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草地と遠隔検知データを活用して,ブドウの植生状態の改善

Tibor Zsigmond1,2,3, Zsófia Bakacsi4,5, Ágota Horel1,2

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標準化差異植生指数 (NDVI) を含む遠隔感知データは,ブドウ畑の植生状態を正確に評価することができます. 草原データを機械学習モデルと統合することで,ブドウのNDVI予測が大幅に改善されます.

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線形回帰機械学習NDVI についてプラントのストレスランダムな森林スペクトル反射度

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科学分野:

  • 農業科学
  • リモートセンシング
  • エコロジー

背景:

  • 植生インデックス (VI) は,植物健康と環境ストレスの監視に不可欠です.
  • 標準化された差異植生指数 (NDVI) は広く使用されていますが,その正確さは土壌の種類や地形などの要因によって制限されます.
  • 草原や衛星画像などの様々な情報源から得られたデータを統合することで,植物の監視能力を向上させることができます.

研究 の 目的:

  • 草原と葡萄園の生態系における特定の植生指標 (VI) を調査する.
  • 牧草地の遠隔検知 (RS) データの可能性を評価し,ブドウ畑におけるNDVI値を精査する.
  • 異なる機械学習モデルの性能を比較する

主な方法:

  • NDVI,光化学反射指数 (PRI),光合成活性放射線 (PAR) のフィールドモニタリング
  • Sentinel-2 (S2) のスペクトルデータの取得と分析
  • 線形回帰 (LR),ランダムフォレスト (RF),XGBoostを含む機械学習技術の適用により,NDVI測定を精製する.

主要な成果:

  • 土壌の化学構造と相関する場所の間で VI の有意な差異が観察されました.
  • NDVIは全体的なキャンピーの活力を示し,PRIは短期的な生理学的変化とストレスに対してより高い感受性を示した.
  • 地上とRSのNDVIは良好な相関関係を示した (r=0. 68).
  • RFモデルは,年の日と草原のデータで訓練され,最高精度 (r=0.787) を達成しました.
  • すべての機械学習モデルは,草原のNDVIをトレーニングに含めると,ブドウのVIの予測が改善されたことを示しました.

結論:

  • 植生インデックスは,生態系タイプと土壌特性に大きく異なります.
  • 草原のリモートセンシングデータは,ブドウ畑の植生監視の精度を高めることができます.
  • 機械学習モデル,特にランダムフォレストは,NDVI測定の精錬とブドウの健康の評価に希望を示しています.