Kotaro Iizuka

The 2024 Noto Peninsula earthquake (Mw 7.5) caused extensive damage in Ishikawa Prefecture, Japan, and surrounding areas, with considerable coastal uplift and tsunami flooding. Past 100 years’ records show no earthquake above Mw 7.0 in the Noto Peninsula, so for everyone alive today, this event is truly without precedent. Therefore, we aimed to support disaster prevention education by developing teaching materials using unmanned aerial vehicles (UAVs) based on digitally archived topographic changes. High-definition topographic data collected from multiple UAV surveys were processed into digital and analog formats, including 3D models, spherical panorama images, and 3D printings. These materials were designed to provide detailed and intuitive representations of post-disaster landforms and were used as educational tools in schools. The learning materials were introduced during a workshop for disaster-affected teachers, featuring hands-on activities to help participants familiarize themselves with the materials, and explore their integration into geography and science classes. Feedback from participants indicated that these tools were highly effective in enhancing classroom learning. The results of this study are expected to contribute to preserving disaster records while enhancing disaster awareness in educational settings and local communities.

Abstract Recent advances in surveying technologies have allowed high precision measurement and monitoring of changes in the Earth's surface position over time. Lateral mass movements remain an under‐explored aspect of topographic analyses despite the plethora of dynamic processes affecting surface position. This research introduces the use of a two‐dimensional optical flow algorithm to estimate the three‐dimensional relationships between a topographic surface and itself after displacement using a time series of bare‐earth digital elevation models (DEMs). Several indices are derived from the motion fields estimated by the optical flow algorithm to define a set of properties that are used to quantitatively characterize surface motion. A preliminary investigation into the efficacy of these surface properties for analysing dynamic topography was conducted on a creeping landslide in Biratori, Hokkaido, Japan. An accuracy assessment demonstrated strong agreement between observed and estimated displacements, with concordance correlation coefficients of 0.87 for both ‐ and ‐axis displacement, and submetre root mean squared error of 0.47 and 0.72 m for the ‐ and ‐axis, respectively. A segmentation algorithm was applied to the translation distance and azimuth angle properties to assess the accuracy with which these variables delineate the landslide, based on the expectation that landslide motion characteristics are spatially contiguous and internally homogeneous. Segments overlapped with the landslide boundary area by up to 70%, and segments within the landslide boundary were consistently among the largest in the segment‐area distribution. The results demonstrated how these surface properties can form simple and effective heuristics for analysing creeping landslides with strong potential for other dynamic surface phenomena.

In recent years, secondary schools and university departments related to geography teach various topics using Geographic Information Systems (GIS). Especially, WebGIS, available online without installing additional software, is a recognized powerful educational tool for educators and students. On the other hand, research on how students perceive and utilize digital maps to understand geographical objects and investigate the complexity of such learning is insufficient. Therefore, we initiated a study to clarify these research questions by implementing Disaster Risk Reduction (DRR) education using a digital hazard map developed with WebGIS technology, focusing on young people in Japan, including secondary school and university students. The results indicate that DRR education using a simple digital map is helpful for a wide range of students regardless of age. Still, some perceive difficulty in learning using a digital hazard map. Map representation strongly affects students’ interpretation of vulnerable areas. The maps’ layers and functions are better to add gradually corresponding to students’ ability and familiarity with GIS in the initial stage of geography education using maps to prevent students’ negative impressions caused by complex issues and technical problems.

Global warming and climate change are significantly impacting local climates, causing more intense heat during the summer season, which poses risks to individuals with pre-existing health conditions and negatively affects overall human health. While various studies have examined the Surface Urban Heat Island (SUHI) phenomenon, these studies often focus on small to large geographic regions using low-to-moderate-resolution data, highlighting general thermal trends across large administrative areas. However, there is a growing need for methods that can detect microscale thermal patterns in environments familiar to urban residents, such as streets and alleys. The temperature-humidity index (THI), which incorporates both temperature and humidity data, serves as a critical measure of human-perceived heat. However, few studies have explored microscale THI variations within urban settings and identified potential THI hotspots at a local level where SUHI effects are pronounced. This research aims to address this gap by estimating THI at a finer resolution scale using data from multiple sensor platforms. We developed a model with the random forest algorithm to assess THI trends at a resolution of 0.5 m, utilizing various variables from different sources, including Landsat 8 land surface temperature (LST), unmanned aerial system (UAS)-derived LST, Sentinel-2 NDVI and NDMI, a wind exposure index, solar radiation modeled from aircraft and UAS-derived Digital Surface Models, and vehicle density and building floor area from social big data. Two models were constructed with different variables: Modelnatural, which includes variables related to only natural factors, and Modelmix, which includes all variables, including anthropogenic factors. The two models were compared to reveal how each source contributes to the model development and SUHI effects. The results show significant improvements, as Modelnatural had a fitting R2 = 0.5846, a root mean square error (RMSE) = 0.5936 and a mean absolute error (MAE) = 0.4294. Moreover, when anthropogenic factors were introduced, Modelmix performed even better, with R2 = 0.9638, RMSE = 0.1751, and MAE = 0.1065 (n = 923). This study contributes to the future of microscale SUHI analysis and offers important insights into urban planning and smart city development.