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J Coast Conserv (2014) 18:299–308 DOI 10.1007/s11852-014-0319-y Comparison of remotely-sensed surveys vs. in situ plot-based assessments of sea grass condition in Barnegat Bay-Little Egg Harbor, New Jersey USA Richard G. Lathrop Jr. & Scott M. Haag & Daniel Merchant & Michael J. Kennish & Benjamin Fertig Received: 11 February 2014 / Accepted: 16 April 2014 / Published online: 11 May 2014 # Springer Science+Business Media Dordrecht 2014 Abstract With the increasing appreciation that sea grass habitats are in global decline, there is a great need to be able to efficiently and effectively assess and characterize the status and trends of sea grass in our coastal ecosystems. This paper examines the utility of remotely sensed vs. in situ plot-based monitoring using the Barnegat Bay-Little Egg Harbor (BBLEH), New Jersey, USA estuarine system as a case study. Eelgrass (Zostera marina) is the dominant species, while widgeon grass (Ruppia maritima) is also common in lower salinity regions of the BB-LEH. Aerial imagery collected during the months of July and August 2009 was interpreted and mapped using object based image analysis techniques, similar to techniques used in the 2003 mapping survey of this system. Boat-based in situ monitoring data were collected concurrently with the aerial photography to assist the image interpretation and for an independent accuracy assessment. We compared the remotely-sensed mapping of sea grass cover change (in 2003 vs. 2009) vs. in situ plot-based monitoring conducted from 2004 through 2009. Comparison of the remotely-sensed vs. the in situ plot-change analysis suggests that the two methodologies had broadly similarly results, with the percent area showing declines in sea grass cover greater than those that exhibited increases. In conclusion, the two studies provide corroborating evidence that sea grass has declined in percent cover in the BB-LEH system during the decade of the 2000’s. While remotely-sensed surveys provide synoptic information for a “big picture” view on sea grass distribution, site specific in situ sampling is required to R. G. Lathrop Jr. (*) : S. M. Haag : D. Merchant Center for Remote Sensing & Spatial Analysis, Rutgers University, New Brunswick, NJ, USA e-mail: [email protected] S. M. Haag : M. J. Kennish : B. Fertig Institute of Marine and Coastal Sciences, Rutgers University, New Brunswick, NJ, USA determine other aspects of sea grass status, e.g. above vs. below-ground biomass, blade length, shoot density, epiphytic loading, etc. Either method alone gives an incomplete picture. As demonstrated in this study, to fully characterize the spatial extent, health, and density of sea grass meadows across the entire estuary, combining remote sensing surveys concomitantly with comprehensive in situ assessment provides the most robust approach. Keywords Sea grass status and trends . Eutrophication . Estuaries . Object based image analysis Introduction Due to the important role that sea grasses play in estuaries, there has been a considerable effort at developing sampling and mapping techniques to quantify the spatial distribution, biomass and health of sea grass communities and monitor changes over time (Dobson et al. 1995; Short et al. 2002). Sea grass is considered a useful measure of estuarine condition as it integrates environmental impacts over measurable and definable timescales (Orth et al. 2006). Accordingly sea grass has been adopted as an ecological indicator in Chesapeake Bay (Orth et al. 2002, 2010), in a number of Mid Atlantic USA coastal lagoons (Wazniak et al. 2007; Fertig et al. 2013), in the Northern Gulf of Mexico (Handley et al. 2007), and more broadly as part of the U.S. National Estuarine Eutrophication Assessment (Bricker et al. 2003; Scavia and Bricker 2006). With the increasing appreciation that sea grass habitats are in global decline (Orth et al. 2006; Waycott et al. 2009), there is a great need to efficiently and effectively assess and characterize the status and trends of sea grass in our coastal ecosystems. To aid in monitoring sea grass meadows in a consistent and comparable fashion, Sea grass Net (Short et al. 2002, 2006) 300 developed a set of in situ sampling protocols. In situ sampling can provide detailed, site-specific information on the aboveand below-ground biomass, blade length, areal cover and related information on epiphytic load, macroalgae, and disease at specific locations. Alternatively, where a big picture perspective is warranted, remote sensing approaches have been employed for “wall-to-wall” mapping of sea grass