光学理論モデルのセミパラメトリック表現に基づく浅水域の汎用水深分布予測法

• 神野 有生

  東京大学大学院新領域創成科学研究科社会文化環境学

• 鯉渕 幸生

  東京大学大学院新領域創成科学研究科社会文化環境学

• 竹内 渉

  東京大学生産技術研究所人間・社会系部門

• 磯部 雅彦

  東京大学大学院新領域創成科学研究科社会文化環境学

書誌事項

タイトル別名

• A Generalized Satellite-based Method of Water Depth Mapping with a Semiparametric Optical Model
• コウガク リロン モデル ノ セミパラメトリック ヒョウゲン ニ モトズク センスイイキ ノ ハンヨウ スイシン ブンプ ヨソクホウ

抄録

Shallow water depth is one of the important factors in science and coastal environmental management. However, in-situ measurement is quite costly and time-consuming. Past research efforts have provided a number of optically-based methods to estimate shallow water depth distribution from satellite image, but they cannot properly handle with the heterogeneity in bottom sediment distribution because they require image-specific assumptions or additional information on bottom reflectivity. It is therefore indispensable to develop a method that can be applied more generally to water areas with inhomogeneous bottom material.<BR>In any application of depth prediction methods, we need depth data for some points to validate the results. A leave-one-out cross validation technique enables us to use the data for predictive model building without degrading the reliability of prediction error evaluation. From this standpoint, we present a new generalized method over the previous methodologies by utilizing depth measurement data.<BR>In the new method, the bottom reflection term of the optical model is assumed to be a nonparametric function of the depth-independent variables (bottom indexes), which can be calculated from the brightness values of the pixels. In this way, the water depth is explained by a semiparametric regression model. The ratios of the diffuse attenuation coefficients, which are needed to calculate the bottom index, are optimized to minimize Generalized Cross-Validation(GCV).<BR>The new method is applied to 3 coral reef areas and artificially generated situations, and the prediction accuracy is compared with those of the methods proposed by Paredes et al., Stumpf et al., and Kanno et al. As a result, the new method is found to have the highest accuracy in cases that enough depth-known pixels are available and that the optical model apply well.

収録刊行物

• 日本リモートセンシング学会誌

  日本リモートセンシング学会誌 29 (3), 459-470, 2009

  一般社団法人 日本リモートセンシング学会

キーワード

• Depth Estimation
• Semiparametric Regression
• Nonuniform Bottom Sediment
• Coral Reef

詳細情報

• CRID: 1390001204666784384
• NII論文ID: 10025320687
• NII書誌ID: AN10035665
• DOI: 10.11440/rssj.29.459
• ISSN: 18831184
• ISSN: 02897911
• NDL書誌ID: 10386669
• Web Site: https://ndlsearch.ndl.go.jp/books/R000000004-I10386669
• 本文言語コード: en
• データソース種別:
  • JaLC
  • NDL
  • CiNii Articles
• 抄録ライセンスフラグ: 使用不可