paper-with-me

Papers

Decadal sink-source shifts of forest aboveground carbon since 1988

2025-06-13 · Zhen Qian, Sebastian Bathiany, Teng Liu, Lana L. Blaschke, Hoong Chen Teo, Niklas Boers

As enduring carbon sinks, forest ecosystems are vital to the terrestrial carbon cycle and help moderate global warming. However, the long-term dynamics of aboveground carbon (AGC) in forests and their sink-source transitions remain highly uncertain, owing to changing disturbance regimes and inconsistencies in observations, data processing, and analysis methods. Here, we derive reliable, harmonized AGC stocks and fluxes in global forests from 1988 to 2021 at high spatial resolution by integrating multi-source satellite observations with probabilistic deep learning models. Our approach simultaneously estimates AGC and associated uncertainties, showing high reliability across space and time. We find that, although global forests remained an AGC sink of 6.2 PgC over 30 years, moist tropical forests shifted to a substantial AGC source between 2001 and 2010 and, together with boreal forests, transitioned toward a source in the 2011-2021 period. Temperate, dry tropical and subtropical forests generally exhibited increasing AGC stocks, although Europe and Australia became sources after 2011. Regionally, pronounced sink-to-source transitions occurred in tropical forests over the past three decades. The interannual relationship between global atmospheric CO2 growth rates and tropical AGC flux variability became increasingly negative, reaching Pearson's r = -0.63 (p < 0.05) in the most recent decade. In the Brazilian Amazon, the contribution of deforested regions to AGC losses declined from 60% in 1989-2000 to 13% in 2011-2021, while the share from untouched areas increased from 33% to 76%. Our findings suggest a growing role of tropical forest AGC in modulating variability in the terrestrial carbon cycle, with anthropogenic climate change potentially contributing increasingly to AGC changes, particularly in previously untouched areas.

📄 PDF Abstract BibTeX arXiv:2506.11879

Code (0)

등록된 구현이 없습니다.

Tasks

Probabilistic Deep Learning

Similar Papers 제목 키워드 기반

Learning to Predict Aboveground Biomass from RGB Images with 3D Synthetic Scenes

2025-11-28 · Silvia Zuffi arxiv

Forests play a critical role in global ecosystems by supporting biodiversity and mitigating climate change via carbon sequestration. Accurate aboveground biomass (AGB) estimation is essential for assessing carbon storage…

Instance Segmentation

A regional model for estimating the aboveground carbon density of Borneo's tropical forests from airborne laser scanning

2017-05-25

Borneo contains some of the world's most biodiverse and carbon dense tropical forest, but this 750,000-km2 island has lost 62% of its old-growth forests within the last 40 years. Efforts to protect and restore the remain…

Estimation of aboveground biomass in a tropical dry forest: An intercomparison of airborne, unmanned, and space laser scanning

2025-10-31 · Nelson Mattié, Arturo Sanchez-Azofeifa, Pablo Crespo-Peremarch, Juan-Ygnacio López-Hernández arxiv

According to the Paris Climate Change Agreement, all nations are required to submit reports on their greenhouse gas emissions and absorption every two years by 2024. Consequently, forests play a crucial role in reducing …

HyBiomass: Global Hyperspectral Imagery Benchmark Dataset for Evaluating Geospatial Foundation Models in Forest Aboveground Biomass Estimation

2025-06-12 · Aaron Banze, Timothée Stassin, Nassim Ait Ali Braham, Rıdvan Salih Kuzu 외

Comprehensive evaluation of geospatial foundation models (Geo-FMs) requires benchmarking across diverse tasks, sensors, and geographic regions. However, most existing benchmark datasets are limited to segmentation or cla…

Benchmarking

Direct Estimation of Tree Volume and Aboveground Biomass Using Deep Regression with Synthetic Lidar Data

2026-03-04 · Habib Pourdelan, Zhengkang Xiang, Hugh Stewart, Cam Nicholson 외 arxiv

Accurate estimation of forest biomass is crucial for monitoring carbon sequestration and informing climate change mitigation strategies. Existing methods often rely on allometric models, which estimate individual tree bi…

Point Clouds