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  • Energy Research
  • 2021-2025

  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Lau Sarmiento, A.I. (Alvaro); Calders, Kim; Bartholomeus, Harm; Martius, Christopher; +5 Authors

    Large uncertainties in tree and forest carbon estimates weaken national efforts to accurately estimate aboveground biomass (AGB) for their national monitoring, measurement, reporting and verification system. Allometric equations to estimate biomass have improved, but remain limited. They rely on destructive sampling; large trees are under-represented in the data used to create them; and they cannot always be applied to different regions. These factors lead to uncertainties and systematic errors in biomass estimations. We developed allometric models to estimate tree AGB in Guyana. These models were based on tree attributes (diameter, height, crown diameter) obtained from terrestrial laser scanning (TLS) point clouds from 72 tropical trees and wood density. We validated our methods and models with data from 26 additional destructively harvested trees. We found that our best TLS-derived allometric models included crown diameter, provided more accurate AGB estimates ( R2 = 0.92���0.93) than traditional pantropical models (R2 = 0.85���0.89), and were especially accurate for large trees (diameter > 70 cm). The assessed pantropical models underestimated AGB by 4 to 13%. Nevertheless, one pantropical model (Chave et al. 2005 without height) consistently performed best among the pantropical models tested ( R2 = 0.89) and predicted AGB accurately across all size classes���which but for this could not be known without destructive or TLS-derived validation data. Our methods also demonstrate that tree height is difficult to measure in situ, and the inclusion of height in allometric models consistently worsened AGB estimates. We determined that TLS-derived AGB estimates were unbiased. Our approach advances methods to be able to develop, test, and choose allometric models without the need to harvest trees.

    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ 4TU.ResearchData | s...arrow_drop_down
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    4TU.ResearchData | science.engineering.design
    Dataset . 2021
    License: CC BY
    Data sources: Datacite
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    4TU.ResearchData | science.engineering.design
    Dataset . 2021
    License: CC BY
    Data sources: Datacite
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Research@WUR
    Dataset . 2021
    Data sources: Research@WUR
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    4TU.ResearchData
    Dataset . 2021
    License: CC BY
    Data sources: 4TU.ResearchData
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    4TU.ResearchData
    Dataset . 2021
    License: CC BY
    Data sources: 4TU.ResearchData
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Smithsonian figshare
    Dataset . 2021
    License: CC BY
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      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ 4TU.ResearchData | s...arrow_drop_down
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      4TU.ResearchData | science.engineering.design
      Dataset . 2021
      License: CC BY
      Data sources: Datacite
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      4TU.ResearchData | science.engineering.design
      Dataset . 2021
      License: CC BY
      Data sources: Datacite
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      Research@WUR
      Dataset . 2021
      Data sources: Research@WUR
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      4TU.ResearchData
      Dataset . 2021
      License: CC BY
      Data sources: 4TU.ResearchData
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      4TU.ResearchData
      Dataset . 2021
      License: CC BY
      Data sources: 4TU.ResearchData
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      Smithsonian figshare
      Dataset . 2021
      License: CC BY
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Daniela Requena Suarez; Danaë M. A. Rozendaal; Veronique De Sy; Mathieu Decuyper; +6 Authors

    AbstractAmazonian forests function as biomass and biodiversity reservoirs, contributing to climate change mitigation. While they continuously experience disturbance, the effect that disturbances have on biomass and biodiversity over time has not yet been assessed at a large scale. Here, we evaluate the degree of recent forest disturbance in Peruvian Amazonia and the effects that disturbance, environmental conditions and human use have on biomass and biodiversity in disturbed forests. We integrate tree‐level data on aboveground biomass (AGB) and species richness from 1840 forest plots from Peru's National Forest Inventory with remotely sensed monitoring of forest change dynamics, based on disturbances detected from Landsat‐derived Normalized Difference Moisture Index time series. Our results show a clear negative effect of disturbance intensity tree species richness. This effect was also observed on AGB and species richness recovery values towards undisturbed levels, as well as on the recovery of species composition towards undisturbed levels. Time since disturbance had a larger effect on AGB than on species richness. While time since disturbance has a positive effect on AGB, unexpectedly we found a small negative effect of time since disturbance on species richness. We estimate that roughly 15% of Peruvian Amazonian forests have experienced disturbance at least once since 1984, and that, following disturbance, have been increasing in AGB at a rate of 4.7 Mg ha−1 year−1 during the first 20 years. Furthermore, the positive effect of surrounding forest cover was evident for both AGB and its recovery towards undisturbed levels, as well as for species richness. There was a negative effect of forest accessibility on the recovery of species composition towards undisturbed levels. Moving forward, we recommend that forest‐based climate change mitigation endeavours consider forest disturbance through the integration of forest inventory data with remote sensing methods.

    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ CGIAR CGSpace (Consu...arrow_drop_down
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Global Change Biology
    Article . 2023 . Peer-reviewed
    License: CC BY NC
    Data sources: Crossref
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Research@WUR
    Article . 2023
    License: CC BY NC
    Data sources: Research@WUR
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Research@WUR
    Other literature type . 2023
    License: CC BY NC
    Data sources: Research@WUR
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Wageningen Staff Publications
    Article . 2023
    License: CC BY NC
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      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ CGIAR CGSpace (Consu...arrow_drop_down
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      Global Change Biology
      Article . 2023 . Peer-reviewed
      License: CC BY NC
      Data sources: Crossref
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      Research@WUR
      Article . 2023
      License: CC BY NC
      Data sources: Research@WUR
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      Research@WUR
      Other literature type . 2023
      License: CC BY NC
      Data sources: Research@WUR
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      Wageningen Staff Publications
      Article . 2023
      License: CC BY NC
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Medha Bulusu; Christopher Martius; Jessica Clendenning;

