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@jpecol.bsky.social

In temperate deserts, there are significant differences in #leaffunctionaltraits and #abovegroundbiomass of the #Erodiumoxyrrhynchum in different plant germination seasons during different stages of #biologicalsoilcrusts.

#phyllospheremicroorganisms | #plantlife_history

doi.org/10.1093/jpe/...

Growth status of Erodium oxyrrhynchum with different life-history strategies across various biocrust types. (a) Individuals of E. oxyrrhynchum exhibiting distinct life-history strategies (spring-germinating vs. autumn-germinating); (b) Morphological characteristics of E. oxyrrhynchum growing in plots dominated by different types of biocrusts.

September 26, 2026 at 2:34 PM

Journal of Plant Ecology

@jpecol.bsky.social

【🎉Latest accepted article】
Improving Forest Aboveground #Carbon, #Nitrogen, and #Phosphorus Stock Estimation by Integrating Radar and Optical Remote Sensing

#AbovegroundBiomass | #BorealForests | #MachineLearning | #SyntheticApertureRadar

@mapjournals.bsky.social

doi.org/10.1093/jpe/...

Improving Forest Aboveground Carbon, Nitrogen, and Phosphorus Stock Estimation by Integrating Radar and Optical Remote Sensing

Abstract. Accurate mapping of forest carbon (C), nitrogen (N), and phosphorus (P) stocks is essential for advancing our understanding of global biogeochemi

doi.org

November 13, 2025 at 4:53 PM

Journal of Plant Ecology

@jpecol.bsky.social

Qian Liu et al. proposed a novel strategy for large-scale estimation of aboveground #Carbon, #Nitrogen and #Phosphorus stocks in #BorealForests using remote sensing data.

#AbovegroundBiomass | #MachineLearning | #SyntheticApertureRadar

doi.org/10.1093/jpe/...

Local detail of the AGB maps estimated using three methods and Landsat images.

April 10, 2026 at 4:25 PM

Ricardo Barros Lourenço

@rblourenco.bsky.social

Title: Aboveground #biomass mapping of Canada with #SAR and optical satellite observations aided by #activelearning 🧪

Aboveground biomass mapping of Canada with SAR and optical satellite observations aided by active learning

Shuhong Qin, Hong Wang , Cheryl Rogers, José Bermúdez, Ricardo Barros Lourenço, Jingru Zhang, Xiuneng Li, Jenny Chau , Piotr Tompalski, Alemu Gonsamo

National forest inventory (NFI) data has become an indispensable reference for model training and validation when estimating forest abovegroundbiomass (AGB) using satellite observations. However, obtaining statistically sufficient NFI data for model training is challenging for countries with vast land areas and extensive forest coverage like Canada. This study aims to directly upscale all available NFI data into high-resolution (30-m) spatially continuous AGB and explicit uncertainties maps across Canada’s treed land, using seasonal Sentinel 1&2 and yearly mosaic of L-band SyntheticApertureRadar (SAR) observations. To address the poor performance with limited training dataset, failure to extrapolate prediction beyond the bound of the training dataset and cannot provide spatially explicit uncertainties that are inherent to the commonly used RandomForest (RF) model, the GaussianProcessRegression (GPR) model and active learning optimization was introduced. The models were trained using stratified 10-fold cross-validation (ST10CV) and optimized by Euclidean distance-based diversity with bidirectional active learning (EBD-BDAL) before extrapolated on the GoogleEarthEngine ( GEE ) platform. The GPR model optimized with EBD-BDAL estimated Canada’s 2020 treed land AGB at 40.68 ± 6.8 Pg, with managed and unmanaged forests accounting for 82 % and 18 %, respectively. Trees outside forest ecosystems account for 2 % (0.8 Pg AGB) of total AGB in Canada’s treed land, and there are 0.1134 Pg AGB within urban treed lands. The uncertainty analysis showed that the GPR model demonstrated superior extrapolation capability for high AGB forests while maintaining lower relative uncertainty. The ST10CV results showed that the GPR model performed better than RF with or without EBD-BDAL optimization. The proposed NFI upscaling framework based on the GPR model and EBD-BDAL optimization shows great potential for national AGB mapping based on limited NFI data and seasonal satellite observations.

Visual Abstract containing maps with the AGB distributions over the Canadian territory

May 24, 2025 at 5:06 PM

@ivojuri.bsky.social

Unlocking the secrets of subalpine forests! 🌲📡 Research uses Aerial LIDAR and Bayesian modeling to improve aboveground biomass estimation in California’s Sierra Nevada, enhancing carbon monitoring & forest management. 🌍🌿

#RemoteSensing #VegetationStructure #CarbonMonitoring #AbovegroundBiomass

https://cbmjournal.biomedcentral.com/articles/10.1186/s13021-024-00286-w

Figure and Table Details:

https://docs.google.com/document/d/1g43Bk0A_IACY-QnXkSVN16rmkehq5vGBUL3B9ATn_E4/edit?tab=t.0

March 4, 2025 at 2:20 AM

Journal of Plant Ecology

@jpecol.bsky.social

❇️ #Rest-grazing in spring ➡️ #SoilMicrobialCommunity🟰 Effect❓
Place: #AlpineGrasslands
Results:
1️⃣ Increased #AbovegroundBiomass ➡️ Improved #PlantDiversity;
2️⃣ Increased #SoilOrganicCarbon ➡️ restored #SoilProperties.
doi.org/10.1093/jpe/...

Map of the study area, experimental layout and before and after rest-grazing of StM and SwM.

Correlation between soil microorganisms of (a, f) Total PLFAs, (b, g) Act, (c, h) Bacteria, (d, i) Fungi, (e, j) AMF and above-/below-ground biomass in StM and SwM in different years of rest-grazing.

May 24, 2025 at 10:45 PM

Journal of Plant Ecology

@jpecol.bsky.social

Jingci Meng et al. combined field data from 125 #SedgeWetland sites🖥️ with remote sensing information on solar radiation and climate to evaluate the impact of abiotic and biotic factors on #AbovegroundBiomass📊.

#SpeciesRichness | #ClimateZone | #EnvironmentalChange

doi.org/10.1093/jpe/...