3.1. Dynamics of Tree Vegetation
It is observed that the basal area values were higher in Area 2 (
Figure 2), which is considered the most conserved area without a history of exploitation for over 50 years. However, over the studied years, these values have been decreasing (
Figure 2D). This result may be associated with tree mortality, as the study period coincides with the occurrence of intense drought events in the region (2012–2019). This could have resulted in higher evapotranspiration rates and, consequently, a negative water balance for the plants [
48,
49].
Such results are observed in the study by Costa Júnior et al. [
50], who analyzed the vegetation dynamics in the same study area and noted high mortality between 2008 and 2019. However, the opposite can be observed for Area 1, which showed an increasing average basal area between 2013 and 2021 (
Figure 2A). This can be explained by the mechanical clearing of the vegetation in 1987, which initiated a process of natural regeneration with a lower tree density. According to Dale et al. [
51], a lower tree density can have significant effects on tree development, as the increased spacing between trees can reduce competition for resources such as water, nutrients, and space, thereby promoting survival and growth even after drought events.
A similar result to the basal area was observed for biomass, with increasing production recorded in Area 1, with an average varying from 345 kg to 398 kg between 2013 and 2021, respectively (
Figure 2B). Considering that light is an essential factor for photosynthesis, the process by which plants convert solar energy into chemical energy necessary for their growth and development, a greater amount of light, enhanced by the lower tree density in Area 1, may have boosted the photosynthetic rate of the plants. This resulted in an increase in carbohydrate production and, consequently, in the growth and production of biomass [
52,
53].
In this context, the higher tree density combined with the drought period recorded for the study region may have led to a decrease in biomass production in Area 2 (
Figure 2E). This is because, in semi-arid regions, water availability is the main limiting factor for biomass increase [
54]. Thus, considering the consecutive drought periods in the region, the continuous water scarcity may lead to an increase in tree and shrub mortality [
55]. This occurs because, during drought periods, trees have adaptation mechanisms to cope with water deficit, such as closing stomata in the leaves and temporary leaf loss (deciduousness) to reduce evapotranspiration [
56]. However, when climatic conditions exceed the plants’ tolerance limits due to increased temperature, vapor pressure, and water deficit [
57], the probability of tree mortality can increase, negatively affecting biomass production, as observed in the study area.
Regarding tree density, Area 1 maintained an almost constant average (
Figure 2C), while Area 2 registered a reduction in tree density between 2013 and 2021 (
Figure 2F). The reduction in tree density results from a mortality rate higher than the recruitment rate, as these rates are strongly influenced by climate seasonality and consecutive years of drought [
58]. In this sense, the decrease in plant density from 2013 to 2020 in Area 2 can be explained by the droughts that occurred in the region from 2012 to 2019 [
49,
59].
3.2. Analysis of Vegetation and Water Indices
The NDVI values ranged between −0.10 and 0.40 from 2013 to 2021 (
Figure 3). Analysis of the NDVI thematic maps reveals values corresponding to different types of land cover and use over the time series in the study area. Water bodies, exposed soil, roads, deforested areas, and other non-vegetative cover [
35] are represented by negative pixel values, ranging from −0.10 to −0.01 (pixels in red tones). Environments with denser vegetation exhibit green tones, with NDVI values close to the maximum recorded for the area (0.40). These high NDVI values, even during dry scenarios, are related to areas with a more humid surface and subsurface hydrological conditions, such as areas near rivers and streams, which can sustain native perennial species and invasive exotic species like
Prosopis juliflora (Sw) DC) [
60,
61]. However, most of the studied area recorded values below 0.40, these environments being occupied by vegetation typical of dry forests that tend to lose their leaves during the dry season and are still susceptible to the effects of intense droughts common in the region [
20,
23,
62].
The NDVI provides a comprehensive measure of vegetation health and status, being one of the first remote sensing-based indicators used for drought detection and monitoring [
37,
63]. The spectral changes associated with this index are related to vegetation patterns, which are strongly influenced by rainfall regimes and moisture conditions in semi-arid regions. In this sense, considering that rainfall plays a fundamental role in the resilience and seasonality of natural vegetation cover in dry forest environments, and consequently the spatio-temporal behavior of the NDVI [
20,
23,
64,
65], the results for the NDVI observed in the present study can be associated with the water deficit common to the region.