    Miombo woodlands are extensive dry forest ecosystems in central and southern Africa covering ≈2.7 million km2. Despite their vast expanse and global importance for carbon storage, the long-term carbon stocks and dynamics have been poorly researched. The objective of this paper was to present and summarize the evidence gathered on aboveground carbon (AGC) and soil organic carbon (SOC) stocks of miombo woodlands from the 1960s to mid-2018 through a literature review. We reviewed the data to find out to what extent aboveground carbon and soil organic carbon stocks are found in miombo woodlands and further investigated if are there differences in carbon stocks based on woodland categories (old-growth, disturbed and re-growth). A review protocol was used to identify 56 publications from which quantitative data on AGC and SOC stocks were extracted. We found that the mean AGC in old-growth miombo (45.8 ± 17.8 Mg C ha−1), disturbed miombo (26.7 ± 15 Mg C ha−1), and regrowth miombo (18.8 ± 16.8 Mg C ha−1) differed significantly. Data on rainfall, stand age, and land-use suggested that the variability in aboveground carbon is site-specific, relating to climatic and geographic conditions as well as land-use history. SOC stocks in both old-growth and re-growth miombo were found to vary widely. It must be noted these soil data are provided only for information; they inconsistently refer to varying soil depths and are thus difficult to interpret. The wide range reported suggests a need for further studies which are much more systematic in method and reporting. Other limitations of the dataset include the lack of systematic sampling and lack of data in some countries, viz. Angola and Democratic Republic of the Congo.

    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ CGIAR CGSpace (Consu...arrow_drop_down
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    Forests
    Article . 2021 . Peer-reviewed
    License: CC BY
    Data sources: Crossref
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Forests
    Article
    License: CC BY
    Data sources: UnpayWall
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    https://doi.org/10.20944/prepr...
    Article . 2021 . Peer-reviewed
    License: CC BY
    Data sources: Crossref
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    https://www.preprints.org/manu...
    Article
    License: CC BY
    Data sources: UnpayWall
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Forests
    Article . 2021
    Data sources: DOAJ
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
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      Forests
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      Forests
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    Authors: Robert N. Masolele; Veronique De Sy; Diego Marcos; Jan Verbesselt; +6 Authors

    National-scale assessments of post-deforestation land-use are crucial for decreasing deforestation and forest degradation-related emissions. In this research, we assess the potential of different satellite data modalities (single-date, multi-date, multi-resolution, and an ensemble of multi-sensor images) for classifying land-use following deforestation in Ethiopia using the U-Net deep neural network architecture enhanced with attention. We performed the analysis on satellite image data retrieved across Ethiopia from freely available Landsat-8, Sentinel-2 and Planet-NICFI satellite data. The experiments aimed at an analysis of (a) single-date images from individual sensors to account for the differences in spatial resolution between image sensors in detecting land-uses, (b) ensembles of multiple images from different sensors (Planet-NICFI/Sentinel-2/Landsat-8) with different spatial resolutions, (c) the use of multi-date data to account for the contribution of temporal information in detecting land-uses, and, finally, (d) the identification of regional differences in terms of land-use following deforestation in Ethiopia. We hypothesize that choosing the right satellite imagery (sensor) type is crucial for the task. Based on a comprehensive visually interpreted reference dataset of 11 types of post-deforestation land-uses, we find that either detailed spatial patterns (single-date Planet-NICFI) or detailed temporal patterns (multi-date Sentinel-2, Landsat-8) are required for identifying land-use following deforestation, while medium-resolution single-date imagery is not sufficient to achieve high classification accuracy. We also find that adding soft-attention to the standard U-Net improved the classification accuracy, especially for small-scale land-uses. The models and products presented in this work can be used as a powerful data resource for governmental and forest monitoring agencies to design and monitor deforestation mitigation measures and data-driven land-use policy.

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    GIScience & Remote Sensing
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    Research@WUR
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    Wageningen Staff Publications
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    Authors: Christopher Martius; Christian Borgemeister; Marcos Jimenez-Martinez; Francis Molua Mwambo; +3 Authors

