overstory. 54Temperate Wet Prairie: These are wetland communties dominated by graminoids, forbs and hydrophyllic species. 55Maidencane Marsh: Maidencane (Panicum hemitomon) marsh is represented by this class. The community is found throughout Florida as a lake fringing marsh and in south Florida in prominent large patches in the Everglades. The community may not be detected when found around lakes when the marsh is to narrow. 56Forb Emergent Marsh: This class represents emergent marsh containing flag species, such as Pontederia cordata, Sagittaria lancifolia, and Thalia geniculata. 57Water lily or Floating Leaved Vegetation: This class represents water lily and floating leaves species such as, Eichhornia crassipes, Hydrocotyle spp., Nuphar luteum, Nymphaea odorata, and Nymphoides aquatica. While different ecologically, the water lilies (Nuphar luteum, Nymphaea odorata, and Nymphoides aquatica) and floating leaved species (Eichhornia crassipes and Hydrocotyle spp.) are difficult to distinguish spectrally due to the high water content of their respective environments. Nevertheless, large patches will tend to be water lily dominated, while small patches and fringing communities will be dominated by floating leaved species. 58Periphyton: This class represents periphyton, an aggregate of calcareous algae. It covers the greatest area and is most obvious in south Florida. 59Sand, Beach: This class represents unvegetated sand and beach. 60Bare soil/Clearcut: Disturbed sites and recent clearcuts generally have a large proportion of area in exposed sand. They appear similar spectrally and are difficult to differentiate. As a result, some agricultural fields and recently developed residential sites may be confused with clearcuts. 61Pavement, Roadside: As one might expect these are transportation corridors including both the pavement and associated cultivated roadside. 62Urban: This class represents predominantly commercial urban areas. 63Urban Residential: Urban residential is as it seems. 64Urban Open/Others: This class represents the open areas and unknown urban uses. 65Agriculture: Row crops, farm roads, and structures are found under this class. 66Pasture/Grassland/Agriculture: This class represents pasture, grassland, and some agriculture. The difficulty of differentiating grassland and some forms of agriculture (e.g. hay) from pasture using spectral data has resulted in this lumped class. The class appears to be primarily pasture, although some overlap with sandhill and other open, graminoid type communities may have occurred. 67Ag/Groves/Ornamental: This class represents orchards (e.g. pecan, peach, pear) and groves (e.g. Citrus). 68Ag/Confined Feeding Operation/ Specialty Farms: This represents cattle feetlots and dairy farms. 69Extractive: This class represents mined areas, including phosphate and sand mines. 70 Recreation 71Cloud: Yes, it happens clouds creep into a coverage and cannot be removed. Numerical value describing the land cover.FGDLCountCountDouble000Number of cells corresponding to value FGDLRedRedDouble000The spectral value which determines the display color for a value.FGDLGreenGreenDouble000The spectral value which determines the display color for a value.FGDLBlueBlueDouble000The spectral value which determines the display color for a value.FGDLClass_namesClass_namesString9600The common name for each type of landcover.FGDL20021206