presence/absence or areal cover over large areas. The most widely adopted approach for operational monitoring has been the visual interpretation and mapping from either digital or analog aerial photography (Ferguson et al. 1993; Dobson et al. 1995; Kendrick et al. 2000; Kurz et al. 2000; Moore et al. 2000). As an alternative to visual interpretation and manual digitizing, Lathrop et al. (2006) employed object based image analysis of digital aerial photographic imagery to map sea grass. This paper examines the utility of remotely sensed vs. in situ plot-based monitoring using the Barnegat Bay-Little Egg Harbor (BB-LEH), New Jersey, USA estuarine system as a case study. In 2003 and again in 2009, we acquired aerial photographic imagery and employed an object-oriented image segmentation approach (Lathrop et al. 2006) to assess change in sea grass status in the BB-LEH estuary. During the intervening time period, an intensive in situ sea grass sampling program was undertaken (Kennish et al. 2008, 2010; Fertig et al. 2013). In this paper, we assess the comparative utility of the remotely-sensed change mapping and the in situ monitoring data, either singly or in combination, for elucidating trends in sea grass status. Methods Remotely-sensed surveys Lathrop et al. (2006) used object based image analysis (Blaschke 2010) of digital aerial photographic imagery to map sea grass presence and characterize sea grass areal cover. Employing similar terminology as Robbins and Bell (1994), the spatial structure of the sea grass habitats was conceptualized at 3 different levels, from the coarser to finer scale: 1) meadow, a spatially contiguous area of sea grass beds of varying % cover composition; 2) bed, a spatially contiguous area of overall similar % cover composition; and 3) patch, a small discrete clump of sea grass. A “neighborhood-based” approach of image segmentation was employed to delineate coarser scale “image super-objects” (i.e., sea grass beds) and finer scale “image objects” (i.e., patches and gaps). Rather than manually classifying each fine scale patch individually, some economy of scale is possible allowing the image analyst to classify coarser scale super-objects and translate the results downward through “super-object classification”. Finer scale patches or gaps of density different than the enclosing sea R.G. Lathrop et al. grass bed are identified and then re classed as appropriate. While the methodology was designed to be easily repeatable, this present paper represents the first attempt at employing the methodology for mapping and quantifying change in the spatial distribution of sea grass area and cover. Film aerial photography was collected by Air Photo graphics, Inc. on June 28, July 14, and August 4, 2009 using a Navajo HS airplane equipped with a Leica RC30 camera shooting AGFA 100 color film. Unfavorable cloud and wind conditions required several over flights to capture the entire study area. The same plane and camera was used for all three imaging missions. The resultant film was then processed and scanned through a high resolution scanner resulting in a digital image with 18,278 by 18,292 pixels in a scale of 1:2,000. These scans were ortho-rectified and projected into Universal Transverse Mercator (Zone 18 North, North American Datum 1983, GRS Spheroid of 1980) with a horizontal positional accuracy at root mean square error of ±1–2 m. The resulting geotiffs were mosaicked into 15 larger blocks for later analysis. During the summer of 2009, a number of field sites were visited to collect reference information to enable the interpretation of the aerial photography (Fig. 1). Reference sites (n= 167) were selected to match a subset of the field references sites selected during the 2003 study (Lathrop et al. 2006). Data from 120 in situ sea grass transect monitoring points collected in June 2009 (Kennish et al. 2010; described below) were also used to aid in the interpretation and mapping. An additional set of 124 field validation points were selected using a stratified random sampling design, stratified on the depth distribution of sea grass within the BB-LEH estuary. This validation dataset was not used in the image mapping and classification process but kept as an independent data set to compare with the map. Field collection for the reference and validation data set was accomplished as follows. At each sampling location, a Lshaped 4 m × 5 m grid was lowered over the side of the boat. The grid coordinate location was determined using Magellan Mobile Mapper 6 (±2–5 m horizontal accuracy) and the grid orientation recorded using a compass to