## 1 Introduction  
Typhoons are the main natural hazard affecting forest ecosystems in eastern Asia [1], and strong winds and heavy rains make forests vulnerable to damage [2, 3]. Although projections under the Intergovernmental Panel on Climate Change (IPCC) A1B scenario show that there will be a decrease in the number of tropical cyclones globally due to climate change, the frequency of intense tropical cyclones is expected to increase by the end of the twenty-first century [4], leading to an increase in windthrow and landslide disturbances affecting forest ecosystems. Remote sensing is a technology that is widely used in different phases of disaster risk management [5, 6] and rapidly delivers information to support planners, scientists, and decision-makers [7, 8]. One of the ways to obtain information on land cover change causedby natural disasters using remotely sensed data is through visual interpretation of satellite data by manual digitalization of patches of change, which can be time-consuming and ambiguous in terms of the necessary criteria, especially when large areas are disturbed [9; 10]. Therefore, the automatic classification of remotely sensed data is more suitable for identifying land cover changes, with some caveats, such as the limited pattern recognition ability compared to that of the human brain [11]. Apart from an automatic classification process, the input data are of extreme importance for change detection analysis. If the temporal resolution is low, it may not be possible to check the effects of certain natural disturbances on land cover [12]. In contrast, higher-spatial-resolution images also reduce the occurrence of mixels [13], improving the identification of damages in the final result. The remote sensing community has also contributed to the rapid evolution of this application by developing more accurate and efficient change detection techniques, such as deep learning using very-high-resolution images [14], unmanned aerial vehicles for mapping [15], and postclassification methods to improve accuracy [16]. Although all these technologies are available, an examination of appropriate methods based on the type of disaster for fast and reliable land cover change detection is still missing [17]. Additionally, the evolution of remote sensing has made this technology accessible to a broader range of end-users [18; 19], which do not necessarily have sufficient skill levels to apply complex algorithms for change detection. In 2016, four typhoons hit the island of Hokkaido, Japan, damaging a total of 9,000 ha of forest and triggering two major types of damage: windthrow and landslides [20]. Images taken from before and after the arrival of typhoons are used to compare the suitability of three different conventional change detection methodologies to identify windthrow and landslide damage: the normalized difference vegetation index (NDVI) filtering method, the spectral angle mapper (SAM) method, and the support vector machine (SVM) method. This study selected PlanetScope imagery with high spatial and temporal resolutions to ensure the reliability of the change detection analysis [21]. The methodologies were chosen based on their relative operational simplicity, fast processing, and satisfactory results in previous studies. The NDVI filtering method presented by [PERSON] [22] was used to preliminarily identify landslides and was able to effectively detect landslides in combination with change vector analysis. For windthrow detection, the SAM classifier was considered fast and relatively easy to apply, yielding satisfactory results in combination with multivariate alteration detection postprocessing [17]. The SVM method, although more sophisticated than the other methods, is implemented as part of the Google Earth Engine platform, which makes it easy to apply, and using cloud computing delivers fast results [23].