ICESat-2データと機械学習に基づいた世界の30m年平均植生高の地図 (2000-2022)
Maria O Hunter1, Leandro Parente2, Yu-Feng Ho2
1Remote Sensing and GIS Laboratory (LAPIG/UFG), Goiânia, Brazil. maria.hunter@ufg.br.
Scientific data
|August 23, 2025
まとめ
科学者たちは衛星データを用いて 森林以外の生態系における 植物の平均高さの世界初の地図を作成しました この新しいツールは,地球規模の生態系構造と生物多様性を理解するための重要なデータを提供します.
科学分野:
- 生態学 と 遠隔 感知
- 地理空間分析
- 環境監視
背景:
- 生態系評価,炭素貯蔵,生物多様性研究のために,正確な植物の高さの測定は不可欠です.
- 既存の世界的な高度モデルは主に森林に焦点を当てており,草木や灌木の生態系にとって重要なデータを無視しています.
- 森林以外の環境における地球規模の植生構造データにはギャップがある.
研究 の 目的:
- 低植生を含むすべての生態系における植物の平均高さの最初のグローバルな見積もりを作成する.
- 2000年から2022年までの年間植物の高さのデータを 30mの解像度で提供する.
- 植物の高さの詳細と異質性をグローバルモデルで強化する.
主な方法:
- 他の地球観測ラスターデータとともに,ICESat-2衛星のLidarとLandsatのクラウドフリーコンポジットを利用しました.
- 10つの独立した抽出で3200万のICESat-2セグメントを使用したアンサンブルグラデントブーストツリー (GBT) モデルを開発しました.
- モデル不確実性を定量化するために推定90%の予測間隔.
主要な成果:
- テストセットで2.35mの平均平方誤差 (RMSE),0.515のR2および0.62のD2回帰スコアを達成した.
- 2000年から2022年にかけての年間平均植生高の地図を30mの解像度で作成した.
- 既存の製品と比較して,短い植物の生態系における詳細性と異質性が示されています.
結論:
- 開発されたモデルは,重要なデータギャップを埋めるために,植物の平均の高さの包括的なグローバル推定を成功裏に提供しています.
- このアプローチは,非森林の生態系における植物の構造の特徴を高めます.
- アウトプットマップ,レファレンスサンプル,訓練されたモデルは,CC-BYライセンスの下で,さらなる研究を支援するために公開されています.
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