Applicability Assessment of a Spatiotemporal Geostatistical Fusion Model for Disaster Monitoring: Two Cases of Flood and Wildfire

A spatial time series geostatistical deconvolution/fusion model (STGDFM), as one of spatiotemporal data fusion model, combines Dense time series data with a Coarse-scale (i.e., DC data) and Sparse time series data with a Fine-scale (i.e., SF data) to generate Synthetic Dense time series data with a Fine-scale (i.e., SDF data). Specifically, STGDFM uses a geostatistics-based spatial time series modeling to capture the temporal trends included in time series DC data. This study evaluated the prediction performance of STGDFM for abrupt changes in reflectance due to disasters in spatiotemporal data fusion, and a spatial and temporal adaptive reflectance fusion model (STARFM) and an enhanced STARFM (ESTARFM) were selected as comparative models. For the applicability assessment, flood and wildfire were selected as case studies. In the case of flood, MODIS-like data (240 m) with spatial resolution converted from Landsat data and Landsat data (30 m) were used as DC and SF data, respectively. In the case of wildfire, MODIS and Landsat data were used as DC and SF data, respectively. The case study results showed that among the three spatiotemporal fusion models, STGDFM presented the best prediction performance with 0.894 to 0.979 at the structure similarity and 0.760 to 0.872 at the R-squared values in the flood- and wildfire-affected areas. Unlike STARFM and ESTARFM that adopt the assumptions for reflectance changes, STGDFM combines the temporal trends using time series DC data. Therefore, STGDFM could capture the abrupt changes in reflectance due to the flood and wildfire. These results indicate that STGDFM can be used for cases where satellite images of appropriate temporal and spatial resolution are difficult to acquire for disaster monitoring.