The number of intense tropical cyclones is expected to increase in the future, causing severe damage to forest ecosystems. Remote sensing plays an important role in detecting changes in land cover caused by these tropical storms. Remote sensing techniques have been widely used in different phases of disaster risk management because they can deliver information rapidly to the concerned parties. Although remote sensing technology is already available, an examination of appropriate methods based on the type of disaster is still missing. Our goal is to compare the suitability of three different conventional classification methods for fast and easy change detection analysis using high-spatial-resolution and high-temporal-resolution remote sensing imagery to identify areas with windthrow and landslides caused by typhoons. In August 2016, four typhoons hit Hokkaido, the northern island of Japan, creating large areas of windthrow and landslides. We compared the normalized difference vegetation index (NDVI) filtering method, the spectral angle mapper (SAM) method, and the support vector machine (SVM) method to identify windthrow and landslides in two different study areas in southwestern Hokkaido. These methodologies were evaluated using PlanetScope data with a resolution of 3 m/px and validated with reference data based on Worldview2 data with a very high resolution of 0.46 m/px. The results showed that all three methods, when applied to high-spatial-resolution imagery, can deliver sufficient results for windthrow and landslide detection. In particular, the SAM method performed better at windthrow detection, and the NDVI filtering method performed better at landslide detection.

The development of UAV technologies offers practical methods to create landcover maps for monitoring and management of areas affected by natural disasters such as landslides. The present study aims at comparing the capability of two different types of UAV to deliver precise information, in order to characterize vegetation at landslide areas over a period of months. For the comparison, an RGB UAV and a Multispectral UAV were used to identify three different classes: vegetation, bare soil, and dead matter, from April to July 2021. The results showed high overall accuracy values (>95%) for the Multispectral UAV, as compared to the RGB UAV, which had lower overall accuracies. Although having lower overall accuracies, the vegetation class of the RGB UAV presented high producer’s and user’s accuracy over time, comparable to the Multispectral UAV results. Image quality played an important role in this study, where higher accuracy values were found on cloudy days. Both RGB and Multispectral UAVs presented similar patterns of vegetation, bare soil, and dead matter classes, where the increase in vegetation class was consistent with the decrease in bare soil and dead matter class. The present study suggests that the Multispectral UAV is more suitable in characterizing vegetation, bare soil, and dead matter classes on landslide areas while the RGB UAV can deliver reliable information for vegetation monitoring.

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