The intensive industrial development in special economic zones, such as Thailand’s Eastern Econom... more The intensive industrial development in special economic zones, such as Thailand’s Eastern Economic Corridor, increases energy consumption, leading to an imbalance of energy supply and a challenge for energy management. Electricity consumption at a local level is crucial for utility planners to manage and invest in the electrical grid. With this study, we propose an electricity consumption estimation model at the district level using machine learning with publicly available statistical data and built-up area (BU), area of lit (AL), and sum of light intensity (SL) data extracted from Landsat 8 and Suomi NPP satellite nighttime light images. The models created from three machine learning algorithms, which included Multiple Linear Regression (MR), Decision Tree (DT), and Support Vector Regression (SVR), were compared. The results show that (1) electricity consumption is highly correlated with SL, AL, and BU; and (2) the DT model demonstrated a better performance in predicting local ele...
The leaf chlorophyll content (LCC) is a critical index to characterize crop growth conditions, ph... more The leaf chlorophyll content (LCC) is a critical index to characterize crop growth conditions, photosynthetic capacity, and physiological status. Its dynamic change characteristics are of great significance for monitoring crop growth conditions and understanding the process of material and energy exchange between crops and the environment. Extensive research has focused on LCC retrieval with hyperspectral data onboard various sensor platforms. Nevertheless, limited attention has been paid to LCC inversion from multispectral data, such as the data from Landsat-8, and the potentials and capabilities of the data for crop LCC estimation have not been fully explored. The present study made use of Landsat-8 Operational Land Imager (OLI) imagery and the corresponding field experimental data to evaluate their capabilities and potentials for LCC modeling using four different retrieval methods: vegetation indices (VIs), machine learning regression algorithms (MLRAs), lookup-table (LUT)-based ...
Combining two or more imaging modalities to provide complementary information has become commonpl... more Combining two or more imaging modalities to provide complementary information has become commonplace in clinical practice and in preclinical and basic biomedical research. By incorporating the structural information provided by computed tomography (CT) or magnetic resonance imaging (MRI), the ill poseness nature of bioluminescence tomography (BLT) can be reduced significantly, thus improve the accuracies of reconstruction and in vivo quantification. In this paper, we present a small animal imaging system combining multi-view and multi-spectral BLT with MRI. The independent MRI-compatible optical device is placed at the end of the clinical MRI scanner. The small animal is transferred between the light tight chamber of the optical device and the animal coil of MRI via a guide rail during the experiment. After the optical imaging and MRI scanning procedures are finished, the optical images are mapped onto the MRI surface by interactive registration between boundary of optical images an...
The last few years have seen satellite platforms with a large number of sensors (e.g. Terra and E... more The last few years have seen satellite platforms with a large number of sensors (e.g. Terra and ENVISAT) coming on-line and the launching of a huge number of satellites with more than one sensor (e.g. IKONOS and QuickBird). Various satellite images with spatial resolutions ranging from 0.5 to 25,000 m are available for different applications. This development offers new and significant changes and challenges in the approach to analysis, integration, and the efficient spatial modelling of these observation data. This paper presents a multi-resolution analysis and classification framework for selecting and integrating suitable information from different spatial resolutions and analytical techniques into classification routines. The proposed framework focuses on the examination of image structural using different spatial analytical techniques in order to select appropriate methods in different stages of classification such as training strategy, feature extraction, scene models, and cla...
This paper presents and compares approaches of estimating true area on the ground and calibrating... more This paper presents and compares approaches of estimating true area on the ground and calibrating quantitative errors of area estimate on categorical maps from the contingency table. Results directly estimated from the contingency table and those from two calibration methods were compared on two maps of 10 different land cover classes with known errors between them. The estimated true area percentage from the contingency table and two calibration approaches showed obvious improvement when compared with uncalibrated values. However, there is no significant difference among the estimates from the contingency table and the two calibration methods. Although the inverse method led to mean estimates closer to the true values for all classes than other methods, comparing the individual area estimates for each class showed that the inverse method did not always produce the most accurate estimate. Homogeneous classes with high classification accuracy have a better chance of achieving more ac...
