Landsat:LiDAR-AGB v0.0.4 Map Accuracy

Map accuracy/agreement assessment following the Riemann et. al. framework.

Description

Map accuracy assessment for the v0.0.4 model-weighted-ensemble Landsat model trained on LiDAR-AGB surfaces. FIA data inventoried between 2013 and 2019 were considered for this assessment.

Model Document

LiDAR-AGB Models

Riemann et. al.

Branch used to produce this document: lj_landsat-lidar-0.0.4

FIA plot inclusion criteria

• All subplots must be sampled.
• Annual assessment vs pooled assessment:
  • For an annual assessment: only FIA plots sampled in the designated year were considered, and compared to the modeled AGB surface for said year.
  • For a pooled assessment: FIA plots inventoried in all years (2013-2019) were aggregated within each hex. Modeled values were extracted from each annual surface to match the inventory year for a particular FIA plot, but aggregated within each hex just the same.

Masking

LCMAP Collection 1.1. 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.

NOTE: Masked AGB pixels are set to 0, and included in the agreement assessment just as any other AGB pixel.

Masked classes:

• 1: Developed
• 5: Water
• 8: Barren

Agreement Statistics (SI corrected)

Table 1: Plot:Pixel Comparison

Group	n	PPH	Mean FIA	MBE	RMSE	MAE	R2	KS	AC	ACs	ACu
target_2013	680	NA	76.71	12.50	49.57	37.61	0.61	0.24	0.56	0.93	0.63
target_2014	661	NA	79.69	12.11	51.83	38.95	0.62	0.26	0.55	0.92	0.63
target_2015	651	NA	75.06	15.04	50.51	38.26	0.63	0.29	0.60	0.92	0.68
target_2016	627	NA	80.54	10.62	49.12	37.57	0.65	0.25	0.57	0.92	0.65
target_2017	608	NA	81.51	10.75	52.67	39.61	0.65	0.27	0.56	0.91	0.66
target_2018	601	NA	77.51	12.89	50.62	38.38	0.62	0.29	0.56	0.92	0.64
target_2019	595	NA	80.97	9.58	48.21	37.35	0.68	0.28	0.60	0.92	0.68
pooled	4423	NA	78.80	11.97	50.39	38.25	0.64	0.27	0.57	0.92	0.65

Table 2: 8660 Ha Hex

Group	n	PPH	Mean FIA	MBE	RMSE	MAE	R2	KS	AC	ACs	ACu
target_2013	645	1.05	76.51	12.67	49.61	37.49	0.61	0.24	0.56	0.93	0.63
target_2014	617	1.07	78.24	12.70	50.32	38.03	0.62	0.26	0.57	0.92	0.64
target_2015	574	1.13	74.87	16.02	49.18	37.30	0.64	0.28	0.61	0.92	0.69
target_2016	564	1.11	79.54	11.07	48.09	36.50	0.65	0.25	0.59	0.93	0.66
target_2017	568	1.07	82.36	10.55	51.89	38.74	0.65	0.26	0.57	0.91	0.66
target_2018	551	1.09	77.10	13.39	49.58	37.71	0.63	0.29	0.58	0.92	0.66
target_2019	520	1.14	81.27	8.89	46.55	36.04	0.69	0.26	0.61	0.92	0.69
pooled	1502	2.94	77.26	12.32	35.04	27.12	0.70	0.14	0.69	0.93	0.75

Table 3: 78100 Ha Hex

Group	n	PPH	Mean FIA	MBE	RMSE	MAE	R2	KS	AC	ACs	ACu
target_2013	190	3.58	71.43	12.54	30.42	23.85	0.71	0.17	0.71	0.95	0.77
target_2014	184	3.59	75.98	14.20	32.74	25.08	0.73	0.17	0.72	0.93	0.79
target_2015	184	3.54	70.38	16.96	34.34	26.63	0.67	0.20	0.67	0.90	0.77
target_2016	180	3.48	79.06	10.13	33.94	25.90	0.71	0.16	0.67	0.92	0.75
target_2017	182	3.34	79.84	9.17	34.41	26.97	0.73	0.16	0.69	0.94	0.75
target_2018	186	3.23	76.60	12.42	34.57	26.55	0.68	0.17	0.65	0.92	0.73
target_2019	179	3.32	80.30	10.47	34.50	26.18	0.70	0.15	0.66	0.94	0.73
pooled	205	21.58	72.37	14.40	24.19	17.81	0.79	0.18	0.78	0.92	0.86

Table 4: 216500 Ha Hex

Group	n	PPH	Mean FIA	MBE	RMSE	MAE	R2	KS	AC	ACs	ACu
target_2013	80	8.50	67.69	11.92	24.64	17.38	0.75	0.19	0.77	0.95	0.82
target_2014	75	8.81	74.61	12.27	25.21	20.18	0.77	0.20	0.75	0.91	0.84
target_2015	77	8.45	69.17	14.91	25.23	19.83	0.74	0.21	0.74	0.91	0.83
target_2016	77	8.14	75.22	12.51	22.06	17.52	0.86	0.16	0.83	0.93	0.90
target_2017	74	8.22	78.20	10.26	21.36	17.30	0.85	0.16	0.83	0.95	0.88
target_2018	77	7.81	70.36	15.70	26.82	19.87	0.74	0.22	0.74	0.91	0.84
target_2019	76	7.83	78.07	12.05	30.01	22.75	0.68	0.20	0.67	0.92	0.74
pooled	85	52.04	70.34	14.23	20.64	16.33	0.85	0.20	0.81	0.90	0.91

ECDF Comparisons Across Scales - FIA Plots vs Mapped (SI corrected)

1:1 and GMFR Lines Across Scales - FIA Plots vs Mapped (SI corrected)

Spatial/Distribution Patterns of Local Differences (SI Corrected)

Target 2013 Distribution

Target 2016 Distribution

Target 2019 Distribution

Pooled Distribution

Spatial Differences

Patterns of Local Variability (SI Corrected)