In the ongoing battle against climate change, quantifying the carbon locked within trees is essential for tracking the effectiveness of our mitigation efforts. A new study presents a method for estimating above-ground carbon (AGC) at an individual tree level, especially in semi-arid regions, by utilizing very high-resolution (VHR) satellite imagery coupled with machine learning algorithms. This approach offers a more precise tool for measuring carbon sequestration, which is critical for climate adaptation and land management strategies on a global scale.Unlocking Precision in Carbon Stock Mapping: A New AI-Driven Approach
January 09, 2025
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In the ongoing battle against climate change, quantifying the carbon locked within trees is essential for tracking the effectiveness of our mitigation efforts. A new study presents a method for estimating above-ground carbon (AGC) at an individual tree level, especially in semi-arid regions, by utilizing very high-resolution (VHR) satellite imagery coupled with machine learning algorithms. This approach offers a more precise tool for measuring carbon sequestration, which is critical for climate adaptation and land management strategies on a global scale.
