An object-based approach to delineate wetlands across landscapes of varied disturbance with high spatial resolution satellite imagery ☆

[1]  Qihao Weng,et al.  An Automated Method to Parameterize Segmentation Scale by Enhancing Intrasegment Homogeneity and Intersegment Heterogeneity , 2015, IEEE Geoscience and Remote Sensing Letters.

[2]  O. Csillik,et al.  Automated parameterisation for multi-scale image segmentation on multiple layers , 2014, ISPRS journal of photogrammetry and remote sensing : official publication of the International Society for Photogrammetry and Remote Sensing.

[3]  Thomas Blaschke,et al.  Geographic Object-Based Image Analysis – Towards a new paradigm , 2014, ISPRS journal of photogrammetry and remote sensing : official publication of the International Society for Photogrammetry and Remote Sensing.

[4]  Joseph F. Knight,et al.  Influence of Multi-Source and Multi-Temporal Remotely Sensed and Ancillary Data on the Accuracy of Random Forest Classification of Wetlands in Northern Minnesota , 2013, Remote. Sens..

[5]  Steven E. Franklin,et al.  A comparison of pixel-based and object-based image analysis with selected machine learning algorithms for the classification of agricultural landscapes using SPOT-5 HRG imagery , 2012 .

[6]  Bo Wang,et al.  Discrepancy measures for selecting optimal combination of parameter values in object-based image analysis , 2012 .

[7]  Geoffrey J. Hay,et al.  How wetland type and area differ through scale: A GEOBIA case study in Alberta's Boreal Plains , 2012 .

[8]  J. Six,et al.  Object-based crop identification using multiple vegetation indices, textural features and crop phenology , 2011 .

[9]  Patricia Gober,et al.  Per-pixel vs. object-based classification of urban land cover extraction using high spatial resolution imagery , 2011, Remote Sensing of Environment.

[10]  Patricia Chow-Fraser,et al.  Transferability of object-based rule sets for mapping coastal high marsh habitat among different regions in Georgian Bay, Canada , 2011, Wetlands Ecology and Management.

[11]  D. King,et al.  Comparison of pixel- and object-based classification in land cover change mapping , 2011 .

[12]  Lenore Fahrig,et al.  Functional landscape heterogeneity and animal biodiversity in agricultural landscapes. , 2011, Ecology letters.

[13]  Xuefei Hu,et al.  Impervious surface area extraction from IKONOS imagery using an object-based fuzzy method , 2011 .

[14]  L. Monika Moskal,et al.  Object-based classification of semi-arid wetlands , 2011 .

[15]  P. Chow-Fraser,et al.  Mapping Floating and Emergent Aquatic Vegetation in Coastal Wetlands of Eastern Georgian Bay, Lake Huron, Canada , 2010, Wetlands.

[16]  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..

[17]  Peter M. Atkinson,et al.  Utilizing image texture to detect land-cover change in Mediterranean coastal wetlands , 2010 .

[18]  Brigitte Poulin,et al.  Wetland monitoring using classification trees and SPOT-5 seasonal time series , 2010 .

[19]  P. Gong,et al.  Accuracy Assessment Measures for Object-based Image Segmentation Goodness , 2010 .

[20]  Thomas Blaschke,et al.  Object based image analysis for remote sensing , 2010 .

[21]  V. Klemas.  Remote Sensing Techniques for Studying Coastal Ecosystems: An Overview , 2010 .

[22]  Timothy A. Warner,et al.  Forest Type Mapping using Object-specific Texture Measures from Multispectral Ikonos Imagery: Segmentation Quality and Image Classification Issues , 2009 .

[23]  Monique Bernier,et al.  Object-based classification of very high resolution panchromatic images for evaluating recent change in the structure of patterned peatlands , 2009 .

[24]  Marguerite Madden,et al.  Hyperspectral image data for mapping wetland vegetation , 2003, Wetlands.

[25]  James P. Gibbs,et al.  Importance of small wetlands for the persistence of local populations of wetland-associated animals , 1993, Wetlands.

[26]  D. Civco,et al.  Integrating multi-temporal spectral and structural information to map wetland vegetation in a lower Connecticut River tidal marsh , 2008 .

[27]  Bethany A. Bradley,et al.  Can remote sensing of land cover improve species distribution modelling? , 2008 .

[28]  John R. Jensen,et al.  Object‐based change detection using correlation image analysis and image segmentation , 2008 .

[29]  Douglas J. King,et al.  Riparian marshland composition and biomass mapping using Ikonos imagery , 2008 .

[30]  M. Baatz,et al.  Progressing from object-based to object-oriented image analysis , 2008 .

[31]  Richard A. Fournier,et al.  An object-based method to map wetland using RADARSAT-1 and Landsat ETM images: test case on two sites in Quebec, Canada , 2007 .

[32]  Richard A. Fournier,et al.  Towards a strategy to implement the Canadian Wetland Inventory using satellite remote sensing , 2007 .

[33]  Martin Volk,et al.  The comparison index: A tool for assessing the accuracy of image segmentation , 2007, Int. J. Appl. Earth Obs. Geoinformation.

[34]  C. Wright,et al.  Improved wetland remote sensing in Yellowstone National Park using classification trees to combine TM imagery and ancillary environmental data , 2007 .

