Hotspots Forecasting Using Autoregressive Integrated Moving Average (ARIMA) for Detecting Forest Fires Hotspots are indicators of forest fires that detect a location that has a relatively higher temperature than the surrounding temperature. Hotspots are usually used as indicators or forest and land fires in an area, so the more hotspots, the more potential land fires occur in an area. Although not always more and more hotspots in an area the more the potential for fires. But the hotspot point can indeed be used to identify the initial occurrence of forest and land fires. Pekanbaru Meteorological Station owned by the Meteorology, Climatology and Geophysics Agency (BMKG) via NASAu0027s Terra and Aqua remote sensing satellite detected 103 hotspots indicating forest and land fires in Riau Province, 97 of which are located in Rokan Hilir District. Therefore, based on these problems, the hotspots forecasting model research will be conducted to prevent the expansion of forest and land fires. Forecasting time series uses historical data to predict future data. The time series forecasting model used in this study applies the BoxJenkins procedure to build ARIMA models that are compatible with the data and applies the ARIMA models obtained to make predictions of the occurrence of hotspots in July  December 2019. Based on the research results, the best model is obtained. ARIMA (2,0,2) with nonzero mean with AICc value of 408.62. Model evaluation is used to determine the accuracy of the ARIMA model (2,0,2) in forecasting by looking at the error value. Obtained the Mean Absolute Error of 3.766891 from the test results, so that the accuracy of the model in forecasting is 96.23%.

An enhanced spatiotemporal fusion method xe2x80x93 Implications for coal fire monitoring using satellite imagery Abstract   Many realworld applications require remotely sensed images at both high spatial and temporal resolutions. This requirement, however, is generally not met by single satellite system. A number of spatiotemporal fusion models have been developed to overcome this constraint. Landsat and Visible Infrared Imaging Radiometer Suite (VIIRS) data have been extensively used for detection and monitoring of active fires at different scales. Fusing the data obtained from these sensors will, therefore, significantly contribute to the satellitebased monitoring of fires. Among the available spatiotemporal fusion methods, the spatial and temporal adaptive reflectance fusion model (STARFM) and enhanced STARFM (ESTARFM) algorithms have been widely used for studying the land surface dynamics in the homogeneous and heterogeneous regions. The present study explores the applicability of STARFM and ESTARFM algorithms for fusing the high spatial resolution Landsat8 OLI data with high temporal resolution VIIRS data in the context of active surface coal fire monitoring. Further, a modified version of ESTARFM algorithm, referred as modifiedESTARFM, is developed to improve the performance of the fusion model. Jharia coalfield (India), known for widespread occurrences of coal fires, is taken as the study area. The qualitative and quantitative assessments of the predicted (synthetic) Landsatlike images from different algorithms (STARFM, modifiedSTARFM, ESTARFM, modifiedESTARFM) indicate that the modifiedESTARFM outperforms the other fusion approaches used in this study. Considering the advantages, limitations and performance of the algorithms used, modifiedESTARFM along with STARFM can be used for surface coal fire monitoring. The study will not only contribute to remote sensing based coal fire studies but also to other applications, such as forest fires, crop residue burning, land cover and land use change, vegetation phenology, etc.