ACCESS NUMBER: 30951-85250

STUDY TITLE: Environmental Sensitivity Index (ESI) Shoreline Classification Using 

New Remote Sensing Data and Techniques 

REPORT TITLE: Feasibility of Using Remote Sensing Techniques for Shoreline 

Delineation and Coastal Habitat Classification for Environmental Sensitivity Index (ESI) 

Mapping 

CONTRACT NUMBER: 1435-01-99-CA-30951-85250 

SPONSORING OCS REGION: Gulf of Mexico 

APPLICABLE PLANNING AREA: Central Gulf of Mexico 

FISCAL YEARS OF PROJECT FUNDING: 2003; 2004; 2005 

COMPLETION DATE OF REPORT: August 2005 

COSTS: FY 2003: $71,022; FY 2004: $29,639; CUMULATIVE PROJECT COST: 

$100,661 

PROJECT MANAGER: D. Braud 

AFFILIATION: Louisiana State University, Coastal Studies Institute 

ADDRESS: 331 Howe-Russell Geoscience Complex, Baton Rouge, Louisiana 70803 

PRINCIPAL INVESTIGATOR: D. Braud 

KEY WORDS: Environmental Sensitivity Index, land/water interface, satellite imagery, 

shoreline types, IKONOS, remote sensing, classification, shoreline delineation, coastal 

habitat, greenness, vegetation index, tasseled cap, Louisiana coast, Central Gulf 

BACKGROUND: Environmental Sensitivity Index (ESI) mapping refers to a shoreline 

classification and sensitivity ranking system that has been a vital component of oil spill 

contingency planning and marine environmental assessment programs nation wide for 

25 years (Halls et al., 1997). The U.S. Minerals Management Service (MMS) currently 

uses ESI data and the ESI classification scheme for environmental assessment studies 

related to Outer Continental Shelf (OCS) activities. Traditional ESI data development 

includes the interpretation of aerial photographs and mapped observations by coastal 

geologists during overflights. This method has been applied successfully to the majority 

of the U.S. coastline. The complex, rapidly changing shoreline of Louisiana, however, 

has made ESI mapping extremely difficult using traditional techniques. As a result, a 

coast-wide ESI shoreline classification has never been developed for Louisiana. This 

represents a major information gap, as oil spill risk and environmental consequences in 

Louisiana are great. 
ACCESS NUMBER: 30951-85250

OBJECTIVES: (1) To develop remote sensing classification procedures supporting ESI 

mapping efforts in Louisiana and elsewhere; (2) To determine if remote sensing 

methods are as reliable as traditional methods, more cost effective, and more efficient; 

(3) To assess the appropriateness of the spectral and spatial resolution of IKONOS 

satellite imagery for ESI mapping; (4) To substantiate the cost savings of archived 

imagery; (5) To ascertain if a viable land/water interface can be developed from 

IKONOS imagery; and (6) To discover if an ESI style product useful for spill response 

and coastal management can be developed. 

DESCRIPTION: The study area comprises the saline regions of the Louisiana coastline 

from Port Fourchon to Lake Barre. Salinity boundaries were obtained from the 1997 

Louisiana Coastal Marsh Vegetative Type Map produced by the Louisiana Department 

of Wildlife & Fisheries, which includes categories of fresh, brackish, intermediate, saline, 

and water. These data define saline as salt marsh having typical vegetation of 

oystergrass (spartina alterniflora), glasswort (avicennia germinans), and saltgrass 

(distichlis spicata)”. This area of coastal Louisiana has experienced high rates of land 

loss and rapid shoreline change that have made traditional ESI classification techniques 

difficult. However, the presence of extensive oil and gas infrastructure and highly 

sensitive habitats and natural resources in this area emphasizes the need for up-to-date 

maps for oil spill planning and response. 

The methodology consisted of satellite image acquisition, land/water interface 

delineation from the satellite imagery, in situ data collection, satellite image 

classification, vector shoreline transfer, validation overflights, and accuracy assessment. 

In contrast, a traditional ESI project methodology consists of identification of existing 

digital land/water or shoreline, overflight classification on topographic quads, field 

classification accuracy assessment, and on-screen ESI attributing of the digital 

shoreline. 