According to observations by Marengo et al. [
66], the Caatinga biome is considered one of the Brazilian ecosystems most susceptible to climate change. This is due to low rainfall levels, which exacerbate environmental degradation and make native vegetation more vulnerable. This is in line with the results obtained in this study, especially for the years 2013 and 2017 when NDVI thematic maps showed a predominantly reddish coloration (
Figure 3), coinciding with periods of extreme drought in the region and, consequently, reduced water supply to plants. Similar findings are noted in the study by Silva et al. [
20], which highlights that from 2016 to 2018, there was lower resilience in areas with natural vegetation in the Caatinga, in the municipality of Capoeiras-PE, with NDVI values below 0.642 when compared to other years.
In 2020, there was a smoothing of the reddish coloration across the entire study area, indicating higher NDVI values, especially around Area 1, which during rainy seasons has the characteristic of retaining a greater amount of water in the soil, providing greater water availability for plants throughout the year. These results indicate the sensitivity of NDVI to climatic variability in dry forests. Several studies using NDVI in semi-arid regions show sensitive monitoring of vegetative biomass and photosynthetically active vegetation covers [
20,
23,
67,
68,
69].
It is possible to observe that for NDWIveg, the values ranged from ≤0.00 to >0.00 (positive and negative) during the period from 2013 to 2021 (
Figure 4), with a predominance of negative values in the study area. Considering the time of year when the images used for index processing were obtained, the low water content in the area can be explained by the region’s dry season. As highlighted by Costa Júnior et al. [
50], assessing the climatic variability of the municipality of Ibimirim-PE, 40 km from the study area, the precipitation recorded in the region for the months of September and October is less than 20 mm.
In 2020, the spectral response of vegetation assessed based on NDWIveg indicated a higher presence of water in plants (
Figure 4). It is important to emphasize that in this year, the northeast region of Brazil was affected by the strong La Niña climatic event [
70], which has the main effect of increasing rainfall volume in the region even during the considered dry season [
71,
72,
73], justifying the result indicated by NDWIveg. However, between the years 2012 and 2019, events of drought with greater intensity and duration of the last 60 years were recorded in northeast Brazil, mainly in the semiarid region, resulting in a high water deficit and consequent stress for vegetation [
23,
74,
75]. In this sense, based on the statements made by Silva et al. [
76], successive episodes of increasing and decreasing rainfall can result in a higher degree of irregularity in rainfall dynamics and, therefore, in higher entropy values.
The low NDWIveg values recorded Indicate a degradative process of native vegetation over the years. Considering the study by Lastovicka et al. [
19], where NDWIveg was particularly useful for detecting disturbed forest and forest recovery after beetle outbreaks, it also provided relevant information about forest health. For the study conditions, the authors observed that undisturbed areas showed NDWIveg values ranging from 0.37 to 0.69, while affected or recovering areas exhibited significantly lower values ranging from −0.12 to 0.28. These results suggest that vegetation in the study area may have been impacted by a physiological disturbance associated with drought, as observed in the evaluated time series.
Through NDWI, it was possible to identify variations between positive and negative values during the period from 2013 to 2021 (
Figure 5). In the thematic maps generated from NDWI, there is only a small strip of positive value, characterized in the pixels of the geospatial maps as areas of minor water coverage, such as environments with some moisture condition around rivers, lakes, and reservoirs [
23].
In monitoring and quantifying vegetation change patterns and water coverage areas by determining physical–hydrological parameters in the northeast region of Brazil, Silva et al. [
23] considered exposed soil, agriculture, and Caatinga vegetation cover when NDWI ranged between −0.2 and −0.01, similar to the present study. Additionally, according to Marin et al. [
11] and Reis et al. [
12], ecosystems in this region are adapted to water scarcity conditions and hot summers. However, in 2012, the onset of one of the most severe droughts ever recorded in the semiarid region of northeast Brazil led to the rapid depletion of vegetation water, which persisted and intensified until 2019 [
35], resulting in reduced water availability for plants in the region, consistent with observations in NDWIveg thematic maps (
Figure 4) and NDWI (
Figure 5).
Negative values for MNDWI, similar to NDWI, were considered as exposed soil, agriculture, and Caatinga vegetation cover (
Figure 6). In the thematic maps of MNDWI, a characteristic of homogenizing agricultural and vegetation cover areas is highlighted, whereas for characterizing water bodies, MNDWI shows greater sensitivity in characterizing water bodies than NDWI, as already observed by Silva et al. [
23]. In this sense, in the spatio-temporal analysis of MNDWI, it was possible to identify water bodies, especially in the environment located in the central region of the study area in the year 2017, which emerged because of the São Francisco River transposition project. This result can be explained by the observations of Titolo [
77]. Studying artificial reservoirs using water indices, the authors mentioned that no pixels were incorrectly recorded in water when using the MNDWI equation, highlighting its efficiency and precision compared to other water indices.
