Remote Sensing of Seagrass to Assess Coastal Environmental Integrity

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1 Remote Sensing of Seagrass to Assess Coastal Environmental Integrity Richard G. Lathrop Scott Haag Rutgers University Jacques Cousteau National Estuarine Research Reserve

2 Outline Human Land Use/Land Cover Change as an environmental stressor Seagrass as a system-wide ecological response indicator Remote sensing as a means of monitoring seagrass Comparing remote sensing with in situ studies: are they telling us the same thing? Collaborators: Mike Kennish, Ben Fertig Funders: Barnegat Bay Partnership, USEPA

3 Barnegat Bay-Little Egg Harbor (BB-LEH) is a shallow (< 2m deep) backbay lagoonal estuary (approx 36,000ha). As part of the United States National Estuary Program, a watershedbased management planning effort was initiated in 1995 to promote the long-term sustainability of BB and its natural resources.

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5 Land Use Change as Driving Coastal Stressor Over 1/3 of the BBEP watershed is developed or otherwise altered Changing surface runoff and groundwater flows Increased nutrient, toxics & sediment inputs Habitat loss, alteration and fragmentation

6 Seagrass as Ecological Response Indicator Due to their ecological importance and recent indications of disease and dieback, seagrasses are considered as an important ecological indicator of overall estuarine health BB contains >75% of NJ s seagrass habitat Part of a nationwide NERR monitoring effort Eelgrass Zostera marina Widgeongrass Ruppia maritima

7 Eutrophication Gradient Graphic by C. Wazniak 2007 Increased watershed nutrient and sediment runoff will lead to eutrophication, resulting in phytoplankton and macroalgal blooms. Light limitation, whatever the cause, will negatively impact seagrass photosynthesis, productivity and abundance

8 Synthesizing Previous SAV Mapping Efforts McLain, P. Bologna, P., R.G. Lathrop. Island Beach S.P. and Rutgers Technique: boat-based GPS-enabled surveys Joseph, J, K. Purdy, and B. Figley. Marine Fisheries, NJDEPE. Technique: boat-based dredge sampling interpolation Macomber, R.T., and D. Allen. Earth Satellite Corp. Wash. DC. Technique: Aerial photo interpretation and field checking U.S. Army Corps of Engineers. Philadelphia District, PA Technique: boat-based surveys

9 Remote Sensing Methods for Characterizing Seagrass High spatial resolution (<1 m GRC) digital airborne and satellite visible imagery for water depth penetration Hybrid of Image Segmentation techniques & Visible Interpretation

10 Seasonal differences in imagery affect seagrass mapping Aerial Photography (Spring 2003) Quickbird Satellite Imagery (Fall 2004)

11 Field Surveys For each field reference point, the following data was collected: GPS location (UTM) Time Date Depth (meters) Substrate (mud/sand) SAV species presence/dominance: Zostera marina or Ruppia maritima or macroalgae Blade Height of 5 tallest blades Shoot density (# of shoots per 1/9 m 2 quadrat) % cover (10 % intervals) Distribution (patchy/uniform) Above- and below-ground biomass Photos by Scott Haag and Paul Montesano

12 Classification Scheme Multi-scale hierarchical view of seagrass landscapes composed of: Sub-objects: individual seagrass patches or bare gaps (Level 6) Objects: seagrass beds of like density (Level 5) Super objects: seagrass meadows of multiple contiguous beds (Level 4)

13 Multi-Scale Image Segmentation of airborne digital camera imagery RS mapping survey undertaken in Lathrop, R.G., P. Montesano, and S. Haag A multi-scale segmentation approach to mapping submerged aquatic vegetation using airborne digital camera imagery. Photogrammetric Engineering and Remote Sensing 72(6):

14 Comparing 2009 vs Seagrass mapped surveys 2003: 5,184 ha sparse 1,971ha moderate 1,139ha dense 2,074ha 2009: 5,253 ha sparse -2,256ha moderate -2,527ha dense - 470ha - Overall amount similar with gains and losses - Trend towards lower cover

15 What is the in situ sampling telling us? ASSESSMENT OF NUTRIENT LOADING AND EUTROPHICATION IN BARNEGAT BAY-LITTLE EGG HARBOR, NEW JERSEY IN SUPPORT OF NUTRIENT MANAGEMENT PLANNING By: Michael J. Kennish, Benjamin M. Fertig and Richard G. Lathrop Draft report released 08/2012 Graphics provided by Mike Kennish & Ben Fertig

16 Comparison of RS vs. in situ data Trend in in situ % Cover RS survey

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20 Inter-annual spatial dynamics: 2003 vs vs What s going on? Graphic provided by S. Haag 2010

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23 Conclusions Seagrass as ecological response indicator Seagrass meadows show longer term decline, as well as inter-annual variability with year-toyear gains and losses While more difficult, RS estimates of %cover are useful in monitoring trends Combination of in situ and RS monitoring needed to more fully characterize areal extent, spatial pattern and condition Remote sensing provides a coarser wall-to-wall picture while in situ sampling provides richer detail The key to restoring Barnegat bay s seagrass meadows is controlling nutrient run-off