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Tetsuji Ota Last modified date:2021.06.11





Homepage
https://kyushu-u.pure.elsevier.com/en/persons/tetsuji-ota
 Reseacher Profiling Tool Kyushu University Pure
Academic Degree
PhD
Field of Specialization
Forest Management
Research
Research Interests
  • Forest monitoring using remote sensing
    keyword : Remote sensing, Forest management
    2012.04~2020.03.
Academic Activities
Papers
1. Tetsuji Ota, Oumer S. Ahmed, Steven E. Franklin, Michael A. Wulder, Tsuyoshi Kajisa, Nobuya Mizoue, Shigejiro Yoshida, Gen Takao, Yasumasa Hirata, Naoyuki Furuya, Takio Sano, Sokh Heng, Ma Vuthy, Estimation of Airborne Lidar-Derived Tropical Forest Canopy Height Using Landsat Time Series in Cambodia, Remote sensing, doi:10.3390/rs61110750, 6, 11, 10750-10772, 2014.11, [URL], In this study, we test and demonstrate the utility of disturbance and recovery information derived from annual Landsat time series to predict current forest vertical structure (as compared to the more common approaches, that consider a sample of airborne Lidar and single-date Landsat derived variables). Mean Canopy Height (MCH) was estimated separately using single date, time series, and the combination of single date and time series variables in multiple regression and random forest (RF) models. The combination of single date and time series variables, which integrate disturbance history over the entire time series, overall provided better MCH prediction than using either of the two sets of variables separately. In general, the RF models resulted in improved performance in all estimates over those using multiple regression. The lowest validation error was obtained using Landsat time series variables in a RF model (R2 = 0.75 and RMSE = 2.81 m). Combining single date and time series data was more effective when the RF model was used (opposed to multiple regression). The RMSE for RF mean canopy height prediction was reduced by 13.5% when combining the two sets of variables as compared to the 3.6% RMSE decline presented by multiple regression. This study demonstrates the value of airborne Lidar and long term Landsat observations to generate estimates of forest canopy height using the random forest algorithm..