3.3. Mann–Kendall Trend Analysis of Field Parameters and Indices Analyzed
Based on the adopted 95% confidence level (|Z| > ±1.96), the basal area (m
2.ha
−1) showed a strong increasing trend (|Z| = 3.02), indicating that the trees in the area’s vegetation exhibited diameter growth (
Figure 7a). Positive Mann–Kendall values indicate an upward trend, while negative values indicate a declining trend. This result can be attributed to the regeneration process the area is undergoing, with lower plant density following the mechanical clearing carried out about three decades ago. However, for Area 2 (
Figure 7b), there was no trend observed significative. These results likely reflect the vegetation’s response to the severe droughts recorded between 2012 and 2016 in the semi-arid region of Brazil [
49], which was more pronounced in Area 2, where there was higher plant density and, consequently, greater competition for water resources (
Figure 2). Under conditions of water deficit, mortality exceeds recruitment, disrupting the dynamic vegetation system.
The higher tree mortality compared to recruitment in the vegetation can also explain the negative trend in the number of trees observed in both areas (
Figure 7c,d). This phenomenon is concerning because the lack of tree recruitment in areas experiencing mortality rates is characteristic of a degrading process without area recovery. In dry tropical forests, such as the study areas with open vegetation and greater exposure to solar radiation, the effects of drought on vegetation are intensified, leading to severe tree mortality, often caused by hydraulic failure [
78]. The risk of hydraulic failure increases proportionally with the exposure of the canopy to light and heating, being more intense in open vegetation. These risks are associated with the plants’ response to water deficit caused by drought events, causing them to close their stomata to prevent hydraulic failure. However, this process can result in a carbon deficit, causing the plant to “starve” [
79]. Regarding biomass production, this showed a negative trend for both areas; however, the values of |Z| less than −1.96 indicate that this trend is not significant at 95% confidence level (
Figure 7e,f).
The values of MNDWI and NDWI, indices sensitive to identifying the presence or absence of water bodies, showed negative trends with negative |Z| values for both study areas (
Figure 8b–d), except for MNDWI in Area 1, which showed a positive trend (
Figure 8a). However, only the negative trend for NDWI in Area 2 was significant at the 95% level. These results can be easily explained by the severe droughts recorded in the region between 2012 and 2016 [
49], which drastically reduced the presence of water bodies in the study area. Regarding the index’s sensitivity to vegetation response, NDWIveg and NDVI generally showed a positive trend (
Figure 8e–h). However, this trend was only positive for NDWIveg and NDVI in Area 2, which may be related to the higher tree density observed when analyzing the number of trees (
Figure 2F and
Figure 8d), resulting in higher vegetation reflectance and consequently higher values for the analyzed indices.
3.4. Regression Analysis
The vegetation indices of the images evaluated in this study did not show considerable predictive power for basal area, tree number, and biomass for the two experimental areas, based on the correlations observed in
Figure 9 and
Figure 10. The low correlation observed in NDVI, NDWIveg, NDWI, and MNDWI mainly occurs because the analysis was conducted during the dry period of each year when rainfall is low or absent in semi-arid regions. As a result, there is a significant reduction in the photosynthetic rate of plants, as well as in their vegetative vigor and biomass. Consistent with the results observed in this study, Barros Santiago et al. [
80], who used biophysical indices and water indices from orbital products in the Araripe National Forest, at the border of Pernambuco and Ceará, highlight that the application of NDVI and NDWI during dry periods showed low values for vegetation characterization. Similarly, Serrano et al. [
81], who applied NDWI to characterize pasture vegetation in the semi-arid region of southern Portugal (annual average between 400 and 600 mm), report that NDWI is efficient in characterizing water bodies in vegetation, but during the dry season, this index is not representative.
It is important to note that the use of indices to characterize water bodies in the soil and vegetation during the dry season in semi-arid regions requires careful analysis. The application of these indices for large semi-arid regions is well-represented, as discussed by Silva et al. [
20] and Melo et al. [
82], who applied biophysical indices to characterize soil and vegetation degradation in semi-arid regions. Similarly, Silva et al. [
23] applied biophysical indices and water indices to characterize the Brazilian semi-arid region using orbital data from the MODIS sensor. These authors highlight the efficiency of applying NDVI and NDWI, for example, to characterize the vegetation of semi-arid regions during dry seasons, as well as the response of FTSS, showing the dynamics of water in the vegetation and soil of these regions.
