日本リモートセンシング学会誌
Online ISSN : 1883-1184
Print ISSN : 0289-7911
ISSN-L : 0289-7911
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選択された号の論文の12件中1~12を表示しています
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森林リモートセンシング特集 序文
森林リモートセンシング特集 総説
  • 林 真智
    2020 年 40 巻 1 号 p. 2-12
    発行日: 2020/01/31
    公開日: 2020/07/07
    ジャーナル フリー

    Accurate estimation of forest biomass is essential for the quantification of global carbon stocks and the assessment of climate change impacts. In the last 3 decades, remote sensing technologies, both airborne and spaceborne, have become the primary source for biomass estimation at large scales. Either optical, LiDAR or SAR sensors are commonly used to retrieve biomass estimation with each sensor type having its advantages and disadvantages. LiDAR delivers highly accurate results but cannot observe forest continuously over large areas, while optical sensors and SAR have a very good coverage but lack accuracy when the biomass exceeds the saturation level. To overcome these problems, in recent years great attention has been paid to the development of methods for fusing two or more sensors, and several global- to continental-scale forest biomass maps are developed. This paper also discusses several planned LiDAR and SAR satellite missions which are expected to greatly advance the global biomass research in the near future.

森林リモートセンシング特集 論文
  • Takeshi HOSHIKAWA, Kazukiyo YAMAMOTO
    2020 年 40 巻 1 号 p. 13-19
    発行日: 2020/01/31
    公開日: 2020/07/07
    ジャーナル フリー

    Pine wilt disease is one of the most destructive disease of pine forests. It is important to detect and exterminate infected trees for preservation of the forest. We demonstrated a novel method combining individual tree detection (ITD) and classification by logistic regression using unmanned aerial vehicle (UAV) images for the mapping of infected trees. In the ITD phase, 50 % and 84 % of damaged trees were automatically detected from the 3D point cloud generated from the UAV images using the local maximum filter. These rates of detection were comparable to previous studies that used UAV imagery. Subsequently, five vegetation indices calculated from multispectral and visible color (RGB) images were used. Among the vegetation indices, normalized difference vegetation index (NDVI), normalized difference red edge index (NDRE), and vegetation atmospherically resistant index (VARI) were preferable explanatory variable in the logistic regression to divide damaged and undamaged trees. The accuracy of these models ranged from 98 % to 100 % and the F-measure ranged from 94 % to 100 %. The best model, the logistic regression model using VARI as the explanatory variable, was then tested using five datasets to evaluate general performance. Each model showed explicitly high accuracy ranging from 95 % to 100 %. The best accuracy when considering the ITD and classification was 84 %. To map pine wilt disease, the proposed method is suitable for practical use due to its high-efficient and low-cost.

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