The development of new methods for estimating precise forest structure parameters is essential for the quantitative evaluation of forest resources. Conventional use of satellite image data, increasing use of terrestrial laser scanning (TLS), and emerging trends in the use of unmanned aerial systems (UASs) highlight the importance of modern technologies in the realm of forest observation. Each technology has different advantages, and this work seeks to incorporate multiple satellite, TLS- and UAS-based remote sensing data sets to improve the ability to estimate forest structure parameters. In this paper, two regression analysis approaches are considered for the estimation: random forest regression (RFR) and support vector regression (SVR). To collect the dependent variable, in situ measurements of individual tree parameters (tree height and diameter at breast height (DBH)) were taken in a Japanese cypress forest using the nondestructive TLS method, which scans the forest to obtain dense and accurate point clouds under the tree canopy. Based on the TLS data, the stem volume was then computed and treated as ground truth information. Topographic and UAS information was then used to calculate various remotely sensed explanatory variables, such as canopy size, canopy cover, and tree height. Canopy cover and canopy shapes were computed via the orthoimages derived from the UAS and watershed segmentation method, respectively. Tree height was computed by combining the digital surface model (DSM) from the UAS and the digital terrain model (DTM) from the TLS data. Topographic variables were computed from the DTM. The backscattering intensity in the satellite imagery was obtained based on L-band (Advanced Land Observing Satellite-2 (ALOS-2) Phased Array type L-band Synthetic Aperture Radar-2 (PALSAR-2)) and C-band (Sentinel-1) synthetic aperture radar (SAR). All satellite (10–25 m resolution), TLS (3.4 mm resolution) and UAS (2.3–4.6 cm resolution) data were then combined, and RFR and SVR were trained; the resulting predictive powers were then compared. The RFR method yielded fitting R2 up to 0.665 and RMSE up to 66.87 m3/ha (rRMSE = 11.95%) depending on the input variables (best result with canopy height, canopy size, canopy cover, and Sentinel-1 data), and the SVR method showed fitting R2 up to 0.519 and RMSE up to 80.12 m3/ha (rRMSE = 12.67%). The RFR outperformed the SVR method, which could delineate the relationship between the variables for better model accuracy. This work has demonstrated that incorporating various remote sensing data to satellite data, especially adding finer resolution data, can provide good estimates of forest parameters at a plot level (10 by 10 m), potentially allowing advancements in precision forestry.

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<p>Effective geomorphological education for young students such as university undergraduates and high school students is crucial for fostering future geomorphologists and the long-term development of Geomorphology. As an outreach activity, geomorphological education for common people is also meaningful. Especially, education related to geomorphological hazards for citizens will lead to disaster risk reduction. We have been developing materials and curricula for geomorphological education using GIS, Internet technology, close-range remote sensing, and virtual reality, and have applied them to practical courses in high school classes and social events in Japan and China. The developed materials include: 1) online resources for learning GIS operations including geomorphometric analysis, 2) Web-based online GIS for a better understanding of flood hazard maps in relation to landforms, 3) explanatory materials of typical landforms in Japan based on photographs and topographic data obtained by Unmanned Aerial Vehicles (UAVs), and 4) visual contents for virtual tours of geomorphological sites such as a coastal cliff and an underground cave. This presentation introduces the main points of our educational activities and discusses their implications to provide future perspectives.</p>

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<p>The 3D models creating by SfM (Structure-from-Motion) photogrammetry became one of the important and convenient methods for any kinds of objects on geomorphology, geoheritage, or geoarchaeological fields. These objects are landforms, monuments, buildings, relics and so on. In order to evaluate these objects, it is necessary to collect morphological characteristics, and then proceeding to decide investigating points or areas of these materials.</p><p>The progress of this methods developed significantly, however, there have been still remained difficulties depending on the objects. For example, it is difficult to create 3D models that the object is too flat, too dark, and/or any restricts of combination of target size and focusing distances. The present study attempts to these difficulties by targeting to narrow and dark underground space. The investigating object is an archaeological man-made cave, called Taya Cave, in central Japan. It was excavated in 13 century originally and used as study areas for Buddhists by making Buddhism bas-reliefs. The cave has a total length of 570 m underground passage with a three-layer structure. The cave also has several domes connected by narrow paths. The present study tried to make a 3D model of this complicated, dark and narrow cave by SfM photogrammetry. In order to concur to make 3D models for the whole area of the cave, it is useful making chunks; separating several areas of simple morphology and then compiled. When facing narrow path, it is better to take photographs not by perpendicularly but by inclinedly. Furthermore, it is important to use strong light with attach to camera. After obtained the image data of the whole cave, the accuracy of the created model was evaluated. The results were that the accuracy of horizontal distances are higher than that of vertical distances. </p>