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Dive into the research topics where Nguyen-Thanh Son is active.

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Featured researches published by Nguyen-Thanh Son.


International Journal of Applied Earth Observation and Geoinformation | 2012

Monitoring agricultural drought in the Lower Mekong Basin using MODIS NDVI and land surface temperature data

Nguyen-Thanh Son; Chi-Farn Chen; Cheng-Ru Chen; Ly-Yu Chang; Vo-Quang Minh

Abstract Drought is a complex natural phenomenon, and its impacts on agriculture are enormous. Drought has been a prevalent concern for farmers in the Lower Mekong Basin (LMB) over the last decades; thus, monitoring drought is important for water planning and management to mitigate impacts on agriculture in the region. This study explored the applicability of monthly MODIS normalized difference vegetation index (NDVI) and land surface temperature (LST) data for agricultural drought monitoring in LMB in the dry season from November 2001 to April 2010. The data were processed using the temperature vegetation dryness index (TVDI), calculated by parameterizing the relationship between the MODIS NDVI and LST data. The daily volumetric surface soil moisture from the Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E) and monthly precipitation from the Tropical Rainfall Measuring Mission (TRMM) were collected and used for verification of the results. In addition, we compared the efficiency of TVDI with a commonly used drought index, the crop water stress index (CWSI), derived from the MODIS LST alone. The results achieved from comparisons between TVDI and AMSR-E soil moisture data indicated acceptable correlations between the two datasets in most cases. There was close agreement between TVDI and TRMM precipitation data through the season, indicating that TVDI was sensitive to precipitation. The TVDI compared to CWSI also yielded close correlations between both datasets. The TVDI was, however, more sensitive to soil moisture stress than CWSI. The results archived by analysis of TVDI indicated that the moderate and severe droughts were spatially scattered over the region from November to March, but more extensive in northeast Thailand and Cambodia. The larger area of severe drought was especially observed for the 2003–2006 dry seasons compared to other years. The results achieved from this study could be important for drought warnings and irrigation scheduling.


IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing | 2015

Mangrove Mapping and Change Detection in Ca Mau Peninsula, Vietnam, Using Landsat Data and Object-Based Image Analysis

Nguyen-Thanh Son; Chi-Farn Chen; Ni-Bin Chang; Cheng-Ru Chen; Ly-Yu Chang; Bui Xuan Thanh

Mangrove forests provide important ecosystem goods and services for human society. Extensive coastal development in many developing countries has converted mangrove forests to other land uses without regard to their ecosystem service values; thus, the ecosystem state of mangrove forests is critical for officials to evaluate sustainable coastal management strategies. The objective of this study is to investigate the multidecadal change in mangrove forests in Ca Mau peninsula, South Vietnam, based on Landsat data from 1979 to 2013. The data were processed through four main steps: 1) data preprocessing; 2) image processing using the object-based image analysis (OBIA); 3) accuracy assessment; and 4) multitemporal change detection and spatial analysis of mangrove forests. The classification maps compared with the ground reference data showed the satisfactory agreement with the overall accuracy higher than 82%. From 1979 to 2013, the area of mangrove forests in the study region had decreased by 74%, mainly due to the boom of local aquaculture industry in the study region. Given that mangrove reforestation and afforestation only contributed about 13.2% during the last three decades, advanced mangrove management strategies are in an acute need for promoting environmental sustainability in the future.


Journal of remote sensing | 2011

Classification of rice cropping systems by empirical mode decomposition and linear mixture model for time-series MODIS 250 m NDVI data in the Mekong Delta, Vietnam

