An Object-Based Paddy Rice Classification Using Multi-Spectral Data and Crop Phenology in Assam, Northeast India
Rice is the staple food for half of the world’s population. Therefore, accurate information of rice area is vital for food security. This study investigates the effect of phenology for rice mapping using an object-based image analysis (OBIA) approach. Crop phenology is combined with high spatial res...
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doaj-7c8e92c0624e4597a5b55f11029b27ea2020-11-24T22:26:36ZengMDPI AGRemote Sensing2072-42922016-06-018647910.3390/rs8060479rs8060479An Object-Based Paddy Rice Classification Using Multi-Spectral Data and Crop Phenology in Assam, Northeast IndiaMrinal Singha0Bingfang Wu1Miao Zhang2University of Chinese Academy of Sciences, Beijing 100049, ChinaDivision for Digital Agriculture, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Olympic Village Science Park, West Beichen Road, Chaoyang District, Beijing 100101, ChinaDivision for Digital Agriculture, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Olympic Village Science Park, West Beichen Road, Chaoyang District, Beijing 100101, ChinaRice is the staple food for half of the world’s population. Therefore, accurate information of rice area is vital for food security. This study investigates the effect of phenology for rice mapping using an object-based image analysis (OBIA) approach. Crop phenology is combined with high spatial resolution multispectral data to accurately classify the rice. Phenology was used to capture the seasonal dynamics of the crops, while multispectral data provided the spatial variation patterns. Phenology was extracted from MODIS NDVI time series, and the distribution of rice was mapped from China’s Environmental Satellite (HJ-1A/B) data. Classification results were evaluated by a confusion matrix using 100 sample points. The overall accuracy of the resulting map of rice area generated by both spectral and phenology is 93%. The results indicate that the use of phenology improved the overall classification accuracy from 2%–4%. The comparison between the estimated rice areas and the State’s statistics shows underestimated values with a percentage difference of −34.53%. The results highlight the potential of the combined use of crop phenology and multispectral satellite data for accurate rice classification in a large area.http://www.mdpi.com/2072-4292/8/6/479object-based image analysisphenologydata fusionpaddy riceclassification |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Mrinal Singha Bingfang Wu Miao Zhang |
spellingShingle |
Mrinal Singha Bingfang Wu Miao Zhang An Object-Based Paddy Rice Classification Using Multi-Spectral Data and Crop Phenology in Assam, Northeast India Remote Sensing object-based image analysis phenology data fusion paddy rice classification |
author_facet |
Mrinal Singha Bingfang Wu Miao Zhang |
author_sort |
Mrinal Singha |
title |
An Object-Based Paddy Rice Classification Using Multi-Spectral Data and Crop Phenology in Assam, Northeast India |
title_short |
An Object-Based Paddy Rice Classification Using Multi-Spectral Data and Crop Phenology in Assam, Northeast India |
title_full |
An Object-Based Paddy Rice Classification Using Multi-Spectral Data and Crop Phenology in Assam, Northeast India |
title_fullStr |
An Object-Based Paddy Rice Classification Using Multi-Spectral Data and Crop Phenology in Assam, Northeast India |
title_full_unstemmed |
An Object-Based Paddy Rice Classification Using Multi-Spectral Data and Crop Phenology in Assam, Northeast India |
title_sort |
object-based paddy rice classification using multi-spectral data and crop phenology in assam, northeast india |
publisher |
MDPI AG |
series |
Remote Sensing |
issn |
2072-4292 |
publishDate |
2016-06-01 |
description |
Rice is the staple food for half of the world’s population. Therefore, accurate information of rice area is vital for food security. This study investigates the effect of phenology for rice mapping using an object-based image analysis (OBIA) approach. Crop phenology is combined with high spatial resolution multispectral data to accurately classify the rice. Phenology was used to capture the seasonal dynamics of the crops, while multispectral data provided the spatial variation patterns. Phenology was extracted from MODIS NDVI time series, and the distribution of rice was mapped from China’s Environmental Satellite (HJ-1A/B) data. Classification results were evaluated by a confusion matrix using 100 sample points. The overall accuracy of the resulting map of rice area generated by both spectral and phenology is 93%. The results indicate that the use of phenology improved the overall classification accuracy from 2%–4%. The comparison between the estimated rice areas and the State’s statistics shows underestimated values with a percentage difference of −34.53%. The results highlight the potential of the combined use of crop phenology and multispectral satellite data for accurate rice classification in a large area. |
topic |
object-based image analysis phenology data fusion paddy rice classification |
url |
http://www.mdpi.com/2072-4292/8/6/479 |
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