Abstract
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.
Highlights
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]
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
We focused on the detection of the windthrow and landslides present immediately after all four typhoons had crossed the island of Hokkaido by 31 August 2016
Summary
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]. One of the ways to obtain information on land cover change caused by 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]. 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]. If the temporal resolution is low, it may not be possible to check the effects of certain natural disturbances on land cover [12]. Higher-spatial-resolution images reduce the occurrence of mixels [13], improving the identification of damages in the final result
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