Chi-Farn Chen; Nguyen-Thanh Son; Ly-Yu Chang; Cheng-Ru Chen

Estimating the area of rice planting is vital for production prediction. This study utilizes time-series MODIS NDVI data from 2002 to 2007 to discriminate rice cropping systems in the Mekong Delta (MD), Vietnam. Data are processed using Empirical Mode Decomposition (EMD) and the Linear Mixture Model (LMM). Various spatial and non-spatial data are also collected for accuracy validation. The results indicate that EMD acts as a well-fitted filter for noise reduction of the time-series NDVI data. The classification results derived from the LMM for 2002 showed an overall classification accuracy of 71.6% and a Kappa coefficient of 0.6. The provincial level area estimates were strongly correlated with the rice statistics. An examination of the change in cropping patterns between 2002 and 2007 showed that 29.0% of the triple irrigated-rice cropping systems had been changed to double irrigated-rice cropping systems and that 12.0% and 9.0% of the double irrigated and rainfed-rice cropping systems, respectively, had been changed to triple rice cropping systems. These changes were verified by visual comparisons with Landsat images.


Journal of remote sensing | 2013

Prediction of rice crop yield using MODIS EVI−LAI data in the Mekong Delta, Vietnam

Nguyen-Thanh Son; Chi-Farn Chen; Cheng-Ru Chen; Ly-Yu Chang; H. N. Duc; L. D. Nguyen

Predicting rice crop yield at the regional scale is important for production estimates that ensure food security for a country. This study aimed to develop an approach for rice crop yield prediction in the Vietnamese Mekong Delta using the Moderate Resolution Imaging Spectroradiometer (MODIS) enhanced vegetation index (EVI) and leaf area index (LAI). Data processing consisted of four main steps: (1) constructing time-series vegetation indices, (2) noise filtering of time-series data using the empirical mode decomposition (EMD), (3) establishment of crop yield models, and (4) model validation. The results indicated that the quadratic model using two variables (EVI and LAI) produced more accurate results than other models (i.e. linear, interaction, pure quadratic, and quadratic with a single variable). The highest correlation coefficients obtained at the ripening period for the spring–winter and autumn–summer crops were 0.70 and 0.74, respectively. The robustness of the established models was evaluated by comparisons between the predicted yields and crop yield statistics for 10 sampling districts in 2006 and 2007. The comparisons revealed satisfactory results for both years, especially for the spring–winter crop. In 2006, the root mean squared error (RMSE), mean absolute error (MAE), and mean bias error (MBE) for the spring–winter crop were 10.18%, 8.44% and 0.9%, respectively, while the values for the autumn–summer crop were 17.65%, 14.06%, and 3.52%, respectively. In 2007, the spring–winter crop also yielded better results (RMSE = 10.56%, MAE = 9.14%, MBE = 3.68%) compared with the autumn–summer crop (RMSE = 17%, MAE = 12.69%, MBE = 2.31%). This study demonstrates the merit of using MODIS data for regional rice crop yield prediction in the Mekong Delta before the harvest period. The methods used in this study could be transferable to other regions around the world.


Giscience & Remote Sensing | 2012

Investigating Rice Cropping Practices and Growing Areas from MODIS Data Using Empirical Mode Decomposition and Support Vector Machines

Chi-Farn Chen; Cheng-Ru Chen; Nguyen-Thanh Son

This study investigated rice cropping practices and rice growing areas in the Vietnamese Mekong Delta using MODIS 250 × 250 m normalized difference vegetation index (NDVI) data acquired during the 2002 and 2007 rice cropping seasons. Data processing was conducted in five main steps: (1) constructing time-series MODIS NDVI data; (2) noise filtering of the time-series MODIS NDVI data using empirical mode decomposition (EMD); (3) extracting and evaluating phenological rice training patterns from the smooth time profiles of NDVI; (4) classifying rice cropping systems using support vector machines (SVMs); and (5) conducting an error analysis using ground reference data and government rice statistics. The results indicated that EMD was an efficient filter for noise removal in the time-series MODIS NDVI data. The filtered temporal NDVI profile characterized the distinct behaviors of the rice cropping systems. The estimated sowing and harvesting dates were compared with the field-survey data and indicated root mean square error (RMSE) values of 7.5 and 8.2 days, respectively. The comparison results between the 2002 classification map and the ground reference data indicated that the overall accuracy for the 2002 data was 92.9% with a Kappa coefficient of 0.89, while in 2007 these values were 93.8% and 0.90, respectively. At the district level, there was good agreement between the MODIS-based estimated areas and government rice statistics for 2002 and 2007 (R 2 ≥ 0.85). An investigation of changes in cropping practices from 2002 to 2007 showed that 12.9% of the area used for double-cropped irrigated rice in 2002 had been converted to triple-cropped irrigated rice by 2007, whereas 27.4% of the area used for triple-cropped irrigated rice in 2002 had been converted to double-cropped irrigated rice by 2007.


