Segmentation and object-oriented classification of wetlands in a karst Florida landscape using multi-season Landsat-7 ETM+ imagery

Segmentation and object-oriented processing of single-season and multi-season Landsat-7 Enhanced Thematic Mapper Plus (ETM+) data was utilized for the classification of wetlands in a 1560 km2 study area of north central Florida. This segmentation and object-oriented classification outperformed the traditional maximum likelihood algorithm (MLC) in accurately mapping wetlands, with overall accuracies of 90.2% (single-season imagery) and 90.8% (multi-season imagery), compared to overall accuracies for the MLC classifiers of 78.4 and 79.0%, respectively. Kappa coefficients were over 1.5-times greater for the segmentation/object-oriented classifications than for the MLC classifications, and producer and user accuracies were also higher. The producer accuracies of the segmentation/object-oriented classifications were 90.8% (single-season) and 91.6% (multi-season), compared to 70.6 and 74.4%, respectively, for the MLC classifications. User accuracies were 73.9 and 73.5% for the single-season and multi-season segmentation/object-oriented classifications, respectively, compared to 54.1% (single-season) and 55.0% (multi-season) for the MLC classifications. The use of multi-seasonal data resulted in only a slight increase in overall accuracy over the single-season imagery. This small increase was primarily due to better discrimination of riparian wetlands in the multi-season data. Segmentation and object-oriented processing provides a low-cost, high-accuracy method for classification of wetlands on a local, regional, or national basis.

[1]  Fernando Bação,et al.  Self-organizing Maps as Substitutes for K-Means Clustering , 2005, International Conference on Computational Science.

[2]  R. Sugumaran,et al.  Using Remote Sensing Data to Study Wetland Dynamics in Iowa , 2004 .

[3]  Martha S. Gilmore,et al.  TIDAL WETLAND CLASSIFICATION FROM LANDSAT IMAGERY USING AN INTEGRATED PIXEL-BASED AND OBJECT-BASED CLASSIFICATION APPROACH , 2006 .

[4]  Andrew K. Skidmore,et al.  Integration of classification methods for improvement of land-cover map accuracy , 2002 .

[5]  Maycira Costa,et al.  Biophysical properties and mapping of aquatic vegetation during the hydrological cycle of the Amazon floodplain using JERS-1 and Radarsat , 2002 .

[6]  Jochen Schiewe.  Integration of multi-sensor data for landscape modeling using a region-based approach , 2003 .

[7]  B. Kartikeyan,et al.  A segmentation approach to classification of remote sensing imagery , 1998 .

[8]  Christel Lingnau,et al.  OBJECT ORIENTED ANALYSIS AND SEMANTIC NETWORK FOR HIGH RESOLUTION IMAGE CLASSIFICATION , 2003 .

[9]  Elijah W. Ramsey,et al.  AVHRR imagery used to identify hurricane damage in a forested wetland of Louisiana , 1997 .

[10]  Alain Pietroniro,et al.  Towards operational monitoring of a northern wetland using geomatics-based techniques , 2005 .

[11]  A. Pekkarinen,et al.  Estimation of timber volume at the sample plot level by means of image segmentation and Landsat TM imagery , 2001 .

[12]  Jinsong Deng,et al.  PCA‐based land‐use change detection and analysis using multitemporal and multisensor satellite data , 2008 .

[13]  Ben Gorte,et al.  A method for object-oriented land cover classification combining Landsat TM data and aerial photographs , 2003 .

[14]  J. R. Jensen,et al.  Measurement of seasonal and yearly cattail and waterlily changes using multidate SPOT panchromatic data , 1993 .

[15]  R. G. Oderwald,et al.  Assessing Landsat classification accuracy using discrete multivariate analysis statistical techniques. , 1983 .

[16]  R. Tiner,et al.  Geographically isolated wetlands of the United States , 2003, Wetlands.

[17]  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 .

[18]  L. Bastin.  Comparison of fuzzy c-means classification, linear mixture modelling and MLC probabilities as tools for unmixing coarse pixels , 1997 .

[19]  Jonas Ardö,et al.  Land use studies in drylands: an evaluation of object‐oriented classification of very high resolution panchromatic imagery , 2008 .

[20]  V. Caron,et al.  United states. , 2018, Nursing standard (Royal College of Nursing (Great Britain) : 1987).

[21]  John R. Jensen,et al.  Introductory Digital Image Processing: A Remote Sensing Perspective , 1986 .

