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無人機と衛星画像を融合した温帯疎林草原の木本および草本植物被覆度リモートセンシング推定

  • role： First author第一作者
  • Affiliation：

    Institute of Desertification Studies, Chinese Academy of Forest, Beijing 100091, China

  • Email：lixiaoya@caf.ac.cn
  • Introduction：李晓雅，研究方向为生态遥感与机器学习。E-mail：lixiaoya@caf.ac.cn
LI Xiaoya1,  
  • Affiliation：

    Institute of Forest Resource Information Techniques, Chinese Academy of Forest, Beijing 100091, China

TIAN Xin2,  
  • Affiliation：

    Institute of Microelectronics of the Chinese Academy of Sciences, Beijing 100029, China

DUAN Tao3,  
  • Affiliation：

    Institute of Desertification Studies, Chinese Academy of Forest, Beijing 100091, China

CAO Xiaoming1,  
  • Affiliation：

    Institute of Desertification Studies, Chinese Academy of Forest, Beijing 100091, China

YANG Kaijie1,  
  • Affiliation：

    Institute of Desertification Studies, Chinese Academy of Forest, Beijing 100091, China

LU Qi1,  
  • role： Corresponding author通信作者
  • Affiliation：

    Institute of Desertification Studies, Chinese Academy of Forest, Beijing 100091, China

  • Email：wangfeng@caf.ac.cn
  • Introduction：王锋，研究方向为干旱区生态遥感和模型。E-mail：wangfeng@caf.ac.cn
WANG Feng1*

サマリー

中国の半乾燥地域に分布する温帯疎林草原生態系は、森林と草原の間の移行型の生態系であり、独特の気候と地形条件の下で砂地に発達した域産的な頂級植生群落である。疎林草原は、樹木、低木および草本植物が混在して生育し、空間的異質性が高いことが特徴である。植生のリモートセンシングによる監視は困難であり、現在に至るまで世界的に最も不正確な土地被覆タイプの一つである。精度と範囲を両立させ、温帯疎林草原の地域スケールで異なるタイプの植物の生育状態を監視することは、現在の乾燥地域の植生リモートセンシングにおけるホットな課題である。本研究は機械学習アルゴリズムに基づき、地上に近いドローンリモートセンシング観測プラットフォームから得られた地表植生タイプ情報を用いて訓練データセットを構築し、高解像度衛星画像と組み合わせて、疎林草原における木本植物と草本植物の被覆度推定モデルを構築した。無人機から衛星までの温帯疎林草原における木本植物および草本植物被覆度の同期推定を実現し、二種類の高解像度衛星画像による疎林草原の木本植物および草本植被被覆度推定結果の差異を比較した。研究結果より、(1) 無人機近接リモートセンシング画像を用いることで地表被覆タイプを高精度に分類でき、地域の温帯疎林草原における木本植物および草本植物被覆度推定モデルに多量の高精度な訓練サンプルデータを提供できること、(2) 機械学習アルゴリズムに基づき、高分六号（GF-6）およびセンチネル2号（Sentinel-2）の二種類の高解像度衛星画像を用いた疎林草原被覆度モデルは木本植物および草本植物の被覆度推定を良好に実現できること。GF-6を用いた疎林草原の木本植物および草本植物の被覆度推定結果の決定係数はそれぞれ0.72および0.66であり、RMSEはそれぞれ6.76％および10.69％、推定精度はそれぞれ46.31％および77.88％だった。Sentinel-2を用いた場合の決定係数はそれぞれ0.72および0.81、RMSEはそれぞれ6.53％および8.20％、推定精度はそれぞれ54.30％および83.17％だった。(3) Sentinel-2衛星画像に基づく疎林草原の木本植物および草本植被被覆度推定精度はGF-6衛星よりやや高く、いずれの衛星画像でも草本植被被覆度の推定精度は木本植物より有意に高かった。本研究は疎林草原の木本植物および草本植物の被覆度を景観スケールから地域スケールに拡張する推定の新たなアプローチを示し、無人機から衛星までのクロススケール協調観測手法は地域の温帯疎林草原における異なる生活型植物の生育状況監視に有効な方法的支援を提供する。将来的には、多時相の高解像度衛星データに基づいて、地域スケールでの温帯疎林草原木本植物および草本植物の動的監視が可能になると期待される。

キーワード

リモートセンシング;ヤマグワ疎林;無人機;高分六号;センチネル2号;ランダムフォレスト;分類および回帰木

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