Research ArticleECOSYSTEMS

Effects of conservation policy on China’s forest recovery

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Science Advances  18 Mar 2016:
Vol. 2, no. 3, e1500965
DOI: 10.1126/sciadv.1500965
  • Fig. 1 Forest cover dynamics in China.

    (A) Pixel-based (250 m per pixel) percent tree cover across China in 2000. The map was derived from the VCF tree cover product based on surface reflectance data collected by MODIS. Polygons correspond to county boundaries. (B) Pixels exhibiting a significant gain or loss in percent tree cover [that is, changes higher than or equal to |20%| and with statistically significant (P < 0.05) positive and negative monotonic trends in percent tree cover] between 2000 and 2010. Polygons correspond to province boundaries.

  • Fig. 2 NPP dynamics.

    Per-pixel relative (that is, percent) change in NPP between 2000 and 2010 among pixels exhibiting a significant gain in forest cover (Fig. 1B). Insets: Areas that exhibited particularly wide-ranging positive/negative trends.

  • Fig. 3 Methodological steps.

    Flow chart depicting the major steps in the procedures used in the study. For details, see Materials and Methods.

  • Table 1 Coefficients of the pixel-based forest loss model.

    Maximum likelihood estimates of the coefficients of the predictor variables obtained in a logistic regression model to assess the probability of forest loss during the 2000–2010 period. See Materials and Methods for details on the logistic regression models developed in the study.

    ParameterEstimateSEWald χ2P > χ2
    Intercept−11.19593.038813.57440.0002
    Easting2.997 × 10−72.66 × 10−71.27010.2598
    Northing3.768 × 10−75.27 × 10−70.51130.4746
    Initial tree cover0.05340.0061475.4652<0.0001
    Distance to main roads−0.002330.002750.71680.3972
    Population density in 20000.0000127.466 × 10−62.72080.0991
    CTI0.05910.05970.98140.3219
    Elevation−0.000190.0000864.96860.0258
    Slope0.0009600.02050.00220.9627
    Aspect−0.001110.01790.00380.9506
    Precipitation0.02750.0084610.57270.0011
    Temperature−0.08760.06821.64940.1990
  • Table 2 Coefficients of the pixel-based forest gain model.

    Maximum likelihood estimates of the coefficients of the predictor variables obtained in a logistic regression model to assess the probability of forest gain during the 2000–2010 period. See Materials and Methods for details on the logistic regression models developed in the study.

    ParameterEstimateSEWald χ2P > χ2
    Intercept1.24641.13941.19660.2740
    Easting1.528 × 10−71.02 × 10−72.24620.1339
    Northing−5.61 × 10−71.652 × 10−711.52980.0007
    Initial tree cover0.004150.002951.97230.1602
    Distance to main roads−0.004880.001747.86910.0050
    Population density in 2000−0.000050.00001510.49240.0012
    CTI−0.20770.034935.4714<0.0001
    Elevation−0.000230.00004427.1876<0.0001
    Slope−0.005230.007810.44850.5031
    Aspect0.01140.008301.86950.1715
    Precipitation−0.002570.004050.40270.5257
    Temperature0.07430.021611.78150.0006
  • Table 3 Coefficients of county-based spatial autoregressive models.

    Estimates of the coefficients of the spatial autoregressive models developed to assess the relationship between county-based independent variables and significant gain and loss of forest cover [presented as coefficient (SE)]. See Materials and Methods for details on these spatial autoregressive models.

    ParameterForest lossForest gain
    R20.4780.742
    Intercept2.61 × 10−5 (0.0006)−0.0004 (0.0019)
    NFCP−0.0007 (0.0004)0.0070* (0.0015)
    GDP per capita in 20000.0014 (0.0005)−0.0023 (0.0018)
    Change (2010–2000) in GDP−2.04 × 10−7 (6.109 × 10−7)1.36 × 10−7 (2.02 × 10−6)
    Grain production in 20004.01 × 10−6 (1.91 × 10−5)−1.10 × 10−5 (6.32 × 10−5)
    Change (2010–2000) in grain production8.43 × 10−7 (2.20 × 10−6)−2.77 × 10−6 (7.18 × 10−6)
    Meat production in 2000−2.29 × 10−5 (0.0001)6.21 × 10−5 (0.0003)
    Change (2010–2000) in meat production−2.17 × 10−6 (1.73 × 10−6)4.10 × 10−6 (5.72 × 10−6)
    Total population in 20001.34 × 10−5 (2.13 × 10−5)8.30 × 10−5 (7.03 × 10−5)
    Change (2010–2000) in total population4.47 × 10−7 (1.48 × 10−5)−3.10 × 10−5 (4.88 × 10−5)
    Rural labor in 2000−3.94 × 10−5 (4.00 × 10−5)−0.0002 (0.0001)
    Change (2010–2000) in rural labor−1.31 × 10−6‡ (6.21 × 10−7)−6.45 × 10−6† (2.05 × 10−6)
    Spatial autoregressive term0.6922* (0.0195)0.8493* (0.0122)

    *P < 0.001.

    P < 0.01.

    P < 0.05.

    Supplementary Materials

    • Supplementary Materials

      This PDF file includes:

      • Fig. S1. Histogram of dynamic pixels.
      • Fig. S2. Distribution of validation polygons.
      • Fig. S3. Validation of the MODIS VCF.
      • Fig. S4. Distribution of model residuals.

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