    L'objectif d'améliorer la sécurité alimentaire en Afrique subsaharienne (Ass) grâce à une agriculture domestique, économe en ressources et à faible émission de carbone est important.Les interventions visant à produire plus de nourriture pourraient avoir un impact sur la base de ressources et entraîner une augmentation des émissions de gaz à effet de serre (GES) des agroécosystèmes.Malgré cela, les méthodes existantes sont limitées dans l'analyse des systèmes agricoles à petite échelle, et cette situation constitue un obstacle à la prise de décision qui vise une agriculture durable.Dans ce document, nous présentons l'approche Emergy-Data Envelopment Analysis (EM-DEA) récemment développée pour évaluer l'efficacité de l'utilisation des ressources (RUE) et durabilité dans les systèmes de production de maïs au Ghana, SSA.Utilisant le simulateur de systèmes de production agricole (APSIM), cinq scénarios d'utilisation des terres et de gestion des ressources ont été modélisés pour représenter les pratiques en tant qu'unités de prise de décision (DMU) dans les systèmes de maïs à petite échelle.L' empreinte carbone des systèmes a été évaluée à l'aide d'une approche, que nous avons adaptée à partir de l'outil de bilan carbone ex ante de la FAO (EX-ACT).La tendance globale des résultats a montré que le rendement, l'emergie totale, les émissions de GES et l'empreinte carbone augmentaient tous avec l'augmentation de l'intensité de l'application d'urée.Toutefois, la relation entre le rendement et l'apport d'urée n'était pas toujours linéaire.Un système qui utilisait plus de ressources renouvelables ou moins de ressources pour produire un rendement égal à celui de son homologue était considéré comme plus efficace et durable en termes relatifs.En particulier, le scénario de statu quo (12 kg/ha/an d'apport de NPK au système de maïs pluvial, c.-à-d. extensif12) était inefficace par rapport aux quatre scénarios contrastés.Le scénario écologique intensif (20 kg/ha/an d'apport d'urée au système de culture intercalaire maïs pluvial-légumine, c.-à-d. Intercrop20) a atteint le rendement marginal le plus élevé, une meilleure RUE et la durabilité.Le scénario d'intrant élevé (100 kg/ha/an) année d'entrée d'urée plus irrigation supplémentaire pour la monoculture de maïs, c.-à-d. intensive100) a produit le rendement le plus élevé, mais la demande d'intrants achetés ainsi que les émissions de GES et l'empreinte carbone étaient les plus élevées. Le scénario sans intrants externes (0 kg/ha/an d'entrée d'urée pour le système de maïs pluvial, c.-à-d. intensive0) et le scénario d'intrants modérés (50 kg/ha/an d'entrée d'urée plus irrigation supplémentaire pour la monoculture de maïs, c.-à-d. intensive50) ont montré les écarts de rendement les plus importants et les moins importants par rapport à Intensive100, respectivement. Sur la base de ces résultats et de l'analyse des compromis, il était évident que Intercrop20 et Intensive50 étaient les deux meilleurs scénarios. En tant que tel, la politique d'utilisation des terres qui vise à l'agriculture durable pourrait recommander Intercrop20 et Intensive50 pour la mise en œuvre dans les systèmes de production de maïs à faible et à fort apport, respectivement. La comparaison entre nos résultats et d'autres études empiriques existantes a révélé des similitudes qui confirment nos résultats. Nous concluons que les informations dérivées en utilisant les approches EM-DEA et EX-ACT pourraient être utiles lors de la prise de décisions éclairées qui visent à l'agriculture durable. Malgré la limitation causée par la rareté des données, l'utilisation de l'approche EM-DEA a conduit à des informations inclusives sur la RUE et la durabilité des DMUs.Hence, l'approche EM-DEA représente une voie à suivre pour mieux évaluer l'empreinte énergétique dans l'utilisation des terres agricoles dans son ensemble. El objetivo de mejorar la seguridad alimentaria en el África subsahariana (ASA) a través de la agricultura doméstica, eficiente en el uso de los recursos y baja en carbono es importante. Las intervenciones para producir más alimentos podrían afectar la base de recursos y conducir a un aumento de las emisiones de gases de efecto invernadero (GEI) de los agroecosistemas. Lamentablemente, los métodos existentes son limitados para analizar los sistemas agrícolas a pequeña escala, y esta situación es un obstáculo para la toma de decisiones que apunta a la agricultura sostenible. En este documento, mostramos el enfoque recientemente desarrollado del Análisis de Envolvimiento de Datos de Emergencia (EM-DEA) para evaluar la eficiencia del uso de los recursos (RUE) y sostenibilidad en los sistemas de producción de maíz en Ghana, SSA. Utilizando el SIMulador de Sistemas de Producción Agrícola (APSIM), se modelaron cinco escenarios de uso de la tierra y gestión de recursos para representar las prácticas como unidades de toma de decisiones (DMU) en sistemas de maíz a pequeña escala. La huella de carbono de los sistemas se evaluó utilizando un enfoque, que adaptamos de la Herramienta de Balance de Carbono Ex-Ante de la FAO (EX-ACT). La tendencia general de los resultados mostró que el rendimiento, la emergencia total, las emisiones de GEI y la huella de carbono aumentaron con el aumento en la intensidad de la aplicación de urea. Sin embargo, la relación entre el rendimiento y la entrada de urea no siempre fue lineal.Un sistema que utilizó más recursos renovables o menos recursos para producir un rendimiento igual al de sus pares se consideró más eficiente y sostenible en términos relativos. En particular, el escenario habitual (12 kg/ha/año de entrada de NPK al sistema de maíz de secano, es decir, Extensive12) fue ineficiente en comparación con los cuatro escenarios contrastantes. El escenario ecológico intensivo (20 kg/ha/año de entrada de urea al sistema de cultivo intercalado de leguminosas de maíz de secano, es decir, Intercrop20) logró el mayor rendimiento marginal, mejor RUDA y sostenibilidad. El escenario de alto insumo (100 kg/ha/el año de entrada de urea más el riego suplementario al monocultivo de maíz, es decir, Intensive100) produjo el mayor rendimiento, pero la demanda de insumos comprados, así como las emisiones de GEI y la huella de carbono fueron mayores. El escenario sin insumos externos (0 kg/ha/año de entrada de urea al sistema de maíz de secano, es decir, Extensive0), y el escenario de insumos moderados (50 kg/ha/año de entrada de urea más el riego suplementario al monocultivo de maíz, es decir, Intensive50) mostraron las mayores y menores brechas de rendimiento en relación con Intensive100, respectivamente. Con base en estos resultados y análisis de compensación, fue evidente que Intercrop20 y Intensive50 fueron los dos mejores escenarios. Por lo tanto, la