Merging land-marine realms: Spatial patterns of seamless coastal habitats using a multispectral LiDAR

Abstract A lack of spatially, structurally and thematically accurate habitat data complicates conservation and management planning as well as ecological research within structurally complex littoral enviromnents. The Scanning Hydrographic Operational Airborne Light Detection And Ranging (LiDAR) Survey (SHOALS) has considerable potential to provide such data, by means of its proficiency in generating high-resolution measurements of emerged and immersed elevations and to create thematic maps. Return signals, i.e. , waveforms, contain signatures and structural information of salt-marsh and benthic cover. This paper focuses on the capability of the SHOALS to assess the structural complexity of emerged and immersed coastal habitats, and to define the contribution of SHOALS data, both elevation and intensity, in order to accurately and seamlessly map these habitats from supratidal to nearshore levels. The study area was selected based upon the variety of littoral cover types, encompassing kelp habitat, eelgrass meadow, beach, salt-marsh, farm and urban coastal environments. Firstly, the LiDAR-derived green waveform, through an ad hoc decision-tree, satisfactorily assessed the structural complexity of littoral habitats (r = 0.75, p

[1]  Chi-Kuei Wang,et al.  Using airborne bathymetric lidar to detect bottom type variation in shallow waters , 2007 .

[2]  R. Macarthur,et al.  On Bird Species Diversity , 1961 .

[3]  Michael Elliott,et al.  Effects of fishing on the structure and functioning of estuarine and nearshore ecosystems , 2000 .

[4]  Ellsworth F. LeDrew,et al.  Spectral Discrimination of Healthy and Non-Healthy Corals Based on Cluster Analysis, Principal Components Analysis, and Derivative Spectroscopy , 1998 .

[5]  W. Cohen,et al.  Patterns of covariance between forest stand and canopy structure in the Pacific Northwest , 2005 .

[6]  E. Barbier,et al.  Impacts of Biodiversity Loss on Ocean Ecosystem Services , 2006, Science.

[7]  Philippe Archambault,et al.  Mapping the Shallow Water Seabed Habitat With the SHOALS , 2008, IEEE Transactions on Geoscience and Remote Sensing.

[8]  B. Long.  Étude hydrodynamique, sédimentologique et biologique des sites de Maria, Saint-Siméon, Bonaventure, Newport et Cap-d'Espoir dans la baie des Chaleurs, Québec, Canada. , 2005 .

[9]  D. J. Segelstein.  The complex refractive index of water , 1981 .

[10]  J. Callaway,et al.  Relationship between topographic heterogeneity and vegetation patterns in a Californian salt marsh , 2004 .

[11]  Norbert Pfeifer,et al.  Repetitive interpolation: A robust algorithm for DTM generation from Aerial Laser Scanner Data in forested terrain☆ , 2007 .

[12]  A. Collin,et al.  Predicting Species Diversity of Benthic Communities within Turbid Nearshore Using Full-Waveform Bathymetric LiDAR and Machine Learners , 2011, PloS one.

[13]  P. Archambault,et al.  Influence of shoreline configuration on spatial variation of meroplanktonic larvae, recruitment and diversity of benthic subtidal communities , 1999 .

[14]  S. Silvestri,et al.  Mapping salt-marsh vegetation by multispectral and hyperspectral remote sensing , 2006 .

[15]  A. Hastings,et al.  Use of lidar to study changes associated with Spartina invasion in San Francisco bay marshes , 2006 .

[16]  S. Thrush,et al.  THE IMPORTANCE OF SMALL‐SCALE HABITAT STRUCTURE FOR MAINTAINING BETA DIVERSITY , 2005 .

[17]  F. Guichard,et al.  Topographic heterogeneity, hydrodynamics, and benthic community structure: a scale-dependent cascade , 1998 .

[18]  H. Middelkoop,et al.  Airborne Laser Scanning as a Tool for Lowland Floodplain Vegetation Monitoring , 2006, Hydrobiologia.

