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Supattra Puttinaovarat
Faculty of Science and Industrial Technology, Prince of Songkla University, Surat Thani Campus, Surat Thani 84000, Thailand

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Journal article
Published: 15 June 2021 in Sustainability
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This study aimed to show maps and analyses that display dengue cases and weather-related factors on dengue transmission in the three southernmost provinces of Thailand, namely Pattani, Yala, and Narathiwat provinces. Data on the number of dengue cases and weather variables including rainfall, rainy day, mean temperature, min temperature, max temperature, relative humidity, and air pressure for the period from January 2015 to December 2019 were obtained from the Bureau of Epidemiology, Ministry of Public Health and the Meteorological Department of Southern Thailand, respectively. Spearman rank correlation test was performed at lags from zero to two months and the predictive modeling used time series Poisson regression analysis. The distribution of dengue cases showed that in Pattani and Yala provinces the most dengue cases occurred in June. Narathiwat province had the most dengue cases occurring in August. The air pressure, relative humidity, rainfall, rainy day, and min temperature are the main predictors in Pattani province, while air pressure, rainy day, and max/mean temperature seem to play important roles in the number of dengue cases in Yala and Narathiwat provinces. The goodness-of-fit analyses reveal that the model fits the data reasonably well. The results provide scientific information for creating effective dengue control programs in the community, and the predictive model can support decision making in public health organizations and for management of the environmental risk area.

ACS Style

Teerawad Sriklin; Siriwan Kajornkasirat; Supattra Puttinaovarat. Dengue Transmission Mapping with Weather-Based Predictive Model in Three Southernmost Provinces of Thailand. Sustainability 2021, 13, 6754 .

AMA Style

Teerawad Sriklin, Siriwan Kajornkasirat, Supattra Puttinaovarat. Dengue Transmission Mapping with Weather-Based Predictive Model in Three Southernmost Provinces of Thailand. Sustainability. 2021; 13 (12):6754.

Chicago/Turabian Style

Teerawad Sriklin; Siriwan Kajornkasirat; Supattra Puttinaovarat. 2021. "Dengue Transmission Mapping with Weather-Based Predictive Model in Three Southernmost Provinces of Thailand." Sustainability 13, no. 12: 6754.

Journal article
Published: 20 May 2021 in International Journal of Online and Biomedical Engineering (iJOE)
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Public Health Office and the risk map created from the patient information. Many provincial hospitals currently have to admit a large number of patients to their emergency room. Each year, the number outgrow limited medical resources, causing tremendous operational delay, and thus undermining quality of medical services. In addition, existing ER flows remain lacking means of communicating with patients’ relatives and notifying them with treatment status of patients under their care. To addresses these concerns, registered nurses with experiences are required not only to make initial patient screening and prioritization, but also to serve as liaison between physicians and patients’ relatives. These double tasks impose great burden to already overloaded medical staffs. An emergency patient classification system, based on support vector machine was developed. It was implemented as a web application, written in PHP, and running on MySQL database. GIS technology was employed to analyze spatial data and producing relevant reports. The proposed system could classify emergency patient into different groups based on their severity, according to the government standard. The resultant recommendation, verified by a nurse on duty, as well as treatment status were presented to patients’ relatives on a digital screen. Moreover, the hospital was able to use the summarized reports, in both standard and spatial forms, for its managerial purposes. The develop system could help the hospital to make the most of their limit resources for treating emergency patients. The produced reports were useful for making relevant policies and executive planning.

ACS Style

Supattra Puttinaovarat; Siwipa Pruitikanee; Jinda Kongcharoen; Paramate Horkaew. Machine Learning Based Emergency Patient Classification System. International Journal of Online and Biomedical Engineering (iJOE) 2021, 17, 133 -146.

AMA Style

Supattra Puttinaovarat, Siwipa Pruitikanee, Jinda Kongcharoen, Paramate Horkaew. Machine Learning Based Emergency Patient Classification System. International Journal of Online and Biomedical Engineering (iJOE). 2021; 17 (05):133-146.

Chicago/Turabian Style

Supattra Puttinaovarat; Siwipa Pruitikanee; Jinda Kongcharoen; Paramate Horkaew. 2021. "Machine Learning Based Emergency Patient Classification System." International Journal of Online and Biomedical Engineering (iJOE) 17, no. 05: 133-146.