política de uso de la tierra que apunta a la agricultura sostenible podría recomendar Intercrop20 e Intensive50 para su implementación en sistemas de producción de maíz de bajo y alto insumo, respectivamente. La comparación entre nuestros resultados y otros estudios empíricos existentes reveló similitudes que confirman nuestros resultados. Concluimos que la información derivada utilizando los enfoques EM-DEA y EX-ACT podría ser útil al tomar decisiones informadas que apunten a la agricultura sostenible. A pesar de la limitación causada por la escasez de datos, el uso del enfoque EM-DEA condujo a información inclusiva sobre RUE y sostenibilidad de las DMU. Por lo tanto, el enfoque EM-DEA representa un camino a seguir para evaluar mejor la huella energética en el uso de la tierra agrícola en su conjunto. The goal to improve food security in sub-Saharan Africa (SSA) through domestic, resource efficient and low carbon agriculture is importance.Interventions to produce more food could impact the resource-base and lead to increase in greenhouse gas (GHG) emissions from agroecosystems.Unfortunately, existing methods are limited in analyzing small-scale agricultural systems, and this situation is an obstacle to decision making which aims at sustainable agriculture.In this paper, we showcase the recently developed Emergy-Data Envelopment Analysis (EM-DEA) approach to assess the resource use efficiency (RUE) and sustainability in maize production systems in Ghana, SSA.Using the Agricultural Production Systems sIMulator (APSIM), five land use and resource management scenarios were modeled to represent practices as decision making units (DMUs) in small-scale maize systems.The carbon footprint of the systems was assessed using an approach, which we adapted from the FAO Ex-Ante Carbon balance Tool (EX-ACT).The overall trend of the results showed that the yield, total emergy, GHG emissions and carbon footprint all increased with increase in urea application intensity.However, the relationship between the yield and urea input was not always linear.A system that used more renewable or fewer resources to produce a yield equal to that of its peer was considered more efficient and sustainable in relative terms.In particular, the business-as-usual scenario (12 kg/ha/yr NPK input to rainfed maize system, i.e.Extensive12) was inefficient when compared to the four contrasting scenarios.The ecological intensive scenario (20 kg/ha/yr urea input to rainfed maize-legume intercropping system, i.e.Intercrop20) achieved the greatest marginal yield, better RUE and sustainability.The high input scenario (100 kg/ha/yr urea input plus supplemental irrigation to maize monoculture, i.e.Intensive100) produced the greatest yield, but the demand for purchased inputs as well as GHG emissions and carbon footprint were greatest.The no external input scenario (0 kg/ha/yr urea input to rainfed maize system, i.e.Extensive0), and the moderate input scenario (50 kg/ha/yr urea input plus supplemental irrigation to maize monoculture, i.e.Intensive50) showed the greatest and least yield gaps relative to Intensive100, respectively.Based on these results and trade-off analysis, it was evident that Intercrop20 and Intensive50 were the two best case scenarios.As such, land use policy that aims at sustainable agriculture could recommend Intercrop20 and Intensive50 for implementation in low and high input maize production systems, respectively.Comparison between our results and other existing empirical studies revealed similarities that confirm our results.We conclude that the information derived using the EM-DEA and EX-ACT approaches could be useful when making informed decisions that aim at sustainable agriculture.Despite the limitation caused by scarcity of data, the use of the EM-DEA approach led to inclusive information on RUE and sustainability of the DMUs.Hence, the EM-DEA approach represents a way forward to better assess energy footprint in agricultural land use as a whole. إن هدف تحسين الأمن الغذائي في أفريقيا جنوب الصحراء الكبرى (SSA) من خلال الزراعة المحلية ذات الكفاءة في استخدام الموارد والمنخفضة الكربون أمر مهم. يمكن أن تؤثر التدخلات لإنتاج المزيد من الغذاء على قاعدة الموارد وتؤدي إلى زيادة انبعاثات غازات الدفيئة من النظم الإيكولوجية الزراعية. لسوء الحظ، فإن الأساليب الحالية محدودة في تحليل النظم الزراعية الصغيرة، وهذا الوضع يمثل عقبة أمام صنع القرار الذي يهدف إلى الزراعة المستدامة. في هذه الورقة، نعرض نهج تحليل البيانات الطارئة (EM - DEA) الذي تم تطويره مؤخرًا لتقييم كفاءة استخدام الموارد (RUE) و الاستدامة في أنظمة إنتاج الذرة في غانا، جنوب الصحراء الكبرى. باستخدام محاكي أنظمة الإنتاج الزراعي (APSIM)، تم تصميم خمسة سيناريوهات لاستخدام الأراضي وإدارة الموارد لتمثيل الممارسات كوحدات صنع القرار (DMUs) في أنظمة الذرة الصغيرة. تم تقييم البصمة الكربونية للأنظمة باستخدام نهج، قمنا بتكييفه من أداة توازن الكربون السابق لمنظمة الأغذية والزراعة (EX - ACT). أظهر الاتجاه العام للنتائج أن العائد، إجمالي الطاقة، انبعاثات غازات الدفيئة وبصمة الكربون زادت جميعها مع زيادة كثافة تطبيق اليوريا. ومع ذلك، فإن العلاقة بين العائد ومدخلات اليوريا لم يكن دائمًا خطيًا. واعتبر النظام الذي يستخدم موارد أكثر متجددة أو أقل لإنتاج عائد مساوٍ لعائد نظيره أكثر كفاءة واستدامة من الناحية النسبية. على وجه الخصوص، فإن سيناريو العمل المعتاد (12 كجم/هكتار/سنة مدخلات NPK إلى نظام الذرة البعلية، أي مكثف 12) كان غير فعال عند مقارنته بالسيناريوهات الأربعة المتناقضة. السيناريو المكثف بيئيًا (20 كجم/هكتار/سنة مدخلات اليوريا إلى نظام زراعة البقول والذرة البعلية، أي Intercrop20) حقق أكبر عائد هامشي، وشق أفضل واستدامة. سيناريو المدخلات العالية (100 كجم/هكتار/سنة أنتجت مدخلات اليوريا السنوية بالإضافة إلى الري التكميلي لذرة الزراعة الأحادية، أي المكثفة 100) أكبر عائد، لكن الطلب على المدخلات المشتراة وكذلك انبعاثات غازات الدفيئة وبصمة الكربون كان أكبر. لم يظهر سيناريو المدخلات الخارجية (0 كجم/هكتار/سنة مدخلات اليوريا في نظام الذرة البعلية، أي المكثفة 0)، وسيناريو المدخلات المعتدلة (50 كجم/هكتار/سنة مدخلات اليوريا بالإضافة إلى الري التكميلي لذرة الزراعة الأحادية، أي المكثفة 50) أكبر وأقل فجوات العائد بالنسبة إلى المكثفة 100، على التوالي. استنادًا إلى هذه النتائج وتحليل المفاضلة، كان من الواضح أن Intercrop20 و كانت المكثفة 50 هي أفضل السيناريوهات. على هذا النحو، يمكن لسياسة استخدام الأراضي التي تهدف إلى الزراعة المستدامة أن توصي بتطبيق Intercrop20 و Intensive50 في أنظمة إنتاج الذرة ذات المدخلات المنخفضة والعالية، على التوالي. كشفت المقارنة بين نتائجنا والدراسات التجريبية الحالية الأخرى عن أوجه تشابه تؤكد نتائجنا. نستنتج أن المعلومات المستمدة باستخدام نهج EM - DEA و EX - ACT يمكن أن تكون مفيدة عند اتخاذ قرارات مستنيرة تهدف إلى الزراعة المستدامة. على الرغم من القيود الناجمة عن ندرة البيانات، أدى استخدام نهج EM - DEA إلى معلومات شاملة عن RUE واستدامة DMUs.Hence، يمثل نهج EM - DEA طريقة للمضي قدمًا لتقييم بصمة الطاقة بشكل أفضل في استخدام الأراضي الزراعية ككل.

