Fusing lidar and digital aerial photography for object-based forest mapping in the Florida Everglades

Figures & data

Figure 1. Map of the Everglades and study area shown as a color infrared (CIR) aerial photograph.

Figure 2. Flowchart for forest mapping using lidar and digital aerial photograph.

Table 1. Classification accuracies using different data sets

Classification accuracies
Experiment #	1	2	3	4	5
Overall accuracy	49%	60%	61%	61%	71%
Kappa value	0.35	0.48	0.50	0.51	0.64
McNemar tests
Z-score value	NA	3.5 (1/2)	3.8 (1/3)	4.3 (1/4)	6.6 (1/5)
		NA	0.3 (2/3)	0.5 (2/4)	3.7 (2/5)
			NA	0.3 (3/4)	4.2 (3/5)
				NA	3.6 (4/5)
Notes: Experiment 1: DOQQ-derived texture measures.
Experiment 2: DOQQ-derived texture measures and lidar-derived elevation information.
Experiment 3: DOQQ-derived texture measures and lidar-derived intensity information.
Experiment 4: DOQQ-derived texture measures and lidar-derived topographic information.
Experiment 5: DOQQ-derived texture measures and lidar-derived elevation, intensity, and topographic information.
Random Forest classifier with k = 150 and m = 4 was used in these experiments.
Z-score value larger than 1.96 means a significant difference in classification at 95% confidence level.

Figure 3. Classification map for the study area (color version available online).

Table 2. Error matrix for the classified map shown in Figure 3

Class #	1	2	3	4	5	6	7	Total	PA (%)
1	14			1	2			17	82
2	1	56		7			3	67	84
3		3	6	8				17	35
4		13	1	50	11			75	67
5				1	23			24	96
6		2		10	3	1		16	6.3
7		5					27	32	84
Total	15	79	7	77	39	1	30	248
UA (%)	93	71	86	65	59	100	90	Overall accuracy: 71%
								Kappa value: 0.64
Notes: PA (%): producer's accuracy; UA (%): user's accuracy.
Classes 1–7 represent mixed wetland hardwood, upland mixed coniferous/hardwood, cabbage palm, oak-cabbage palm, live oak, upland hardwood, and palmetto prairies, respectively.