Application of Wavelet Transform for Paddy Area Classification Using MODIS NDVI Data Series (original) (raw)
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CIGR-AgEng 2012
The classification of land use in agriculture usually requires high resolution satellite, therefore the cost tends to be expensive and that is the barrier of remote sensing use in agriculture. The author Utilize the time frequency analysis to the NDVI data series observed by Moderate Resolution Imaging Spectroradiometer (MODIS), and then presents an automatic and cost free method for the classification of paddy fields in this paper. This research is conducted in Mekong Delta, in the southern part of Vietnam. 10-days composite images (250m spatial resolution) from the MODIS sensor onboard the NASA EOS Aqua and Terra satellite were used. The wavelet analysis is used as the method for time frequency analysis. The calculated wavelet powers of the waves of NDVI are used to get the characteristics of paddy area which corresponding to the rice cropping calendar. For example in the level -3 at 80 days period, the wavelet powers of paddy area become very high in contrast with other area because the rice cropping calendar is approximately 90 days. After getting features of paddy field Linear Discriminant Analysis (LDA) is assess for automatic land use classification using statistical NDVI data series and calculated wavelet powers. The result shows that the combination of wavelet power and statistic of NDVI data for discriminant analysis work well for the classification even a low resolution satellite images were used. Furthermore by using the wavelet analysis it is possible to distinguish whether the paddy field is single or multiple cropping by utilizing the value of wavelet powers in time.
Agriculture is one of the most important sectors in the economy of Southeast Asia countries, especially Thailand and Vietnam. These two countries have been the largest rice suppliers in the world and played a critical role in global food security. Yearly rice crop monitoring to provide policymakers with information on rice growing areas is thus important to timely devise plans to ensure food security. This study aimed to develop an approach for regional mapping of cropping patterns from time-series MODIS data. Data were processed through three steps: (1) noise filtering of time-series MODIS NDVI data with wavelet transform, (2) image classification of cropping patterns using artificial neural networks (ANNs), and (3) classification accuracy assessment using ground reference data. The results by a comparison between classification map and ground reference data indicated the overall accuracy of 80.3% and Kappa coefficient of 0.76.
Remote Sensing, 2013
Rice crop monitoring is an important activity for crop management. This study aimed to develop a phenology-based classification approach for the assessment of rice cropping systems in Mekong Delta, Vietnam, using Moderate Resolution Imaging Spectroradiometer (MODIS) data. The data were processed from December 2000, to December 2012, using empirical mode decomposition (EMD) in three main steps: (1) data pre-processing to construct the smooth MODIS enhanced vegetation index (EVI) time-series data; (2) rice crop classification; and (3) accuracy assessment. The comparisons between the classification maps and the ground reference data indicated overall accuracies and Kappa coefficients, respectively, of 81.4% and 0.75 for 2002, 80.6% and 0.74 for 2006 and 85.5% and 0.81 for 2012. The results by comparisons between MODIS-derived rice area and rice area statistics were slightly overestimated, with a relative error in area (REA) from 0.9-15.9%. There was, however, a close correlation between the two datasets (R 2 ≥ 0.89). From 2001 to 2012, the areas of triple-cropped rice increased approximately 31.6%, while those of the single-cropped rain-fed rice, double-cropped irrigated rice and double-cropped rain-fed rice decreased roughly −5.0%, −19.2% and −7.4%, respectively. This study demonstrates the validity of such an approach for rice-crop monitoring with MODIS data and could be transferable to other regions.
International Journal of Electrical and Computer Engineering (IJECE), 2019
This paper presents the paddy field classification model using the approach based on periodic plant life cycle events and how these elevations in climate as well as habitat factors, such as elevation. The data used are MODIS-Terra two tiles of H28v09 and H29v09 of 2016, consist of 46 series of 8-daily data, with 500 meter resolution in Java region. The paddy field classification method based on the phenological model is done by Maximum Likelihood on the transformed annual multi-temporal image of the reflectance data, index data, and the combination of reflectance and index data. The results of the study showed that, with the reference of the Paddy Field Map from the Ministry of Agriculture (MoA), the overall accuracies of the paddy field classification results using the combination of reflectance and index data provide the highest (85.4%) among the reflectance data (83.5%) and index data (81.7%). The accuracy levels were varied; these depend on the slope and the types of paddy fields. Paddy fields on the slopes of 0-2% could be well identified by MODIS-Terra data, whereas it was difficult to identify the paddy fields on the slope >2%. Rain-fed lowland paddy field type has a lower user accuracy than irrigated paddy fields. This study also performed correlation (r2) between the analysis results and the statistical data based on district and provincial boundaries were >0.85 and >0.99 respectively. These correlations were much higher than the previous study results, which reached 0.49-0.65 (hilly-flat areas of county-level), and 0.80-0.88 (hilly-flat areas of provincial level) for China, and reached 0.44 for Indonesia.
