The broader goal of this stewardship project was to develop and apply geospatial information and tools to direct strategies for climate change adaptation with respect to rising sea level in the MidAtlantic region, using the Edwin B. Forsythe National Wildlife Reserve (EBFNWR) and Jacques Cousteau National Estuarine Research Reserve (JCNERR) as a case study. Our more specific objective was to model and map the potential impact of sea level rise on the distribution of habitat of target species that depend on coastal marsh and barrier island habitats.
Sea level rise is a physical reality that is impacting the New Jersey and the entire MidAtlantic (New Jersey, Delaware, Pennsylvania, and New York) coastline (Titus and Strange, 2008; NOAA, 2012). Through their land use planning, development and management decisions, refuge managers will greatly influence future impacts of sea level rise and global climate change on wildlife populations.
Mitigating the impact of sea level rise is a local decision-making challenge and is going to require site-specific remedies. Faced with a variety of conflicting mandates and uncertainty as appropriate responses, managers will greatly benefit from place-based decision support system tools that outline a range of geographically targeted management options.
To address this priority, this study was undertaken to assess the potential of emerging geospatial information techniques (i.e., LiDAR and semi-automated object based classification) to developing relevant geospatial data to support science-based management of coastal marsh and barrier island habitat.
The approach itself consisted of three major phases: 1) undertaking a highly detailed mapping of relevant land cover information using remotely sensed imagery over the entire EBFNWR area, 2) mapping the current distribution of target wildlife species based on that detailed land cover data, and 3) applying existing sea level rise (SLR) models to these habitat maps to characterize the potential impacts to individual species. While our simple assessment was informative, we suggest that more sophisticated modeling of the response of salt marsh and barrier island beach environments to long term sea level rise and episodic storms is needed to better inform conservation managers on the future availability of critical coastal habitats.
Object-oriented image analysis and high spatial resolution digital orthophotography (1 foot ground resolution cell) was used to create highly detailed map of land cover/habitat features for the 512-sq mi study area. In terms of spatial detail, features as small as 1/10 acre were delineated and classified into one of 27 different land cover categories (focusing primarily on coastal marsh and barrier island landscapes) with an acceptable degree of accuracy. While the digital orthophotography for a high level of detail, inconsistency in radiometric response across individual photo frames made for a challenge in developing universally applicable spectral signatures for individual land cover classes. The semi-automated object-based land cover mapping process using Definiens eCognition software also requires high level of image analyst training and development. The classification rule-base developed for this project was documented for potential adoption and application elsewhere.
The resulting land cover GIS data is made available by request to Rick Lathrop, CRSSA Director: . The metadata documentation and report PDF are available on this webpage.
We would like to recognize the important role of Scott M. Haag in initiating this research project. We also gratefully acknowledge the contributions of U.S. Fish & Wildlife Service Biologists Paul Castelli and Vincent Turner of Edwin B. Forsythe National Wildlife Reserve for providing their insight on target species and their habitat requirements. We would like to thank Lisa Auermuller of the Jacques Cousteau national Estuarine Research Reserve for hosting the March 22, 2013 workshop.
This work was supported by the National Fish & Wildlife Foundation under Grant #2013-0111-001.
Director, Center for Remote Sensing and Spatial Analysis (CRSSA)
Web site composed by the Grant F. Walton Center for Remote Sensing and Spatial Analysis (CRSSA), Rutgers University