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    Journal of Cleaner Production
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Lau Sarmiento, A.I. (Alvaro); Calders, Kim; Bartholomeus, Harm; Martius, Christopher; +5 Authors

    Large uncertainties in tree and forest carbon estimates weaken national efforts to accurately estimate aboveground biomass (AGB) for their national monitoring, measurement, reporting and verification system. Allometric equations to estimate biomass have improved, but remain limited. They rely on destructive sampling; large trees are under-represented in the data used to create them; and they cannot always be applied to different regions. These factors lead to uncertainties and systematic errors in biomass estimations. We developed allometric models to estimate tree AGB in Guyana. These models were based on tree attributes (diameter, height, crown diameter) obtained from terrestrial laser scanning (TLS) point clouds from 72 tropical trees and wood density. We validated our methods and models with data from 26 additional destructively harvested trees. We found that our best TLS-derived allometric models included crown diameter, provided more accurate AGB estimates ( R2 = 0.92���0.93) than traditional pantropical models (R2 = 0.85���0.89), and were especially accurate for large trees (diameter > 70 cm). The assessed pantropical models underestimated AGB by 4 to 13%. Nevertheless, one pantropical model (Chave et al. 2005 without height) consistently performed best among the pantropical models tested ( R2 = 0.89) and predicted AGB accurately across all size classes���which but for this could not be known without destructive or TLS-derived validation data. Our methods also demonstrate that tree height is difficult to measure in situ, and the inclusion of height in allometric models consistently worsened AGB estimates. We determined that TLS-derived AGB estimates were unbiased. Our approach advances methods to be able to develop, test, and choose allometric models without the need to harvest trees.

    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ 4TU.ResearchData | s...arrow_drop_down
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    4TU.ResearchData | science.engineering.design
    Dataset . 2021
    License: CC BY
    Data sources: Datacite
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    4TU.ResearchData | science.engineering.design
    Dataset . 2021
    License: CC BY
    Data sources: Datacite
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
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    Research@WUR
    Dataset . 2021
    Data sources: Research@WUR
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
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    4TU.ResearchData
    Dataset . 2021
    License: CC BY
    Data sources: 4TU.ResearchData
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    4TU.ResearchData
    Dataset . 2021
    License: CC BY
    Data sources: 4TU.ResearchData
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Smithsonian figshare
    Dataset . 2021
    License: CC BY
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      4TU.ResearchData | science.engineering.design
      Dataset . 2021
      License: CC BY
      Data sources: Datacite
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      4TU.ResearchData | science.engineering.design
      Dataset . 2021
      License: CC BY
      Data sources: Datacite
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
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      Research@WUR
      Dataset . 2021
      Data sources: Research@WUR
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      4TU.ResearchData
      Dataset . 2021
      License: CC BY
      Data sources: 4TU.ResearchData
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      4TU.ResearchData
      Dataset . 2021
      License: CC BY
      Data sources: 4TU.ResearchData
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      Smithsonian figshare
      Dataset . 2021
      License: CC BY
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Daniela Requena Suarez; Danaë M. A. Rozendaal; Veronique De Sy; Mathieu Decuyper; +6 Authors

    AbstractAmazonian forests function as biomass and biodiversity reservoirs, contributing to climate change mitigation. While they continuously experience disturbance, the effect that disturbances have on biomass and biodiversity over time has not yet been assessed at a large scale. Here, we evaluate the degree of recent forest disturbance in Peruvian Amazonia and the effects that disturbance, environmental conditions and human use have on biomass and biodiversity in disturbed forests. We integrate tree‐level data on aboveground biomass (AGB) and species richness from 1840 forest plots from Peru's National Forest Inventory with remotely sensed monitoring of forest change dynamics, based on disturbances detected from Landsat‐derived Normalized Difference Moisture Index time series. Our results show a clear negative effect of disturbance intensity tree species richness. This effect was also observed on AGB and species richness recovery values towards undisturbed levels, as well as on the recovery of species composition towards undisturbed levels. Time since disturbance had a larger effect on AGB than on species richness. While time since disturbance has a positive effect on AGB, unexpectedly we found a small negative effect of time since disturbance on species richness. We estimate that roughly 15% of Peruvian Amazonian forests have experienced disturbance at least once since 1984, and that, following disturbance, have been increasing in AGB at a rate of 4.7 Mg ha−1 year−1 during the first 20 years. Furthermore, the positive effect of surrounding forest cover was evident for both AGB and its recovery towards undisturbed levels, as well as for species richness. There was a negative effect of forest accessibility on the recovery of species composition towards undisturbed levels. Moving forward, we recommend that forest‐based climate change mitigation endeavours consider forest disturbance through the integration of forest inventory data with remote sensing methods.

