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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 indices (VIs) 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. Maximum Likelihood Classifier (MLC) was used to classify Mangrove and Non-Mangrove area. The results demonstrated that the best accuracy (96.78%) was from combination between 4 THEOS's spectral bands and some vegetation indices including NDVI, SR and SAVI.

Earthquake is the natural disaster which causes damage to human lives and their properties, domestic animals and buildings in the areas near the epicentre. The ability to predict the earthquake can greatly reduce in catastrophic damages, but nowadays, earthquake prediction is still the unsolvable problem. However, the earthquake prediction is still an interesting topic for scientists all over the world. One of the important earthquakes precursors in earthquake preparatory phase is thermal anomaly. The thermal region data from remote sensing have been employed recently based on the concept of stress accumulation in the active plate tectonics region, which can be transformed as temperature variation prior event. Moderate Resolution Imaging Spectroradiometer (MODIS) Land Surface Temperature (LST) data have been commonly used to locate thermal anomalies prior to occurrence of earthquake event. In Thailand, 2014 Mae Lao Earthquake, the largest earthquake in the Thailand historical record with magnitude Mw 6.1, shook the area of Mae Lao District, Chiang Rai Province on 5th May 2014. To locate possibility of thermal anomalies, the daily data of MODIS MYD11A1 product for 30 days before and after the earthquake were processed and analysed. Average LST before and after earthquake events were used for removing background temperature in the area and comparative method was performed to detect the thermal anomalies. The result found that this simple technique detected the thermal anomaly occurrence during 12-23 days prior to the earthquake and 9-28 days after the earthquake. Nevertheless, in order to understand furthermore about earthquake mechanism, it is necessity of discovered thermal precursors.

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 and Kathu district, Phuket, Thailand was the study area. The land cover was classified into 7 groups as forest, built-up, road, water, agriculture, grassland and bare land. The result indicated that the overall accuracy of THEOS with 15 m was slightly higher than Landsat-5 TM with 30 m resolution (90.65% and 89.00%, respectively). The outcome of the study 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 the correct interpretation of any classification results.

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