Using image texture to map farmland field size: a case study in Eastern Europe

Eastern Europe provides unique opportunities to study changes in land use patterns, because much farmland became parcelized in the post-socialist period (i.e. large fields were broken up into smaller ones). Classification-based remote sensing approaches, however, do not capture such land cover modifications and new approaches based on continuous indicators are needed. Our goal is to develop a novel method to map farmland field size based on image texture. We fitted linear regression models to relate field size to Landsat-based image texture for a study area in the border region of Poland, Slovakia and Ukraine. Texture explained up to 93% of the variability in field size. Our field size map revealed marked differences among countries and these differences appear to be related to socialist land-ownership patterns and post-socialist land reform strategies. Image texture has great potential for mapping land use patterns and may contribute to a better understanding of land cover modifications in Eastern Europe and elsewhere.

[1]  D. Munroe,et al.  Changing Rural Landscapes in Albania: Cropland Abandonment and Forest Clearing in the Postsocialist Transition , 2008 .

[2]  B. Turner,et al.  Correction for Turner et al., Land Change Science Special Feature: The emergence of land change science for global environmental change and sustainability , 2008, Proceedings of the National Academy of Sciences.

[3]  P. Hostert,et al.  Post-socialist forest disturbance in the Carpathian border region of Poland, Slovakia, and Ukraine. , 2007, Ecological applications : a publication of the Ecological Society of America.

[4]  Patricia Balvanera,et al.  The future of production systems in a globalized world , 2007 .

[5]  R. DeFries,et al.  Amazonia revealed: forest degradation and loss of ecosystem goods and services in the Amazon Basin , 2007 .

[6]  Volker C. Radeloff,et al.  High-resolution image texture as a predictor of bird species richness , 2006 .

[7]  Peter H. Verburg,et al.  Simulating feedbacks in land use and land cover change models , 2006, Landscape Ecology.

[8]  Patrick Hostert,et al.  Cross-border comparison of land cover and landscape pattern in Eastern Europe using a hybrid classification technique , 2006 .

[9]  C. Woodcock,et al.  Resolution dependent errors in remote sensing of cultivated areas , 2006 .

[10]  M. Keller,et al.  Selective Logging in the Brazilian Amazon , 2005, Science.

[11]  M. Turner Landscape ecology in North America: past, present, and future , 2005 .

[12]  S. Carpenter,et al.  Global Consequences of Land Use , 2005, Science.

[13]  Luisa J. Elliott,et al.  METHODOLOGICAL INSIGHTS: The role of satellite image‐processing for national‐scale estimates of gene flow from genetically modified crops: rapeseed in the UK as a model , 2004 .

[14]  Christopher Small,et al.  The Landsat ETM+ spectral mixing space , 2004 .

[15]  Craig A. Coburn,et al.  A multiscale texture analysis procedure for improved forest stand classification , 2004 .

[16]  Peter M. Atkinson,et al.  A comparison of texture measures for the per-field classification of Mediterranean land cover , 2004 .

[17]  Ronald R Rindfuss,et al.  Developing a science of land change: challenges and methodological issues. , 2004, Proceedings of the National Academy of Sciences of the United States of America.

[18]  I. Penov The Use of Irrigation Water in Bulgaria’s Plovdiv Region During Transition , 2004, Environmental management.

[19]  Maciej Augustyn,et al.  Anthropogenic changes in the environmental parameters of Bieszczady Mountains , 2004 .

[20]  W. Cohen,et al.  Landsat's Role in Ecological Applications of Remote Sensing , 2004 .

[21]  Pol Coppin,et al.  Review ArticleDigital change detection methods in ecosystem monitoring: a review , 2004 .

[22]  H. Nagendra,et al.  Land cover change and landscape fragmentation—comparing the utility of continuous and discrete analyses for a western Honduras region , 2004 .

[23]  Anders Wästfelt,et al.  Assessing Village Authenticity with Satellite Images: A Method to Identify Intact Cultural Landscapes in Europe , 2003, Ambio.

[24]  Gregory P. Asner,et al.  Scale dependence of biophysical structure in deforested areas bordering the Tapajós National Forest, Central Amazon , 2003 .

[25]  Rob J. Dekker,et al.  Texture analysis and classification of ERS SAR images for map updating of urban areas in The Netherlands , 2003, IEEE Trans. Geosci. Remote. Sens..

[26]  C. Csáki,et al.  The Agricultural Sector of Slovakia on the Eve of EU Accession , 2003 .

[27]  van Terry Dijk Scenarios of Central European land fragmentation , 2003 .

[28]  T. Benton,et al.  Farmland biodiversity: is habitat heterogeneity the key? , 2003 .

[29]  Gerard Govers,et al.  Modelling sediment supply to rivers and reservoirs in Eastern Europe during and after the collectivisation period , 2003, Hydrobiologia.

[30]  K. Seto,et al.  Modeling the Drivers of Urban Land Use Change in the Pearl River Delta, China: Integrating Remote Sensing with Socioeconomic Data , 2003, Land Economics.