[35]  Patricia Chow-Fraser,et al.  Use of IKONOS Imagery to Map Coastal Wetlands of Georgian Bay , 2007 .

[36]  S. Pickett,et al.  Spatial heterogeneity in urban ecosystems: reconceptualizing land cover and a framework for classification , 2007 .

[37]  Desmond Power,et al.  Comparison of C-band SAR algorithms to derive surface wind vectors and initial findings in their use in marine search and rescue , 2007 .

[38]  Zhang Xiangmin,et al.  Comparison of pixel‐based and object‐oriented image classification approaches—a case study in a coal fire area, Wuda, Inner Mongolia, China , 2006 .

[39]  P. Gong,et al.  Object-based Detailed Vegetation Classification with Airborne High Spatial Resolution Remote Sensing Imagery , 2006 .

[40]  S. Sanjeevi,et al.  A comparison of the classification of wetland characteristics by linear spectral mixture modelling and traditional hard classifiers on multispectral remotely sensed imagery in southern India , 2006 .

[41]  Ramanathan Sugumaran,et al.  Classification of Iowa wetlands using an airborne hyperspectral image: a comparison of the spectral angle mapper classifier and an object-oriented approach , 2005 .

[42]  Laura Chasmer,et al.  Vegetation class dependent errors in lidar ground elevation and canopy height estimates in a boreal wetland environment , 2005 .

[43]  Waqar Ahmad,et al.  A COMPARISON OF OBJECT-ORIENTED AND PIXEL-BASED CLASSIFICATION METHODS FOR MAPPING LAND COVER IN NORTHERN AUSTRALIA. , 2005 .

[44]  Peng Gong,et al.  Integration of object-based and pixel-based classification for mapping mangroves with IKONOS imagery , 2004 .

[45]  Ana M. Cingolani,et al.  Mapping vegetation in a heterogeneous mountain rangeland using landsat data: an alternative method to define and classify land-cover units , 2004 .

[46]  Stacy L. Ozesmi,et al.  Satellite remote sensing of wetlands , 2002, Wetlands Ecology and Management.

[47]  Stephen J. Walsh,et al.  Remote sensing of forested wetlands: application of multitemporal and multispectral satellite imagery to determine plant community composition and structure in southeastern USA , 2001, Plant Ecology.

[48]  M. Brock,et al.  Drain it, dam it: alteration of water regime in shallow wetlands on the New England Tableland of New South Wales, Australia , 1999, Wetlands Ecology and Management.

[49]  U. Benz,et al.  Multi-resolution, object-oriented fuzzy analysis of remote sensing data for GIS-ready information , 2004 .

[50]  Nathan J. Heinert,et al.  Extending satellite remote sensing to local scales: land and water resource monitoring using high-resolution imagery , 2003 .

[51]  A. Skidmore,et al.  Spectral discrimination of vegetation types in a coastal wetland , 2003 .

[52]  J. Zedler.  Wetlands at your service: reducing impacts of agriculture at the watershed scale , 2003 .

[53]  Xavier Cufí,et al.  Strategies for image segmentation combining region and boundary information , 2003, Pattern Recognit. Lett..

[54]  S. Franklin,et al.  Classification of wetland habitat and vegetation communities using multi-temporal Ikonos imagery in southern Saskatchewan , 2002 .

[55]  Giles M. Foody,et al.  Status of land cover classification accuracy assessment , 2002 .

[56]  J. Wickham,et al.  Impacts of Patch Size and Land-Cover Heterogeneity on Thematic Image Classification Accuracy , 2002 .

[57]  Filipe Aires,et al.  Remote sensing of global wetland dynamics with multiple satellite data sets , 2001 .

[58]  Philip A. Townsend,et al.  Mapping Seasonal Flooding in Forested Wetlands Using Multi-Temporal Radarsat SAR , 2001 .

[59]  W. Mitsch,et al.  The value of wetlands: importance of scale and landscape setting. , 2000 .

[60]  Paul V. Bolstad,et al.  Coordinating methodologies for scaling landcover classifications from site-specific to global: Steps toward validating global map products , 1999 .

[61]  R. D. Semlitsch,et al.  Are Small, Isolated Wetlands Expendable? , 1998 .

[62]  C. Woodcock,et al.  Combining Spectral and Texture Data in the Segmentation of Remotely Sensed Images , 1996 .

[63]  S. Sader,et al.  Accuracy of landsat-TM and GIS rule-based methods for forest wetland classification in Maine , 1995 .

[64]  K. Rutchey,et al.  Inland Wetland Change Detection in the Everglades Water Conservation Area 2A Using a Time Series of Normalized Remotely Sensed Data , 1995 .

[65]  M. Barson,et al.  Remote sensing of Australian wetlands: An evaluation of Landsat TM data for inventory and classification , 1993 .

[66]  D. S. Gilmer,et al.  Enumeration of prairie wetlands with Landsat and aircraft data , 1980 .

[67]  James R. Anderson,et al.  A land use and land cover classification system for use with remote sensor data , 1976 .

[68]  Robert M. Haralick,et al.  Textural Features for Image Classification , 1973, IEEE Trans. Syst. Man Cybern..

[69]  J. A. Schell,et al.  Monitoring vegetation systems in the great plains with ERTS , 1973 .