Journal article
Published: 12 June 2020 in International Journal of Emerging Technologies in Learning (iJET)
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The development of a mangrove ecology self-learning application integrates the advantages of mobile-based learning and the benefits of micro-learning into the virtual learning of mangrove ecology. The system was designed based on a case study in the Leeled mangrove forest, Thailand and encour-ages young learners to understand the value of mangrove forests, and to help to preserve them. The system developed uses a virtual learning environment and accommodates young learner’s behaviours, favouring micro-learning with the content organized into the learning units which take a maximum of 15 minutes to complete. The application therefore allows the learning to be integrated into the learners’ daily activities and can contribute to their life-long learning. It allows learners to conduct self-learning and gain experience from performance in a virtual environment which simulates a real mangrove forest. This approach is better suited to the needs of present day young peo-ple than traditional approaches to environmental education.

ACS Style

Supaporn Chai-Arayalert; Supattra Puttinaovarat. Designing Mangrove Ecology Self-Learning Application Based on a Micro-Learning Approach. International Journal of Emerging Technologies in Learning (iJET) 2020, 15, 29 .

AMA Style

Supaporn Chai-Arayalert, Supattra Puttinaovarat. Designing Mangrove Ecology Self-Learning Application Based on a Micro-Learning Approach. International Journal of Emerging Technologies in Learning (iJET). 2020; 15 (11):29.

Chicago/Turabian Style

Supaporn Chai-Arayalert; Supattra Puttinaovarat. 2020. "Designing Mangrove Ecology Self-Learning Application Based on a Micro-Learning Approach." International Journal of Emerging Technologies in Learning (iJET) 15, no. 11: 29.

Journal article
Published: 03 January 2020 in IEEE Access
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ACS Style

Supattra Puttinaovarat; Paramate Horkaew. Flood Forecasting System Based on Integrated Big and Crowdsource Data by Using Machine Learning Techniques. IEEE Access 2020, 8, 5885 -5905.

AMA Style

Supattra Puttinaovarat, Paramate Horkaew. Flood Forecasting System Based on Integrated Big and Crowdsource Data by Using Machine Learning Techniques. IEEE Access. 2020; 8 ():5885-5905.

Chicago/Turabian Style

Supattra Puttinaovarat; Paramate Horkaew. 2020. "Flood Forecasting System Based on Integrated Big and Crowdsource Data by Using Machine Learning Techniques." IEEE Access 8, no. : 5885-5905.

Journal article
Published: 15 November 2019 in International Journal of Interactive Mobile Technologies (iJIM)
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Nowadays, natural disasters tend to increase and become more severe. They do affect life and belongings of great numbers of people. One kind of such disasters that hap-pen frequently almost every year is floods in all regions across the world. A prepara-tion measure to cope with upcoming floods is flood forecasting in each particular area in order to use acquired data for monitoring and warning to people and involved per-sons, resulting in the reduction of damage. With advanced computer technology and remote sensing technology, large amounts of applicable data from various sources are provided for flood forecasting. Current flood forecasting is done through computer processing by different techniques. The famous one is machine learning, of which the limitation is to acquire a large amount big data. The one currently used still requires manpower to download and record data, causing delays and failures in real-time flood forecasting. This research, therefore, proposed the development of an automatic big data downloading system from various sources through the development of applica-tion programming interface (API) for flood forecasting by machine learning. This research relied on 4 techniques, i.e., maximum likelihood classification (MLC), fuzzy logic, self-organization map (SOM), and artificial neural network with RBF Kernel. According to accuracy assessment of flood forecasting, the most accurate technique was MLC (99.2%), followed by fuzzy logic, SOM, and RBF (97.8%, 96.6%, and 83.3%), respectively.

ACS Style

Supattra Puttinaovarat; Paramate Horkaew. Application Programming Interface for Flood Forecasting from Geospatial Big Data and Crowdsourcing Data. International Journal of Interactive Mobile Technologies (iJIM) 2019, 13, 137 -156.

AMA Style

Supattra Puttinaovarat, Paramate Horkaew. Application Programming Interface for Flood Forecasting from Geospatial Big Data and Crowdsourcing Data. International Journal of Interactive Mobile Technologies (iJIM). 2019; 13 (11):137-156.

Chicago/Turabian Style

Supattra Puttinaovarat; Paramate Horkaew. 2019. "Application Programming Interface for Flood Forecasting from Geospatial Big Data and Crowdsourcing Data." International Journal of Interactive Mobile Technologies (iJIM) 13, no. 11: 137-156.

