Field-oriented assessment of agricultural crops through temporal segmentation of MODIS VI data

Monitoring agricultural crops constitutes a vital task for the general understanding of land use spatio-temporal dynamics. This paper presents an approach for the enhancement of current crop monitoring capabilities on a regional scale, in order to allow for the analysis of environmental and socio-economic drivers and impacts of agricultural land use. This work discusses the advantages and current limitations of using 250m VI data from the Moderate Resolution Imaging Spectroradiometer (MODIS) for this purpose, with emphasis in the difficulty of correctly analyzing pixels whose temporal responses are disturbed due to certain sources of interference such as mixed or heterogeneous land cover. It is shown that the influence of noisy or disturbed pixels can be minimized, and a much more consistent and useful result can be attained, if individual agricultural fields are identified and each field's pixels are analyzed in a collective manner. As such, a method is proposed that makes use of image segmentation techniques based on MODIS temporal information in order to identify portions of the study area that agree with actual agricultural field borders. The pixels of each portion or segment are then analyzed individually in order to estimate the reliability of the temporal signal observed and the consequent relevance of any estimation of land use from that data. The proposed method was applied in the state of Mato Grosso, in mid-western Brazil, where extensive ground truth data was available. Experiments were carried out using several supervised classification algorithms as well as different subsets of land cover classes, in order to test the methodology in a comprehensive way. Results show that the proposed method is capable of consistently improving classification results not only in terms of overall accuracy but also qualitatively by allowing a better understanding of the land use patterns detected. It thus provides a practical and straightforward procedure for enhancing crop-mapping capabilities using temporal series of moderate resolution remote sensing data.

[1]  David G. Stork,et al.  Pattern Classification , 1973 .

[2]  John C. Platt,et al.  Fast training of support vector machines using sequential minimal optimization, advances in kernel methods , 1999 .

[3]  Gilberto Câmara,et al.  Spring: integrating remote sensing and gis by object-oriented data modelling , 1996, Comput. Graph..

[4]  Guaraci J. Erthal,et al.  Satellite Imagery Segmentation: a region growing approach , 1996 .

[5]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[6]  B. Wardlow,et al.  Analysis of time-series MODIS 250 m vegetation index data for crop classification in the U.S. Central Great Plains , 2007 .

[7]  Josef Strobl,et al.  What’s wrong with pixels? Some recent developments interfacing remote sensing and GIS , 2001 .

[8]  Isabelle Herlin,et al.  Regional scale land use/land cover classification using temporal series of MODIS data at the High Taquari Basin, MS, Brazil , 2005 .

[9]  J. Ross Quinlan,et al.  C4.5: Programs for Machine Learning , 1992 .

[10]  David G. Stork,et al.  Pattern Classification (2nd ed.) , 1999 .

[11]  A. Savitzky,et al.  Smoothing and Differentiation of Data by Simplified Least Squares Procedures. , 1964 .

[12]  R. DeFries,et al.  Cropland expansion changes deforestation dynamics in the southern Brazilian Amazon , 2006, Proceedings of the National Academy of Sciences.

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

[14]  P. C. Doraiswamy,et al.  Crop classification in the U.S. Corn Belt using MODIS imagery , 2007, 2007 IEEE International Geoscience and Remote Sensing Symposium.

[15]  Shigeo Abe DrEng.  Pattern Classification , 2001, Springer London.

[16]  James C. Tilton,et al.  Interactive analysis of hierarchical image segmentation , 2000, IGARSS 2000. IEEE 2000 International Geoscience and Remote Sensing Symposium. Taking the Pulse of the Planet: The Role of Remote Sensing in Managing the Environment. Proceedings (Cat. No.00CH37120).

[17]  V. Dubreuil,et al.  Detecting outliers and asserting consistency in agriculture ground truth information by using temporal vi data from MODIS , 2008 .

[18]  M. Neubert,et al.  A COMPARISON OF SEGMENTATION PROGRAMS FOR HIGH RESOLUTION REMOTE SENSING DATA , 2004 .

[19]  Yosio Edemir Shimabukuro,et al.  Deforestation detection in Brazilian Amazon region in a near real time using Terra MODIS daily data , 2004, IGARSS 2004. 2004 IEEE International Geoscience and Remote Sensing Symposium.

[20]  A. Strahler.  MODIS Land Cover Product Algorithm Theoretical Basis Document (ATBD) Version 5.0 , 1994 .

[21]  Egidio Arai,et al.  Cover: Multitemporal fraction images derived from Terra MODIS data for analysing land cover change over the Amazon region , 2005 .