Using variance analysis of multitemporal MODIS images for rice field mapping in Bali Province, Indonesia

Existing methods for rice field classification have some limitations due to the large variety of land covers attributed to rice fields. This study used temporal variance analysis of daily Moderate Resolution Imaging Spectroradiometer (MODIS) satellite images to discriminate rice fields from other land uses. The classification result was then compared with the reference data. Regression analysis showed that regency and district comparisons produced coefficients of determination (R 2) of 0.97490 and 0.92298, whereas the root mean square errors (RMSEs) were 1570.70 and 551.36 ha, respectively. The overall accuracy of the method in this study was 87.91%, with commission and omission errors of 35.45% and 17.68%, respectively. Kappa analysis showed strong agreement between the results of the analysis of the MODIS data using the method developed in this study and the reference data, with a kappa coefficient value of 0.8371. The results of this study indicated that the algorithm for variance analysis of multitemporal MODIS images could potentially be applied for rice field mapping.

[1]  Ping Chen,et al.  Application of multitemporal ERS-2 synthetic aperture radar in delineating rice cropping systems in the Mekong River Delta, Vietnam , 1998, IEEE Trans. Geosci. Remote. Sens..

[2]  Alfredo Huete,et al.  Assessment of vegetation and soil water regimes in partial canopies with optical remotely sensed data. , 1990 .

[3]  Russell G. Congalton,et al.  Assessing the accuracy of remotely sensed data : principles and practices , 1998 .

[4]  Hongling Fang,et al.  Rice crop area estimation of an administrative division in China using remote sensing data , 1998 .

[5]  M. Boschetti,et al.  Multi-year monitoring of rice crop phenology through time series analysis of MODIS images , 2009 .

[6]  Alexandre Bouvet,et al.  Monitoring of the Rice Cropping System in the Mekong Delta Using ENVISAT/ASAR Dual Polarization Data , 2009, IEEE Transactions on Geoscience and Remote Sensing.

[7]  Changsheng Li,et al.  Quantitative relationships between field-measured leaf area index and vegetation index derived from VEGETATION images for paddy rice fields , 2002 .

[8]  K. Okamoto Estimation of rice-planted area in the tropical zone using a combination of optical and microwave satellite sensor data , 1999 .

[9]  R. G. Oderwald,et al.  Assessing Landsat classification accuracy using discrete multivariate analysis statistical techniques. , 1983 .

[10]  R. K. Gupta,et al.  Comparative study of AVHRR ratio vegetation index and normalized difference vegetation index in district level agricultural monitoring , 1993 .

[11]  S.C. Liew,et al.  Application of multitemporal ERS synthetic aperture radar in delineating rice cropping systems in the Mekong river delta , 1997, IGARSS'97. 1997 IEEE International Geoscience and Remote Sensing Symposium Proceedings. Remote Sensing - A Scientific Vision for Sustainable Development.

[12]  John R. Jensen Introductory Digital Image Processing , 2004 .

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

[14]  Tim R. McVicar,et al.  Remote Sensing Of Rice-Based Irrigated Agriculture: A Review , 2005 .

[15]  H. Fang,et al.  Using NOAA AVHRR and landsat TM to estimate rice area year-by-year , 1998 .

[16]  Thuy Le Toan,et al.  Rice crop mapping and monitoring using ERS-1 data based on experiment and modeling results , 1997, IEEE Trans. Geosci. Remote. Sens..

[17]  Cheng-Chien Liu,et al.  Using FORMOSAT-2 Satellite Data to Estimate Leaf Area Index of Rice Crop , 2008 .

[18]  A. Huete,et al.  Assessment of biophysical soil properties through spectral decomposition techniques , 1991 .

[19]  J. Knox,et al.  Using a Crop/Soil Simulation Model and GIS Techniques to Assess Methane Emissions from Rice Fields in Asia. IV. Upscaling to National Levels , 2000, Nutrient Cycling in Agroecosystems.

[20]  A. Huete,et al.  Overview of the radiometric and biophysical performance of the MODIS vegetation indices , 2002 .

[21]  F. Baret,et al.  Potentials and limits of vegetation indices for LAI and APAR assessment , 1991 .

[22]  Dominique Bachelet,et al.  Rice paddy inventory in a few provinces of China using AVHRR data , 1995 .

[23]  Liu Chuang,et al.  Study on extraction of crop information using time-series MODIS data in the Chao Phraya Basin of Thailand , 2010 .

[24]  S. Frolking,et al.  Sensitivity of vegetation indices to atmospheric aerosols: Continental-scale observations in Northern Asia , 2003 .

[25]  Changsheng Li,et al.  Mapping paddy rice agriculture in southern China using multi-temporal MODIS images , 2005 .

[26]  G. Khush What it will take to Feed 5.0 Billion Rice consumers in 2030 , 2005, Plant Molecular Biology.

[27]  Satoshi Uchida,et al.  Discriminating different landuse types by using multitemporal NDXI in a rice planting area , 2010 .

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

[29]  A. Huete A soil-adjusted vegetation index (SAVI) , 1988 .

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

[31]  Philip Lewis,et al.  Geostatistical classification for remote sensing: an introduction , 2000 .