Generating land cover boundaries from remotely sensed data using object-based image analysis: overview and epidemiological application.

Satellite imagery and aerial photography represent a vast resource to significantly enhance environmental mapping and modeling applications for use in understanding spatio-temporal relationships between environment and health. Deriving boundaries of land cover objects, such as trees, buildings, and crop fields, from image data has traditionally been performed manually using a very time consuming process of hand digitizing. Boundary detection algorithms are increasingly being applied using object-based image analysis (OBIA) technology to automate the process. The purpose of this paper is to present an overview and demonstrate the application of OBIA for delineating land cover features at multiple scales using a high resolution aerial photograph (1 m) and a medium resolution Landsat image (30 m) time series in the context of a pesticide spray drift exposure application.

[1]  Fionn Murtagh,et al.  Digital change detection with the aid of multiresolution wavelet analysis , 2001 .

[2]  Beate Ritz,et al.  Historical pesticide exposure in California using pesticide use reports and land-use surveys: an assessment of misclassification error and bias. , 2003, Environmental health perspectives.

[3]  Giuseppe Zanin,et al.  Role of hedgerows in intercepting spray drift: Evaluation and modelling of the effects , 2008 .

[4]  A. Rango,et al.  Combining Decision Trees with Hierarchical Object-oriented Image Analysis for Mapping Arid Rangelands , 2007 .

[5]  C. V. D. Sande,et al.  A segmentation and classification approach of IKONOS-2 imagery for land cover mapping to assist flood risk and flood damage assessment , 2003 .

[6]  N. Sotherton,et al.  Measurements of pesticide spray drift deposition into field boundaries and hedgerows: 2. Autumn applications , 1997 .

[7]  A. Olshan,et al.  Pesticides and childhood cancers. , 1997, Environmental health perspectives.

[8]  J. Briggs,et al.  An Object-oriented Approach to Urban Forest Mapping in Phoenix , 2007 .

[9]  R. Kettig,et al.  Classification of Multispectral Image Data by Extraction and Classification of Homogeneous Objects , 1976, IEEE Transactions on Geoscience Electronics.

[10]  Neural Tube Defects and Maternal Residential Proximity to Agricultural Pesticide Applications , 2006 .

[11]  Randolph H. Wynne,et al.  Comparison of segment and pixel-based non-parametric land cover classification in the brazilian amazon using multitemporal landsat TM/ETM+ imagery , 2007 .

[12]  Vincent Herbreteau,et al.  Thirty years of use and improvement of remote sensing, applied to epidemiology: from early promises to lasting frustration. , 2007, Health & place.

[13]  L. Kolonel,et al.  Mammographic density and breast cancer risk: the multiethnic cohort study. , 2005, American journal of epidemiology.

[14]  S. Weichenthal,et al.  Pesticides and Childhood Cancer: An Update of Zahm and Ward's 1998 Review , 2007, Journal of toxicology and environmental health. Part B, Critical reviews.

[15]  J. Freudenheim,et al.  Pesticide exposure and risk of breast cancer: a nested case-control study of residentially stable women living on Long Island. , 2004, Environmental research.

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

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

[18]  Pierre Goovaerts,et al.  Use of land surface remotely sensed satellite and airborne data for environmental exposure assessment in cancer research , 2010, Journal of Exposure Science and Environmental Epidemiology.

[19]  G. L. Schmidt,et al.  A multi‐scale segmentation approach to filling gaps in Landsat ETM+ SLC‐off images , 2007 .

[20]  S. Melly,et al.  Using GIS and historical records to reconstruct residential exposure to large-scale pesticide application , 2002, Journal of Exposure Analysis and Environmental Epidemiology.

[21]  A. G. Lane,et al.  The effect of plant structure on the drift of pesticides at field boundaries. , 2000 .

[22]  R. Platt,et al.  An Evaluation of an Object-Oriented Paradigm for Land Use/Land Cover Classification , 2008 .

[23]  Joanne S. Colt,et al.  Proximity to Crops and Residential Exposure to Agricultural Herbicides in Iowa , 2006, Environmental health perspectives.

[24]  D. Al-Khudhairy,et al.  Structural Damage Assessments from Ikonos Data Using Change Detection, Object-Oriented Segmentation, and Classification Techniques , 2005 .

[25]  C. Field,et al.  Relationships Between NDVI, Canopy Structure, and Photosynthesis in Three Californian Vegetation Types , 1995 .

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

[27]  Beate Ritz,et al.  Parkinson's disease and residential exposure to maneb and paraquat from agricultural applications in the central valley of California. , 2009, American journal of epidemiology.

[28]  A. Hursthouse,et al.  Transport and dynamics of toxic pollutants in the natural environment and their effect on human health: research gaps and challenge , 2009, Environmental geochemistry and health.

[29]  S. Carozza,et al.  Agricultural pesticides and risk of childhood cancers. , 2009, International journal of hygiene and environmental health.

[30]  Ryan S. Miller,et al.  Linkage of the California Pesticide Use Reporting Database with Spatial Land Use Data for Exposure Assessment , 2007, Environmental health perspectives.

[31]  B. Markham,et al.  Summary of Current Radiometric Calibration Coefficients for Landsat MSS, TM, ETM+, and EO-1 ALI Sensors , 2009 .

[32]  S. Dobrowski,et al.  Mapping mountain vegetation using species distribution modeling, image-based texture analysis, and object-based classification , 2008 .

[33]  J. R. Jensen.  Remote Sensing of the Environment: An Earth Resource Perspective , 2000 .

[34]  Stanisław Lewiński.  Object-oriented classification of Landsat ETM+ satellite image , 2006 .

[35]  D. Flanders,et al.  Preliminary evaluation of eCognition object-based software for cut block delineation and feature extraction , 2003 .

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