Research ArticleENVIRONMENTAL STUDIES

Congo Basin forest loss dominated by increasing smallholder clearing

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Science Advances  07 Nov 2018:
Vol. 4, no. 11, eaat2993
DOI: 10.1126/sciadv.aat2993
  • Fig. 1 Forest disturbance driver.

    (A) Reference disturbance driver for each sampled pixel. (B) National estimates of 2000 to 2014 forest loss area by disturbance driver. Area estimates along with SEs are presented in table S2A. Forest clearing for small-scale rotational agriculture includes clearing for charcoal production, the contribution of which does not exceed 10% of the class area (42).

  • Fig. 2 Predisturbance forest type.

    (A) Reference predisturbance type for sampled pixels identified as forest loss. (B) National estimates of 2000 to 2014 forest loss area by predisturbance forest type. Area estimates expressed in hectares along with SEs are presented in table S2A.

  • Fig. 3 Three-year moving average of annual forest loss area for the major disturbance categories in all countries.

    Each major disturbance category contributes >0.5 Mha to the total 2000 to 2014 forest loss area. Forest clearing for small-scale rotational agriculture includes clearing for charcoal production, the contribution of which does not exceed 10% of the class area (42). Error bands represent ±SE. Annual area estimates along with SEs are presented in table S2B. Prim., primary; sec., secondary; woodl., woodlands.

  • Fig. 4 Expansion of small-scale agriculture into recently undisturbed forests and woodlands (lines) and population growth in the region by country (bar chart).

    Solid lines connect the annual forest loss area estimates and dashed lines represent the linear trend based on ordinary least squares regression. Forest clearing for small-scale rotational agriculture includes clearing for charcoal production, the contribution of which does not exceed 10% of the class area (42). Error bars on the area estimates represent 1 SE.

  • Table 1 Food production and trade indicators.

    Data source: FAOSTAT Database (http://www.fao.org/faostat). Food production index 2014 (2004 to 2006 = 100) shows the relative level of the aggregate volume of agricultural production for the year 2014 in comparison with the base period 2004 to 2006.

    CountryFood production index 2014
    (2004–2006 = 100),
    net per capita
    Agricultural products export value
    base price per capita, 2013
    ($ per person)
    Agricultural products import value
    base price per capita, 2013
    ($ per person)
    Food aid shipments, 2014, per
    capita (kg per person)
    CAM12626380.6
    CAR95276.1
    DRC780.281.0
    EQG90
    GAB7328215
    RoC822581.8
    Brazil12326324
    Indonesia1257835
  • Table 2 Annual area of small-scale forest clearing for agriculture in primary and mature secondary dense HTFs and primary woodlands and dry forests (thousand hectares ± SE) by 5-year epochs.

    Forest clearing for small-scale rotational agriculture includes clearing for charcoal production, the contribution of which does not exceed 10% of the class area (42). EQG had only 20 sampled pixels identified as forest loss, and this small sample size did not yield adequately precise estimated annual loss rates by 5-year epochs.

    2000–20052005–20102010–2014
    DRC321 ± 26403 ± 27462 ± 33
    CAR64 ± 1788 ± 2080 ± 12
    CAM28 ± 737 ± 769 ± 16
    RoC9 ± 324 ± 835 ± 9
    GAB17 ± 57 ± 34 ± 2

Supplementary Materials

  • Supplementary material for this article is available at http://advances.sciencemag.org/cgi/content/full/4/11/eaat2993/DC1

    Fig. S1. Conceptual diagram of forest loss cases distinguishable via visual interpretation of a single 30-m Landsat pixel.

    Fig. S2. Examples of predisturbance forest types.

    Fig. S3. Examples of forest disturbance drivers.

    Fig. S4. Study area and sampling strata.

    Fig. S5. Availability of cloud-free 16-day Landsat observations for the sampled pixels.

    Fig. S6. Sampled pixels with high and low confidence of presence/absence of forest loss.

    Fig. S7. Comparison of annual forest loss estimates for DRC.

    Table S1. Summary of selected socioeconomic indicators for the study countries.

    Table S2A. Total 2001 to 2014 forest disturbance area by disturbance driver and predisturbance forest type (million hectares ± SE).

    Table S2B. Annual forest loss area by forest disturbance driver and predisturbance forest type in all countries (million hectares ± SE).

    Table S3. Comparison of forest loss estimates for DRC.

    Table S4. Major sources of uncertainty during sample interpretation and measures to address them.

    Table S5. Distribution of sampled pixels (nh) among the country poststrata and three sampling design strata (loss, probable loss, and no loss) and strata sizes (Nh).

  • Supplementary Materials

    This PDF file includes:

    • Fig. S1. Conceptual diagram of forest loss cases distinguishable via visual interpretation of a single 30-m Landsat pixel.
    • Fig. S2. Examples of predisturbance forest types.
    • Fig. S3. Examples of forest disturbance drivers.
    • Fig. S4. Study area and sampling strata.
    • Fig. S5. Availability of cloud-free 16-day Landsat observations for the sampled pixels.
    • Fig. S6. Sampled pixels with high and low confidence of presence/absence of forest loss.
    • Fig. S7. Comparison of annual forest loss estimates for DRC.
    • Table S1. Summary of selected socioeconomic indicators for the study countries.
    • Table S2A. Total 2001 to 2014 forest disturbance area by disturbance driver and predisturbance forest type (million hectares ± SE).
    • Table S2B. Annual forest loss area by forest disturbance driver and predisturbance forest type in all countries (million hectares ± SE).
    • Table S3. Comparison of forest loss estimates for DRC.
    • Table S4. Major sources of uncertainty during sample interpretation and measures to address them.
    • Table S5. Distribution of sampled pixels (nh) among the country poststrata and three sampling design strata (loss, probable loss, and no loss) and strata sizes (Nh).

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