allow for a precise placement of the sampling grid on the benthos to a higher level of accuracy than a boat-based GPS unit. The diver then visited each of eight sub-grids and recorded information on SAV presence/absence (yes no), percent cover of sea grass species (R. maritima and Z. marina) (0 to 100 in 10 % increments), and percent coverage macro-algae (0 to 100 in 10 % increments). A composite percent areal cover of sea grass was then calculated. For more detail on the methods employed, please refer to Lathrop and Haag (2011). The rectified mosaicked color aerial photography was imported into e Cognition™ to support image segmentation and classification. e Cognition™ is an image analysis software package that segments raster image data in an unsupervised Comparison of remotely-sensed vs. in situ plot-based sea grass monitoring 301 Fig. 1 Map of Barnegat Bay – Little Egg Harbor (BB-LEH) study area with 2009 reference and validation sites for remotelysensed survey method minimizing the intra-polygon (image object) variance while maximizing inter-polygon (image object) variance. Multiple scale image objects can be created by running a multiple resolution segmentation procedure. The fine scale parameter was selected to meet the target minimum mapping unit of .05 ha (500 m2). The minimum mapping unit defines the smallest feature delineated in the map. A super-object classification approach was employed where the coarser scale image objects were first manually classified using e Cognition™. Each image object was visually interpreted and assigned to one of four classes of sea grass areal cover (high 100–80 % percent cover, medium <80–40 % cover, sparse <40–10 % cover, and no sea grass <10–0 %). The field reference data were used to inform the interpretation. Areas of extremely sparse sea grass (i.e., 5–10 % cover) were not discernible from the remotely sensed imagery and were thereby considered as no sea grass. The coarser scale image object classifications were then forced down into the finer scale image objects based on the nested polygon structure. Smaller image objects on edge areas and internal to the larger image objects were then manually reclassified when necessary. This method sped up the manual classification effort allowing large contiguous areas of sea grass to be classified quickly while also allowing precise classification on sea grass edge and gap areas. To create the final GIS dataset, the finerscale image objects were exported to Environmental Research Institute ESRI™ shapefile format. To determine how well the object based image analysis described sea grass presence/absence and areal cover across the BB-LEH, an accuracy assessment was undertaken using the 124 validation sites. The validation dataset was compared with the final sea grass cover map to create an accuracy assessment matrix. Error of omission and commission, overall accuracy assessment, and a Kappa coefficient were calculated. The Kappa coefficient is a measure of agreement between two 302 categorical datasets correcting for the random chance that categories will agree. An un-weighted Kappa statistic was used to normalize the influence of categories that cover a disproportionate area. R.G. Lathrop et al. Table 1 Seagrass change matrix showing seven categories of percent cover change categories between 2003 and 2009 (or 2004 and 2009 for plot data). Note: that the original percent cover categories were: Dense (80–100 %), Medium (40–80 %), Sparse (10–40 %) and No Seagrass (<10 %) In situ monitoring During 2004–2009, in situ monitoring was conducted along 12 transects in three disjunct sub-areas, and each transect consisted of 10 sampling stations for a total of 120 sample points (Fig. 1) (Kennish et al. 2010). Each station was geo located with a Differential Global Positioning System (Trimble®GeoXTTM handheld unit). Note that transects 1–6 were sampled in 2004, transects 7–12 were sampled in 2005, and transects 1–12 were sampled in 2006 and 2008–2009. No samples were collected in 2007. Sampling efforts were based on the Sea grass Net monitoring and sampling protocols of Short et al. (2002). The main modification of methods was establishing transects perpendicular to shore rather than parallel, to identify differences along a clearly defined depth gradient. Eelgrass samples were collected during each of 3 time periods (June–July, August– September, and October–November) in all years. The following eelgrass characteristics were recorded on all sampling dates at each sampling station: above-ground and below-ground biomass, shoot density, blade length, and areal cover. For comparing