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Ground surface changes caused by freeze-thaw action affect agriculture and forestry, as well as artificial structures such as roads. In this study, an area is examined in which reforestation is urgently needed but the growth of naturally restored seedlings and planted trees is impaired by freeze-thaw action. Thus, a method of measuring freeze-thaw induced ground surface changes and mitigating their negative impacts is needed. Real-time kinematic unmanned aerial vehicle and structure-from-motion multiview stereophotogrammetry are used on slope-failure sites in forest areas to observe the ground surface changes caused by freeze-thaw action over a wide area, in a nondestructive manner. The slope characteristics influencing the ground-surface changes were examined, and it was confirmed that it is possible to observe minute topographical changes of less than ±5 cm resulting from freeze-thaw action. Statistical models show that the amount of freeze-thaw action is mostly linked to the cumulative solar radiation, daily ground-surface temperature range, and topographic-wetness index, which influence the microscale dynamics of the ground surface. The proposed method will be useful for future quantitative assessments of ground-surface conditions. Further, efficient reforestation could be implemented by considering the effects of the factors identified on the amount of freeze-thaw action.
Yasutaka Nakata; Masato Hayamizu; Nobuo Ishiyama; Hiroyuki Torita. Observation of Diurnal Ground Surface Changes Due to Freeze-Thaw Action by Real-Time Kinematic Unmanned Aerial Vehicle. Remote Sensing 2021, 13, 2167 .
AMA StyleYasutaka Nakata, Masato Hayamizu, Nobuo Ishiyama, Hiroyuki Torita. Observation of Diurnal Ground Surface Changes Due to Freeze-Thaw Action by Real-Time Kinematic Unmanned Aerial Vehicle. Remote Sensing. 2021; 13 (11):2167.
Chicago/Turabian StyleYasutaka Nakata; Masato Hayamizu; Nobuo Ishiyama; Hiroyuki Torita. 2021. "Observation of Diurnal Ground Surface Changes Due to Freeze-Thaw Action by Real-Time Kinematic Unmanned Aerial Vehicle." Remote Sensing 13, no. 11: 2167.
The Great East Japan Tsunami, triggered by the earthquake that occurred on March 11, 2011 in the Pacific Ocean, caused significant fatalities and socioeconomic damage. As recovery of a disaster area requires significant time, all possible mitigation measures must be prepared in advance for future events. As a tsunami countermeasure, coastal forests have been acknowledged to considerably reduce tsunami energy and decrease tsunami-related damage. In the Great East Japan tsunami, many trees of coastal forests were damaged by trunk breakage and overturning. This led to further infrastructural damage as the debris were transported landward and seaward by floodwaters. To better protect coastal areas from the secondary effects of tsunamis and reduce tsunami energy, coastal forests must exhibit higher resistance. This research investigated the effect of forestry management by applying different levels of thinning of trees as a means of resistance to tree damage under tsunami events. In October of 1999, study plots were established with different thinning intensities in a mature coastal forest of Pinus thunbergii trees. As a useful indicator of the resistance of coastal forests to tsunamis, the threshold tsunami velocities at which trees in these study plots begin to be destroyed were calculated using a mechanistic model. The results revealed that trunk diameter is the most important parameter for increasing resistance to tsunamis. An analysis of the generalized linear model for diameter growth showed that heavy thinning best enhanced the diameter growth. Therefore, heavy thinning is the most effective approach to increasing the resistance of trees to tsunamis. Considering the relationship between resistance to tsunami and inundation depth, the resistance to tsunami decreased rapidly with increasing inundation depth in all plots. Differences in the resistance to the tsunami were not observed across all plots when the inundation depth exceeded the mean tree height.
Hiroyuki Torita; Kazuhiko Masaka; Norio Tanaka; Kenta Iwasaki; Satosi Hasui; Masato Hayamizu; Yasutaka Nakata. Assessment of the effect of thinning on the resistance of Pinus thunbergii Parlat. trees in mature coastal forests to tsunami fluid forces. Journal of Environmental Management 2021, 284, 111969 .
AMA StyleHiroyuki Torita, Kazuhiko Masaka, Norio Tanaka, Kenta Iwasaki, Satosi Hasui, Masato Hayamizu, Yasutaka Nakata. Assessment of the effect of thinning on the resistance of Pinus thunbergii Parlat. trees in mature coastal forests to tsunami fluid forces. Journal of Environmental Management. 2021; 284 ():111969.
Chicago/Turabian StyleHiroyuki Torita; Kazuhiko Masaka; Norio Tanaka; Kenta Iwasaki; Satosi Hasui; Masato Hayamizu; Yasutaka Nakata. 2021. "Assessment of the effect of thinning on the resistance of Pinus thunbergii Parlat. trees in mature coastal forests to tsunami fluid forces." Journal of Environmental Management 284, no. : 111969.
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.
Kotaro Iizuka; Yuichi S. Hayakawa; Takuro Ogura; Yasutaka Nakata; Yoshiko Kosugi; Taichiro Yonehara. Integration of Multi-Sensor Data to Estimate Plot-Level Stem Volume Using Machine Learning Algorithms–Case Study of Evergreen Conifer Planted Forests in Japan. Remote Sensing 2020, 12, 1649 .
AMA StyleKotaro Iizuka, Yuichi S. Hayakawa, Takuro Ogura, Yasutaka Nakata, Yoshiko Kosugi, Taichiro Yonehara. Integration of Multi-Sensor Data to Estimate Plot-Level Stem Volume Using Machine Learning Algorithms–Case Study of Evergreen Conifer Planted Forests in Japan. Remote Sensing. 2020; 12 (10):1649.
Chicago/Turabian StyleKotaro Iizuka; Yuichi S. Hayakawa; Takuro Ogura; Yasutaka Nakata; Yoshiko Kosugi; Taichiro Yonehara. 2020. "Integration of Multi-Sensor Data to Estimate Plot-Level Stem Volume Using Machine Learning Algorithms–Case Study of Evergreen Conifer Planted Forests in Japan." Remote Sensing 12, no. 10: 1649.