3.1. WorldView-2 Image
To identify all vegetation cover types in the WorldView-2 image, both the segmentation and diversity of plant covers were considered. In total, 389 training fields were used for the 19 different spectral signatures to include the spectral variability of each class. The results for the digital levels in the different bands obtained from the training fields for each spectral signature are displayed in
Figure 2. Greater separation of each signature can be seen in band 7 (NIR), which registers plant activity parameters and is sensitive to moisture. This explains why this part of the spectrum generates high reflectivity [
25].
The sicklebush and sicklebush with aroma (
Vachellia farnesiana) categories obtained an average digital level in the NIR band of 583.64 and 514.71, respectively. These values are below those for the other classes of vegetation and reflect differences in the properties of the leaves, canopy structure, and orientation. For example, the portion of radiation reflected in different parts of the spectrum (the reflectivity pattern) depends on a leaf’s pigmentation, density, and composition (cellular structure) as well as the quantity of water in its tissues [
26]. As such, our adopted spectral classification approach was able to confidently distinguish sicklebush from other species.
After recording the training pixels for the different categories of each class, spectral signatures were assessed using the transformed divergence separability method (
Table 3). This showed that with the sicklebush class as baseline, the classes with lower spectral distances were
Acacia plantation (PA), mamoncillo, mango, other (MMO), and sicklebush and aroma (MA). The values obtained for these classes were 1723, 1805, and 1855, respectively. Based on these values, the AP class had poor separability, whereas the MYA and MMO classes had medium separability.
The poor separation in
Acacia, sicklebush, and aroma classes can be explained by the similarity in the alternate pinnate leaves of these species, both paripinnate and imparipinnate, which is a common characteristic of the
Fabaceae family [
27]. This implies that the reflectivity of these leaves will share similarly high values, making it impossible to obtain a greater level of separation within these classes.
Following the assessment of the signatures, an assignment was performed to select the best classifier. The parallelepiped method left a large number of pixels unclassified for sicklebush, producing a highly fragmented thematic image with fairly extensive regions of pixels with no specific assignment. The minimum distance method also gave an erroneous classification of pixels, including those belonging to other categories, and as a result, classes could not be determined for a large number of isolated pixels. To address this issue, two classifiers were employed. First, the maximum likelihood classifier was applied as a parametric rule, and the parallelepiped classifier was applied as a non-parametric rule. These separated each of the vegetation and land-use categories by assigning each pixel with an established category so that none were left unclassified. This follows the results of Ayala and Menenti [
22], which suggest that these classifiers perform the best, as measured using the performance index.
The spatial distribution of sicklebush is shown in red in
Figure 3, showing a homogeneous distribution in combination with other types of vegetation. In areas where sicklebush was found, the canopy was almost entirely classified as sicklebush. This indicates a correct supervised classification. While juvenile sicklebush growth occurs in some areas, the corresponding spectral values could not be acquired because the training fields only included adult growth, which was more easily identified from the satellite images.
To validate the accuracy of the resulting classification, 257 pixels were randomly selected to calculate the reliability and degree of accuracy of each class using a confusion matrix (
Table 4). The data in
Table 4 represent the degree of association between the classified and baseline classes, yielding an overall accuracy of 88.7%. This exceeds the 85% value recommended by Anderson et al. [
28]. For sicklebush, an accuracy of 94.7% was obtained. Of all the pixels in this category, 94.7% coincided with pixels classified by the applied algorithms, while the remaining pixels were classified as the aroma class. As previously stated, such misclassification likely resulted from the similar physiological characteristics of these two species of
Fabaceae. The accuracy obtained by the Kappa coefficient was 87.4%, close to the perfect level of reliability according to the value classification for the coefficient, as discussed by Landis and Koch [
29].
Visual inspection of the classification revealed salt-and-pepper noise for some cover types despite image segmentation and pixel grouping. This noise was unavoidable and is similar to the observations of Estoque [
30], who used different classifiers. Salt-and-pepper noise is more apparent in the classifiers used in this study than in those based on objects from high-resolution spectral images. Consequently, we conclude that our classification did not give a higher overall reliability level, as some of the sample pixels were likely affected by this noise even when their surroundings were correctly classified, based on field observations.
