A Low-Cost and Robust Landsat-Based Approach to Study Forest Degradation and Carbon Emissions from Selective Logging in the Venezuelan Amazon
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Selective logging in the tropics is a major driver of forest degradation by altering forest structure and function, including significant losses of aboveground carbon. In this study, we used a 30-year Landsat time series (1985–2015) to analyze forest degradation and carbon emissions due to selective logging in a Forest Reserve of the Venezuelan Amazon. Our work was conducted in two phases: the first, by means of a direct method we detected the infrastructure related to logging at the sub-pixel level, and for the second, we used an indirect approach using buffer areas applied to the results of the selective logging mapping. Pre- and post-logging forest inventory data, combined with the mapping analysis were used to quantify the effects of logging on aboveground carbon emissions for three different sources: hauling, skidding and tree felling. With an overall precision of 0.943, we demonstrate the potential of this method to efficiently map selective logging and forest degradation with commission and omission errors of +7.6 ± 4.5 (Mean ± SD %) and −7.5% ± 9.1 respectively. Forest degradation due to logging directly affected close to 24,480 ha, or about ~1% of the total area of the Imataca Forest Reserve. On average, with a relatively low harvest intensity of 2.8 ± 1.2 trees ha−1 or 10.5 ± 4.6 m3 ha−1, selective logging was responsible for the emission of 61 ± 21.9 Mg C ha−1. Lack of reduced impact logging guidelines contributed to pervasive effects reflected in a mean reduction of ~35% of the aboveground carbon compared to unlogged stands. This research contributes to further improve our understanding of the relationships between selective logging and forest degradation in tropical managed forests and serves as input for the potential implementation of projects for reducing emissions from deforestation and forest degradation (REDD+).
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Forest degradation directly impacted 24,480 ha of the Imataca forest reserve in Venezuela's Amazon.
“The findings indicated that forest degradation directly impacted 24,480 ha of the Imataca forest reserve.”
From Strategies to achieve a carbon neutral society: a review (2022) · cites this paper as Pacheco-Angulo et al. 2021
With a harvest intensity of 2.8 ± 1.2 trees ha−1, selective logging released around 61 ± 21.9 MgC ha−1.
“With a harvest intensity of 2.8 ± 1.2 trees ha−1, selective logging released around 61 ± 21.9 MgC ha−1.”
From Strategies to achieve a carbon neutral society: a review (2022) · cites this paper as Pacheco-Angulo et al. 2021
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- Authors
- Pacheco-Angulo, C., Plata-Rocha, W., Serrano, J., Vilanova, E., Monjardin-Armenta, S., González, A., & Camargo, C.
- Affiliations
- Facultad de Ciencias Forestales y Ambientales, Universidad de los Andes, Mérida 5110, Venezuela
- Universidad Autónoma de Sinaloa (MX)
- Universidad de Los Andes (VE)
- University of California, Berkeley (US)
- Facultad de Ciencias de la Tierra y el Espacio, Universidad Autónoma de Sinaloa, Sinaloa M-80029, Mexico
- ORCID
- Venue
- Remote Sensing
- Publisher
- MDPI AG
- Published
- 2021-04-08
- Volume
- 13
- Issue
- 8
- Pages
- 1435
- ISSN
- 2072-4292 (ISSN-L)
- OpenAlex
- W3155157871
- Cited by
- 12
- References
- 66
- Citations by year
- 2026: 2 · 2025: 3 · 2024: 2 · 2023: 2 · 2022: 3
- FWCI
- 0.72
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Remote Sensing (publishedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Global Environment Facility
- Topics
- Physical Sciences › Environmental Science › Global and Planetary Change › Conservation, Biodiversity, and Resource Management
- Physical Sciences › Environmental Science › Environmental Engineering › Remote Sensing and LiDAR Applications
- Physical Sciences › Environmental Science › Ecology › Remote Sensing in Agriculture
- Keywords
- selective logging, forest degradation, Landsat time series, buffer zones, reduced impact logging, Venezuelan Amazon, REDD+, harvest intensity
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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