Application of wavelet analysis to the multi-temporal MODIS data for detecting the rice phenology
aars.org
We examined the spatial pattern of rice phenology in the Red River Delta (RRD) by using time-series MODIS data in 2003. We applied the Wavelet based Filter for determining Crop Phenology (WFCP) to assess the seasonal change of EVI data reflecting from the crop status. Time courses of smoothed EVI derived by WFCP showed two peaks on April-March and August-September, indicating that double rice cropping is mainly being performed in RRD. It was also shown that there are winter-spring rice cropping areas in eastern RRD (Hai Duong province, Hai Phong province and Thai Binh province) where cultivation period is about two weeks earlier than the other provinces. Furthermore, spatially heterogeneous distribution of heading dates for rainy season rice was detected by the spatio-temporal EVI. MODIS EVI with WFCP enable us to grasp the geographical characteristics of agricultural activity in RRD i.e. spatial pattern of rice cropping system, phenology and its recent changing trend with high accuracy.
Classification and Mapping of Paddy Rice by Combining Landsat and SAR Time Series Data
Remote Sensing
Rice is an important food resource, and the demand for rice has increased as population has expanded. Therefore, accurate paddy rice classification and monitoring are necessary to identify and forecast rice production. Satellite data have been often used to produce paddy rice maps with more frequent update cycle (e.g., every year) than field surveys. Many satellite data, including both optical and SAR sensor data (e.g., Landsat, MODIS, and ALOS PALSAR), have been employed to classify paddy rice. In the present study, time series data from Landsat, RADARSAT-1, and ALOS PALSAR satellite sensors were synergistically used to classify paddy rice through machine learning approaches over two different climate regions (sites A and B). Six schemes considering the composition of various combinations of input data by sensor and collection date were evaluated. Scheme 6 that fused optical and SAR sensor time series data at the decision level yielded the highest accuracy (98.67% for site A and 93.87% for site B). Performance of paddy rice classification was better in site A than site B, which consists of heterogeneous land cover and has low data availability due to a high cloud cover rate. This study also proposed Paddy Rice Mapping Index (PMI) considering spectral and phenological characteristics of paddy rice. PMI represented well the spatial distribution of paddy rice in both regions. Google Earth Engine was adopted to produce paddy rice maps over larger areas using the proposed PMI-based approach.
Using temporal MODIS data to detect paddy rice in Red River Delta
2012
Information on the area and spatial distribution of paddy rice fields is needed for food security, management of water resources, and estimation of Methan emission as well. MODIS remote sensing data including visible bands, near infrared band and short wave infrared band is foundation of calculating vegetation indices such as NDVI, EVI and LSWI. These remote sensing indices are very sensitive and strongly correlative to physiological status of plant, they are useful means for detecting and mapping paddy rice. This paper focus on an algorithm that uses time series of these vegetation indices to identify paddy rice areas based on sensivity of LSWI to the increased surface moisture during the period of flooding and rice transplanting.
Remote Sensing, 2016
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.
2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 2016
Presently, there is a need to explore the possibility to maximize the use of MODIS (Moderate Resolution Imaging Spectroradiometer) data as it has very good spectral (36 bands) and temporal resolution whereas its spatial resolution is moderate i.e. 250m, 500m, and 1km. Because of its moderate spatial resolution, its application for land cover classification is limited. Therefore, in this paper, an attempt has been made to enhance its spatial resolution and utilize the information contained in the different bands together to achieve good land cover classification accuracy, so that, in future, MODIS data can be used more effectively. For resolution enhancement, modified dual tree complex wavelet transform (DT-CWT) has been employed, where DT-CWT has been modified by critically analyzing the effect of weight factor of the DT-CWT coefficients on land cover classification. For this purpose, image statistics parameter like Mean of the image has also been considered. The proposed technique has been applied on the six bands of MODIS data which have spatial resolution of 500m. It is observed that weight factor of the high-frequency sub-bands is quite sensitive for computation of classification accuracy.
A crop phenology detection method using time-series MODIS data
Remote Sensing of Environment, 2005
Information of crop phenology is essential for evaluating crop productivity and crop management. Therefore we developed a new method for remotely determining phenological stages of paddy rice. The method consists of three procedures: (i) prescription of multitemporal MODIS/Terra data; (ii) filtering time-series Enhanced Vegetation Index (EVI) data by time-frequency analysis; and (iii) specifying the phenological stages by detecting the maximum point, minimal point and inflection point from the smoothed EVI time profile. Applying this method to MODIS data, we determined the planting date, heading date, harvesting date, and growing period in 2002. And we validated the performance of the method against statistical data in 30 paddy fields. As for the filtering, we adopted wavelet and Fourier transforms. Three types of mother wavelet (Daubechies, Symlet and Coiflet) were used in Wavelet transform. As the results of validation, the wavelet transform performed better than the Fourier transform. Specifically, the case using Coiflet (order = 4) gave remarkably good results in determining phenological stages and growing periods. The root mean square errors of the estimated phenological dates against the statistical data were: 12.1 days for planting date, 9.0 days for heading date, 10.6 days for harvesting date, and 11.0 days for growing period. The method using wavelet transform with Coiflet (order = 4) allows the determination of regional characteristics of rice phenology. We proposed this new method using the wavelet transform; Wavelet based Filter for determining Crop Phenology (WFCP).