The Southwest Monsoon is a major driver of rainfall in Thailand and plays a critical role in water resources, agriculture, and disaster preparedness. However, increasing climate variability has made monsoon behaviour more irregular, resulting in unpredictable rainfall patterns and heightened risks of floods and droughts. This study develops a Southwest Monsoon Index (SWMI) based on the Sea Surface temperature (SST) contrast between two strategically selected regions of the Indian Ocean that influence moisture transport toward Thailand. Monthly satellite-derived SST imagery was processed using K-means colour segmentation to extract representative SST values from each region, and the resulting SST difference was used to construct the SWMI. Rainfall data from 1042 telemetry stations operated by the Hydro-Informatics Institute (HII) across Thailand were analysed to evaluate the relationship between the SWMI and monsoon-season rainfall. The results show a statistically significant negative correlation between the SWMI and accumulated rainfall (r =-0.74, p = 1.29 & times; 10-21), indicating that a stronger negative SST contrast is associated with increased monsoon rainfall. Spatial clustering of rainfall stations reveals that western and southern Thailand exhibit the strongest sensitivity to SWMI variations. This study demonstrates that the SWMI is a robust climatic indicator capable of capturing monsoon-driven rainfall variability in Thailand. The index provides a foundation for future seasonal and long-term rainfall forecasting, including integration with machine learning models. Its operational application has strong potential to support water-resource planning, agricultural management, and disaster risk reduction in Thailand.

Space-based assets have been integrated into a sensor web to monitor flooding in Thailand. In this approach, the moderate resolution imaging spectrometer data from the Terra and Aqua satellites are used to perform broad-scale monitoring for flood tracking at the regional level (250 m/pixel) to generate flood detections/alerts. Based on these alerts, the Earth Observing-1 (EO-1) mission is autonomously tasked to acquire higher-resolution (10-30 m/pixel) advanced land imager data, and a number of other assets have imagery automatically requested, with yet further assets requested only in a semiautomated fashion. Based on these alerts, these data are then automatically processed to derive products such as surface water extent and volumetric water estimates in shapefile formats to enable interpretation in geographic information systems. These products are then automatically pushed to organizations in Thailand for use in damage estimation, relief efforts, and damage mitigation. To date, Terra, Aqua, EO-1, Landsat, Ikonos, WorldView-1, WorldView-2, GeoEye-1, and Radarsat-2 have been used in some fashion in the sensor web. The overall autonomous detection, tasking, data acquisition, and processing sensor web framework are described, as well as ongoing work to extend to in situ sensor networks. How the automatic triggering of targeted higher-resolution observations enables higher temporal and spatial resolution tracking of flooding events is also documented.

The Earth Observing One (EO-1) mission has been a pathfinder in demonstrating autonomous operations paradigms. In 2010-2012 (and continuing), EO-1 has been supporting sensorweb operations to enable autonomous tracking of flooding in Thailand. In this approach, the Moderate Imaging Spectrometer (MODIS) is used to perform broad-scale monitoring to track flooding at the regional level (500 m/pixel) and EO-1 is autonomously tasked in response to alerts to acquire higher resolution (30 m/pixel) Advanced Land Imager (ALI) data. This data is then automatically processed to derive products such as surface water extent and volumetric water estimates. These products are then automatically pushed to relevant authorities in Thailand for use in damage estimation, relief efforts, and damage mitigation. EO-1 has served as a testbed and pathfinder to this type of sensorweb operations. Beginning with EO-1, these techniques for monitoring are being extended to other space sensors (such as Radarsat-2, Landsat, Worldview-2, TRMM) and integrated with hydrological models, and integration with in-situ sensors.

