View In:
ArcGIS JavaScript
ArcGIS Online map viewer
ArcGIS Earth
ArcMap
ArcGIS Explorer
View Footprint In:
ArcGIS Online map viewer
Service Description:
Map Name: Layers
Legend
All Layers and Tables
Dynamic Legend
Dynamic All Layers
Layers:
Description:
Copyright Text:
Spatial Reference:
102100
(3857)
Single Fused Map Cache: false
Initial Extent:
XMin: -1.0932426894033713E7
YMin: 3285048.9152486613
XMax: -1.0397875675956236E7
YMax: 3586321.318405046
Spatial Reference:
102100
(3857)
Full Extent:
XMin: -1.0902666284994975E7
YMin: 3218537.8184050457
XMax: -1.0427636284994975E7
YMax: 3568807.8184050457
Spatial Reference:
102100
(3857)
Units: esriMeters
Supported Image Format Types: PNG32,PNG24,PNG,JPG,DIB,TIFF,EMF,PS,PDF,GIF,SVG,SVGZ,BMP
Document Info:
Title:
Author:
Comments: Coastal zone managers and researchers often require detailed information regarding emergent marsh vegetation types for modeling habitat capacities and needs of marsh-reliant wildlife (such as waterfowl and alligator). Detailed information on the extent and distribution of marsh vegetation zones throughout the Texas coast has been historically unavailable. In response, the U.S. Geological Survey, in collaboration with the Gulf Coast Joint Venture, the University of Louisiana-Lafayette, Ducks Unlimited, Inc., and Texas A&M University Kingsville, has produced a classification of marsh vegetation types along the middle and upper Texas coast from Corpus Christi Bay to the Sabine River. This study incorporates approximately 1,000 ground reference locations collected via helicopter surveys in coastal marsh areas and about 2,000 supplemental locations from fresh marsh, water, and “other” (that is, nonmarsh) areas. About two-thirds of these data were used for training, and about one-third were used for assessing accuracy. Decision-tree analyses using Rulequest See5 were used to classify emergent marsh vegetation types by using these data, multitemporal satellite-based multispectral imagery from 2009 to 2011, a bare-earth digital elevation model (DEM) based on airborne light detection and ranging (lidar), alternative contemporary land cover classifications, and other spatially explicit variables believed to be important for delineating the extent and distribution of marsh vegetation communities. Image objects were generated from segmentation of high-resolution airborne imagery acquired in 2010 and were used to refine the classification. The classification is dated 2010 because the year is both the midpoint of the multitemporal satellite-based imagery (2009–2011) classified and the date of the high-resolution airborne imagery that was used to develop image objects. Overall accuracy corrected for bias (accuracy estimate incorporates true marginal proportions) was 91 percent (95 percent confidence interval [CI]: 89.2–92.8), with a kappa statistic of 0.79 (95 percent CI: 0.77–0.81). The classification performed best for saline marsh (user’s accuracy 81.5 percent; producer’s accuracy corrected for bias 62.9 percent) but showed a lesser ability to discriminate intermediate marsh (user’s accuracy 47.7 percent; producer’s accuracy corrected for bias 49.5 percent). Because of confusion in intermediate and brackish marsh classes, an alternative classification containing only three marsh types was created in which intermediate and brackish marshes were combined into a single class. Image objects were reattributed by using this alternative three-marsh-type classification. Overall accuracy, corrected for bias, of this more general classification was 92.4 percent (95 percent CI: 90.7–94.2), and the kappa statistic was 0.83 (95 percent CI: 0.81–0.85). Mean user’s accuracy for marshes within the four-marsh-type and three-marsh-type classifications was 65.4 percent and 75.6 percent, respectively, whereas mean producer’s accuracy was 56.7 percent and 65.1 percent, respectively. This study provides a more objective and repeatable method for classifying marsh types of the middle and upper Texas coast at an extent and greater level of detail than previously available for the study area. The seamless classification produced through this work is now available to help State agencies (such as the Texas Parks and Wildlife Department) and landscape-scale conservation partnerships (such as the Gulf Coast Prairie Landscape Conservation Cooperative and the Gulf Coast Joint Venture) to develop and (or) refine conservation plans targeting priority natural resources. Moreover, these data may improve projections of landscape change and serve as a baseline for monitoring future changes resulting from chronic and episodic stressors.
Subject: A seamless and standardized classification of marsh vegetation salinity zones (i.e., fresh, intermediate, brackish, and saline) along the mid- and upper Texas coast from Corpus Christi Bay, Texas to the Sabine River, 2010
Category:
Keywords: coastal marsh, salinity, Texas
AntialiasingMode: None
TextAntialiasingMode: Force
Supports Dynamic Layers: true
MaxRecordCount: 1000
MaxImageHeight: 8192
MaxImageWidth: 8192
Supported Query Formats: JSON, AMF, geoJSON
Min Scale: 0
Max Scale: 0
Supports Datum Transformation: true
Child Resources:
Info
Dynamic Layer
Supported Operations:
Export Map
Identify
QueryDomains
Find
Return Updates
Generate KML