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    Global Change Biology
    Article . 2023 . Peer-reviewed
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    Research@WUR
    Article . 2023
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    Wageningen Staff Publications
    Article . 2023
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      Global Change Biology
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      Wageningen Staff Publications
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    Authors: Medha Bulusu; Christopher Martius; Jessica Clendenning;

    Miombo woodlands are extensive dry forest ecosystems in central and southern Africa covering ≈2.7 million km2. Despite their vast expanse and global importance for carbon storage, the long-term carbon stocks and dynamics have been poorly researched. The objective of this paper was to present and summarize the evidence gathered on aboveground carbon (AGC) and soil organic carbon (SOC) stocks of miombo woodlands from the 1960s to mid-2018 through a literature review. We reviewed the data to find out to what extent aboveground carbon and soil organic carbon stocks are found in miombo woodlands and further investigated if are there differences in carbon stocks based on woodland categories (old-growth, disturbed and re-growth). A review protocol was used to identify 56 publications from which quantitative data on AGC and SOC stocks were extracted. We found that the mean AGC in old-growth miombo (45.8 ± 17.8 Mg C ha−1), disturbed miombo (26.7 ± 15 Mg C ha−1), and regrowth miombo (18.8 ± 16.8 Mg C ha−1) differed significantly. Data on rainfall, stand age, and land-use suggested that the variability in aboveground carbon is site-specific, relating to climatic and geographic conditions as well as land-use history. SOC stocks in both old-growth and re-growth miombo were found to vary widely. It must be noted these soil data are provided only for information; they inconsistently refer to varying soil depths and are thus difficult to interpret. The wide range reported suggests a need for further studies which are much more systematic in method and reporting. Other limitations of the dataset include the lack of systematic sampling and lack of data in some countries, viz. Angola and Democratic Republic of the Congo.

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    Forests
    Article . 2021 . Peer-reviewed
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    Forests
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    https://doi.org/10.20944/prepr...
    Article . 2021 . Peer-reviewed
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    Forests
    Article . 2021
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      Forests
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      https://doi.org/10.20944/prepr...
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      Forests
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    Authors: Robert N. Masolele; Veronique De Sy; Diego Marcos; Jan Verbesselt; +6 Authors

    National-scale assessments of post-deforestation land-use are crucial for decreasing deforestation and forest degradation-related emissions. In this research, we assess the potential of different satellite data modalities (single-date, multi-date, multi-resolution, and an ensemble of multi-sensor images) for classifying land-use following deforestation in Ethiopia using the U-Net deep neural network architecture enhanced with attention. We performed the analysis on satellite image data retrieved across Ethiopia from freely available Landsat-8, Sentinel-2 and Planet-NICFI satellite data. The experiments aimed at an analysis of (a) single-date images from individual sensors to account for the differences in spatial resolution between image sensors in detecting land-uses, (b) ensembles of multiple images from different sensors (Planet-NICFI/Sentinel-2/Landsat-8) with different spatial resolutions, (c) the use of multi-date data to account for the contribution of temporal information in detecting land-uses, and, finally, (d) the identification of regional differences in terms of land-use following deforestation in Ethiopia. We hypothesize that choosing the right satellite imagery (sensor) type is crucial for the task. Based on a comprehensive visually interpreted reference dataset of 11 types of post-deforestation land-uses, we find that either detailed spatial patterns (single-date Planet-NICFI) or detailed temporal patterns (multi-date Sentinel-2, Landsat-8) are required for identifying land-use following deforestation, while medium-resolution single-date imagery is not sufficient to achieve high classification accuracy. We also find that adding soft-attention to the standard U-Net improved the classification accuracy, especially for small-scale land-uses. The models and products presented in this work can be used as a powerful data resource for governmental and forest monitoring agencies to design and monitor deforestation mitigation measures and data-driven land-use policy.

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    GIScience & Remote Sensing
    Article . 2022 . Peer-reviewed
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    Wageningen Staff Publications
    Article . 2022
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    GIScience & Remote Sensing
    Article . 2022 . Peer-reviewed
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    Authors: Christopher Martius; Christian Borgemeister; Marcos Jimenez-Martinez; Francis Molua Mwambo; +3 Authors

