| dc.description.abstract | Rice is globally one of the most important economic and food crops. It is the main source of employment and income for rural people in many countries worldwide. Yearly estimation of rice growing areas and delineation of spatial distribution of rice crops are needed to devise agricultural economic plans and to ensure security of food supply. The main objective of this study is to develop approaches for mapping rice-cropping systems at sub-national and regional scales by using multi-temporal satellite data. Three case studies were carried out under different rice growing conditions. The objective of the first case study was to develop a phenology-based approach for rice crop mapping in the upper Mekong River Delta (MRD) region in South Vietnam using Moderate Resolution Imaging Spectroradiometer (MODIS). The second case study was to use time-series MODIS−SPOT fusion data for rice crop mapping using the phenology-based method. In the third case study, an approach was developed to map complex rice cropping systems in Low Mekong Countries (LMC), including Vietnam, Lao, Thailand and Cambodia, from the time-series MODIS data using artificial neural networks (ANN).
The data processing was basically carried out through four main steps: (1) data preprocessing to account for geometric and radiometric errors, and to use MODIS data with Satellite Pour l′Observation de la Terre (SPOT) data using the spatial-temporal adaptive reflectance fusion model (STARFM) for the case study in Taiwan; (2) construction of the smooth time-series vegetation indices (VIs) using the empirical mode decomposition (EMD) and wavelet transform (WT),respectively regarding to the scale of study area and the application of the classifier; (3) rice crop classification using phenology-based algorithm and ANN due to availability of detail crop calendar, and (4) accuracy assessment of the mapping results using ground reference data and rice area statistics provided by the government.
The research findings confirmed that EMD and WT algorithms were efficient at filtering out noise from the time-series VI MODIS data. The mapping results compared with the ground reference data indicated the validity of adapting strategy, including data preprocessing, noise filtering and classification methods, regarding to the availability of detailed regional crop calendar for rice crop mapping at the subnational and regional scales. The results achieved for the first case study in upper MRD, in which has larger and homogeneous rice fields, using the phenology-based approach revealed the overall accuracy and Kappa coefficient value of 93.8% and 0.90, respectively. A close agreement between the MODIS-based rice areas and the district-level rice area statistics was observed (R2 = 0.91), reaffirming the effectiveness of this approach for automatically delineating rice-cropping systems in the study region.
The phenology-based approach was applied to the time-series MODIS−SPOT fusion data to delineate small-scale rice fields in Taiwan. The results indicated a close correlation between the mapping results and the government rice statistics (the R2 and RMSE for the first and second crops, were 0.98 and 115.7 ha. and 0.90 and 284.39 ha. respectively). The overall accuracies and Kappa coefficients achieved for the first and second crops were 89.6% and 0.79, and 83.2% and 0.66, respectively. The ANN applied to the filtered MODIS VI data to map complex rice cropping systems in LMC showed the overall accuracy and Kappa coefficient of 84.9% and 0.8, respectively. The comparison results between MODIS-derived rice area and rice area statistics at the provincial level also reaffirmed by the validity of ANN algorithm (R2 = 0.91).
The accuracy level of the mapping results was lowered by some error sources including data preprocessing, mixed-pixel problems and resolution bias between the mapping results and the ground reference data constructed using high-resolution satellite data or aerial photos. The results, however, achieved from this study could provide quantitative information on rice cropping systems that may be useful for agronomic planners to devise strategies for rice crop management to enhance national food security and rice grain exports. Such approaches were thus proposed for monitoring rice cropping activities in the study regions and other places worldwide.
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