THEOS Data user Workshop
Chairman :
Damrongrit Niammuad (d.niammuad AT
Co-Chair: Chris Elvidge (chris.elvidge AT
Members :
Earth Monitoring Working Group
Objectives :
The THEOS satellite was launched in 2008. Since that time the THEOS sensor has acquired more than 300,000 image of the earth. This workshop will provide an overview of the THEOS panchromatic and multispectral imaging capabilities, data quality, data access, and user experiences.
Target Audience :
Scientists engaged in the study of natural resources, urban areas, forests, agriculture and fisheries.
Expected Number of Participants :
Agenda :
  1. An Application of THEOS Data to Rubber Plantation areas in Mukdahan Province, Northeast Thailand.


    Abstract :

    A rapid expansion of rubber plantation areas in Northeast Thailand in the preceding 15 years has resulted from high return in combination with technologically feasible rubber tree growth in the areas. Spatial information on the rubber plantation areas can be effectively incorporated into land use planning and decision-making. The study aims to establish a spatial database of the rubber plantation areas with the different tree ages using THEOS data. Covering an area of approximately 4339 km2, Mukdahan province located in the Northeast is currently the area of study. THEOS panchromatic images acquired during the period 2009-2011 were used to identify the plantation areas. Through a process of on-screen digitizing for selected images, textures of the images were based on visual analysis. Using the textures, identification of different tree ages was possible, and distinct line spacing of rubber tree pattern could be observed. The panchromatic THEOS data is capable of identifying the rubber sapling by observation of plow line spacing (3m x 7m). We could classify the rubber tree into 3 classes of age: less than 5, 5-10 and over 10 years. The result indicated that areas planted to rubber trees accounted for roughly 11.21, 3.77 and 2.84 % for <5, 5-10 and > 10 years of plant ages respectively. The panchromatic THEOS data with 2m resolution proved to be effective in identifying the rubber tree ages. Slides

  2. Impacts of spatial resolution on land cover classification.

    Chanida Suwanprasit and Naiyana Srichai

    Abstract :

    Regularly updated land cover information is a requirement for various land management application. Remote sensing scenes can provide information highly useful for real-time modeling of the earth environment. However, the spatial resolution is also a very important factor to acquire the information on satellite imagery. This paper summarizes the basic conclusions of work in which the spatial resolution of satellite imagery, related to the factor of scale for land cover classification, was investigated. Optical data collected by two different sensors (THEOS with 15-m resolution and Landsat 5 TM with resolution 30-m) in 2010 were tested against the ability to correctly classify specific land cover classes at different scales of observation. Support Vector Machines (SVMs) classifier was used. Kathu district, Phuket, Thailand was the selected area. The land cover was classified into 5 groups including forest, agriculture, water bodies, settlement, and other land cover types. The result can be discussed further to assess the suitable spatial resolution for land cover classification mapping of Kathu district. Understanding the role of scale on the spectral signatures of satellite data will help secure the correct interpretation of any classification results. Slides

  3. Vegetation classification on Prathong Island, Phang Nga, Thailand.

    Naiyana Srichai and Chanida Suwanprasit

    Abstract :

    Habitats mapping as defined by plant communities is a common component of the planning and monitoring for conservation management. This study provided the test of two classifiers for producing the vegetation mapping in a tropical island. The selected area is Prathong Island, Phang Nga Province, Thailand. The performance of the support vector machines (SVMs) image classification technique for vegetation classifying was assessed and compared to maximum likelihood classifier (MLC). The vegetation was grouped into 7 categories include tropical forest, beach forest, swamp forest, mangrove forest, wetland, savanna and grassland, and other land-use type. The overall and individual classification results of this approach were compared to find out the suitable classifier for producing a vegetation map. THEOS multispectral image with 15-m resolution achieved on 19th January 2009 was used for this study. The results are useful to identify the boundary of each ecosystem on Prathong Island. Additional research is needed to assess the full potential of both classifiers and THEOS imagery for exploring potential applications on other tropical environments. Slides

  4. Comparison of vegetation indices for mangrove mapping using THEOS data.

    Jiraporn Kongwongjun, Chanida Suwanprasit and Pun Thongchumnum

    Abstract :

    The increasing application of remote sensing for mangrove mapping and monitoring is practical for sustainable management of the biological resources. Over the past few decades, the emergence of several vegetation index (VI) has certainly given significant impacts on mangrove and other forest mappings. In this study, five different vegetation indices including Normalized Different Vegetation Index (NDVI), Simple Ratio (SR), Soil Adjusted Vegetation Index (SAVI), Perpendicular Vegetation Index (PVI) and Triangular Vegetation Index (TVI) were compared to discover a suitable vegetation index for identifying mangrove area in Pa Khlok sub-district, Phuket, Thailand. THEOS imagery with 15-m resolution from 2010 was utilized in this study. Maximum Likelihood Classifier (MLC) was used to classify mangrove and non-mangrove area. The results demonstrated the accuracy, reliability, and utility of the vegetation indices of THEOS products for producing mangrove mapping.Slides

  5. Expanding Access to THEOS with Free Educational Software.

    Sally E. Goldin and Kurt T. Rudahl

    Abstract :

    The deployment of THEOS greatly enlarged the potential user base for remote sensing imagery in Thailand. Governmental and non-governmental organizations, educational institutions and commercial entities can now access large volumes of high resolution multispectral imagery at an affordable price. However, these new users may lack the skills, knowledge and software facilities necessary to realize the benefits of this resource. OpenDragon is a free remote sensing image analysis package designed for education and research. With its simple, well-documented, Thai-language user interface, extensive suite of operations, and extensibility via the Dragon Programmer's Toolkit, OpenDragon can help individuals and organizations to gain the experience they need in order to make the most productive use of THEOS data. We will briefly describe OpenDragon's capabilities and then present several case studies showing how we have used Dragon and the Toolkit for education of users ranging from high-school students to geoinformatics professionals. Slides

Remarks :
WiFi and VTC access.

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