Carbon-water trade-off in Eucalyptus dunnii plantations: A bi-objective optimization framework using remote sensing and machine learning models
ユーカリ・ダニー植林地における炭素と水のトレードオフ：リモートセンシングと機械学習を用いた多目的最適化フレームワーク (AI 翻訳)
Andrés Baietto, Andrés Hirigoyen, Mauricio Acuña, Rafael M. Navarro‐Cerrillo
🤖 gxceed AI 要約
日本語
ウルグアイのユーカリ植林地を対象に、リモートセンシングと機械学習を用いて炭素蓄積と水利用のトレードオフを最適化する収穫スケジューリング手法を開発。バランス型管理により水利用効率が向上し、炭素吸収を維持しつつ水消費を削減できることを示した。
English
This study develops a bi-objective optimization framework for harvest scheduling in Eucalyptus plantations in Uruguay, integrating remote sensing and machine learning. A balanced scenario improves water-use efficiency and reduces evapotranspiration with minimal carbon loss, offering a scalable tool for sustainable forest management.
Unofficial AI-generated summary based on the public title and abstract. Not an official translation.
📝 gxceed 編集解説 — Why this matters
日本のGX文脈において
日本では森林吸収源の活用がカーボンニュートラル戦略に位置づけられており、本手法はJ-クレジットや森林経営計画における炭素吸収と水資源保全の両立に応用可能。リモートセンシングとAIを組み合わせた効率的なモニタリング手法は、国内の森林管理にも示唆を与える。
In the global GX context
This study contributes to global climate mitigation by demonstrating a scalable method to optimize carbon sequestration and water use in plantations, relevant to REDD+ and sustainable forest management frameworks. It highlights the potential of remote sensing and ML for nature-based solutions.
👥 読者別の含意
🔬研究者:Provides a novel bi-objective optimization approach integrating remote sensing and ML for forest carbon-water trade-offs.
🏢実務担当者:Offers a cost-effective tool for optimizing harvest schedules to balance carbon and water objectives in plantation management.
🏛政策担当者:Informs policies on sustainable forest management and carbon accounting by quantifying trade-offs and providing a decision-support framework.
📄 Abstract（原文）
: Commercial Eucalyptus plantations have expanded significantly in South America, with most of them being managed under short-rotation regimes for pulp production. Despite the contribution of Eucalyptus plantations to carbon sequestration, there are concerns about high water use and its potential impacts on hydrological processes. In this context, this study aims to develop a spatially explicit bi-objective linear programming framework for stand-level biophysical harvest scheduling in Eucalyptus dunnii plantations for pulp production in Uruguay. This framework integrates Sentinel-1 synthetic aperture radar, Sentinel-2 multispectral imagery, SoilGrids soil property data, and accumulated evapotranspiration (ETa) estimates derived from geeSEBAL-MODIS. Random forest models were applied to estimate above-ground carbon stock (AGC) and stand age (SA), while ETa was derived for 226 forest stands (699 ha) located on Aquic Argiudolls and Typic Albaqualfs soils across a chronosequence of 6 to 9 years. A Pareto-optimal frontier was used to simultaneously maximize the normalized AGC accumulation and the inverted normalized ETa (water-use reduction). The results indicate that a balanced forest management scenario, in which both objectives have equal weight, consists of a 7-year rotation length, corresponding to the peak in water-use efficiency (WUE). Compared to the typical 8-year fixed-rotation scheme, this balanced scenario substantially improved mean WUE (0.723 vs. 0.658 g·L −1 ) and reduced mean ETa (8,407 vs. 9,401 mm), with only a slight reduction in AGC (60.35 vs. 61.38 Mg·ha −1 ). This study provides a scalable and cost-effective tool for optimizing harvest scheduling, mitigating potential hydrological impacts while sustaining carbon sequestration in short-rotation Eucalyptus plantations.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.1016/j.fecs.2026.100528first seen 2026-08-17 04:58:28
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