Unmanned aerial vehicles (UAV) for assessment of qualitative classification of Norway spruce in temperate forest stands

Figures & data

Figure 1. Framework of the study.

Notes: NDVI – normalized differential vegetation index; OBIA – object-based image analysis. Green blocks are input data, blue blocks are objectives, and gray blocks are outputs.

Figure 2. Location of study area and the two selected sites for field and UAV surveys.

Table 1. The basic stand dendrometric characteristics including mean tree stem diameter at breast height (DBHm), mean tree height H, stand basal area (G), and stand tree density (N) for the studied plots.

Parameter	Plot A				Plot B
	Spruce	Fir	Beech	Total	Spruce	Fir	Beech	Total
DBHm (cm)	22.6	22.8	15.9	22.5	23.1	19.5	16.8	22.5
H (m)	20.5	19.5	17.6	20.4	20.5	19.4	17.2	20.3
G (m^2/ha)	33.3	21.9	0.9	56	36.9	2.6	1.2	40.7
N (1/ha)	832	536	44	1412	880	88	56	1024

Note: Data are shown by the individual tree species and totally.

Figure 3. Box-and-Whisker plots of NDVI (left) and PDC raster (right) for tree species category including alive specimen of Norway spruce, Silver fir and European beech, and standing dead trees.

Notes: The letters a, b, and c indicate significant differences between the categories (at p < 0.05) from the post hoc test of ANOVA. The length of each box shows the range within which the central 50% of the values fall, with the box edges (hinges) at the first and third quartiles. The line in the box indicates the median of the data. Outside values are plotted with asterisk. Far outside values are plotted with empty circles.

Table 2. Confusion matrixes of species classification.

(a) Input for classification is VNIR data
Species	Spruce	Beech	Fir	Dead tree	Total
Spruce	114	2	31	0	147
Beech	1	10	3	0	14
Fir	19	0	74	0	93
Dead tree	0	0	0	2	2
Total	134	12	107	2	255
(b) Input for classification is VNIR+PDC raster
Species	Spruce	Beech	Fir	Dead tree	Total
Spruce	94	0	23	0	117
Beech	1	9	0	0	10
Fir	39	3	85	0	127
Dead tree	0	0	0	2	2
Total	134	12	107	2	255

Notes: (a) confusion matrix for classification with input VNIR data, (b) confusion matrix for classification with input VNIR+PDC raster. The correct classifications are in bold font along the upper left to lower right diagonal.

Table 3. Species classification accuracy metrics.

Item	Input for classification	Spruce	Beech	Fir	Dead tree
Errors of commission	VNIR	0.224	0.286	0.215	0
	VNIR+PDC raster	0.197	0.100	0.338	0
Errors of omission	VNIR	0.149	0.167	0.318	0
	VNIR+PDC raster	0.298	0.250	0.215	0
Overall accuracy	VNIR	0.784
	VNIR+PDC raster	0.745

Figure 4. Example of species classification using object-oriented image analysis: (a) RGB image; (b) segmentation; (c) classification.

Figure 5. Testing of UAV data products (PDC raster and NDVI) against tree health indicators.