Journal article
Published: 01 August 2019 in International Journal of Electrical and Computer Engineering (IJECE)
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This research presented a system development approach for facility maintenance management system based on GIS and indoor map in the form of web applications that can be used with all devices and no worries about time limitations. The capabilities of GIS, indoor map, and geospatial data visualization help speeding up facility maintenance management process and create benefits to all concerned parties, i.e., users can notify and follow the data of facility errors at the time; or officers in charge can operate quickly because they can access real-time data. Indoor map display makes it easier to access locations or places of damaged facilities. In addition, the data from the model system presented in this research can also be applied to planning and decision-making of executives.

ACS Style

Supattra Puttinaovarat; Suwat Jutapruet; Aekarat Saeliw; Siwipa Pruitikanee; Jinda Kongcharoen; Watchara Jiamsawat; Suchakree Limpasamanon. Facility maintenance management system based on GIS and indoor map. International Journal of Electrical and Computer Engineering (IJECE) 2019, 9, 3323 -3332.

AMA Style

Supattra Puttinaovarat, Suwat Jutapruet, Aekarat Saeliw, Siwipa Pruitikanee, Jinda Kongcharoen, Watchara Jiamsawat, Suchakree Limpasamanon. Facility maintenance management system based on GIS and indoor map. International Journal of Electrical and Computer Engineering (IJECE). 2019; 9 (4):3323-3332.

Chicago/Turabian Style

Supattra Puttinaovarat; Suwat Jutapruet; Aekarat Saeliw; Siwipa Pruitikanee; Jinda Kongcharoen; Watchara Jiamsawat; Suchakree Limpasamanon. 2019. "Facility maintenance management system based on GIS and indoor map." International Journal of Electrical and Computer Engineering (IJECE) 9, no. 4: 3323-3332.

Research article
Published: 25 June 2019 in Earth Science Informatics
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Characterization of oil palm plantation is a crucial step toward many geographical based management strategies, ranging from determining regional planting and appropriate species to irrigation and logistics planning. Accurate and most updated plantation identification enables well informed and effective measures for such schemes. This paper proposes a computerized method for detecting oil-palm plantation from remotely sensed imagery. Unlike other existing approaches, where imaging features were retrieved from spectral data and then trained with a machine learning box for region of interest extraction, this paper employed 2-stage detection. Firstly, a deep learning network was employed to determine a presence of oil-palm plantation in a generic Google satellite image. With irrelevant samples being disregarded and thus the problem space being so contained, the images with detected oil-palm had their plantation delineated at higher accuracy by using a support vector machine, based on Gabor texture descriptor. The proposed coupled detection-delineation was benchmarked against different feature descriptors and state-of-the-art supervised and unsupervised machine learning techniques. The validation was made by comparing the extraction results with those ground surveyed by an authority. It was shown in the experiments that it could detect and delineate the plantations with an accuracy of 92.29% and precision, recall and Kappa of 91.16%, 84.97%, and 0.81, respectively.

ACS Style

Supattra Puttinaovarat; Paramate Horkaew. Deep and machine learnings of remotely sensed imagery and its multi-band visual features for detecting oil palm plantation. Earth Science Informatics 2019, 12, 429 -446.

AMA Style

Supattra Puttinaovarat, Paramate Horkaew. Deep and machine learnings of remotely sensed imagery and its multi-band visual features for detecting oil palm plantation. Earth Science Informatics. 2019; 12 (4):429-446.

Chicago/Turabian Style

Supattra Puttinaovarat; Paramate Horkaew. 2019. "Deep and machine learnings of remotely sensed imagery and its multi-band visual features for detecting oil palm plantation." Earth Science Informatics 12, no. 4: 429-446.

Journal article
Published: 21 May 2019 in International Journal of Interactive Mobile Technologies (iJIM)
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The common problem that is mostly found in urban area and the organizations service numbers of people such as government places, university, department store, and hospital, is the insufficient car parking. This problem is the result of the continuing increasing number of vehicle. Further, the car parking management is insufficient so the service users waste their time and fuel trying to look for the available parking. The objective of this research was to develop mobile application for smart car parking using Radio-Frequency Identification (RFID) and Internet of Thing (IoT) which can detect the available parking lot so it is time saving for people. Moreover, the parking area management is more efficient as it minimizes the limitation of traditional system which the users have to access web application which is unable to automatically alert when the parking lot status has changed. Further, the data can be applied to the management and planning such as analyzing numbers of vehicle daily to compare with the parking lot if it is sufficient or not in order to improve and provide more parking space appropriately.

