Project Design and Methods
To support the image interpretation and mapping, extensive field reference data were collected in the weeks before and after the image acquisition. Existing maps of seagrass distribution derived from boat based surveys from the mid 1990's were used to plan the reference data collection. A series of transects were established to sample full range of conditions in the Barnegat Bay-Little Egg Harbor study area. Eighteen transects were visited, and data points were recorded at intervals of approximately 250 meters. The transects, perpendicular to the eastern (barrier island) shoreline, extended from shallow inshore areas, across the seagrass beds and into deeper mid-bay water. These intervals were not strict, and often data points were recorded in between the intervals at areas where there appeared to be a noticeable change in seagrass coverage. Additional reference points were also collected to spot check areas of uncertainty. In general, field reference points were collected in areas (i.e., approximately 5 x 5 meters area) where the seagrass bed was reasonably consistent in coverage and distribution. ESRI's ArcMap and Trimble's GPS Pathfinder were used to support the field reference data collection.
All transect endpoints and individual check points were first mapped on the GPS, endpoints were then loaded onto a GPS for navigation on the water. Real time data collection (approximately ± 1-3 meter accuracy) was a sufficient level of accuracy for our purposes. A total of 245 transect and individual points were collected (Figure 2). The objective was to understand how bed characteristics changed from shallow to deep water, and to be able to understand the difference in visual signal on the imagery between beds in shallow (less than or equal to 1.5 m) and deep water (greater than 1.5 m). All 245 points were used to support the interpretation and mapping, none were reserved as independent validation. For each field reference point, the following data were collected:
GPS location (UTM)
SAV species presence/dominance: Zostera marina or Ruppia maritima or Algae
% cover (10 % intervals) determined by visual estimation
Blade Height of 5 tallest seagrass blades
Shoot density (# of shoots per 1/9 m2 quadrat that was extracted and counted on the boat)
From a landscape ecology perspective as well as from our object-oriented classification approach, the spatial structure of the seagrass habitats was conceptualized at 3 different levels: 1) bed, a spatially contiguous area of seagrass of varying % cover composition; 2) density class, a spatially contiguous area of overall similar % cover composition; and 3) patch, small discrete clumps of seagrass or areas of open bay bottom. This conceptual spatial framework was then broadened to develop a hierarchical classification scheme to encompass the larger bay system for mapping purposes. The bay was categorized into 4 levels of attribute detail (Figure 3). Level 1 differentiated land and emergent wetlands from open water. Level 2 differentiated deep water/channels (> 1.5-2 m depth) and shallow water (<1.5-2m depth) bottom habitats. At Level 3, the shallow bottom habitats were then differentiated into: 1) shallow sand/mud flats (<1.5m depth); macro algae beds (i.e. Ulva lactuca and assorted macro algae dominated; scattered seagrass may be present) and seagrass beds (i.e., Zostera marina and Ruppia maritima). At Level 4, the seagrass beds were partitioned into 3 categories based on the % cover: dense (80-100 % coverage), moderate (40-80% coverage), sparse (10-40% coverage). At Level 5, were the individual patches of seagrass or bare bottom. The very detailed Level 5 delineations were not included in the final output maps.
While this seagrass classification does not represent equal % cover intervals, the class breaks were based on thresholds that appeared to be consistently discernable in both the image interpretation and corresponding field data. These seagrass density class ranges are similar to the scheme used by Moore et. al., 2000. The relative dominance of Zostera vs. Ruppia was not distinguished. The shallow sand/mud flats can in some ways be considered as potential seagrass habitat as our field surveys showed that seagrass was often present at low levels (i.e., < 10% cover). In these cases, the seagrass generally did not form cohesive clumps but rather a sparse and/or discontinuous covering of individual seagrass plants. Previous experience has shown that some of these areas develop denser cover of seagrass later in the growing season. This may especially be true in the more mesohaline areas of the bay where Ruppia is the dominant seagrass.
An object-oriented classification approach was performed using eCognition software (Standard Version 3.0) to segment the image into image objects. Image objects are delineated to minimize within object variance and maximize between object variances. A multi-resolution segmentation can be used to create a hierarchical framework of decomposable image objects (Benz et al., 2004). In other words, a super-object is composed of objects which in turn can be composed of sub-objects. As sub-objects are aggregated to form an object, interior boundaries disappear but exterior boundaries remain stable. This multi-resolution approach was adopted to segment the water portion of the image into 3 general levels of spatial detail using what is termed a classification-based multi-resolution segmentation (CBMS). The first step was to segment the image at a fine level of detail, which corresponded with our conceptual Level 5, i.e., the individual patches of seagrass. The size of a minimum mapping unit for the individual seagrass beds was on the order of 1 ha in size. Next, the segmentation was coarsened to the next higher level of aggregation (determined by the scale parameter), corresponding to conceptual Level 4 where individual sub-object (patches) are combined to create image objects (macro-patches) of similar density class. Finally, the objects (macro-patches) were combined into super-objects to correspond with the conceptual model Level 3 seagrass beds.
