研究等業績 - 原著論文 - 河合　隆行

Diet of Siberian marmot in the forest-steppe zone of Mongolia: grass or forb?

Takatsuki S. ,Takayuki Kawai, Ryouta Tsuchiya, Tserenpurev Bat-Oyun

Mammal Study ( Mammal Study )  50 ( 4 ) 381 - 385   2025年07月  [査読有り]

研究論文（学術雑誌）  

DOI

Sand movement analysis of the Tottori Sand Dunes using aerial photogrammetry by UAVs

Tadaomi SAITO, Takayuki KAWAI, Yoshiki TAKAHARA, Dai NAGAMATSU, Koji Inosako

Journal of Arid Land Studies ( 日本沙漠学会 )  34 ( S ) 87 - 91   2024年  [査読有り]

研究論文（学術雑誌）  

<p>The Tottori Sand Dunes, located along Japan’s Sanin Coast, are a UNESCO Global Geopark and a popular tourist attraction with millions of annual visitors. Monitoring of environmental conditions including sand movement is needed for the protection and utilization of the dunes. The objectives of this paper were (i) accuracy evaluation of aerial photogrammetry using three UAVs (Unmanned Aerial Vehicles: Drones) and two types of structure from motion (SfM) software and (ii) sand movement analysis in the dunes using the best results. For each flight, about 1800 vertical photographs (side overlap: 60%, and top-bottom overlap: 80%) were taken at altitudes of approximately 100 m above ground level using three UAVs for a 1500 km² area of the dunes. The obtained images were processed using two SfM software, PhotoScan Professional (currently Metashpae: Agisoft) and Pix4Dmapper (Pix4D), for building 3D models. The position data of survey piles in the dunes were used as the GCP (ground control point) setting for image processing and model evaluation. The results revealed that the accuracy of the models was significantly affected by the difference in the SfM software but was not greatly influenced by the difference in UAVs. The models generated from PhotoScan generally curved and had larger errors at the center parts and around the edges of the models (average RMSE=1.74 m), while the models generated from Pix4Dmapper had smaller errors throughout the entire areas (average RMSE=0.38 m). From the comparison of the models obtained from Pix4Dmapper, the characteristics of sand movement such as the movement of the ridge line towards the inland area, were elucidated in the dunes from 2016 to 2018.</p>

DOI CiNii Research

Improving satellite-based global rainfall erosivity estimates through merging with gauge data

Fenta A.A.

Journal of Hydrology ( Journal of Hydrology )  620   2023年05月

研究論文（学術雑誌）  

Rainfall erosivity is a key factor that influences soil erosion by water. Rain-gauge measurements are commonly used to estimate rainfall erosivity. However, long-term gauge records with sub-hourly resolutions are lacking in large parts of the world. Satellite observations provide spatially continuous estimates of rainfall, but they are subject to biases that affect estimates of rainfall erosivity. We employed a novel approach to map global rainfall erosivity based on a high-temporal-resolution (30-min), long-term (2001–2020) satellite-based precipitation product—the Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (GPM-IMERG)—and mean annual rainfall erosivity from the Global Rainfall Erosivity Database (GloREDa) stations (n = 3286). We used a residual-based merging scheme to integrate GPM-IMERG-based rainfall erosivity with GloREDa using Geographically Weighted Regression (GWR). The accuracy of the GWR-based merging scheme was evaluated with a 10-fold cross-validation against GloREDa stations. Based on GPM-IMERG-only, the global mean annual rainfall erosivity was estimated to be 1173 MJ mm ha−1 h−1 yr−1 with a standard deviation of 1736 MJ mm ha−1 h−1 yr−1. The mean value estimated via GPM-IMERG merged with GloREDa was 2020 MJ mm ha−1 h−1 yr−1 with a standard deviation of 3415 MJ mm ha−1 h−1 yr−1. Overall, GPM-IMERG-only estimates underestimated rainfall erosivity. The underestimations were greatest in areas of high rainfall erosivity. The accuracy of rainfall erosivity estimates from GPM-IMERG merged with GloREDa substantially improved (Nash-Sutcliffe efficiency = 0.83, percent bias = −2.4%, and root mean square error = 1122 MJ mm ha−1 h−1 yr−1) compared to estimates by GPM-IMERG-only (Nash-Sutcliffe efficiency = 0.51, percent bias = 27.8%, and root mean square error = 1730 MJ mm ha−1 h−1 yr−1). Improving satellite-based global rainfall erosivity estimates through integrating with gauge data is relevant as it can contribute to enhancing soil erosion modeling and, in turn, support land degradation neutrality programs.

DOI

Occurrence of wet and dry spells of rainfall in Bagan, Myanmar

Yasuda H.

Journal of Earth System Science ( Journal of Earth System Science )  132 ( 1 )   2023年03月

研究論文（学術雑誌）  

DOI

Water level change of Lake Tana, source of the Blue Nile: Prediction using teleconnections with sea surface temperatures

Yasuda H.

Journal of Great Lakes Research ( Journal of Great Lakes Research )  48 ( 2 ) 468 - 477   2022年04月  [査読有り]

研究論文（学術雑誌）   国際共著

Water level change of Lake Tana, the source of the Blue Nile was analyzed. Correlations between the water level change and global sea surface temperature (GSST) were calculated and teleconnections were found. Prediction of water level was performed using a recurrent artificial neural network model. First, the seasonal change of water level was divided into three phases, the rising, recession 1, and recession 2 phases. The water level increased during the rising phase, decreased rapidly during the recession 1 phase, and decreased at a uniform rate during the recession 2 phase. To find teleconnections of the water level change in the rising phase with GSST, correlations between the level change and GSST were calculated. Sea regions on the Pacific Ocean indicated significant correlations with the level change at lag 0–1 month and lag 6–7 months. There was a strongly correlated sea zone over the western Pacific Ocean at time lags of 6–7 months. To predict water level change, SST time series of the correlated zone was applied to a recurrent neural network model. Predictions of changes of the rise of the water level of Lake Tana during the rainy season from teleconnections with SSTs via the neural network model simulated the observed changes well (r = 0.795). Prediction of the changes of Lake Tana's water level with a lead time of 6–7 months can greatly facilitate management of the lake's water resources.

DOI

Teleconnection of rainfall time series in the central Nile Basin with sea surface temperature

Yasuda H.

Paddy and Water Environment ( Paddy and Water Environment )  16 ( 4 ) 805 - 821   2018年10月  [査読有り]

研究論文（学術雑誌）   国際共著

DOI