Map accuracy/agreement assessment following the Riemann et. al. framework.
Map accuracy assessment for the v0.0.99 model-weighted-ensemble Landsat model trained on v1.4 lidar-agb predictions. FIA data inventoried between 2013 and 2019 were considered for this assessment.
Branch used to produce this document: lj_landsat-lidar-0.0.99
LCMAP Collection 1.2 primary classification layers were used to mask out water, developed, and barren areas i.e. “!class %in% c(1, 5, 8)” pixels from the modeled AGB surfaces. Annual AGB surfaces were matched to the LMCAP layer from the same year.
AOA masking was also implemented, and results are shown both with and without AOA masking.
NOTE: Masked AGB pixels are set to NA, and excluded from the analysis.
Masked LCMAP classes:
LCPRI | n | Mean FIA | %RMSE | RMSE | % MAE | MAE | MBE | R\(^2\) |
---|---|---|---|---|---|---|---|---|
Cropland | 32 | 25.38 | 168.92 | 42.88 | 150.47 | 38.19 | 30.33 | -0.61 |
Grass/Shrub | 34 | 32.35 | 133.13 | 43.07 | 114.76 | 37.13 | 32.18 | 0.02 |
Tree cover | 1823 | 142.47 | 39.14 | 55.77 | 30.99 | 44.15 | 1.45 | 0.27 |
Wetland | 126 | 112.11 | 53.37 | 59.83 | 43.06 | 48.28 | -9.91 | 0.22 |
LCPRI | n | Mean FIA | %RMSE | RMSE | % MAE | MAE | MBE | R\(^2\) |
---|---|---|---|---|---|---|---|---|
Cropland | 31 | 25.99 | 165.52 | 43.02 | 147.83 | 38.42 | 30.29 | -0.59 |
Grass/Shrub | 27 | 38.71 | 94.21 | 36.47 | 82.11 | 31.78 | 24.39 | 0.40 |
Tree cover | 1821 | 142.41 | 39.41 | 56.12 | 31.22 | 44.46 | 1.91 | 0.27 |
Wetland | 125 | 112.35 | 53.16 | 59.73 | 42.87 | 48.16 | -9.22 | 0.23 |
Dist | n | PPH | Mean FIA | %RMSE | RMSE | %MAE | MAE | MBE | R\(^2\) |
---|---|---|---|---|---|---|---|---|---|
Plot:pixel | 2015 | 136.85 | 40.67 | 55.66 | 32.30 | 44.20 | 1.72 | 0.34 | |
10 | 1053 | 1.91 | 137.41 | 35.12 | 48.26 | 27.45 | 37.72 | 1.29 | 0.38 |
25 | 260 | 7.75 | 134.17 | 25.59 | 34.34 | 18.56 | 24.90 | 3.21 | 0.48 |
50 | 79 | 25.51 | 135.29 | 17.32 | 23.44 | 12.52 | 16.94 | 2.23 | 0.64 |
100 | 27 | 74.63 | 125.98 | 12.52 | 15.77 | 8.60 | 10.84 | 4.84 | 0.78 |
Dist | n | PPH | Mean FIA | %RMSE | RMSE | %MAE | MAE | MBE | R\(^2\) |
---|---|---|---|---|---|---|---|---|---|
Plot:pixel | 2004 | 137.34 | 40.74 | 55.95 | 32.35 | 44.43 | 1.95 | 0.32 | |
10 | 1052 | 1.9 | 137.74 | 35.39 | 48.75 | 27.68 | 38.13 | 1.59 | 0.37 |
25 | 260 | 7.71 | 134.48 | 25.96 | 34.91 | 18.77 | 25.24 | 3.52 | 0.46 |
50 | 79 | 25.37 | 135.59 | 17.54 | 23.78 | 12.79 | 17.34 | 2.65 | 0.63 |
100 | 27 | 74.22 | 126.32 | 12.93 | 16.33 | 9.08 | 11.47 | 5.00 | 0.77 |
If you see mistakes or want to suggest changes, please create an issue on the source repository.
For attribution, please cite this work as
Johnson (2022, May 6). CAFRI Labs: Landsat:LiDAR-AGB v0.0.99 Map Accuracy. Retrieved from https://cafri-labs.github.io/acceptable-growing-stock/posts/landsatlidar-agb-v0099-map-accuracy/
BibTeX citation
@misc{johnson2022landsat:lidar-agb, author = {Johnson, Lucas}, title = {CAFRI Labs: Landsat:LiDAR-AGB v0.0.99 Map Accuracy}, url = {https://cafri-labs.github.io/acceptable-growing-stock/posts/landsatlidar-agb-v0099-map-accuracy/}, year = {2022} }