3.5. Principal Component Analysis (PCA)
According to the Kaiser–Meyer–Olkin (KMO) adequacy test for principal component analysis (PCA), the components for Areas 1 and 2 showed a moderate fit (KMO between 0.70 and 0.79), with KMO values of 0.721 and 0.732, respectively (
Table 3). As for the Bartlett test, both PCA analyses showed significant results in both areas (
p-value < 0.01). Based on these results, the PCA established in this study is adequate for characterizing and representing the analyzed variables. Pandorfi et al. [
83] highlight the importance of using the KMO test for sampling adequacy, where the authors validated their results with a KMO value of 0.70. Additionally, significance in the Bartlett test is crucial (<0.01) for representing the stability of the established PCA, according to the authors.
Based on the adequacy of the data sampling, the principal components (PCs) were established for Areas 1 and 2, respectively. According to
Table 4, the eigenvalues, variances, and cumulative variances of the variables for the established PCs in this study are presented. It is noted that six PCs were generated, but only the first three components have significant informational load to be analyzed in this study. According to Kaiser [
45], PCs only possess significant informational load if they have an eigenvalue above 1. Therefore, only PCs 1, 2, and 3 meet the criterion established by Kaiser [
45], with eigenvalues of 2.70, 2.06, and 1.62, respectively, for Area 1, and 2.57, 2.29, and 1.26, respectively, for Area 2. Corroborating the results of this study, Melo et al. [
82], when analyzing the degradative effects using biophysical indices in the dairy basin of the state of Pernambuco, highlighted that the correlation between the biophysical indices observed through principal component analysis was significant, with an eigenvalue of 2.949 for PC1.
Regarding the cumulative variance of the PCs, six components were established, which together account for 100% of the explanatory variance of the dataset. However, for plotting the component graph, only PCs 1 and 2 are used, with their cumulative values for these two in Areas 1 and 2 being 67.89% and 69.40%, respectively. Supporting these findings, Silva et al. [
84], who utilized biophysical indices via Sentinel-2, soil physical data, and morphometric variables of forage palm, reported that PCs 1 and 2 exhibit the highest variance rates and are therefore recommended for generating the component graph.
Total accumulated variance values above 50% are representative, as evidenced in a study by Silva et al. [
84], which focused on soil physical attributes, biophysical indices, and morphometric variables of cacti in the semiarid region of northeast Brazil. Their study observed that total variance values ranging from 50% to 60% of the total accumulated variance of the principal components were sufficient to significantly observe correlations between variables and to predict a model for estimating cactus leaf area in a semi-arid region.
It is noted that both the NDVI and NDWIveg showed strong correlations (>0.80) for both study areas (
Figure 11a–d), which is because both indices are directly proportional. Additionally, it can be observed that the year 2020 was the most representative for NDVI and NDWIveg, mainly influenced by the extreme La Niña effect that affected the entire northeastern region of Brazil [
70]. Corroborating the results observed in this study, an analysis of the spatio-temporal variability of rainfall and the occurrence of extreme rainfall events in the state of Pernambuco by Silva et al. [
73] highlights that the year 2020 was atypical with anomalies in the intensity of extreme rainfall.
A correlation is observed between biomass, basal area, and tree density for both study areas, regardless of the year of study. This effect occurs due to the correlation of biomass with the variables, meaning that the greater the density and basal area, the higher the estimated biomass in the field. This result is expected, as the biomass behaves similarly when the tree density increases or decreases in an area since more or fewer trees contribute to the total production of organic matter [
85]. Basal area is related to the size and density of the trees, and these factors directly influence the amount of accumulated biomass [
86]. Furthermore, the basis for calculating basal area is the tree diameter at 1.30m above the ground, which is also a component in all equations used in the estimation (
Table 1).
From the observations made in the principal component analysis (
Figure 11), it is noted that the years 2013 to 2019 and 2021 did not show directly proportional differences in the physical–hydric parameters on the surface recorded for Area 1 (
Figure 11a,b) and Area 2 (
Figure 11c,d). This period includes the great drought in northeastern Brazil from 2012 to 2016, which extended until 2019. Corroborating the results of this study, Silva et al. [
35] report the effects of the great droughts in northeastern Brazil through the NDWI and highlight that the droughts continued until 2019.