ACS Style

Aekarat Saeliw; Watcharasuda Hualkasin; Supattra Puttinaovarat; Kanit Khaimook. Smart Car Parking Mobile Application based on RFID and IoT. International Journal of Interactive Mobile Technologies (iJIM) 2019, 13, 4 -14.

AMA Style

Aekarat Saeliw, Watcharasuda Hualkasin, Supattra Puttinaovarat, Kanit Khaimook. Smart Car Parking Mobile Application based on RFID and IoT. International Journal of Interactive Mobile Technologies (iJIM). 2019; 13 (5):4-14.

Chicago/Turabian Style

Aekarat Saeliw; Watcharasuda Hualkasin; Supattra Puttinaovarat; Kanit Khaimook. 2019. "Smart Car Parking Mobile Application based on RFID and IoT." International Journal of Interactive Mobile Technologies (iJIM) 13, no. 5: 4-14.

Journal article
Published: 14 May 2019 in International Journal of Online and Biomedical Engineering (iJOE)
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The need to treat illnesses or deterioration of the legs caused by accidents, rheumatism, muscular weakness, paralysis or bone diseases has increased. Paraplegic patients need physical therapy to mitigate their condition. This study developed a gamification smartphone application for leg physical therapy. The application was implemented for patients using Android Operating System smartphones. The smartphone was attached to the walking support equipment. The accelerometer sensor of the smartphone was utilized to measure distance, time, and number of steps. The application acquired data from the sensor, and processed and stored the data in a server, to enable assessment of critical conditions. Moreover, this system would report the evaluation of physical therapy on a weekly basis. Alerts of physiotherapy treatment could be set in this application. The advantages of the application include increasing the patient’s motivation for the therapy, performed by themselves at home, and the results could be used for planning treatments by a physician.

ACS Style

Jinda Kongcharoen; Siwipa Pruitikanee; Supattra Puttinaovarat; Yanin Tubtiang; Pattarakorn Chankeaw. Gamification Smartphone Application for Leg Physical Therapy. International Journal of Online and Biomedical Engineering (iJOE) 2019, 15, 31 -41.

AMA Style

Jinda Kongcharoen, Siwipa Pruitikanee, Supattra Puttinaovarat, Yanin Tubtiang, Pattarakorn Chankeaw. Gamification Smartphone Application for Leg Physical Therapy. International Journal of Online and Biomedical Engineering (iJOE). 2019; 15 (8):31-41.

Chicago/Turabian Style

Jinda Kongcharoen; Siwipa Pruitikanee; Supattra Puttinaovarat; Yanin Tubtiang; Pattarakorn Chankeaw. 2019. "Gamification Smartphone Application for Leg Physical Therapy." International Journal of Online and Biomedical Engineering (iJOE) 15, no. 8: 31-41.

Journal article
Published: 14 September 2018 in Open Geosciences
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Road geometry is pertinent information in various GIS studies. Reliable and updated road information thus calls for conventional on-site survey being replaced by more accurate and efficient remote sensing technology. Generally, this approach involves image enhancement and extraction of relevant features, such as elongate gradient and intersecting corners. Thus far, its implication is often impeded by wrongly extraction of other urban peripherals with similar pixel characteristics. This paper therefore proposes the fusion of THEOS satellite image and topographic derivatives, obtained from underlying Digital Surface Models (DSM). Multi-spectral indices in thematic layers and surface properties of designated roads were both fed into state-of-the-art machine learning algorithms. The results were later fused, taken into account consistently leveled road surface. The proposed technique was thus able to eliminate irrelevant urban structures such as buildings and other constructions, otherwise left by conventional index based extraction. The numerical assessment indicates recall of 84.64%, precision of 97.40% and overall accuracy of 97.78%, with 0.89 Kappa statistics. Visual inspection reported herewith also confirms consistency with ground truth reference.

ACS Style

Supattra Puttinaovarat; Paramate Horkaew. Multi-spectral and Topographic Fusion for Automated Road Extraction. Open Geosciences 2018, 10, 461 -473.

AMA Style

Supattra Puttinaovarat, Paramate Horkaew. Multi-spectral and Topographic Fusion for Automated Road Extraction. Open Geosciences. 2018; 10 (1):461-473.

Chicago/Turabian Style

Supattra Puttinaovarat; Paramate Horkaew. 2018. "Multi-spectral and Topographic Fusion for Automated Road Extraction." Open Geosciences 10, no. 1: 461-473.

