Improving impervious surface estimation: an integrated method of classification and regression trees (CART) and linear spectral mixture analysis (LSMA) based on error analysis

Figures & data

Figure 1. Location of the study areas in Chicago (America), Venice (Italy), and Guangzhou (China) with training and validation area.

Figure 2. Process of ISA estimation with CART: (a) one of the training areas in QuickBird imagery (Guangzhou); (b) binary map of impervious surfaces in training area extracted using O-O classifier; (c) grid corresponding to Landsat8 Oli imagery in training areas; (d) density of impervious surfaces in training area (30 m resolution) for training; (e) one of the validation areas in QuickBird imagery (Chicago); (f) density of impervious surfaces in validation area (30 m resolution) for accuracy validation.

Figure 3. Procedure of improved CART algorithm by LSMA and error analysis.

Figure 4. Impervious surfaces estimated by CART and LSMA.

Figure 5. Accuracy assessment scatter plot of (a) CART and (b) LSMA.

Figure 6. (a) Box plot of RE_c, (b) quantity of pixels aggregated by ABS of RE_c, and (c) ISA_c.

Figure 7. MAE of CART and LSMA estimation in different ISA.

Figure 8. Improved estimation of impervious surfaces in (a) Chicago, (b) Venice, (c) Guangzhou, and (d) accuracy assessment.

Table 1. Accuracy of impervious estimation.

Methods	Overall/High-density MAE (%)	Overall/High-density RMSE (%)	Fitting Curve Function	R^2
LSMA	5.4/7.1	9.3/10.5	y = 0.99x−0.01	0.94
CART	5.3/10.0	8.6/13.0	y = 0.94x+0.03	0.95
Improved CART	4.1/6.6	6.8/8.8	y = 1.02x+0.01	0.96

Table 2. Accuracy assessment comparison of different methods tested in study area of Chicago.

Methods	Overall/High-density MAE (%)	Overall/High-density RMSE (%)	Overall/High-density ME (%)
ANN	7.49/9.08	9.61/12.39	−3.65/−3.76
Random Forests	5.82/9.92	8.81/13.71	−2.74/−4.39
CART	5.09/9.64	7.56/12.41	−2.70/−3.78

Table 3. Accuracy assessment comparison of different methods in related research.

Methods	MAE (%)	RMSE (%)	Location & Time	References
LSMA	8.6	10.0	Guangzhou, China (2009)	Fan et al. (2015)
LSMA	5.4	9.3	/	This study
CART	9.5	11.9	Suzhou, China (2010)	She and Xiaoning (2013)
CART	6.1	10.8	Raba, Poland (2009)	Drzewiecki (2016)
CART	5.3	8.6	/	This study
ANN	8.0	11.8	Indianapolis, US (2001)	Hu and Weng (2009)
ANN	6.8	11.3	Raba, Poland (2009)	Drzewiecki (2016)
Random Forests	5.7	/	Virginia, US (2006)	Deng and Changshan (2013)
Random Forests	6.5	11.2	Raba, Poland (2009)	Drzewiecki (2016)
Improved CART	4.1	6.8	/	This study

Figure 9. Small-scale comparison of the estimation results of conventional and improved CART in Guangzhou, China.

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