Among water quality parameters, total suspended matter is important for the evaluation of inland ... more Among water quality parameters, total suspended matter is important for the evaluation of inland waters. A recently launched satellite sensor, HJ-CCD, by China possesses high temporal resolution, medium spatial resolution, and wide swath, so it is convenient for monitoring this parameter in large inland waters. However, no operational method currently exists for retrieving the total suspended matter concentration of turbid inland waters from HJ-CCD data. Using Lake Taihu in Eastern China as a study area, we obtained and analyzed optical properties of the lake during all seasons, and found that the absorption coefficient of suspended matter, chlorophyll, and colored dissolved organic matter of the near-infrared band may approximate zero. Based on this analysis, we found that a single band method of retrieving concentration using the near-infrared band was suitable using HJ-CCD data. We parameterized the single band method with specific inherent optical properties of Lake Taihu, and v...
International journal of environmental research and public health, Nov 9, 2016
Identification of the sources of soil mercury (Hg) on the provincial scale is helpful for enactin... more Identification of the sources of soil mercury (Hg) on the provincial scale is helpful for enacting effective policies to prevent further contamination and take reclamation measurements. The natural and anthropogenic sources and their contributions of Hg in Chinese farmland soil were identified based on a decision tree method. The results showed that the concentrations of Hg in parent materials were most strongly associated with the general spatial distribution pattern of Hg concentration on a provincial scale. The decision tree analysis gained an 89.70% total accuracy in simulating the influence of human activities on the additions of Hg in farmland soil. Human activities-for example, the production of coke, application of fertilizers, discharge of wastewater, discharge of solid waste, and the production of non-ferrous metals-were the main external sources of a large amount of Hg in the farmland soil.
The intensive industrial development in special economic zones, such as Thailand’s Eastern Econom... more The intensive industrial development in special economic zones, such as Thailand’s Eastern Economic Corridor, increases energy consumption, leading to an imbalance of energy supply and a challenge for energy management. Electricity consumption at a local level is crucial for utility planners to manage and invest in the electrical grid. With this study, we propose an electricity consumption estimation model at the district level using machine learning with publicly available statistical data and built-up area (BU), area of lit (AL), and sum of light intensity (SL) data extracted from Landsat 8 and Suomi NPP satellite nighttime light images. The models created from three machine learning algorithms, which included Multiple Linear Regression (MR), Decision Tree (DT), and Support Vector Regression (SVR), were compared. The results show that (1) electricity consumption is highly correlated with SL, AL, and BU; and (2) the DT model demonstrated a better performance in predicting local ele...
The leaf chlorophyll content (LCC) is a critical index to characterize crop growth conditions, ph... more The leaf chlorophyll content (LCC) is a critical index to characterize crop growth conditions, photosynthetic capacity, and physiological status. Its dynamic change characteristics are of great significance for monitoring crop growth conditions and understanding the process of material and energy exchange between crops and the environment. Extensive research has focused on LCC retrieval with hyperspectral data onboard various sensor platforms. Nevertheless, limited attention has been paid to LCC inversion from multispectral data, such as the data from Landsat-8, and the potentials and capabilities of the data for crop LCC estimation have not been fully explored. The present study made use of Landsat-8 Operational Land Imager (OLI) imagery and the corresponding field experimental data to evaluate their capabilities and potentials for LCC modeling using four different retrieval methods: vegetation indices (VIs), machine learning regression algorithms (MLRAs), lookup-table (LUT)-based ...