Journal of Applied Remote Sensing | 2011

Mapping double-cropped irrigated rice fields in Taiwan using time-series Satellite Pour I'Observation de la Terre data

Chi-Farn Chen; Su-Wei Huang; Nguyen-Thanh Son; Li-Yu Chang

Rice is the most important food crop in Taiwan. During recent decades, rice production in Taiwan has sharply declined because of industrialization and urbanization. Monitoring the areas of rice cultivation thus becomes important due to the official initiatives to ensure food supply. This study aims to develop a remote sensing classification approach for mapping double-cropped irrigated rice fields in Taiwan using time-series SPOT (Satellite Pour l’Observation de la Terre) data. Three study sites with different farming conditions in Taitung, Chiayi, and Taoyuan counties were chosen to test the new classification method. Data processing steps include: 1. filtering time-series SPOT-based normalized difference vegetation index (NDVI) using empirical mode decomposition (EMD) and wavelet transform, 2. classifying double-cropped irrigated rice fields using statistical methods (i.e., correlation analysis and sign-test statistics), and 3. assessing classification accuracy. The comparisons between the classification maps and ground-truth maps in 2005 indicated that classification using the EMD-based filtered NDVI time-series data yielded more accurate results than did the wavelet transform-based filtered data.


Journal of Applied Remote Sensing | 2011

Wavelet filtering of time-series moderate resolution imaging spectroradiometer data for rice crop mapping using support vector machines and maximum likelihood classifier

Chi-Farn Chen; Nguyen-Thanh Son; Cheng-Ru Chen; Ly-Yu Chang

Rice is the most important economic crop in Vietnams Mekong Delta (MD). It is the main source of employment and income for rural people in this region. Yearly estimates of rice growing areas and delineation of spatial distribution of rice crops are needed to devise agricultural economic plans and to ensure security of the food supply. The main objective of this study is to map rice cropping systems with respect to monitoring agricultural practices in the MD using time-series moderate resolution imaging spectroradiometer (MODIS) normalized difference vegetation index (NDVI) 250-m data. These time-series NDVI data were derived from the 8-day MODIS 250-m data acquired in 2008. Various spatial and nonspatial data were also used for accuracy verification. The method used in this study consists of the following three main steps: 1. filtering noise from the time-series NDVI data using wavelet transformation (Coiflet 4); 2. classification of rice cropping systems using parametric and nonparametric classification algorithms: the maximum likelihood classifier (MLC) and support vector machines (SVMs); and 3. verification of classification results using ground truth data and government rice statistics. Good results can be found using wavelet transformation for cleaning rice signatures. The results of classification accuracy assessment showed that the SVMs outperformed the MLC. The overall accuracy and Kappa coefficient achieved by the SVMs were 89.7% and 0.86, respectively, while those achieved by the MLC were 76.2% and 0.68, respectively. Comparison of the MODIS-derived areas obtained by the SVMs with the government rice statistics at the provincial level also demonstrated that the results achieved by the SVMs (R2 = 0.95) were better than the MLC (R2 = 0.91). This study demonstrates the effectiveness of using a nonparametric classification algorithm (SVMs) and time-series MODIS NVDI data for rice crop mapping in the Vietnamese MD.