3.2. Landsat-8 Image
The Landsat-8 image was cropped to the size of the WorldView-2 image to use the previously acquired classifications as training fields to obtain spectral signatures for the different vegetation types. A working legend (
Table 5) was established for different vegetation types and characteristics of the Landsat-8 image based on its ability to discriminate one object from another since the spatial resolution of the first classified image was 0.5 m, that is, the size of the pixel, and the Landsat-8 image had a spatial resolution of 30 m (
Appendix A,
Figure A2). Therefore, the number of classes or vegetation types was reduced to those that could be differentiated. Thus, 70 training fields were obtained for the 10 different spectral signatures to account for the spectral variability of each class.
The spectral signatures obtained from the training areas (
Figure 4) show that sicklebush (MA) and the combination of sicklebush and aroma (MYA) were distinguishable from the other classes in band 5 (NIR) and gave the lowest reflectivity, 0.245 and 0.228, respectively. The same result was obtained using the WorldView-2 image, in which the lowest reflectivity values were obtained for the same classes.
These reflectivity patterns are typical for vegetation and reveal low reflectivity in the visible bands, especially in the red portion, but high reflectivity in the NIR band [
31]. The low reflectivity in the visible portion of the spectrum results from absorption by leaf pigments, primarily chlorophyll [
32]. High reflectivity in the NIR region may be due to the internal structure of leaves (e.g., the spongy mesophyll layer), in which internal air cavities affect the diffusion and dispersion of radiation [
33].
To confirm that the signatures obtained for the different vegetation and land-use types were separated spectrally from the sicklebush category, the transformed divergence separability method was applied. Good separability between the different vegetation types was apparent (
Table 5); the majority of values exceeded 1900, as established by [
21], apart from the sicklebush and aroma class, which exhibited a value of 1086, placing it in the poor separability category. This likely resulted from the combined signatures of both sicklebush and aroma, the growth of which were closely associated in the study area. Another explanation may be the choice of the training fields and pixel size given that the problem did not appear in the WorldView-2 image. This implies that the sites selected as training sites in the first classification had a greater cover of aroma than sicklebush, with the opposite apparent in the Landsat-8 image. In terms of pixel size, it is possible that the range of the sensor did not allow sicklebush and aroma to be differentiated, given their similar morphological characteristics, including bipinnate compound leaves.
The assignment stage was undertaken to obtain the supervised classification using the maximum likelihood method as a parametric rule and the parallelepiped method as a non-parametric rule. This approach successfully separated the vegetation and land-use categories. While the classification was conducted for all vegetation types used in the spectral signatures, only the distribution of sicklebush was mapped in this study (
Figure 5). Thus, sicklebush was distributed across the analysed area, mostly localised to the north, where the valley is superseded by the Escambray Sierra. The results obtained from the original Landsat-8 image demonstrated that sicklebush was colonising areas with low vegetation density or where vegetation is highly fragmented. This finding is in agreement with those of a study by Godinez et al. [
34], who noted that as an invasive species, sicklebush colonises open ground by forming a structure of inaccessible branches and thorns and highlighted the prevalence of this species in areas of secondary scrubland.
Finally, the classification was checked using a confusion matrix produced with 240 random pixels verified in the field. The validation of the maximum likelihood classification of the Landsat-8 image is shown in
Table 6, which yielded an overall accuracy of 93.7%, higher than that of Anderson et al. [
28], who suggested that an accuracy of at least 85% is required (both overall and for each category) if results are to be used for future land-use management and planning. An accuracy of 91% was obtained for the Kappa coefficient, which, based on Landis and Koch [
29], indicates very high reliability, where six of the ten categories exceeded 90%. This suggests that the visual interpretation and criteria used to separate the different categories were correct, reducing the likelihood of the misclassification of pixels.
After acquiring the spectral signature and spatial distribution of sicklebush by cropping the Landsat-8 image, supervised classification was performed using the maximum likelihood method (
Figure 6). Sicklebush was present in most of the municipality (shown in red in
Figure 6), covering 52.7% of the total area analysed, and was mainly present in the northeast and west (Escambray Sierra) and south (seaside marshes) of the Valle de los Ingenios area, which is clearly delimited (
Figure 6).
Given the scope of the project, the distribution of infestation could not be verified across non-cropped areas. Therefore, it was impossible to generate a matrix of confusion to determine the overall reliability of our model and to determine the effect of transferring the information from a small to a larger area. This is because there may be other types of vegetation in non-accessible zones, such as the sierra and marsh areas, which had not been distinguished spectrally in the model. Therefore, it is necessary to generate alternative signatures to avoid erroneous classification in the study area. However, our model was able to distinguish between local crops and marabou covers, which was the main goal of this study.