Object-based class modelling for multi-scale riparian forest habitat mapping

Abstract Object-based class modelling allows for mapping complex, hierarchical habitat systems. The riparian zone, including forests, represents such a complex ecosystem. Forests within riparian zones are biologically high productive and characterized by a rich biodiversity; thus considered of high community interest with an imperative to be protected and regularly monitored. Satellite earth observation (EO) provides tools for capturing the current state of forest habitats such as forest composition including intermixture of non-native tree species. Here we present a semi-automated object based image analysis (OBIA) approach for the mapping of riparian forests by applying class modelling of habitats based on the European Nature Information System (EUNIS) habitat classifications and the European Habitats Directive (HabDir) Annex 1. A very high resolution (VHR) WorldView-2 satellite image provided the required spatial and spectral details for a multi-scale image segmentation and rule-base composition to generate a six-level hierarchical representation of riparian forest habitats. Thereby habitats were hierarchically represented within an image object hierarchy as forest stands, stands of homogenous tree species and single trees represented by sunlit tree crowns. 522 EUNIS level 3 (EUNIS-3) habitat patches with a mean patch size (MPS) of 12,349.64 m2 were modelled from 938 forest stand patches (MPS = 6868.20 m2) and 43,742 tree stand patches (MPS = 140.79 m2). The delineation quality of the modelled EUNIS-3 habitats (focal level) was quantitatively assessed to an expert-based visual interpretation showing a mean deviation of 11.71%.

[1]  Manfred F. Buchroithner,et al.  Towards automated forest-type mapping – a service within GSE Forest Monitoring based on SPOT-5 and IKONOS data , 2009 .

[2]  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..

[3]  R. Naiman,et al.  The Ecology of Interfaces: Riparian Zones , 1997 .

[4]  James G. Miller.  The nature of living systems , 1971 .

[5]  Kirsten L. Jones,et al.  Characterisation and mapping of forest communities by clustering individual tree crowns , 2010 .

[6]  Tobias Langanke,et al.  Combined object-based classification and manual interpretation–synergies for a quantitative assessment of parcels and biotopes , 2009 .

[7]  Jian-guo Wu.  Hierarchy and scaling: Extrapolating informa-tion along a scaling ladder , 1999 .

[8]  Cynthia E. Davies,et al.  EUNIS HABITAT CLASSIFICATION REVISED 2004 , 2004 .

[9]  André Stumpf,et al.  bject-oriented mapping of urban trees using Random Forest lassifiers , 2013 .

[10]  R. O'Neill,et al.  Landscape Ecology Explained@@@Landscape Ecology in Theory and Practice: Pattern and Process , 2001 .

[11]  Alexandre Carleer,et al.  Exploitation of Very High Resolution Satellite Data for Tree Species Identification , 2004 .

[12]  H. Nagendra,et al.  Remote sensing for conservation monitoring: Assessing protected areas, habitat extent, habitat condition, species diversity, and threats , 2013 .

[13]  Thomas Blaschke,et al.  Multi-scale object-based image analysis – a key to the hierarchical organisation of landscapes , 2004 .

[14]  Roman M. Palenichka,et al.  Hierarchical multi-scale segmentation of LiDAR images in forest areas , 2012, 2012 IEEE International Geoscience and Remote Sensing Symposium.

[15]  H. Nagendra.  Using remote sensing to assess biodiversity , 2001 .

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

[17]  Ana Belen Garcia Hernando,et al.  Objects-based Image Analysis for Mapping Natura 2000 Habitats to Improve Forest Management , 2012 .

[18]  Marek K. Jakubowski,et al.  Tradeoffs between lidar pulse density and forest measurement accuracy , 2013 .

[19]  N. Coops,et al.  What is the Value of a Good Map? An Example Using High Spatial Resolution Imagery to Aid Riparian Restoration , 2007, Ecosystems.

[20]  K. Tockner,et al.  Riverine landscape diversity , 2002 .

[21]  N. Coops,et al.  High Spatial Resolution Remotely Sensed Data for Ecosystem Characterization , 2004 .

[22]  N. Coops,et al.  Application of high spatial resolution satellite imagery for riparian and forest ecosystem classification , 2007 .

[23]  Arno Schäpe,et al.  Multiresolution Segmentation : an optimization approach for high quality multi-scale image segmentation , 2000 .

[24]  A. Cierjacks,et al.  Knowledge-based classification of remote sensing data for the estimation of below- and above-ground organic carbon stocks in riparian forests , 2012, Wetlands Ecology and Management.

[25]  Aniruddha Ghosh,et al.  A comparison of selected classification algorithms for mapping bamboo patches in lower Gangetic plains using very high resolution WorldView 2 imagery , 2014, Int. J. Appl. Earth Obs. Geoinformation.

[26]  K. Tockner,et al.  Riverine flood plains: present state and future trends , 2002, Environmental Conservation.

[27]  D. Hölbling,et al.  Object-based Class Modeling for Cadastre-constrained Delineation of Geo-objects , 2010 .

[28]  G. Hay,et al.  An automated object-based approach for the multiscale image segmentation of forest scenes , 2005 .

[29]  C. Burnett,et al.  A multi-scale segmentation/object relationship modelling methodology for landscape analysis , 2003 .

[30]  Birgit Kleinschmit,et al.  Object-based classification of QuickBird data using ancillary information for the detection of forest types and NATURA 2000 habitats , 2008 .

[31]  D. Leckie,et al.  The Individual Tree Crown Approach Applied to Ikonos Images of a Coniferous Plantation Area , 2006 .

[32]  H. Simon,et al.  The Organization of Complex Systems , 1977 .

[33]  D. Leckie,et al.  Automated tree recognition in old growth conifer stands with high resolution digital imagery , 2005 .

[34]  D. Tiede,et al.  Characterising mountain forest structure using landscape metrics on LiDAR-based canopy surface models , 2008 .

[35]  G. J. Hay,et al.  A multiscale framework for landscape analysis: Object-specific analysis and upscaling , 2001, Landscape Ecology.

[36]  J R Healey,et al.  The repeatability of vegetation classification and mapping. , 2011, Journal of environmental management.

[37]  Daniel Clewley,et al.  Updating the Phase 1 habitat map of Wales, UK, using satellite sensor data , 2011 .

[38]  L. Bruzzone,et al.  Tree species classification in the Southern Alps based on the fusion of very high geometrical resolution multispectral/hyperspectral images and LiDAR data , 2012 .

[39]  Stefan Lang,et al.  Class modelling of complex riparian forest habitats , 2014 .

[40]  R. O'Neill.  A Hierarchical Concept of Ecosystems. , 1986 .

[41]  Stefan Lang,et al.  Multiscale Object Feature Library for Habitat Quality Monitoring in Riparian Forests , 2014, IEEE Geoscience and Remote Sensing Letters.