Comparison of DEMS derived from USGS DLG, SRTM, a statewide photogrammetry program, ASTER GDEM and LiDAR: implications for change detection

Figures & data

Table 1. DEM datasets evaluated in this study.

Dataset	Date	Native cell size	Creation method	Target accuracy	Estimated accuracy	Season of collection
DLGDEM	1976	30 m	Aerial photogrammetry	<3.09 m	<3.05 m^a	Leaf-off
SRTMDEM	2000	30 m	C-band IFSAR	16 m	9 m^b	February
SAMBDEM	2003	3 m	Aerial photogrammetry	±10 ft	2.091 m^c	April
GDEM	2009	30 m	Satellite photogrammetry	25 m	10.45 m^d	Multiple
LiDARDEM	2010	3 m	LiDAR	<0.3 m	0.209 m^e	Leaf-off

Notes: ^aUSGS (2006).

^bRodríguez, Morris, and Belz (2006); Bolstad and Stowe (1994); Blanchard, Rogan, and Woodcock (2010); Hofton et al. (2006); Passini, Jacobsen, and Passini (2007).

^cFedorko (2005).

^dAbrams et al. (2010).

^eDiCicco (2011).

Figure 1. Location of the study area within the 7.5′ topographic quadrangle. For full color versions of the figures in this paper, please see the online version.

Figure 2. Error rasters generated from the LiDARDEM minus each DEM calculation. (A) LiDAR – DLGDEM; (B) LiDAR – SRTMDEM; (C) LiDAR – GDEM; (D) LiDAR – SAMBDEM.

Table 2. OLS regression of elevation and error, slope and error, and aspect and error. There is a positive, significant relationship between elevation and error in the DLGDEM and SRTMDEM and between slope and error in the SRTMDEM.

Variables	Metric	DLGDEM	SRTMDEM	SAMBDEM	GDEM
Elevation and error
	R^2	0.048	0.020	0.003	0.001
	p > |t|	<0.0001*	<0.0001*	0.064	0.425
	Coefficient	0.024	0.021	0.003	0.005
Slope and error
	R^2	0.002	0.038	0.005	0.009
	p > |t|	0.165	<0.0001*	0.034*	0.004*
	Coefficient	0.036	0.021	0.030	−0.146
Aspect and error
	R^2	0.001	0.001	0.0001	0.0001
	p > |t|	<0.0001*	<0.0001*	0.219	<0.0001*
	Coefficient	1.233	−13.142	−0.177	−12.943

Notes: no significance (ns): p > 0.05, *p ≤ 0.05, ** p ≤ 0.01, *** p ≤ 0.001, **** p ≤ 0.0001.

Table 3. Descriptive statistics of the error values in meters for each DEM, broken out by land cover.

	Land Cover	Developed	Forest	Grassland/pasture	Shrub/crop
DLG	Minimum	−10.33	−17.59	−15.46	1.11
	Maximum	13.43	22.98	9.15	15.56
	Mean	−0.95	1.76	−0.88	8.34
	Standard deviation	5.74	5.95	4.64	10.22
SRTM	Minimum	−29.44	−36.17	−24.51	−6.20
	Maximum	3.40	9.59	2.42	−1.38
	Mean	−13.36	−12.76	−8.84	−3.79
	Standard deviation	6.66	8.01	6.33	3.41
SAMB	Minimum	−5.62	−20.95	−4.15	−0.69
	Maximum	7.50	13.00	5.85	5.85
	Mean	−0.33	0.28	0.21	2.58
	Standard deviation	3.10	3.09	2.32	4.62
GDEM	Minimum	−24.22	−49.19	−22.27	−10.65
	Maximum	9.98	24.28	12.59	−5.72
	Mean	−7.06	−12.73	−3.49	−8.19
	Standard deviation	8.95	11.41	8.57	3.48

Figure 3. Histograms of the number of grid cells with values in increments of 5 m, for each DEM.

Table 4. Statistical measures of each error raster. Error rasters with a non-normal distribution are highlighted in gray; however, both normal and non-normal measures are calculated for all error rasters. While the majority of these statistical measures utilize the signed difference in elevation between the reference surface and the tested DEM surface (indicated by ∆h), the non-normal error distribution measures of the 68.3 percentile and the 95th percentile show the absolute value of the errors (indicated by ).

			SAMBDEM	DLGDEM	SRTMDEM	GDEM
Descriptive statistic	Maximum	∆h	13.00	22.98	9.59	24.28
	Minimum	∆h	−20.96	−17.59	−36.17	−49.19
		∆h	0.25	1.66	−12.59	−12.17
	Skewness	∆h	−0.123	0.136	0.016	−0.078
Test for normalcy	W-score					0.99^ns
	t-Score
Normal distribution measure	RMSE (m)	∆h	3.05	6.14	14.90	16.77
	Mean error	∆h	0.25	1.69	−12.59	−12.17
	Σ	∆h	3.04	5.91	7.97	11.54
Non-normal error distribution measure	Median (50%)	∆h	0.14	1.44	−12.74	−12.14
	NMAD	∆h	2.81	5.77	8.28	11.81
	68.3%		2.81	6.08	16.49	17.86
	95%		5.83	12.50	25.57	31.76

Notes: ns: p > 0.05, *p ≤ 0.05, **p ≤ 0.01, ***p ≤ 0.001, ****p ≤ 0.0001.

Figure 4. Comparisons of the DEM along a transect across a ridge.

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