the in situ vs. the remotely-sensed approaches, we used the in situ measure of areal cover for the years 2004–2009. To determine areal cover of both eelgrass and macroalgae, a 0.25-m2 metal quadrat was randomly tossed overboard at each sampling station. A diver estimated the percentage of the quadrate covered by eelgrass in increments of 5 along a scale of 0–100. Comparing remotely-sensed surveys with in situ monitoring The in situ monitoring data were binned into the same sea grass percent areal cover categories as employed in the remotely sensed surveys: Dense (80–100 %), Medium (40–80 %), Sparse (10–40 %) and No Sea grass (<10 %). To visualize changes in sea grass cover between the 2003 and 2009 remotely-sensed surveys, we classed the mapped data into seven change categories (Table 1). The in situ monitoring data for the 2004 to 2009 time period were also classed into the same seven change categories (Table 1). The in situ plot data were geolocated and the corresponding remotely-sensed 2003– 2009 change category extracted for comparison purposes with a final sample size of 120 plots. Results 2009 remotely-sensed survey The 2009 sea grass remotely-sensed survey (Fig. 2, Table 2) classified 5,260 ha of sea grass (2,266 ha of sparse, 2,523 ha of moderate, and 471 ha of dense cover). A high level of agreement was obtained (overall accuracy of 87 % and a Kappa value of 0.73 between the mapped and in situ validation presence/absence data (Table 3). A moderate level of agreement was obtained (overall accuracy of 70 % and a Kappa statistic of 0.47) for the four-class sea grass density map, (Table 4). Comparison of 2009 and 2003 remotely-sensed surveys Comparison of the results of the 2009 and 2003 remotelysensed surveys indicate that the overall area of mapped sea grass (i.e., sea grass present) was similar (5,122 ha in 2003 vs. 5,260 ha in 2009) (Table 2). 1,463 ha were mapped as sea grass in 2003 (and not in 2009), 1,601 in 2009 (and not in 2003) and 3,659 ha were mapped in both 2003 and 2009 (Fig. 2). Examination of the more detailed four-class sea grass cover maps shows a decline in the area of dense (80–100 % cover) sea grass in 2009 vs. 2003 (471 ha in 2009 vs. 2,074 ha in 2003; a nearly 60 % decline). The loss in area of dense cover translated to an increase in medium (40–80 % cover) density meadows (1,093 ha in 2003 vs. 2,523 ha in 2009; an increase of 130 %). The 2009 imagery collection was a challenge due to meteorological events (cloud cover), which caused three separate imaging attempts (June 28th, July 7th, and August 4th) before good aerial photography could be obtained. Areas of high water turbidity were observed in Little Egg Harbor and southern Barnegat Bay, obscuring the Comparison of remotely-sensed vs. in situ plot-based sea grass monitoring 303 Fig. 2 Sea grass habitat mapped in 2003 and 2009 over the study area clear delineation of bottom features and complicating the interpretation of the sea grass areal coverage. The degree to which this apparent thinning in the density of the sea grass meadows is real or an artifact of the poorer image quality in the 2009 imagery and the resulting lower accuracy in mapping dense sea grass meadows is uncertain. Table 2 Area of seagrass cover types mapped in the 2009 and 2003 remote sensing surveys Seagrass cover Sparse (10–39 %) Moderate (40–79 %) Dense (80–100 %) Total 2009 2003 Class (ha) % Total seagrass Class (ha) % Total seagrass 2,266 2,523 471 5,260 43 % 48 % 9% 1,955 1,093 2,074 5,122 38 % 22 % 40 % Comparison of remotely-sensed vs. in situ trend results Comparison of the remotely-sensed vs. the in situ plot change analysis suggests that the two methodologies had broadly similar results with the overall percent area showing decreases in percent sea grass cover greater than those that exhibited increases (Table 5; Fig. 3). The remotely sensed change analysis showed approximately 47 % of the study area with a decrease in density vs. 37 % that exhibited an increase (Fig. 3). Examining the 120 plots only, the remote sensed change analysis showed 65 plots (54 %) with a decrease in cover vs. 19 plots (16 %) with an increase (Table 5). The in situ plotbased change analysis showed approximately 56 plots (47 %) with a decrease in density vs. 27 (22 %) that exhibited an increase. Note that 13 out of the 120 plots (11 %) were not mapped as sea grass in either 2003 or 2009 and thereby classed as no change. Likewise, 6 of the in situ plots (5 %) did not contain sea grass at any point during the 2004 to 2009 time period and were classed as no change. Plot-by-plot level 304 R.G. Lathrop et al. Table 3 