An Object-Based Image Analysis Method for Monitoring Land Conversion by Artificial Sprawl Use of RapidEye and IRS Data

Abstract: In France, in the peri-urban context, urban sprawl dynamics are particularly strong with huge population growth as well as a land crisis. The increase and spreading of built-up areas from the city centre towards the periphery takes place to the detriment of natural and agricultural spaces. The conversion of land with agricultural potential is all the more worrying as it is usually irreversible. The French Ministry of Agriculture therefore needs reliable and repeatable spatial-temporal methods to locate and quantify loss of land at both local and national scales. The main objective of this study was to design a repeatable method to monitor land conversion characterized by artificial sprawl: (i) We used an object-based image analysis to extract artificial areas from satellite images; (ii) We built an artificial patch that consists of aggregating all the peripheral areas that characterize artificial areas. The “artificialized” patch concept is an innovative extension of the urban patch concept, but differs in the nature of its components and in the continuity distance applied; (iii) The diachronic analysis of artificial patch maps enables characterization of artificial sprawl. The method was applied at the scale of four departments (similar to provinces) along the coast of Languedoc-Roussillon, in the South of France, based on two satellite datasets, one acquired in 1996–1997 (Indian Remote Sensing) and the other in 2009 (RapidEye). In the four departments, we measured an increase in artificial areas of from 113,000 ha in 1997 to 133,000 ha in 2009,

[1]  S. Durrieu,et al.  Object-based image analysis for operational fine-scale regional mapping of land cover within river corridors from multispectral imagery and thematic data , 2012 .

[2]  Stefan W. Maier,et al.  Comparing object-based and pixel-based classifications for mapping savannas , 2011, Int. J. Appl. Earth Obs. Geoinformation.

[3]  Birgit Kleinschmit,et al.  An Object-Based Classification Approach for Mapping Migrant Housing in the Mega-Urban Area of the Pearl River Delta (China) , 2011, Remote. Sens..

[4]  F. Jarrige,et al.  Dynamiques urbaines et mutations des espaces agricoles en Languedoc-Roussillon (France) , 2010 .

[5]  F. Jarrige,et al.  Mutation du foncier agricole en frange urbaine : élaboration et mise à l'épreuve d'une politique de régulation territoriale , 2009 .

[6]  Laurent Durieux,et al.  A method for monitoring building construction in urban sprawl areas using object-based analysis of Spot 5 images and existing GIS data , 2008 .

[7]  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 .

[8]  B. Koch,et al.  Landscape structure assessment with image grey‐values and object‐based classification at three spatial resolutions , 2005 .

[9]  Wenzhong Shi,et al.  Quality assessment for geo‐spatial objects derived from remotely sensed data , 2005 .

[10]  C. Napoléone,et al.  Rente foncière et anticipations dans le périurbain , 2005 .

[11]  R. Congalton.  Putting the Map Back in Map Accuracy Assessment , 2004 .

[12]  Klaus Steinnocher,et al.  Object-oriented land cover classification of panchromatic KOMPSAT-1 and SPOT-5 data , 2003, IGARSS 2003. 2003 IEEE International Geoscience and Remote Sensing Symposium. Proceedings (IEEE Cat. No.03CH37477).

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

[14]  M. Dobson,et al.  Contribution of space remote sensing to river studies , 1993 .

[15]  Russell G. Congalton,et al.  A review of assessing the accuracy of classifications of remotely sensed data , 1991 .

[16]  Xinhua Zhuang,et al.  Image Analysis Using Mathematical Morphology , 1987, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[17]  Darrel L. Williams,et al.  The effects of spatial resolution on the classification of Thematic Mapper data , 1985 .

[18]  Corinne Lampin-Maillet,et al.  Mapping wildland-urban interfaces at large scales integrating housing density and vegetation aggregation for fire prevention in the South of France. , 2010, Journal of environmental management.

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

[20]  M. Marie,et al.  DÉMÉTER : une démarche originale pour maîtriser la consommation du foncier agricole liée à l'étalement urbain. , 2010 .

[21]  Philippe Pointereau,et al.  Abandon et artificialisation des terres agricoles. , 2009 .

[22]  M. Grenier,et al.  ACCURACY ASSESSMENT METHOD FOR WETLAND OBJECT-BASED CLASSIFICATION , 2008 .

[23]  P. Hofmann,et al.  Detecting informal settlements from QuickBird data in Rio de Janeiro using an object based approach , 2008 .

[24]  Stefan Lang,et al.  Object Fate Analysis – a Virtual Overlay Method for the Categorisation of Object Transition and Object-based Accuracy Assessment , 2006 .

[25]  Dirk Tiede,et al.  Supervised and forest type-specific multi-scale segmentation for a one-level-representation of single trees , 2006 .

[26]  A. Marangoz,et al.  COMPARISON OF PIXEL-BASED AND OBJECT-ORIENTED CLASSIFICATION APPROACHES USING LANDSAT-7 ETM SPECTRAL BANDS , 2004 .

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

[28]  Russell G. Congalton,et al.  Classifying and Mapping Forest Cover Types Using Ikonos Imagery in the Northeastern United States , 2002 .

[29]  Dennis D. Truax,et al.  COMPARING SPECTRAL AND OBJECT BASED APPROACHES FOR CLASSIFICATION AND TRANSPORTATION FEATURE EXTRACTION FROM HIGH RESOLUTION MULTISPECTRAL IMAGERY , 2002 .

[30]  G. Willhauck,et al.  Comparison of object oriented classification techniques and standard image analysis for the use of change detection between SPOT multispectral satellite images and aerial photos. , 2000 .

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

[32]  Limin Yang,et al.  Accuracy assessment for the U.S. Geological Survey Regional Land-Cover Mapping Program: New York and New Jersey Region , 2000 .

[33]  R. M. Hoffer,et al.  Computer-based classification accuracy due to the spatial resolution using per-point versus per-field classification techniques. [for photomapping , 1981 .