Figures (6)  Tables (5)

    • Figure 1. 

      Distribution of global literature.

    • Figure 2. 

      Workflow of the meta-analysis. (a) Literature screening and search process; (b) extracting relevant data from the literature; (c) calculating the ES and 95% confidence interval (CI); (d) result.

    • Figure 3. 

      AGB estimation systems using close-range remote sensing.

    • Figure 4. 

      Effects of different research scales on forest AGB estimation: (a) Single-tree scale; (b) plot scale; (c) stand scale. * indicates that a fixed effects model was used.

    • Figure 5. 

      Effects of different forest types on forest AGB estimations: (a) broadleaf forests; (b) coniferous forests; (c) mixed forests; (d) shrubs. * indicates that a fixed effects model was used.

    • Figure 6. 

      AGB estimation accuracy of different research methods, different independent variables, and different platforms. (a) Different research methods. PM, parametric method; NPM, non-parametric method, AGE, allometric growth equation. (b) Different independent variables. (c) Accuracy comparison between multi-source remote sensing and individual remote sensing data sources.

    • Scale of study Sensor type Number of samples Sample proportion (%) I2 (%)
      Tree UAV lidar 38 19.90 97.6
      Ground lidar 42 21.99 75.8
      RGB 24 12.57 95.8
      Spectra 4 2.09 72.7
      Plot UAV lidar 31 16.23 97.0
      Ground lidar 7 3.66 73.5
      RGB 13 6.81 59.5
      Spectra 12 6.28 68.3
      Stand UAV lidar 13 6.81 86.6
      Ground lidar 2 1.05 88.3
      RGB 3 1.57 0.0
      Spectra 2 1.05 90.8
      Total 191 100

      Table 1. 

      Statistical table of sample size at different research scales.

    • Type of forest Sensor type Number of samples Sample proportion (%) I2 (%)
      Broadleaf forests UAV lidar 22 11.52 98.2
      Ground lidar 14 7.33 42.1
      RGB 11 5.76 97.7
      Spectra 6 3.14 83.3
      Coniferous forests UAV lidar 20 10.47 85.8
      Ground lidar 12 6.28 42.1
      RGB 11 5.76 77.4
      Spectra 4 2.09 83.3
      Mixed forests UAV lidar 31 16.23 97.7
      Ground lidar 17 8.90 80.9
      RGB 6 3.14 0.0
      Spectra 3 1.57 99.6
      Shrubs UAV lidar 9 4.71 47.4
      Ground lidar 8 4.19 80.6
      RGB 12 6.28 88.8
      Spectra 5 2.62 86.0
      Total 191 100

      Table 2. 

      Statistical table of samples in different forest types.

    • Research method Number of samples Sample proportion (%) I2
      Parametric method 95 40.77 98.6
      Non-parametric method 62 26.61 91.9
      Allometric growth equation 76 32.62 97.2
      Total 233 100

      Table 3. 

      Statistical table of samples with different research methods.

    • Independent variable Number of samples Sample proportion I2
      H 14 18.42% 98.4
      D 10 13.16% 77.4
      D2H 28 37.04% 93.5
      Other 24 31.58% 95.3
      Total 76 100%

      Table 4. 

      Statistical table of sample with different independent variables.

    • Sensor Number of samples Sample proportion I2
      UAV lidar 82 35.19% 97.3
      Ground lidar 52 22.32% 75
      RGB 40 17.17% 93.5
      Spectra 18 7.73% 99.7
      Multi-source 41 17.60% 95.5
      Total 233 100%

      Table 5. 

      Statistical table of samples using different sensors.