Presence/absence accuracy assessment matrix for the 2009 seagrass survey. Un-weighted Kappa statistic 0.73 GIS MAP Field reference Seagrass absent Seagrass present Producer’s accuracy Seagrass absent Seagrass present User’s accuracy 69 7 91 % 9 39 81 % 88 % 85 % 87 % comparison between the mapped vs. in situ plot results shows a low degree of correspondence with only 27 (22.5 %) of the plots matching the mapped results (Table 5). Given the difference in scales with the minimum mapping unit for the remotely-sensed survey at 500 m2 (0.05 ha) vs. the <1 m2 size for the in situ plots, we relaxed the definition of correctness to include a more “fuzzy similarity” (i.e., exact match as well as ± one class difference). The fuzzy similarity showed greater degree of correspondence with 75 (62.5 %) of the plots ± one class from the remotely-sensed data (Table 5). Discussion NOAA protocols (Finkbeiner et al. 2001) suggest that overall thematic accuracy should be greater than 85 % and a Kappa of >0.5. The sea grass presence/absence map meets these criteria, while the four-class sea grass density map is slightly below. Table 3 suggests that most of the errors of omission (i.e., producer’s accuracy) and commission (i.e., user’s accuracy) for the presence/absence sea grass map are similar. Table 4 suggests that the four-class map does not consistently differentiate between moderate and dense sea grass habitat and thereby underestimates the amount of dense sea grass. While every effort was undertaken to reduce the spatial error in relating the field reference data to the imagery (i.e., ±1–2 m for imagery geo registration and ±2–5 m for geo locating the field reference data collection location), this positional error Table 4 Class accuracy assessment matrix for the 2009 seagrass survey. Un-weighted Kappa statistic 0.47 GIS MAP Field reference Seagrass Seagrass Seagrass Seagrass User’s absent sparse moderate dense accuracy Seagrass absent Seagrass sparse Seagrass moderate Seagrass dense Producer’s accuracy 69 5 2 0 91 % 7 7 4 0 39 % 2 4 6 2 43 % 0 1 10 5 31 % 88 41 27 71 70 % % % % % coupled with the fine scale patchiness of some sea grass beds can result in a disagreement between the reference data and the mapping. Increasing the size of the sampling grid used in the remotely sensed field surveys, while more cumbersome to deploy, we deemed to be more effective in capturing the fine scale spatial heterogeneity for comparison with the remotely sensed imagery. The procedure to select the validation sites (random vs. targeted) can drive which error (omission and commission vs. categorical) is better constrained. In the 2009 survey, we opted for a stratified random sampling to provide a better estimate on the total errors of omission and commission of sea grass presence/absence across the entire estuary. Consequently, the sample sizes within the individual sea grass density categories (i.e., sparse, moderate and dense) were limited while areas of no sea grass were oversampled. Future work should attempt to collect a larger number of samples in known sea grass habitat to provide more information on the accuracy of the areal cover density mapping. Over the past decade, both periodic remotely-sensed surveys and in situ monitoring have been employed to chart trends in the a real cover and spatial extent of sea grass within the BB-LEH estuary. One objective of this paper is to characterize the change in sea grass distribution and cover as determined from remotely-sensed surveys taken in 2003 and 2009. We employed similar aerial photographic imagery and an object based image analysis approach to assess change in sea grass distribution for both time periods. The remotelysensed change analysis suggests that the overall area of mapped sea grass cover was similar between the two time periods. In directly comparing the two mapped data sets, there were areas of apparent gain and loss, though the bulk of the sea grass area was stable (i.e., was present in both 2003 and 2009) (Fig. 2). Differences in the seasonal period of image acquisition may account for some of the differences in the mapped area as imagery for the 2003 survey was acquired early in the growing season (May 4–5th), while the 2009 survey was acquired later in the growing season on July 14, and August 4 (northern section of the BB study area). The remotely-sensed 2003–2009 trend map (Fig. 4) suggests an increase in the percent cover density of sea grass meadows in the north-central portions of BB-LEH, while sea grass meadows in the central portions (near the Barnegat Inlet area) and in the southern portions of Barnegat Bay and Little