Discriminating different landuse types by using multitemporal NDXI in a rice planting area

Research has shown that the use of multitemporal images could obtain better classification over single date images and that seasonal variation of the normalized difference vegetation index (NDVI) could help improve classification accuracy. On consideration of different crop phenology and the seasonal changing characteristics of vegetation, water and bare soil indices from the Landsat Thematic Mapper (TM) images, the present paper uses multitemporal NDXI (NDVI, normalized difference water index (NDWI) and normalized difference soil index (NDSI)) to discriminate different landuse types in a rice planting area. From a comparison of the overall accuracy, Kappa coefficient and producer and user accuracies of different approaches, it is found that the NDXI approach is superior to the traditional classification that uses the original un-transformed images in the discrimination of different landuse types and agricultural land types in the rice planting area. The approach is expected to be more applicable to multitemporal Moderate Resolution Imaging Spectroradiometer (MODIS) images and to be used in discriminating different cropping systems in paddy areas.

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

[2]  Tim R. McVicar,et al.  Determining temporal windows for crop discrimination with remote sensing: a case study in south-eastern Australia , 2004 .

[3]  Y. Hirosawa,et al.  Application of standardized principal component analysis to land-cover characterization using multitemporal AVHRR data , 1996 .

[4]  T. Santos,et al.  SATSTAT: Exploratory Analysis of Envisat-MERIS Data for Land Cover Mapping of Portugal in 2003 , 2005 .

[5]  Jennifer L. Dungan,et al.  Seasonal LAI in slash pine estimated with landsat TM , 1992 .

[6]  B. Csatho,et al.  Knowledge discovery in urban environments from fused multi-dimensional imagery , 2007, 2007 Urban Remote Sensing Joint Event.

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

[8]  S. Liang,et al.  Calculating environmental moisture for per-field discrimination of rice crops , 2003 .

[9]  P. Chavez.  An improved dark-object subtraction technique for atmospheric scattering correction of multispectral data , 1988 .

[10]  William Salas,et al.  Fourier analysis of multi-temporal AVHRR data applied to a land cover classification , 1994 .

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

[12]  C. Tucker,et al.  Satellite remote sensing of primary production , 1986 .

[13]  Tammo S. Steenhuis,et al.  Unsupervised classification of saturated areas using a time series of remotely sensed images , 2007 .

[14]  John A. Silander,et al.  Delineating forest canopy species in the northeastern united states using multi-temporal TM imagery , 1998 .

[15]  Guofan Shao,et al.  Optimizing unsupervised classifications of remotely sensed imagery with a data-assisted labeling approach , 2008, Comput. Geosci..

[16]  James S. Kagan,et al.  Map-Guided Classification of Regional Land Cover with Multi-Temporal AVHRR Data , 1998 .

[17]  C. Tucker.  Red and photographic infrared linear combinations for monitoring vegetation , 1979 .

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

[19]  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 .

[20]  Heather M. Cheshire,et al.  A comparison of single date and multitemporal satellite image classifications in a semi-arid grassland , 2001 .

[21]  Jeffrey W. Hollister,et al.  Assessing the Accuracy of National Land Cover Dataset Area Estimates at Multiple Spatial Extents , 2004 .

[22]  J. Scepan,et al.  Thematic validation of high-resolution Global Land-Cover Data sets , 1999 .

[23]  K. Price,et al.  Grasslands discriminant analysis using Landsat TM single and multitemporal data , 2003 .

[24]  A. Rogers,et al.  Reducing signature variability in unmixing coastal marsh Thematic Mapper scenes using spectral indices , 2004 .

[25]  A. Huete,et al.  A review of vegetation indices , 1995 .