An ontological system for interoperable spatial generalisation in biodiversity monitoring

Semantic heterogeneity remains a barrier to data comparability and standardisation of results in different fields of spatial research. Because of its thematic complexity, differing acquisition methods and national nomenclatures, interoperability of biodiversity monitoring information is especially difficult. Since data collection methods and interpretation manuals broadly vary there is a need for automatised, objective methodologies for the generation of comparable data-sets. Ontology-based applications offer vast opportunities in data management and standardisation. This study examines two data-sets of protected heathlands in Germany and Belgium which are based on remote sensing image classification and semantically formalised in an OWL2 ontology. The proposed methodology uses semantic relations of the two data-sets, which are (semi-)automatically derived from remote sensing imagery, to generate objective and comparable information about the status of protected areas by utilising kernel-based spatial reclassification. This automatised method suggests a generalisation approach, which is able to generate delineation of Special Areas of Conservation (SAC) of the European biodiversity Natura 2000 network. Furthermore, it is able to transfer generalisation rules between areas surveyed with varying acquisition methods in different countries by taking into account automated inference of the underlying semantics. The generalisation results were compared with the manual delineation of terrestrial monitoring. For the different habitats in the two sites an accuracy of above 70% was detected. However, it has to be highlighted that the delineation of the ground-truth data inherits a high degree of uncertainty, which is discussed in this study. HighlightsKernel reclassification algorithm for creation of interoperable Natura 2000 habitats.Combination of spatial reclassification and ontology-based data handling.The results were compared with the manual delineation of terrestrial monitoring.For the different habitats in the two sites an accuracy of above 70% was detected.

[1]  Angela Schwering,et al.  Measuring Semantic Similarity Between Geospatial Conceptual Regions , 2005, GeoS.

[2]  Helen Couclelis,et al.  Ontologies of geographic information , 2010, Int. J. Geogr. Inf. Sci..

[3]  Michael Förster,et al.  Remote sensing for mapping natural habitats and their conservation status - New opportunities and challenges , 2015, Int. J. Appl. Earth Obs. Geoinformation.

[4]  Inge Uljee,et al.  Inferring urban land use using the optimised spatial reclassification kernel , 2011, Environ. Model. Softw..

[5]  M. Lutz,et al.  Overcoming semantic heterogeneity in spatial data infrastructures , 2009, Comput. Geosci..

[6]  H. Nagendra,et al.  High resolution satellite imagery for tropical biodiversity studies: the devil is in the detail , 2008, Biodiversity and Conservation.

[7]  Silvana Castano,et al.  Geographic Ontology Matching with iG-Match , 2007, SSTD.

[8]  Marinos Kavouras,et al.  Comparing categories among geographic ontologies , 2005, Comput. Geosci..

[9]  Travis E. Oliphant,et al.  Python for Scientific Computing , 2007, Computing in Science & Engineering.

[10]  J. Lamarque,et al.  Global Biodiversity: Indicators of Recent Declines , 2010, Science.

[11]  Terence L. van Zyl,et al.  Earth Observation Scientific Workflows in a Distributed Computing Environment , 2012, Trans. GIS.

[12]  Surya S. Durbha,et al.  A framework for semantic reconciliation of disparate earth observation thematic data , 2009, Comput. Geosci..

[13]  Dirk S. Schmeller,et al.  The national responsibility approach to setting conservation priorities—Recommendations for its use , 2014 .

[14]  Kian Pakzad,et al.  Using information layers for mapping grassland habitat distribution at local to regional scales , 2015, Int. J. Appl. Earth Obs. Geoinformation.

[15]  Riccardo Albertoni,et al.  A multilingual/multicultural semantic-based approach to improve Data Sharing in a SDI for Nature Conservation , 2010 .

[16]  Thomas Blaschke,et al.  A comparison of three image-object methods for the multiscale analysis of landscape structure , 2003 .

[17]  Sebastian Schmidtlein,et al.  Mapping the floristic continuum : Ordination space position estimated from imaging spectroscopy , 2007 .

[18]  Krzysztof Janowicz,et al.  Observation‐Driven Geo‐Ontology Engineering , 2012, Trans. GIS.

[19]  Vera Andrejchenko,et al.  Towards a framework for agent-based image analysis of remote-sensing data , 2015, International journal of image and data fusion.

[20]  Annekatrin Metz,et al.  Remote sensing of scattered Natura 2000 habitats using a one-class classifier , 2014, Int. J. Appl. Earth Obs. Geoinformation.

[21]  Cecilia Zanni-Merk,et al.  Towards a Semi-automatic Semantic Approach for Satellite Image Analysis , 2013, KES.

[22]  L. Durieux,et al.  Advances in Geographic Object-Based Image Analysis with ontologies: A review of main contributions and limitations from a remote sensing perspective , 2013 .

[23]  Alejandra Cechich,et al.  Ontology-driven geographic information integration: A survey of current approaches , 2009, Comput. Geosci..

[24]  Toon Spanhove,et al.  Can remote sensing estimate fine-scale quality indicators of natural habitats? , 2012 .

