We investigated the feasibility of using the Himawari-8, a Japanese meteorological geostationary satellite with high temporal resolution (receiving data at 10-minute intervals), to measure the time from fire outbreak to extinction. We selected the study area in Central Kalimantan, in Indonesia, containing peatland. We first investigated which band of Advanced Himawari Imager (AHI) is more sensitive for detecting fire areas and which band is suitable for tracking time-series change of fire area. Therefore, we first used the Landsat 8 data of 2015/10/22 for visual interpretation of the fire area and then selected pixels corresponding to the fire area from the AHI data acquired at about the same time as the Landsat 8 data. We found that band 7 of the AHI is the most suitable for detecting fire area even if the ratio of fire area in one pixel is low, and we assumed that not only high values of band 7 but also the short-term fluctuations are characteristics of fires. To establish this assumption, we separated the value of band 7 into 2 components, mean value and short-term fluctuations, μtm, and Dtm, respectively. Then, we applied these two indices to the data for September 2015 and confirmed how fire occurrence and extinguishment were captured by these two indices.
The Normalized Difference Vegetation Index (NDVI) is effective for expressing vegetation status and quantified vegetation attributes. However, optical remote sensing imagery is limited by cloud contamination. On the other hand, synthetic aperture radar (SAR) can work under all weather conditions and overcome this disadvantage of optical remote sensing while it is difficult to recognize the land cover types visually due to the mechanisms of SAR imaging and the speckle noise. In this study, the image-to-image translation methods (pix2pix and CycleGAN) were used to convert Sentinel-1 C-SAR images into Sentinel-2 NDVI images. The results show that the combination of CycleGAN and VH polarization data works well during the growing season of beetroots and the simulated NDVI values were significantly correlated with the real NDVI values.