[22]  A. Rango,et al.  Object-oriented image analysis for mapping shrub encroachment from 1937 to 2003 in southern New Mexico , 2004 .

[23]  Thomas Blaschke,et al.  An Object-based Methodology for Mapping Mires Using High Resolution Imagery , 2003 .

[24]  Katarzyna Dabrowska-Zielinska,et al.  Mapping vegetation of a wetland ecosystem by fuzzy classification of optical and microwave satellite images supported by various ancillary data , 2003, SPIE Remote Sensing.

[25]  V. K. Verma,et al.  Mapping, monitoring and conservation of Harike wetland ecosystem, Punjab, India, through remote sensing , 2001 .

[26]  Daniel L. Civco,et al.  Artificial Neural Networks for Land-Cover Classification and Mapping , 1993, Int. J. Geogr. Inf. Sci..

[27]  Ross S. Lunetta,et al.  Application of multi-temporal Landsat 5 TM imagery for wetland identification , 1999 .

[28]  T. M. Lillesand,et al.  Rapid maximum likelihood classification , 1991 .

[29]  Jiaguo Qi,et al.  Optimal classification methods for mapping agricultural tillage practices , 2004 .

[30]  E. L. Hunter,et al.  An assessment of two classification methods for mapping Thames Estuary intertidal habitats using CASI data , 2002 .

[31]  A. Pekkarinen,et al.  A method for the segmentation of very high spatial resolution images of forested landscapes , 2002 .

[32]  Marvin E. Bauer,et al.  Integrating Contextual Information with per-Pixel Classification for Improved Land Cover Classification , 2000 .

[33]  P. Switzer,et al.  A transformation for ordering multispectral data in terms of image quality with implications for noise removal , 1988 .

[34]  R. Hill.  Image segmentation for humid tropical forest classification in Landsat TM data , 1999 .

[35]  R. Congalton,et al.  Accuracy assessment: a user's perspective , 1986 .

[36]  T. M. Lillesand,et al.  Remote Sensing and Image Interpretation , 1980 .

[37]  S. P. S. Kushwaha,et al.  Evaluation of various digital image processing techniques for detection of coastal wetlands using ERS-1 SAR data , 2000 .

[38]  A. Troy,et al.  An object‐oriented approach for analysing and characterizing urban landscape at the parcel level , 2008 .

[39]  John R. Jensen,et al.  Nontidal wetland mapping in South Carolina using airborne multispectral scanner data , 1984 .

[40]  D. Stow,et al.  Object‐based classification of residential land use within Accra, Ghana based on QuickBird satellite data , 2007, International journal of remote sensing.

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

[42]  G. Foody.  A fuzzy sets approach to the representation of vegetation continua from remotely sensed data : an example from lowland health , 1992 .

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

[44]  R. Lawrence,et al.  Mapping wetlands and riparian areas using Landsat ETM+ imagery and decision-tree-based models , 2006, Wetlands.

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

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

[47]  C. Barbosa,et al.  Dual-season mapping of wetland inundation and vegetation for the central Amazon basin , 2003 .

[48]  Siamak Khorram,et al.  Hierarchical maximum-likelihood classification for improved accuracies , 1997, IEEE Trans. Geosci. Remote. Sens..

[49]  Jinmu Choi,et al.  A hybrid approach to urban land use/cover mapping using Landsat 7 Enhanced Thematic Mapper Plus (ETM+) images , 2004 .

[50]  Kazuo Oki,et al.  Comparison between several feature extraction/classification methods for mapping complicated agricultural land use patches using airborne hyperspectral data , 2007 .

[51]  Andrew K. Skidmore,et al.  Improved understorey bamboo cover mapping using a novel hybrid neural network and expert system , 2009 .

[52]  N. Emrahoğlu,et al.  Comparison of a new algorithm with the supervised classifications , 2003 .

[53]  C. Özkan,et al.  Comparison of maximum likelihood classification method with supervised artificial neural network algorithms for land use activities , 2004 .

[54]  Yoshifumi Yasuoka,et al.  ASTER Data Utilization for Wetland Mapping and Forest Mapping , 2001 .

[55]  Junhua Li,et al.  A rule-based method for mapping Canada's wetlands using optical, radar and DEM data , 2005 .

[56]  Curt H. Davis,et al.  A combined fuzzy pixel-based and object-based approach for classification of high-resolution multispectral data over urban areas , 2003, IEEE Trans. Geosci. Remote. Sens..