Several space-based assets (Terra, Aqua, Earth Observing One) have been integrated into a sensorweb to monitor flooding in Thailand. In this approach, the Moderate Imaging Spectrometer (MODIS) data from Terra and Aqua is used to perform broad-scale monitoring to track flooding at the regional level (250m/pixel) and EO-1 is autonomously tasked in response to alerts to acquire higher resolution (30m/pixel) Advanced Land Imager (ALI) data. This data is then automatically processed to derive products such as surface water extent and volumetric water estimates. These products are then automatically pushed to organizations in Thailand for use in damage estimation, relief efforts, and damage mitigation. More recently, this sensorweb structure has been used to request imagery, access imagery, and process high-resolution (several m to 30m), targetable asset imagery from commercial assets including Worldview-2, Ikonos, Radarsat-2, Landsat-7, and Geo-Eye-1. We describe the overall sensorweb framework as well as new workflows and products made possible via these extensions.

This paper addresses a computer system capable of integrating, storing, analyzing, and displaying geographic information. Currently, the Geographic Information System is not only limited to cartography but also related to various data management such as natural resource management, environmental impact assessment, and etc. The Internet system allows more geographic information sharing as many users can access at the same time. The Internet GIS system including Management Information is a research and application area that utilizes the Internet systems to facilitate the access, processing, and dissemination of geographic or non-geographic information. In this study, the Internet GIS application has been developed for the water resource management as it gives users better understanding of the overall country watershed. Internet GIS; Geographic Information System; Management Information System; water resource management;

Thailand's climate is tropical; it has high temperature and humidity and is dominated by monsoons. The average rainfall, recorded from 1950 to 1997, is 1,374mm/year or equivalent to 702,610 Million Cubic Meter (MCM), which is much greater than the global average rainfall of 990mm/year. During the past 10 years, the annual rainfall has been positively deviant from the annual average (Fig. 1). In addition, results from climate-model simulations for the 21st century (Bates, Kundzewicz, Wu, & Palutikof, 2008) show that droughts should not be a problem in Thailand because of increased precipitation, soil moisture, and runoff and Thailand only experiences slight increase in evaporation. Therefore, it can be said that “droughts” change from country to country.

The most general definition of climate change is a change in the statistical properties of the climate system when considered over periods of decades or longer, regardless of cause. However this change could be very different from one region to another. Glaciers are considered among the most sensitive indicators of climate change, advancing when climate cools and retreating when climate warms.

This paper describes the capability of computer system for integrating, analyzing, and displaying geographically referenced information. The Internet GIS application has been developed for water resource management as it gives users better understanding of the overall country watershed. In addition, an integration of the GIS information technologies is also presented in this paper.

Abstract The bioinformatics research area is now faced with a mountain of ever-increasing and distributed information. For example, finding a single gene of the Oryza sativa (rice) genome one must spend weeks, if not months, wandering through approximately 40 million base pairs. These data are scattered in many data repositories. Thus, not only do we need an efficient tool to visualize and analyze DNA data, but the integration and exchange of information on a particular gene or coding regions from different international collaborative databases needs to be done in a careful, but robust manner as well. This research suggests a feasible means to overcome these problems by employing two main technologies. To support the exchange and communication between several sources of data, the grid database technology will be employed on the fast Internet2 backbone. Then, XML-based DNA data will be transported between collaborative sources for further analysis and representation. A preliminary version of our Web-based viewer for the XML data of the Oryza sativa genome is presented to illustrate the idea.

Thailand’s climate is tropical; it has high temperature and humidity and is dominated by monsoons. The average rainfall, recorded from 1950 to 1997, is 1,374mm/year or equivalent to 702,610 Million Cubic Meter (MCM), which is much greater than the global average rainfall of 990mm/year. During the past 10 years, the annual rainfall has been positively deviant from the annual average (Fig. 1). In addition, results from climate-model simulations for the 21st century (Bates, Kundzewicz, Wu, & Palutikof, 2008) show that droughts should not be a problem in Thailand because of increased precipitation, soil moisture, and runoff and Thailand only experiences slight increase in evaporation. Therefore, it can be said that ‘‘droughts’’ change from country to country. From the data of the Department of Disaster Prevention and Mitigation, it is evident that several parts of Thailand continue to suffer drought problems repeatedly and some parts experience both flood and drought, mostly in the same year (Fig. 2). Droughts often occur during the dry season