    L'objectif d'améliorer la sécurité alimentaire en Afrique subsaharienne (Ass) grâce à une agriculture domestique, économe en ressources et à faible émission de carbone est important.Les interventions visant à produire plus de nourriture pourraient avoir un impact sur la base de ressources et entraîner une augmentation des émissions de gaz à effet de serre (GES) des agroécosystèmes.Malgré cela, les méthodes existantes sont limitées dans l'analyse des systèmes agricoles à petite échelle, et cette situation constitue un obstacle à la prise de décision qui vise une agriculture durable.Dans ce document, nous présentons l'approche Emergy-Data Envelopment Analysis (EM-DEA) récemment développée pour évaluer l'efficacité de l'utilisation des ressources (RUE) et durabilité dans les systèmes de production de maïs au Ghana, SSA.Utilisant le simulateur de systèmes de production agricole (APSIM), cinq scénarios d'utilisation des terres et de gestion des ressources ont été modélisés pour représenter les pratiques en tant qu'unités de prise de décision (DMU) dans les systèmes de maïs à petite échelle.L' empreinte carbone des systèmes a été évaluée à l'aide d'une approche, que nous avons adaptée à partir de l'outil de bilan carbone ex ante de la FAO (EX-ACT).La tendance globale des résultats a montré que le rendement, l'emergie totale, les émissions de GES et l'empreinte carbone augmentaient tous avec l'augmentation de l'intensité de l'application d'urée.Toutefois, la relation entre le rendement et l'apport d'urée n'était pas toujours linéaire.Un système qui utilisait plus de ressources renouvelables ou moins de ressources pour produire un rendement égal à celui de son homologue était considéré comme plus efficace et durable en termes relatifs.En particulier, le scénario de statu quo (12 kg/ha/an d'apport de NPK au système de maïs pluvial, c.-à-d. extensif12) était inefficace par rapport aux quatre scénarios contrastés.Le scénario écologique intensif (20 kg/ha/an d'apport d'urée au système de culture intercalaire maïs pluvial-légumine, c.-à-d. Intercrop20) a atteint le rendement marginal le plus élevé, une meilleure RUE et la durabilité.Le scénario d'intrant élevé (100 kg/ha/an) année d'entrée d'urée plus irrigation supplémentaire pour la monoculture de maïs, c.-à-d. intensive100) a produit le rendement le plus élevé, mais la demande d'intrants achetés ainsi que les émissions de GES et l'empreinte carbone étaient les plus élevées. Le scénario sans intrants externes (0 kg/ha/an d'entrée d'urée pour le système de maïs pluvial, c.-à-d. intensive0) et le scénario d'intrants modérés (50 kg/ha/an d'entrée d'urée plus irrigation supplémentaire pour la monoculture de maïs, c.-à-d. intensive50) ont montré les écarts de rendement les plus importants et les moins importants par rapport à Intensive100, respectivement. Sur la base de ces résultats et de l'analyse des compromis, il était évident que Intercrop20 et Intensive50 étaient les deux meilleurs scénarios. En tant que tel, la politique d'utilisation des terres qui vise à l'agriculture durable pourrait recommander Intercrop20 et Intensive50 pour la mise en œuvre dans les systèmes de production de maïs à faible et à fort apport, respectivement. La comparaison entre nos résultats et d'autres études empiriques existantes a révélé des similitudes qui confirment nos résultats. Nous concluons que les informations dérivées en utilisant les approches EM-DEA et EX-ACT pourraient être utiles lors de la prise de décisions éclairées qui visent à l'agriculture durable. Malgré la limitation causée par la rareté des données, l'utilisation de l'approche EM-DEA a conduit à des informations inclusives sur la RUE et la durabilité des DMUs.Hence, l'approche EM-DEA représente une voie à suivre pour mieux évaluer l'empreinte énergétique dans l'utilisation des terres agricoles dans son ensemble. El objetivo de mejorar la seguridad alimentaria en el África subsahariana (ASA) a través de la agricultura doméstica, eficiente en el uso de los recursos y baja en carbono es importante. Las intervenciones para producir más alimentos podrían afectar la base de recursos y conducir a un aumento de las emisiones de gases de efecto invernadero (GEI) de los agroecosistemas. Lamentablemente, los métodos existentes son limitados para analizar los sistemas agrícolas a pequeña escala, y esta situación es un obstáculo para la toma de decisiones que apunta a la agricultura sostenible. En este documento, mostramos el enfoque recientemente desarrollado del Análisis de Envolvimiento de Datos de Emergencia (EM-DEA) para evaluar la eficiencia del uso de los recursos (RUE) y sostenibilidad en los sistemas de producción de maíz en Ghana, SSA. Utilizando el SIMulador de Sistemas de Producción Agrícola (APSIM), se modelaron cinco escenarios de uso de la tierra y gestión de recursos para representar las prácticas como unidades de toma de decisiones (DMU) en sistemas de maíz a pequeña escala. La huella de carbono de los sistemas se evaluó utilizando un enfoque, que adaptamos de la Herramienta de Balance de Carbono Ex-Ante de la FAO (EX-ACT). La tendencia general de los resultados mostró que el rendimiento, la emergencia total, las emisiones de GEI y la huella de carbono aumentaron con el aumento en la intensidad de la aplicación de urea. Sin embargo, la relación entre el rendimiento y la entrada de urea no siempre fue lineal.Un sistema que utilizó más recursos renovables o menos recursos para producir un rendimiento igual al de sus pares se consideró más eficiente y sostenible en términos relativos. En particular, el escenario habitual (12 kg/ha/año de entrada de NPK al sistema de maíz de secano, es decir, Extensive12) fue ineficiente en comparación con los cuatro escenarios contrastantes. El escenario ecológico intensivo (20 kg/ha/año de entrada de urea al sistema de cultivo intercalado de leguminosas de maíz de secano, es decir, Intercrop20) logró el mayor rendimiento marginal, mejor RUDA y sostenibilidad. El escenario de alto insumo (100 kg/ha/el año de entrada de urea más el riego suplementario al monocultivo de maíz, es decir, Intensive100) produjo el mayor rendimiento, pero la demanda de insumos comprados, así como las emisiones de GEI y la huella de carbono fueron mayores. El escenario sin insumos externos (0 kg/ha/año de entrada de urea al sistema de maíz de secano, es decir, Extensive0), y el escenario de insumos moderados (50 kg/ha/año de entrada de urea más el riego suplementario al monocultivo de maíz, es decir, Intensive50) mostraron las mayores y menores brechas de rendimiento en relación con Intensive100, respectivamente. Con base en estos resultados y análisis de compensación, fue evidente que Intercrop20 y Intensive50 fueron los dos mejores escenarios. Por lo tanto, la política de uso de la tierra que apunta a la agricultura sostenible