[25]  Caspar A. Mücher,et al.  Integrating remote sensing in Natura 2000 habitat monitoring: prospects on the way forward , 2011 .

[26]  Christian Schuster,et al.  Towards Detecting Swath Events in TerraSAR-X Time Series to Establish NATURA 2000 Grassland Habitat Swath Management as Monitoring Parameter , 2011, Remote. Sens..

[27]  Boyan Brodaric,et al.  Finding Science with Science: Evaluating a Domain and Scientific Ontology User Interface for the Discovery of Scientific Resources , 2013, Trans. GIS.

[28]  Pol Coppin,et al.  Endmember variability in Spectral Mixture Analysis: A review , 2011 .

[29]  Damien Arvor,et al.  Towards an Ontological Approach for Classifying Remote Sensing Images , 2012, 2012 Eighth International Conference on Signal Image Technology and Internet Based Systems.

[30]  C. Small.  Estimation of urban vegetation abundance by spectral mixture analysis , 2001 .

[31]  Heiner Stuckenschmidt,et al.  Ontologies for geographic information processing , 2002 .

[32]  S. T. Buckland,et al.  Long-term datasets in biodiversity research and monitoring: assessing change in ecological communities through time. , 2010, Trends in ecology & evolution.

[33]  Iphigenia Keramitsoglou,et al.  Habitat mapping using machine learning-extended kernel-based reclassification of an Ikonos satellite image , 2006 .

[34]  Francesco M. Donini,et al.  Complexity of Reasoning , 2003, Description Logic Handbook.

[35]  Hannes Feilhauer,et al.  Combining Isomap ordination and imaging spectroscopy to map continuous floristic gradients in a heterogeneous landscape , 2011 .

[36]  Birgit Kleinschmit,et al.  Approaches to utilising QuickBird data for the monitoring of NATURA 2000 habitats , 2008 .

[37]  Ashish Ghosh,et al.  Aggregation Pheromone Density Based Pattern Classification , 2009, Fundam. Informaticae.

[38]  M. Bock,et al.  Object-oriented methods for habitat mapping at multiple scales – Case studies from Northern Germany and Wye Downs, UK , 2005 .

[39]  Boris Motik,et al.  Hypertableau Reasoning for Description Logics , 2009, J. Artif. Intell. Res..

[40]  Charalambos Kontoes,et al.  Kernel based re-classification of Earth observation data for fine scale habitat mapping , 2005 .

[41]  Liping Di,et al.  Semantic Web-based geospatial knowledge transformation , 2009, Comput. Geosci..

[42]  Thomas Blaschke,et al.  Ontology-Based Classification of Building Types Detected from Airborne Laser Scanning Data , 2014, Remote. Sens..

[43]  Dirk Tiede,et al.  ESP: a tool to estimate scale parameter for multiresolution image segmentation of remotely sensed data , 2010, Int. J. Geogr. Inf. Sci..

[44]  Alex M. Lechner,et al.  Remote sensing of small and linear features: Quantifying the effects of patch size and length, grid position and detectability on land cover mapping , 2009 .

[45]  T. Vetter,et al.  Impact of climate change on soil moisture dynamics in Brandenburg with a focus on nature conservation areas , 2009 .

[46]  Birgit Kleinschmit,et al.  Significance Analysis of Different Types of Ancillary Geodata Utilized in a Multisource Classification Process for Forest Identification in Germany , 2014, IEEE Transactions on Geoscience and Remote Sensing.

[47]  Madhur Anand,et al.  Uncertainty in ecosystem mapping by remote sensing , 2013, Comput. Geosci..

[48]  F. Volpe,et al.  The influence of principal component analysis on the spatial structure of a multispectral dataset , 1999 .

[49]  Samuel Andrés,et al.  Towards a Semantic Interpretation of Satellite Images by Using Spatial Relations Defined in Geographic Standards , 2013 .

[50]  Isabel F. Cruz,et al.  Structural Alignment Methods with Applications to Geospatial Ontologies , 2008, Trans. GIS.

[51]  Germain Forestier,et al.  Coastal image interpretation using background knowledge and semantics , 2013, Comput. Geosci..

[52]  David G Steel,et al.  Aggregation and Ecological Effects in Geographically Based Data , 2010 .

[53]  Birgit Kleinschmit,et al.  Using ontological inference and hierarchical matchmaking to overcome semantic heterogeneity in remote sensing-based biodiversity monitoring , 2015, Int. J. Appl. Earth Obs. Geoinformation.

[54]  Paul Scheunders,et al.  Habitat mapping and quality assessment of heathlands using a modified kernel-based reclassification technique , 2010, 2010 IEEE International Geoscience and Remote Sensing Symposium.

[55]  Markus Neteler,et al.  Remotely sensed spectral heterogeneity as a proxy of species diversity: Recent advances and open challenges , 2010, Ecol. Informatics.

[56]  S. Barr,et al.  INFERRING URBAN LAND USE FROM SATELLITE SENSOR IMAGES USING KERNEL-BASED SPATIAL RECLASSIFICATION , 1996 .