4.1. Assessment of Postfire Regrowth Dynamics Using PFIR
In
Figure 3a, the territory of almost the entire fire area is classified with HRI, which determines better vegetation status of the forest ecosystems several days before the fire. A large part of the test site is classified with LRI, distinguishing the burnt areas a few days after the fire (
Figure 3b) and showing that the Ardino test site was significantly affected by the fire [
19]. The largest part of the test area is classified with HRI and MRI in
Figure 3c—one year after the fire, typically for the initial regrowth process—starting with herbaceous and shrubby vegetation [
3,
14,
22]. Sanitary logging was conducted in 2018 removing the damaged forest stands [
14], resulting in LRI over the large part of the test site (
Figure 3d). In the next few years (
Figure 3e–h), the PFIR exhibits less favorable condition for intensive regrowth.
The worst vegetation status of the forest ecosystems (resulting from a tornado and bark beetle outbreak in the previous years) is observed in Bistritsa test site, which is verified by PFIR raster before the fire (
Figure 4a). The completely dry forests were the perfect fuel for the wildfire, benefited from hot and dry climatic conditions [
14]. The most affected by the fire forests are those of the Bistritsa test site as well, distinguished by LRI over the entire test site (
Figure 4b). The PFIR after the wildfire demonstrates the completely destroyed forests. Although there was no sanitary logging in Bistritsa test site, due to its nature reserve protective status, the PFIR exhibits low recovery rates during the first years of the monitoring (
Figure 4c–e). The post-ire regrowth vegetation is presented predominantly by annual herbaceous plants [
14] mainly influencing the PFIR fluctuations during the study period of the monitoring (
Figure 4f–l).
Ecosystems with limited water resources and low gross primary productivity, such as grassland ecosystems, exhibit higher dependency on hydro-climatic changes influencing vegetation status. They are distinguished by essential productivity reduction under drought impact [
23]. This is the reason for the higher dynamics in the PFIR in the Bistritsa test site. Assessing the impact of local forest ecology on the postfire regrowth dynamics using DI, Chen et al., 2022 [
12] found a correlation between DI and topographic and climatic factors. In mountainous areas, colder habitats are distinguished by lower recovery rates than warmer ones. The results of postfire regrowth dynamics monitoring in this article confirm the results of Chen et al., 2022 [
12]. Similarly, the results obtained in this study confirm that forest regrowth depends on climatic factors [
14,
24].
The PFIR exhibits HRI distinguishing good vegetation status of the study forests in the Perperek test site before the fire event (
Figure 5a). After the fire, almost the entire test site area is distinguished by LRI, which confirms the negative ecologic impact of the wildfire over the forests (
Figure 5b). The PFIR demonstrates optimal condition for regrowth dynamics (
Figure 5c–i). In particular areas, the PFIR exhibits LRI due to sanitary logging in 2017 (
Figure 5d) [
14]. Burnt forest stands were removed in some small areas during the sanitary logging [
14] and the PFIR indicates them with LRI that persists during the entire period of the regrowth monitoring (
Figure 5d–i).
The PFIR distinguishes areas with sanitary logging, indicating them with LRI. However, the PFIR could not distinguish if the regrowth is due to forest vegetation regrowth or other types of vegetation (herbaceous, shrubby vegetation).
4.2. Accuracy Metrics Discussion
Errors of omission represent the real-life objects that were not correctly classified. The results of the present study show that the greater the heterogeneity, the greater the EO is. These observations were confirmed also on the test site level. The lowest EO was found at the test site with the most homogenous environmental conditions throughout the territory and, hence, the most uniformly distributed regrowth categories (Bistritsa test site). Due to the cold and wet conditions for vegetation development, the regrowth intensity in Bistritsa is slower compared with the other test sites. In addition, the greatest part of the territory was identified namely with MRI on the date used for accuracy assessment procedure (
Table 2,
Figure 4h), which undoubtedly influenced the lower EO for this test site. On the contrary, the greatest EO for the Bistritsa test site was found in the HRI class, which was the class with the lowest territorial representation on the date used for accuracy assessment procedure (
Figure 4h). EO increased with the heterogeneity in the environmental conditions and, hence, in the intensity of forest regrowth. Perperek was the test site with the most preserved forest stands after the fire outbreak and the most diverse environmental conditions for forest regrowth after that (
Figure 5i). This effect can be observed also in the results shown in
Table 6. The EO in Perperek was the greatest for all three regrowth categories amongst the studied test sites.
In general, it is observed that in assessing the errors, the values for the individual test sites are mostly influenced by the number of sample points used in the determination of threshold values. An exception is the EC value at MRI for Bistritsa test site. In the assessment of accuracies, this trend is not always observed. However, PA and UA are complements of the OE and CE. Hence, it can be concluded that the results obtained about the individual test sites should be interpreted carefully.
Taking into consideration the differences in the percentage reliability of the individual threshold categories, the lowest accuracy can be predicted for the classification of the HRI and the highest—for MRI. Barely 61% of the test points with a PFIR value below 1 are determined as HRI, whereas the confidence in the classification of MRI is 81.6% (
Table 4). These expectations were confirmed by the results after the calculation of the different accuracy metrics (
Table 6).
The results show that the classified raster thematic maps are distinguished by a good performance in monitoring the regrowth intensity with an average overall accuracy of 62.1%. The PFIR is distinguished by very good PA in the classification of HRI (93.5%) and LRI (75.5%). The UA is highest for MRI classification. Due to possible influence of the number of sample points used in the determination of threshold values, the obtained results related to the accuracy metrics for the individual test sites should be interpreted carefully. However, the final results for the accuracy metrics, including all three test sites, normalize these differences and can be taken as representative of the PFIR performance generally.