Journal article
Published: 08 June 2018 in International Journal on Advanced Science, Engineering and Information Technology
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ACS Style

Supattra Puttinaovarat; Paramate Horkaew. Oil-Palm Plantation Identification from Satellite Images Using Google Earth Engine. International Journal on Advanced Science, Engineering and Information Technology 2018, 8, 720 .

AMA Style

Supattra Puttinaovarat, Paramate Horkaew. Oil-Palm Plantation Identification from Satellite Images Using Google Earth Engine. International Journal on Advanced Science, Engineering and Information Technology. 2018; 8 (3):720.

Chicago/Turabian Style

Supattra Puttinaovarat; Paramate Horkaew. 2018. "Oil-Palm Plantation Identification from Satellite Images Using Google Earth Engine." International Journal on Advanced Science, Engineering and Information Technology 8, no. 3: 720.

Journal article
Published: 27 September 2017 in ISPRS International Journal of Geo-Information
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Reliable water surface extraction is essential for river delineation and flood monitoring. Obtaining such information from fine resolution satellite imagery has attracted much interest for geographic and remote sensing applications. However, those images are often expensive and difficult to acquire. This study proposes a more cost-effective technique, employing freely available Landsat images. Despite its extensive spectrum and robust discrimination capability, Landsat data are normally of medium spatial resolution and, as such, fail to delineate smaller hydrological features. Based on Multivariate Mutual Information (MMI), the Landsat images were fused with Digital Surface Model (DSM) on the spatial domain. Each coinciding pixel would then contain not only rich indices but also intricate topographic attributes, derived from its respective sources. The proposed data fusion ensures robust, precise, and observer-invariable extraction of water surfaces and their branching, while eliminating spurious details. Its merit was demonstrated by effective discrimination of flooded regions from natural rivers for flood monitoring. The assessments we completed suggest improved extraction compared to traditional methods. Compared with manual digitizing, this method also exhibited promising consistency. Extraction using Dempster–Shafer fusion provided a 91.81% F-measure, 93.09% precision, 90.74% recall, and 98.25% accuracy, while using Majority Voting fusion resulted in an 84.91% F-measure, 75.44% precision, 97.37% recall, and 97.21% accuracy.

ACS Style

Paramate Horkaew; Supattra Puttinaovarat. Entropy-Based Fusion of Water Indices and DSM Derivatives for Automatic Water Surfaces Extraction and Flood Monitoring. ISPRS International Journal of Geo-Information 2017, 6, 301 .

AMA Style

Paramate Horkaew, Supattra Puttinaovarat. Entropy-Based Fusion of Water Indices and DSM Derivatives for Automatic Water Surfaces Extraction and Flood Monitoring. ISPRS International Journal of Geo-Information. 2017; 6 (10):301.

Chicago/Turabian Style

Paramate Horkaew; Supattra Puttinaovarat. 2017. "Entropy-Based Fusion of Water Indices and DSM Derivatives for Automatic Water Surfaces Extraction and Flood Monitoring." ISPRS International Journal of Geo-Information 6, no. 10: 301.

Journal article
Published: 01 April 2017 in Pattern Recognition and Image Analysis
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Accurate urban areas information is important for a variety of applications, especially city planning and natural disaster prediction and management. In recent years, extraction of urban structures from remotely sensed images has been extensively explored. The key advantages of this imaging modality are reduction of surveying expense and time. It also elevates restrictions on ground surveys. Thus far, much research typically extracts these structures from very high resolution satellite imagery, which are unfortunately of relatively poor spectral resolution, resulting in good precision yet moderate accuracy. Therefore, this paper investigates extraction of buildings from middle and high resolution satellite images by using spectral indices (Normalized Difference Building Index: NDBI, Normalized Difference Vegetation Index: NDVI, Soil Adjustment Vegetation Index: SAVI, Modified Normalized Difference Index: MNDWI, and Global Environment Monitoring Index: GEMI) by means of various Machine Learning methods (Artificial Neural Network: ANN, K-Nearest Neighbor: KNN, and Support Vector Machine: SVM) and Data Fusion (i.e., Majority Voting). Herein empirical results suggested that suitable learning methods for urban areas extraction are in preferring order Data Fusion, SVM, KNN, and ANN. Their accuracies were 85.46, 84.86, 84.66, and 84.91%, respectively.

ACS Style

S. Puttinaovarat; Paramate Horkaew. Urban areas extraction from multi sensor data based on machine learning and data fusion. Pattern Recognition and Image Analysis 2017, 27, 326 -337.