3.5.1. Principal Component Regression (PCR)
Area 1
Table 5 presents the correlation matrix by PCs of the variables studied with the six components established in Area 1. It is noted that from PC4 onwards, the correlation values of the variables are almost null, which is consistent with
Table 4, where the eigenvalues from PC4 onwards are less than 1, indicating almost no contribution of these components to the set of variables studied. On the other hand, PCs 1, 2, and 3 showed variables with significant correlation loads, where high positive correlations of one or more variables indicate that these variables are strongly correlated with each other.
In Principal Component 1 (PC1), the variables basal area, tree number, biomass, NDWIveg, and NDVI showed significant correlations with values of approximately 0.85, 0.73, 0.89, 0.50, and 0.51, respectively. In PC2, residual information is generated, as stated by Kaiser [
45], where we observed higher inversely proportional correlations in the variables NDWI and NDVI, with respective values of 0.89 and −0.81. In PC3, the variables MNDWI and NDWIveg exhibited strong proportional correlations with values of approximately 0.92 and 0.73, respectively.
In
Table 6, the principal component regression (PCR) results are presented for the response variables basal area, tree number, and biomass. For all three response variables, the component that best fit the variables was PC1, with coefficients of determination (R2) values of 0.73, 0.53, and 0.80, respectively. The regressions were highly significant (
p-value < 0.01) for all variables in question.
Based on the observations from
Table 6, the component that best represents the established response variables for predicting multiple regression models was determined. Consequently, the predictor variables that showed the highest correlation with the response variables were the vegetation indices NDWIveg and NDVI (
Table 5).
The analysis of variance (ANOVA) for each of the multiple models for basal area, tree number, and biomass response variables is presented in
Table 7. All models had a
p-value < 0.01, indicating significance. However, the R-squared values were low for each response variable, with values of 0.04, 0.02, and 0.02 for basal area, tree number, and biomass, respectively. It is evident that, consistent with the findings from a simple regression (
Figure 9), the models developed using principal component analysis to identify physical–hydrological predictors for each response variable (basal area, tree number, and biomass) performed unsatisfactorily. As noted by Oliveira et al. [
47] in their modeling of biomass and carbon stock using LiDAR metrics in dry tropical forest areas of Brazil, principal component regression showed the lowest R-squared values among the regression techniques analyzed.
Area 2
In
Table 8, it is possible to observe that, similar to Area 1 starting from PC4, the correlation values of the variables are almost null. It is noted that in these cases, the eigenvalues are less than 1, indicating a minimal or nearly negligible contribution of these components to the regression. On the other hand, primarily PCs 1 and 2, and for some variables PC 3 (MNDWI and NDWI), exhibited variables with high correlation loads, indicating a strong correlation among these variables.
In Principal Component 1 (PC1), the variables NDWIveg, NDWI, and NDVI showed significant correlations with values of 0.92, −0.78, and 0.92, respectively. In PC2, inverse proportional correlations are observed, where the variables basal area, tree number, and biomass had higher loadings with values of 0.82, 0.68, and 0.93, respectively. PC3 exhibited strong proportional correlations between the variables MNDWI and NDWI, with values of 0.77 and 0.54, respectively. It is evident that for the dataset obtained in Area 2, the correlation loadings for the variables in each PC were defined differently between the physical–hydrological indices and the field variables.
Table 9 presents the statistical values for principal component regression (PCR) for each response variable (basal area, tree number, and biomass). For all three response variables, PC2 provided the best fit, with coefficients of determination (R
2) of 0.67, 0.46, and 0.86, respectively, for basal area, tree number, and biomass. The regressions were highly significant (
p-value < 0.01) for all analyzed variables.
As observed in
Table 8, there is no significant correlation between the response variables and the predictor indices in PC2, nor in any of the other components. This result is consistent with what was observed in the simple regression results (
Figure 10), which indicated low predictive power of the evaluated indices to estimate the response variables. These findings, along with the low R
2 values in the proposed regressions also for Area 1, support the hypothesis that the sensitivity of the indices to the presence and absence of water, combined with the fact that the analyzed images and raw data are from the dry period of each evaluated year (2013 to 2021), complicates the adequacy of the dataset for regression analyses. Therefore, we recommend future studies to better understand the correlation between field-obtained data and physical–hydrological indices, evaluating images both in dry and rainy seasons in the study area. Additionally, the use of indices more sensitive to the characteristics of dry forest vegetation is recommended.