[19]  Frédéric Bretar,et al.  Full-waveform topographic lidar : State-of-the-art , 2009 .

[20]  Scott A. Lassau,et al.  Using high‐resolution multi‐spectral imagery to estimate habitat complexity in open‐canopy forests: can we predict ant community patterns? , 2005 .

[21]  Lee A. Vierling,et al.  The use of airborne lidar to assess avian species diversity, density, and occurrence in a pine/aspen forest , 2008 .

[22]  Alan M. Friedlander,et al.  Using bathymetric lidar to define nearshore benthic habitat complexity: Implications for management of reef fish assemblages in Hawaii , 2008 .

[23]  M. Palmer,et al.  The Influence of Environmental Heterogeneity on Patterns and Processes in Streams , 1997, Journal of the North American Benthological Society.

[24]  C. Jacobi,et al.  Habitat geometry of benthic substrata: effects on arrival and settlement of mobile epifauna , 1996 .

[25]  T. Webster,et al.  Object-oriented land cover classification of lidar-derived surfaces , 2006 .

[26]  K. R. Clarke,et al.  Change in marine communities : an approach to statistical analysis and interpretation , 2001 .

[27]  P. Archambault,et al.  Temporal variation in the structure of intertidal assemblages following the removal of sewage , 2001 .

[28]  S. Running,et al.  What does Remote Sensing Do for Ecology , 1991 .

[29]  J.-P. Agnard,et al.  High‐resolution remote sensing of intertidal ecosystems: A low‐cost technique to link scale‐dependent patterns and processes , 2000 .

[30]  James Parrish,et al.  Habitat characteristics affecting fish assemblages on a Hawaiian coral reef , 1998 .

[31]  C. Wayne Wright,et al.  Relationships Between Reef Fish Communities and Remotely Sensed Rugosity Measurements in Biscayne National Park, Florida, USA , 2006, Environmental Biology of Fishes.

[32]  G. Hays,et al.  Critical evaluation of the nursery role hypothesis for seagrass meadows , 2003 .

[33]  Stephen V. Stehman,et al.  Selecting and interpreting measures of thematic classification accuracy , 1997 .

[34]  A. Rowden,et al.  THE IMPORTANCE OF SEAGRASS BEDS AS A HABITAT FOR FISHERY SPECIES , 2001 .

[35]  E. Bonsdorff,et al.  Drifting algal mats as an alternative habitat for benthic invertebrates: Species specific responses to a transient resource. , 2000, Journal of experimental marine biology and ecology.

[36]  R. O'Neill,et al.  The value of the world's ecosystem services and natural capital , 1997, Nature.

[37]  W J Lillycrop,et al.  Airborne lidar bathymetry : The SHOALS system , 2000 .

[38]  Antoine Collin,et al.  Salt-marsh characterization, zonation assessment and mapping through a dual-wavelength LiDAR , 2010 .

[39]  Simon J. Pittman,et al.  Using Lidar Bathymetry and Boosted Regression Trees to Predict the Diversity and Abundance of Fish and Corals , 2009 .

[40]  C. Raman,et al.  A Change of Wave-length in Light Scattering , 1928, Nature.

[41]  J. Zedler,et al.  Topographic heterogeneity influences fish use of an experimentally restored tidal marsh. , 2008, Ecological applications : a publication of the Ecological Society of America.

[42]  Steven Degraer,et al.  Habitat suitability modelling as a mapping tool for macrobenthic communities: An example from the Belgian part of the North Sea , 2008 .

[43]  Paul E. LaRocque,et al.  MEETING THE ACCURACY CHALLENGE IN AIRBORNE LIDAR BATHYMETRY , 2000 .

[44]  Jeana Gross,et al.  Pigments in Vegetables: Chlorophylls and Carotenoids , 1995 .

[45]  Ibon Galparsoro,et al.  Coastal and estuarine habitat mapping, using LIDAR height and intensity and multi-spectral imagery , 2008 .