AMA Style

S. Puttinaovarat, Paramate Horkaew. Urban areas extraction from multi sensor data based on machine learning and data fusion. Pattern Recognition and Image Analysis. 2017; 27 (2):326-337.

Chicago/Turabian Style

S. Puttinaovarat; Paramate Horkaew. 2017. "Urban areas extraction from multi sensor data based on machine learning and data fusion." Pattern Recognition and Image Analysis 27, no. 2: 326-337.

Conference paper
Published: 01 October 2015 in 2015 IEEE International Conference on Signal and Image Processing Applications (ICSIPA)
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Water resource is crucial to the existence of every life form and also valuable to our daily life. Among its many advantages, there exist those in environmental, agricultural, industrial, and household activities as well as in climate monitoring. Nonetheless, water is also a causal factor in several major natural disasters. In order to effectively make an educated planning, remote sensing technology which are able to offer immediate and accurate means of determining water resources, are generally adopted. This paper presents detailed analyses and treatments on an adaptive water body extraction from remotely sensed images based on various water indices (NDWI, NDWI2, MNDWI and NDPI). In our framework, relaxation labeling was incorporated in order to ensure reliable and robust classification while suppressing spurious artefacts, inherited from the imaging modality. The subsequent assessment suggested that NDWI2 index yielded the most accurate classification and hence water extraction. The technique was then compared against labour intensive yet accurate manual tracing with promising consistency.

ACS Style

Supattra Puttinaovarat; Kanit Khaimook; Weerapong Polnigongit; Paramate Horkaew. Robust water body extraction from landsat imagery by using gradual assignment of water index and DSM. 2015 IEEE International Conference on Signal and Image Processing Applications (ICSIPA) 2015, 122 -126.

AMA Style

Supattra Puttinaovarat, Kanit Khaimook, Weerapong Polnigongit, Paramate Horkaew. Robust water body extraction from landsat imagery by using gradual assignment of water index and DSM. 2015 IEEE International Conference on Signal and Image Processing Applications (ICSIPA). 2015; ():122-126.

Chicago/Turabian Style

Supattra Puttinaovarat; Kanit Khaimook; Weerapong Polnigongit; Paramate Horkaew. 2015. "Robust water body extraction from landsat imagery by using gradual assignment of water index and DSM." 2015 IEEE International Conference on Signal and Image Processing Applications (ICSIPA) , no. : 122-126.

Journal article
Published: 01 January 2015 in Journal of Applied Remote Sensing
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Hydrological flow characteristic is one of the prime indicators for assessing flood. It plays a major part in determining drainage capability of the affected basin and also in the subsequent simulation and rainfall-runoff prediction. Thus far, flow directions were typically derived from terrain data which for flat landscapes are obscured by other man-made structures, hence undermining the practical potential. In the absence (or diminutive) of terrain slopes, water passages have a more pronounced effect on flow directions than elevations. This paper, therefore, presents detailed analyses and implementation of hydrological flow modeling from satellite and topographic images. Herein, gradual assignment based on support vector machine was applied to modified normalized difference water index and a digital surface model, in order to ensure reliable water labeling while suppressing modality-inherited artifacts and noise. Gradient vector flow was subsequently employed to reconstruct the flow field. Experiments comparing the proposed scheme with conventional water boundary delineation and flow reconstruction were presented. Respective assessments revealed its advantage over the generic stream burning. Specifically, it could extract water body from studied areas with 98.70% precision, 99.83% recall, 98.76% accuracy, and 99.26% F-measure. The correlations between resultant flows and those obtained from the stream burning were as high as 0.80±0.04 (p≤0.01 in all resolutions).

ACS Style

Supattra Puttinaovarat; Paramate Horkaew; Kanit Khaimook; Weerapong Polnigongit. Adaptive hydrological flow field modeling based on water body extraction and surface information. Journal of Applied Remote Sensing 2015, 9, 095041 -095041.

AMA Style

Supattra Puttinaovarat, Paramate Horkaew, Kanit Khaimook, Weerapong Polnigongit. Adaptive hydrological flow field modeling based on water body extraction and surface information. Journal of Applied Remote Sensing. 2015; 9 (1):095041-095041.

Chicago/Turabian Style

Supattra Puttinaovarat; Paramate Horkaew; Kanit Khaimook; Weerapong Polnigongit. 2015. "Adaptive hydrological flow field modeling based on water body extraction and surface information." Journal of Applied Remote Sensing 9, no. 1: 095041-095041.