podría recomendar Intercrop20 e Intensive50 para su implementación en sistemas de producción de maíz de bajo y alto insumo, respectivamente. La comparación entre nuestros resultados y otros estudios empíricos existentes reveló similitudes que confirman nuestros resultados. Concluimos que la información derivada utilizando los enfoques EM-DEA y EX-ACT podría ser útil al tomar decisiones informadas que apunten a la agricultura sostenible. A pesar de la limitación causada por la escasez de datos, el uso del enfoque EM-DEA condujo a información inclusiva sobre RUE y sostenibilidad de las DMU. Por lo tanto, el enfoque EM-DEA representa un camino a seguir para evaluar mejor la huella energética en el uso de la tierra agrícola en su conjunto. The goal to improve food security in sub-Saharan Africa (SSA) through domestic, resource efficient and low carbon agriculture is importance.Interventions to produce more food could impact the resource-base and lead to increase in greenhouse gas (GHG) emissions from agroecosystems.Unfortunately, existing methods are limited in analyzing small-scale agricultural systems, and this situation is an obstacle to decision making which aims at sustainable agriculture.In this paper, we showcase the recently developed Emergy-Data Envelopment Analysis (EM-DEA) approach to assess the resource use efficiency (RUE) and sustainability in maize production systems in Ghana, SSA.Using the Agricultural Production Systems sIMulator (APSIM), five land use and resource management scenarios were modeled to represent practices as decision making units (DMUs) in small-scale maize systems.The carbon footprint of the systems was assessed using an approach, which we adapted from the FAO Ex-Ante Carbon balance Tool (EX-ACT).The overall trend of the results showed that the yield, total emergy, GHG emissions and carbon footprint all increased with increase in urea application intensity.However, the relationship between the yield and urea input was not always linear.A system that used more renewable or fewer resources to produce a yield equal to that of its peer was considered more efficient and sustainable in relative terms.In particular, the business-as-usual scenario (12 kg/ha/yr NPK input to rainfed maize system, i.e.Extensive12) was inefficient when compared to the four contrasting scenarios.The ecological intensive scenario (20 kg/ha/yr urea input to rainfed maize-legume intercropping system, i.e.Intercrop20) achieved the greatest marginal yield, better RUE and sustainability.The high input scenario (100 kg/ha/yr urea input plus supplemental irrigation to maize monoculture, i.e.Intensive100) produced the greatest yield, but the demand for purchased inputs as well as GHG emissions and carbon footprint were greatest.The no external input scenario (0 kg/ha/yr urea input to rainfed maize system, i.e.Extensive0), and the moderate input scenario (50 kg/ha/yr urea input plus supplemental irrigation to maize monoculture, i.e.Intensive50) showed the greatest and least yield gaps relative to Intensive100, respectively.Based on these results and trade-off analysis, it was evident that Intercrop20 and Intensive50 were the two best case scenarios.As such, land use policy that aims at sustainable agriculture could recommend Intercrop20 and Intensive50 for implementation in low and high input maize production systems, respectively.Comparison between our results and other existing empirical studies revealed similarities that confirm our results.We conclude that the information derived using the EM-DEA and EX-ACT approaches could be useful when making informed decisions that aim at sustainable agriculture.Despite the limitation caused by scarcity of data, the use of the EM-DEA approach led to inclusive information on RUE and sustainability of the DMUs.Hence, the EM-DEA approach represents a way forward to better assess energy footprint in agricultural land use as a whole. إن هدف تحسين الأمن الغذائي في أفريقيا جنوب الصحراء الكبرى (SSA) من خلال الزراعة المحلية ذات الكفاءة في استخدام الموارد والمنخفضة الكربون أمر مهم. يمكن أن تؤثر التدخلات لإنتاج المزيد من الغذاء على قاعدة الموارد وتؤدي إلى زيادة انبعاثات غازات الدفيئة من النظم الإيكولوجية الزراعية. لسوء الحظ، فإن الأساليب الحالية محدودة في تحليل النظم الزراعية الصغيرة، وهذا الوضع يمثل عقبة أمام صنع القرار الذي يهدف إلى الزراعة المستدامة. في هذه الورقة، نعرض نهج تحليل البيانات الطارئة (EM - DEA) الذي تم تطويره مؤخرًا لتقييم كفاءة استخدام الموارد (RUE) و الاستدامة في أنظمة إنتاج الذرة في غانا، جنوب الصحراء الكبرى. باستخدام محاكي أنظمة الإنتاج الزراعي (APSIM)، تم تصميم خمسة سيناريوهات لاستخدام الأراضي وإدارة الموارد لتمثيل الممارسات كوحدات صنع القرار (DMUs) في أنظمة الذرة الصغيرة. تم تقييم البصمة الكربونية للأنظمة باستخدام نهج، قمنا بتكييفه من أداة توازن الكربون السابق لمنظمة الأغذية والزراعة (EX - ACT). أظهر الاتجاه العام للنتائج أن العائد، إجمالي الطاقة، انبعاثات غازات الدفيئة وبصمة الكربون زادت جميعها مع زيادة كثافة تطبيق اليوريا. ومع ذلك، فإن العلاقة بين العائد ومدخلات اليوريا لم يكن دائمًا خطيًا. واعتبر النظام الذي يستخدم موارد أكثر متجددة أو أقل لإنتاج عائد مساوٍ لعائد نظيره أكثر كفاءة واستدامة من الناحية النسبية. على وجه الخصوص، فإن سيناريو العمل المعتاد (12 كجم/هكتار/سنة مدخلات NPK إلى نظام الذرة البعلية، أي مكثف 12) كان غير فعال عند مقارنته بالسيناريوهات الأربعة المتناقضة. السيناريو المكثف بيئيًا (20 كجم/هكتار/سنة مدخلات اليوريا إلى نظام زراعة البقول والذرة البعلية، أي Intercrop20) حقق أكبر عائد هامشي، وشق أفضل واستدامة. سيناريو المدخلات العالية (100 كجم/هكتار/سنة أنتجت مدخلات اليوريا السنوية بالإضافة إلى الري التكميلي لذرة الزراعة الأحادية، أي المكثفة 100) أكبر عائد، لكن الطلب على المدخلات المشتراة وكذلك انبعاثات غازات الدفيئة وبصمة الكربون كان أكبر. لم يظهر سيناريو المدخلات الخارجية (0 كجم/هكتار/سنة مدخلات اليوريا في نظام الذرة البعلية، أي المكثفة 0)، وسيناريو المدخلات المعتدلة (50 كجم/هكتار/سنة مدخلات اليوريا بالإضافة إلى الري التكميلي لذرة الزراعة الأحادية، أي المكثفة 50) أكبر وأقل فجوات العائد بالنسبة إلى المكثفة 100، على التوالي. استنادًا إلى هذه النتائج وتحليل المفاضلة، كان من الواضح أن Intercrop20 و كانت المكثفة 50 هي أفضل السيناريوهات. على هذا النحو، يمكن لسياسة استخدام الأراضي التي تهدف إلى الزراعة المستدامة أن توصي بتطبيق Intercrop20 و Intensive50 في أنظمة إنتاج الذرة ذات المدخلات المنخفضة والعالية، على التوالي. كشفت المقارنة بين نتائجنا والدراسات التجريبية الحالية الأخرى عن أوجه تشابه تؤكد نتائجنا. نستنتج أن المعلومات المستمدة باستخدام نهج EM - DEA و EX - ACT يمكن أن تكون مفيدة عند اتخاذ قرارات مستنيرة تهدف إلى الزراعة المستدامة. على الرغم من القيود الناجمة عن ندرة البيانات، أدى استخدام نهج EM - DEA إلى معلومات شاملة عن RUE واستدامة DMUs.Hence، يمثل نهج EM - DEA طريقة للمضي قدمًا لتقييم بصمة الطاقة بشكل أفضل في استخدام الأراضي الزراعية ككل.

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    Journal of Cleaner Production
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