Journal of Applied Remote Sensing | 2012

Urban growth mapping from Landsat data using linear mixture model in Ho Chi Minh City, Vietnam

Nguyen-Thanh Son; Chi-Farn Chen; Cheng-Ru Chen; Li-Yu Chang; Bui Xuan Thanh

Rapid urbanization in Ho Chi Minh City (HCMC), Vietnam, is creating societal impacts on the environment attributed to the increasing population. Understanding spatio-temporal dimensions of land-use changes that shape the urbanization is thus critical to the process of urban planning. We explore the urban growth in HCMC through Landsat images for 1990, 2002, and 2010 using the linear mixture model (LMM). The data are processed through four steps: (1) data pre-processing, (2) image classification by LMM using endmembers extracted from the original image using minimum noise fraction, (3) accuracy assessment of the classification results using field verification data, and (4) urban growth analysis to understand the spatial changes of land cover. The results achieved by comparisons between the classification results and ground reference data indicate that the overall accuracy and Kappa coefficient obtained for 1990 were 87.1% and 0.83, respectively, while those for 2002 were 92.5% and 0.89, and those for 2010 were 89.6% and 0.86. The results of urban growth analysis indicate that high albedo class (i.e., built-up areas) expanded from 12.3% in 1990 to 27.2% in 2002 and to 31.1% in 2010. When investigating land-cover conversions to high albedo class from 1990 to 2002, the largest conversion is observed for soil class (9.2%), followed by vegetation class (7.2%), and low albedo class (2.2%). From 2002 to 2010, 4.5% area of soil class was converted to high albedo class, while conversions from vegetation and low albedo classes were 3.5% and 2.5%, respectively.


Geocarto International | 2017

Assessment of Sentinel-1A data for rice crop classification using random forests and support vector machines

Nguyen-Thanh Son; Chi-Farn Chen; Cheng-Ru Chen; Vo-Quang Minh

Abstract This study developed an approach to map rice-cropping systems in An Giang and Dong Thap provinces, South Vietnam using multi-temporal Sentinel-1A (S1A) data. The data were processed through four steps: (1) data pre-processing, (2) constructing smooth time series VH backscatter data, (3) rice crop classification using random forests (RF) and support vector machines (SVM) and (4) accuracy assessment. The results indicated that the smooth VH backscatter profiles reflected the temporal characteristics of rice-cropping patterns in the study region. The comparisons between the classification results and the ground reference data indicated that the overall accuracy and Kappa coefficient achieved from RF were 86.1% and 0.72, respectively, which were slightly more accurate than SVM (overall accuracy of 83.4% and Kappa coefficient of 0.67). These results were reaffirmed by the government’s rice area statistics with the relative error in area (REA) values of 0.2 and 2.2% for RF and SVM, respectively.


Geocarto International | 2016

Mapping urban growth of the capital city of Honduras from Landsat data using the impervious surface fraction algorithm

Nguyen-Thanh Son; Chi-Farn Chen; Cheng-Ru Chen; Shou-Hao Chiang

This study developed an impervious surface fraction algorithm (ISFA) for automatic mapping of urban areas from Landsat data. We processed the data for 2001 and 2014 to trace the urbanization of Tegucigalpa, the capital city of Honduras, using a four-step procedure: (1) data pre-processing to perform image reflectance normalization, (2) quantification of impervious surface area (ISA) using ISFA, (3) accuracy assessment of mapping results and (4) change analysis of urban growth. The mapping results compared with the ground reference data confirmed the validity of ISFA for automatic delineation of ISA in the study region. The overall accuracy and Kappa coefficient achieved for 2001 were 92.8% and 0.86, while the values for 2014 were 91.8% and 0.84, respectively. The results of change detection between the classification maps indicated that ISA increased approximately 1956.7 ha from 2001 to 2014, mainly attributing to the increase of the city’s population.

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Chi-Farn Chen

National Central University

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Cheng-Ru Chen

National Central University

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Ly-Yu Chang

National Central University

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Shou-Hao Chiang

National Central University

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Li-Yu Chang

National Central University

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Bui Xuan Thanh

Ho Chi Minh City University of Technology

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Ya-Wen Lin

National Central University

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Cheng-Cru Chen

National Central University

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L. V. Kinh

National Central University

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