Egg Harbor experienced declines in percent cover. The areas in northern Barnegat Bay mapped as increasing sea grass cover are likely due to an artifact of the later image acquisition date for the 2009 survey. Our calibration plots document that Ruppia dominates in these lower salinity waters of northern Barnegat Bay and the mid-summer image acquisition (as compared to mid-spring acquisition in 2003) may have mapped higher cover of this later maturing species due to a timing issue and not a “real” change (Lathrop and Haag 2011). Unfortunately, we do not have long-term in situ Comparison of remotely-sensed vs. in situ plot-based sea grass monitoring 305 Table 5 Site-level comparison between the mapped vs. plot change classification results monitoring plots in this portion of the study area to serve as comparison. While the overall area of sea grass appears to be comparatively stable (as mapped in 2003 and 2009), both the remotely sensed and in situ monitoring document an overall declining trend in percent cover in the BB-LEH system during the decade of the 2000’s. Analysis of the more detailed four-class sea grass cover change data (Fig. 4) shows a decline in the area of dense sea grass in 2009 vs. 2003 (i.e., 471 ha in 2009 vs. 2,074 ha in 2003; a nearly 60 % decline). The degree to which this apparent thinning in the density of the sea grass meadows is real or an artifact of the poorer image quality in the 2009 imagery (in selected locations) and the resulting lower accuracy in mapping Fig. 3 Comparison of remotelysensed (2003–2009; absolute area in ha and by % of total) vs. in situ plot (2004–2009; number of plots and by % of total)) dense sea grass is uncertain. However, comparison of the remotely-sensed surveys vs. the in situ monitoring at a series of permanent plots during roughly the same time period (between 2004 and 2009) provide corroborating evidence that sea grass has declined in percent cover. The remotely-sensed and in situ change analysis showed 54 and 47 % of the monitoring plots, respectively, with some decrease in percent cover vs. only 16 and 22 %, respectively, exhibited an increase (Table 5). The in situ monitoring data also documents a decline in biomass and other indicators of sea grass health (Fertig et al. 2013). Short (2007) found similar patterns in Great Bay, NH with Zostera biomass and percent cover declining between 1995 and 2005, while sea grass distribution remained relatively constant. It is 306 R.G. Lathrop et al. Fig. 4 Sea grass 2003–2009 remotely-sensed change map for entire BB-LEH study area interesting to note that Macomber and Allen (1979), using visual interpretation of summer season black & white aerial photography (field checked by low altitude sea plane reconnaissance), mapped over 6,550 ha of dense (>80 %) cover Zostera and Ruppia meadows in the BB-LEH system (Lathrop et al. 2001). Comparing the 1979 with the 2003 and 2009 mapping (which must be done with caution due to the differences in methodology employed) suggests a decline of dense sea grass meadows of approximately 68 and 93 % in 2003 and 2009, respectively. While the remotely-sensed and in situ monitoring showed similar trends, a lot of spatial and temporal dynamics can be missed in the intervening years between remotely sensed surveys (i.e., in this case between 2003 and 2009). In situ monitoring done in the intervening years (2004–2010) showed a declining sea grass cover and biomass across the estuary, reaching a low point in 2006 (Fertig et al. 2013). There was some rebound by 2008 followed by consistent declines with 2010 levels reaching the lowest recorded over the six-year time period. Fertig et al. (2013, 2014) suggest that this consistent estuarine-wide rate of decline was related to nutrient loading and associated symptoms of eutrophication. Examination of the remotely sensed trend map (Fig. 4) and the in situ monitoring data shows areas of moderate to major Comparison of remotely-sensed vs. in situ plot-based sea grass monitoring decrease in southern Barnegat Bay and in Little Egg Harbor that closely align with areas of high total nitrogen concentration (as displayed in Fertig et al. 2013), strongly supporting their general conclusion that eutrophication is the major factor in sea grass decline in the overall BB-LEH system. However, the hotspot of decreasing sea grass cover bay ward of Barnegat Inlet observed in both the remotely sensed survey (Fig. 4) and in situ monitoring does not follow this general pattern. Fertig et al. (2013) found consistently low total nitrogen concentrations and Lathrop et al. (2001) observed higher water