SIGNIFICANT CONCLUSIONS: The spatial and spectral resolutions of IKONOS 

imagery adequately captured four broad habitat classes, but did not extract the same 

level of detail as traditional ESI mapping methods. The usefulness and applicability of 

these broad ESI classifications are dependent on the question at hand. For oil spill 

response, the relatively current imagery provides a valuable base map that is greatly 

improved over other available maps. This feature is particularly important in coastal 

areas experiencing rapid shoreline change. One of the biggest challenges of 

responders in these areas is access to maps that accurately reflect current conditions 

and shoreline locations so that equipment and personnel can be deployed to 

appropriate cleanup sites. Currently, responders obtain oblique digital photography 

during overflights, then print out the photographs to use as base maps for deploying 

equipment and mapping the distribution of oil. Recent imagery would provide a more 

uniform base map. A high resolution shoreline vector was also extracted from the 

imagery and served as the base for shoreline classes. Creation of an actual shoreline 

is also of value to spill responders, since miles of shoreline oiled is a common metric 

used to measure the spill impact on linear shoreline types and track cleanup progress. 
ACCESS NUMBER: 30951-85250

Both archived and new-collect imagery were used in this study. The average price for 

archived imagery was $6.85 per km2 whereas the average price for new-collect imagery 

was $12.89 per km2. The archived imagery is significantly cheaper, however, the 

seasons and tides cannot be chosen and fieldwork cannot coincide with the collection of 

the imagery. On the other hand, it is still difficult to predict and plan for new-collect 

satellite imagery to coincide with the desired seasons, tides and cloud free days, and 

the small swath width of the IKONOS satellite cannot collect imagery for a large area in 

a single pass. 

General recommendations to improve the classification of all ESI habitat types include 

the use of more robust sensors, sensors that can extract vertical structures, ancillary 

elevation data, texture analysis, and an exposure index. The Compact Airborne 

Spectrographic Imager (CASI and CASI 2) and Light Detecting and Ranging (LiDAR) 

are robust airborne sensors that have the potential to extract ESI classes with greater 

detail. Larsen and Erickson (1998) used the CASI sensor to distinguish between 

substrate types such as course sand, mud, and gravel beaches and man-made 

features. LiDAR collects elevation data, which may be useful in classifying vertical 

structures. Applying an exposure index to the shoreline can further define the modified 

classes into sheltered and exposed. 

STUDY RESULTS: Ten ESI habitat types were observed during the field work and 

overflights. The habitat characteristics were recorded for each type, and the technical 

performance of the imagery and classifications techniques were documented. Figures 

illustrate IKONOS imagery of each ESI habitat with a second image depicting the 

vectorized shoreline attributed with the ESI classification. In addition, an oblique aerial 

photograph, taken during the overflights, is shown for the same general area of the 

imagery along with a representative ground photograph of each ESI habitat. 

The tide at which the imagery was acquired significantly affected the accuracy of the 

mud and tidal flat mapping. IKONOS imagery accurately classified mud and tidal flats 

when the imagery was acquired at low tide. When the imagery was acquired at high 

tides, few flats were classified. The validation overflights, conducted during low tide, 

confirmed the extensive areas of mud and tidal flats found within the coastal region of 

Louisiana. 

The modified ESI class of beaches and man-made structures was the most difficult and 

least accurate classification type. The spatial and spectral resolutions of IKONOS 

imagery are not as effective at distinguishing between spectrally bright features and/or 

narrow or vertical structures. Gravel and sand beaches, urban infrastructures, riprap, 

and seawalls all exhibit similar reflectance values within the multi-spectral IKONOS 

imagery. Suspended sediment in shallow water caused additional problems. 

Overall, scrub-shrub habitats were easily identified and classified from the IKONOS 

imagery, and the classification of salt marsh habitats was successful. Salt marsh was 

the dominant habitat class found within the study area. Identifying vegetation 
ACCESS NUMBER: 30951-85250

phenology of the dominant vegetation types may help refine the classification. The 

classification of broken marsh areas was less accurate than areas of solid salt marsh. 

STUDY PRODUCTS: Research Planning, Inc. and Louisiana State University. 2005. 

Feasibility of Using Remote Sensing Techniques for Shoreline Delineation and Coastal 

Habitat Classification for Environmental Sensitivity Index (ESI) Mapping. OCS Study 

Report MMS 2005-047. A final report for the U.S. Department of the Interior, Minerals 

Management Service Gulf of Mexico OCS Region, Metairie, LA. Contract No. 1435-01-

99-CA-30951-85250. 71 pp. 

Geospatial Data: Vectorized shoreline in shapefile format attributed by ESI habitat for 

saline marsh region of study area. Vectorized polygons in shapefile format of mud flats 

for saline marsh region of study area. Both GIS layers are supplied with accompanying 

metadata.. 
ACCESS NUMBER: 30951-85250

STUDY AREA FOR FEASIBILITY OF USING REMOTE SENSING TECHNIQUES 

FOR SHORELINE DELINEATION AND COASTAL HABITAT CLASSIFICATION 

FOR ENVIRONMENTAL SENSITIVITY INDEX (ESI) MAPPING.