Processing of near real time land surface temperature and its application in forecasting forest fire danger conditions Near real time (NRT) remote sensing derived land surface temperature (Ts) data has an utmost importance in various applications of natural hazards and disasters. Spacebased instrument MODIS (moderate resolution imaging spectroradiometer) acquired NRT data products of Ts are made available for the users by LANCE (Land, Atmosphere Near realtime Capability) for Earth Observing System (EOS) of NASA (National Aeronautics and Space Administration) free of cost. Such Ts products are swath data with 5 min temporal increments of satellite acquisition, and the average latency is 60125 min to be available in public domain. The swath data of Ts requires a specialized tool, i.e., HEG (HDFEOS to GeoTIFF conversion tool) to process and make the data useful for further analysis. However, the file naming convention of the available swath data files in LANCE is not appropriate to download for an area of interest (AOI) to be processed by HEG. In this study, we developed a methodalgorithm to overcome such issues in identifying the appropriate swath data files for an AOI that would be able to further processes supported by the HEG. In this case, we used Terra MODIS acquired NRT swath data of Ts, and further applied it to an existing framework of forecasting forest fires (as a case study) for the performance evaluation of our processed Ts. We were successful in selecting appropriate swath data files of Ts for our study area that was further processed by HEG, and finally were able to generate fire danger map in the existing forecasting model. Our proposed methodalgorithm could be applied on any swath data product available in LANCE for any location in the world.

Visualizing and labeling dense multisensor earth observation time series: The EO Time Series Viewer Abstract   Multispectral spaceborne sensors with different spatial resolutions produce Earth observation (EO) time series (TS) with global coverage. The interactive visualization and interpretation of TS is essential to better understand changes in landuse and landcover and to extract reference information for model calibration and validation. However, available software tools are often limited to specific sensors or optimized for applicationspecific visualizations. To overcome these limitations, we developed the EO Time Series Viewer, a free and open source QGIS plugin for userfriendly visualization, interpretation and labeling of multisensor TS data. The EO Time Series Viewer (i) combines advantages of spatial, spectral and temporal data visualization concepts that are so far not available in a single tool, (ii) provides maximum flexibility in terms of supported data formats, (iii) minimizes the userinteractions required to load and visualize multisensor TS data and (iv) speedsup labeling of TS data based on enhanced GIS vector tools and formats.