transparency where ocean waters entering through Barnegat Inlet are continually mixing with bay waters. The spatial patterns of change in sea grass cover and distribution near Barnegat Inlet as revealed in the remotely sensed imagery suggest that tidal inlet/delta dynamics (i.e., shifting currents and sediment deposition/erosion) may be playing a more important role than eutrophication in explaining the decline of sea grass meadows in this section of the BB-LEH system. Conclusions Trends in Sea grass areal extent (primarily Zostera) within the northeastern United States is highly variable with some locations showing new growth and expansion of existing or transplanted beds, such as in the Virginia and Maryland Coastal Bays (Orth and McGlathery 2012; Wazniak et al. 2007), while other areas such as Piscataqua River NH and Chesapeake Bay (Beem and Short 2008; Orth et al. 2010) have experienced significant declines in extent. Though there have been considerable spatial dynamics with areas of both increase and decline, the results of our study suggest an overall declining trend in percent sea grass cover in the Barnegat Bay-Little Egg Harbor (BB-LEH) system during the decade of the 2000’s. The primary causes for sea grass decline and or expansion have been related to site specific changes in water quality, temperature, direct alterations, and or disease (Burkholder et al. 2007; Short 2007; Beem and Short 2008; Orth et al. 2010). Determining the causative factors for sea grass decline and expansion can be difficult in that sea grass habitat integrates the ecological signal over a multi-year period of time. While nutrient enrichment and accompanying estuary-wide eutrophication may build over a multi-decadal time period, other phenomena such as harmful algae blooms (Gastrich et al. 2004) or tidal channel dynamics may be more restricted in space and time. To understand what causative factors drive sea grass distribution and health and in a specific estuary, it is necessary to have a dedicated monitoring program. This should be done on an annual or semiannual basis to avoid missing major changes in the extent and health of sea grass habitats. As Orth et al. (2010) have demonstrated, annual aerial mapping surveys 307 over several decades (1984 to present) have allowed researchers within the Chesapeake Bay to assess the trends in sea grass distribution through time, compare the success of various transplant and relocation efforts, and compare sea grass decline to in situ water quality parameters. However, for many estuary systems annual remote sensed surveys are cost prohibitive and are undertaken on a more irregular basis. For the BB-LEH system, mapping surveys every five to ten years are more the norm (Lathrop et al. 2001; 2006). Intensive in situ monitoring (i.e., multiple visits spaced over the growing season to scores of plots over multiple years) is potentially even more expensive. Unfortunately, this level of sampling is not routinely collected in the BB-LEH system, nor in many estuaries, due to budgetary constraints. When monitoring sea grass, coastal management agencies have a choice, do they invest in remotely sensed surveys or in situ sampling or can they do both? Based on our results, we conclude that object based image analysis provides an effective alternative approach, as compared to visual interpretation and manual digitizing, to map sea grass on a repetitive basis. This methodology provides useful quantitative information on both areal extent and percent cover, though the accuracy of the latter needs further improvement. While remotely-sensed surveys provide synoptic information for a “big picture” view on sea grass distribution, site specific in situ sampling is required to determine other aspects of sea grass status, e.g. above vs. below-ground biomass, blade length, shoot density, epiphytic loading, etc. Either method alone gives an incomplete picture. As demonstrated in this study, to more fully characterize the spatial extent, health, and density of sea grass meadows across the entire estuary, combining remote sensing surveys concomitantly with comprehensive in situ assessment provides the most robust approach. Acknowledgments This project was funded by the Barnegat Bay Partnership, the New England Interstate Water Pollution Control Commission and the New Jersey Agricultural Experiment Station. We express our sincere appreciation for the assistance of Gina Petruzzelli, Greg Sakowicz, Chris Huch and the staff at Rutgers University Marine Field Station in the collection of the field reference data. 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