Within the eCognition software environment, segmentation parameters can be weighted to take into account object scale, color and shape factors; resulting in drastically different image objects. Optimizing these parameters for the study at hand was an iterative trial and error process. While there was no clear correct set of parameters, certain parameter combinations (affected heavily by the scale parameter) made for more useful image object arrangements than others. These parameters differed from one image mosaic to the next because each image mosaic's radiometry and geographic extent were unique. Once the objects are delineated, they can then be classified using a rules-based approach. While initially we proposed to develop a "universal" set of rules to classify the seagrass and bottom types in a comparatively automated classification approach across the entire study area, we realized that a more manual, analyst assisted approach was necessary. As in determining the segmentation parameters, due to the variability in spectral response between the individual digital photos and the image mosaics as well as the spectral variation of seagrass across varying % cover, water clarity, depth and substrate, it was difficult to determine a set of universally applicable rules.
The following approach was adopted to map bottom types:
1) the entire image was segmented at a fine (i.e, Level 5);
2) using the clear distinction between land and water in the near infrared
waveband image, a simple NIR membership rule was established to mask
3) the image segmentation was then coarsened to merge areas of like classes
(i.e., Level 4);
4) the Level 4 image objects were visually interpreted and manual encoded as to
the appropriate bottom type (Figure 2) with the help of field reference
5) the class coding was "forced down" to the level 5 sub-objects;
6) the Level 5 sub-objects were then visually evaluated atop the original imagery
to ensure that a proper identification was made and the classification
revised where necessary; and
7) the revised Level 5 sub-objects were then transmitted back up the hierarchy
and the Level 4 image objects revised accordingly (this was done by
specifying "existence based on sub-objects" as a rule for each class).
This approach expedited the process by undertaking the manual classification at a coarser scale with fewer objects to code but without losing the boundary detail afforded by the more detailed segmentation. Using the above approach, each of the 14 image mosaics were classified independently and merged to create a complete bay-wide classification. In addition, to the difficulty in developing consistent classification rules across mosaics, a super-mosaic of all 14 sub-areas would have required enormous computational power and time for a multi-resolution segmentation.
The resulting maps were compared with the 245 field reference points. All 245 reference points were used to support the interpretation and mapping in some fashion and so can not be truly considered as completely independent validation. The resulting maps were also compared with an independent set of 41 bottom sampling points collected as part of a seagrass-sediment study conducted during the summer of 2003 (Smith and Friedman, 2004). These additional 41 bottom sample points were collected in an area along the eastern shore of central Barnegat Bay in an area deemed of high image quality. At each sampling point, a sediment grab sample was taken and the presence/absence of seagrass determined for an approximately 5m2 area. The spatial locations of the 41 sampling points were recorded using a non-differentially collected GPS receiver (Garmin Map 12) with an approximate positional error of ± 15m. The presence/absence data for the 245 and 41 sampling points were compared with the same location from the digital seagrass map and summarized in a contingency table and producer's/user's accuracy and Kappa statistic (a measure of agreement corrected for chance agreement) computed.
Spatial Pattern Analysis
Using the resulting study area-wide classified GIS map, we examined the spatial structure of the seagrass beds by analyzing the spatial pattern of seagrass density classes and their shared edge lengths. The health and productivity of seagrass is highly dependent on an adequate amount of solar illumination which in turn is heavily influenced by the water clarity (Dennison et al., 1993). Seagrass beds in deeper water or seagrass at the deep water edge of the bed are therefore more vulnerable to turbid water conditions and a limited light environment. The classified map was used to analyze seagrass adjacency to deep water to highlight areas of greatest vulnerability as well as to examine within bed spatial structure. The amount of border to each contiguous seagrass patch was calculated, and expressed as a percentage of the total border. The Level 3 seagrass beds (i.e., 3 classes of seagrass density grouped together) were coarsened to 5m grid cell resolution and analyzed using the ArcINFO Version 8.3.
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