Using multisource remotely sensed data and cloud computing approaches to map non-native species in the semi-arid savannah rangelands of Mpumalanga, South Africa

Figures & data

Figure 1. Location setting of the study sites in South Africa.

Table 1. Field sampled points.

Cover class	# of points collected	Training data (70%)	Validation data (30%)
Non-native woody plants (invasive)	428	300	128
Native woody plants	324	227	97
Bare surface	110	77	33
Water	20	14	6
Cropland	13	9	4
Built-up areas	10	7	3

Figure 2. Flow chart of the methodology.

Figure 3. (a) Mean indices values per class extracted from the median composite image using training data; while (b) shows mean spectral reflectance extracted from the median composite image for all the classes in the study area.

Figure 4. Separability of the six land cover classes, namely non-native woody plants; native plants; bare surface; water; built-up areas; and croplands for the variables Sentinel-1.

Figure 5. The producer’s accuracy (a) and user’s accuracy (b) from the model incorporating Sentinel-1 and Sentinel-2 data.

Figure 6. Output of the multispectral integration of Sentinel-1 and Sentinel-2 classification map at 10 m of geometrical resolution of the study area using object-based.

Table 2. The error metrics obtained from different land use classes, wherein the elements are the classification accuracies.

	Non-native species	Native Species	Bare Surface	Water	Built-up	Croplands	Total
Non-native species	276	10	3	0	0	0	289
Native Species	96	37	0	0	0	0	133
Bare Surface	40	7	24	0	0	0	71
Water	1	1	0	14	0	0	16
Built-up	0	0	0	0	8	0	8
Croplands	0	0	0	0	0	4	4
Total	413	55	27	14	8	4	521

Figure 7. The aerial coverage of the six classes in percentage from the output of Sentinel-2 and Sentinel-1 data.