[31]  R. Sabates‐Wheeler Consolidation initiatives after land reform: responses to multiple dimensions of land fragmentation in Eastern European agriculture , 2002 .

[32]  Mark Berman,et al.  Segmenting multispectral Landsat TM images into field units , 2002, IEEE Trans. Geosci. Remote. Sens..

[33]  M. Keller,et al.  Remote sensing of selective logging in Amazonia: Assessing limitations based on detailed field observations, Landsat ETM+, and textural analysis , 2002 .

[34]  E. Lambin,et al.  Proximate Causes and Underlying Driving Forces of Tropical Deforestation , 2002 .

[35]  Konstantinos Perakis,et al.  Assessment of crop damage using space remote sensing and GIS , 2002 .

[36]  S. Franklin,et al.  Supervised Classification of Multisource Satellite Image Spectral and Texture Data for Agricultural Crop Mapping in Buenos Aires Province, Argentina , 2001 .

[37]  J. W. Bruce,et al.  The causes of land-use and land-cover change: moving beyond the myths , 2001 .

[38]  Z. Lerman Agriculture in transition economies: from common heritage to divergence , 2001 .

[39]  Eric F. Lambin,et al.  Impact of Macroeconomic Change on Deforestation in South Cameroon: Integration of Household Survey and Remotely-Sensed Data , 2000 .

[40]  Peter M. Atkinson,et al.  The integration of spectral and textural information using neural networks for land cover mapping in the Mediterranean , 2000 .

[41]  Mario Chica-Olmo,et al.  Computing geostatistical image texture for remotely sensed data classification , 2000 .

[42]  J. Cihlar Land cover mapping of large areas from satellites: Status and research priorities , 2000 .

[43]  R. Hall,et al.  Incorporating texture into classification of forest species composition from airborne multispectral images , 2000 .

[44]  A. Trzeciak-Duval A decade of transition in central and eastern European agriculture , 1999 .

[45]  Cong Cao Red or Expert: Membership in the Chinese Academy of Sciences , 1999 .

[46]  Pablo Pacheco,et al.  The effects of structural adjustment on deforestation and forest degradation in lowland Bolivia , 1999 .

[47]  J. Swinnen,et al.  The Economics of Agricultural Decollectivization in East Central Europe and the Former Soviet Union , 1998, Economic Development and Cultural Change.

[48]  S. Franklin,et al.  Aerial Image Texture Information in the Estimation of Northern Deciduous and Mixed Wood Forest Leaf Area Index (LAI) , 1998 .

[49]  W. Parton,et al.  Agricultural intensification and ecosystem properties. , 1997, Science.

[50]  D. He,et al.  Evaluation of textural and multipolarization radar features for crop classification , 1995, IEEE Trans. Geosci. Remote. Sens..

[51]  Z. Lerman Land reform and farm restructuring in Ukraine , 1995 .

[52]  Andrea Baraldi,et al.  An investigation of the textural characteristics associated with gray level cooccurrence matrix statistical parameters , 1995, IEEE Transactions on Geoscience and Remote Sensing.

[53]  Peng Gong,et al.  A comparison of spatial feature extraction algorithms for land-use classification with SPOT HRV data , 1992 .

[54]  Alan J. Miller Subset Selection in Regression , 1991 .

[55]  P. Burman A comparative study of ordinary cross-validation, v-fold cross-validation and the repeated learning-testing methods , 1989 .

[56]  G. Schwarz Estimating the Dimension of a Model , 1978 .

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

[58]  Eric F. Lambin,et al.  Land-use and land-cover change : local processes and global impacts , 2010 .

[59]  Eric F. Lambin,et al.  Land-Use and Land-Cover Change , 2006 .

[60]  W. Steffen,et al.  Science Plan and Implementation Strategy , 2006 .

[61]  Elinor Ostrom,et al.  Seeing the forest and the trees : human-environment interactions in forest ecosystems , 2005 .

[62]  Gershon Feder,et al.  Evolving farm structures and land use patterns in former socialist countries , 2004 .

[63]  Paul J. Curran,et al.  Merging spectral and textural information for classifying remotely sensed images , 2004 .

[64]  D. Turnock Ecoregion-based conservation in the Carpathians and the land-use implications , 2002 .

[65]  Deborah J. Pain,et al.  The Common Agricultural Policy, EU enlargement and the conservation of Europe's farmland birds , 2002 .

[66]  Russell G. Congalton,et al.  Classification of multi-temporal SPOT-XS satellite data for mapping rice fields on a West African floodplain , 1998 .

[67]  Timothy N. Ash Land and agricultural reform in Ukraine , 1998 .

[68]  A. Bannari,et al.  Cartographie des zones urbaines a l'aide des images aeroportees MEIS-II , 1998 .

[69]  J. Swinnen,et al.  Agricultural privatisation, land reform and farm restructuring in Central and Eastern Europe: a comparative analysis. , 1997 .

[70]  A. Atkinson Subset Selection in Regression , 1992 .