Combining two or more imaging modalities to provide complementary information has become commonpl... more Combining two or more imaging modalities to provide complementary information has become commonplace in clinical practice and in preclinical and basic biomedical research. By incorporating the structural information provided by computed tomography (CT) or magnetic resonance imaging (MRI), the ill poseness nature of bioluminescence tomography (BLT) can be reduced significantly, thus improve the accuracies of reconstruction and in vivo quantification. In this paper, we present a small animal imaging system combining multi-view and multi-spectral BLT with MRI. The independent MRI-compatible optical device is placed at the end of the clinical MRI scanner. The small animal is transferred between the light tight chamber of the optical device and the animal coil of MRI via a guide rail during the experiment. After the optical imaging and MRI scanning procedures are finished, the optical images are mapped onto the MRI surface by interactive registration between boundary of optical images an...
The last few years have seen satellite platforms with a large number of sensors (e.g. Terra and E... more The last few years have seen satellite platforms with a large number of sensors (e.g. Terra and ENVISAT) coming on-line and the launching of a huge number of satellites with more than one sensor (e.g. IKONOS and QuickBird). Various satellite images with spatial resolutions ranging from 0.5 to 25,000 m are available for different applications. This development offers new and significant changes and challenges in the approach to analysis, integration, and the efficient spatial modelling of these observation data. This paper presents a multi-resolution analysis and classification framework for selecting and integrating suitable information from different spatial resolutions and analytical techniques into classification routines. The proposed framework focuses on the examination of image structural using different spatial analytical techniques in order to select appropriate methods in different stages of classification such as training strategy, feature extraction, scene models, and cla...
This paper presents and compares approaches of estimating true area on the ground and calibrating... more This paper presents and compares approaches of estimating true area on the ground and calibrating quantitative errors of area estimate on categorical maps from the contingency table. Results directly estimated from the contingency table and those from two calibration methods were compared on two maps of 10 different land cover classes with known errors between them. The estimated true area percentage from the contingency table and two calibration approaches showed obvious improvement when compared with uncalibrated values. However, there is no significant difference among the estimates from the contingency table and the two calibration methods. Although the inverse method led to mean estimates closer to the true values for all classes than other methods, comparing the individual area estimates for each class showed that the inverse method did not always produce the most accurate estimate. Homogeneous classes with high classification accuracy have a better chance of achieving more ac...
Among water quality parameters, total suspended matter is important for the evaluation of inland ... more Among water quality parameters, total suspended matter is important for the evaluation of inland waters. A recently launched satellite sensor, HJ-CCD, by China possesses high temporal resolution, medium spatial resolution, and wide swath, so it is convenient for monitoring this parameter in large inland waters. However, no operational method currently exists for retrieving the total suspended matter concentration of turbid inland waters from HJ-CCD data. Using Lake Taihu in Eastern China as a study area, we obtained and analyzed optical properties of the lake during all seasons, and found that the absorption coefficient of suspended matter, chlorophyll, and colored dissolved organic matter of the near-infrared band may approximate zero. Based on this analysis, we found that a single band method of retrieving concentration using the near-infrared band was suitable using HJ-CCD data. We parameterized the single band method with specific inherent optical properties of Lake Taihu, and v...
International journal of environmental research and public health, Nov 9, 2016
Identification of the sources of soil mercury (Hg) on the provincial scale is helpful for enactin... more Identification of the sources of soil mercury (Hg) on the provincial scale is helpful for enacting effective policies to prevent further contamination and take reclamation measurements. The natural and anthropogenic sources and their contributions of Hg in Chinese farmland soil were identified based on a decision tree method. The results showed that the concentrations of Hg in parent materials were most strongly associated with the general spatial distribution pattern of Hg concentration on a provincial scale. The decision tree analysis gained an 89.70% total accuracy in simulating the influence of human activities on the additions of Hg in farmland soil. Human activities-for example, the production of coke, application of fertilizers, discharge of wastewater, discharge of solid waste, and the production of non-ferrous metals-were the main external sources of a large amount of Hg in the farmland soil.
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Papers by Dongmei Chen