**3. Results**

#### **3.1 Magnitudes of spatial dynamics**

Globally, each index showed a different interseason sensitivity to dynamics for the two main magnitudes of change in the vegetation distribution. The indices detecting vigor, biochemical composition, and water content, i.e., PVR, PBI and GVMI, that are highly correlated with MSAVI2, invaded the upper medium area for the increasing magnitude, while their increasing patterns were more concentrated on the south and lower medium area (**Figure 4a** and **b**). Besides, the indices detecting canopy chlorophyll, carotenoids, anthocyanin, nitrogen, and water stress, with a positive (mCRIG)

*Dynamics, Anomalies and Boundaries of the Forest-Savanna Transition: A Novel Remote… DOI: http://dx.doi.org/10.5772/intechopen.105074*

#### **Figure 4.**

*a. Decrease trends of CVA per index. The background layers are from 2021 to 2022. b. Increase trends of CVA per index. The background layers are from 2021 to 2022.*

or non-significant (IREIP1, LWCI) correlation to MSAVI2, gave a general decreasing or increasing patterns on the whole subset (**Figure 4a** and **b**). Whereas, the indices targeting the lack of chlorophyll, carotenoids, anthocyanin, pigment or the plant senescence, and that were negatively correlated to MSAVI2, i.e., MARI, SRPI and PSRI, cover the South area for the decreasing patterns, and the North part for the increasing patterns (**Figure 4a** and **b**).

Moreover, all magnitudes agreed to the same total areas for all indices, when adding the unchanged magnitudes. For the extreme cases, LWCI indicates a brutal

#### **Figure 5.**

*Areas(a), percentages(b) and hierarchy of changes (c) based on the CVA decrease (row1) and increase (row2) magnitudes.*

decrease in between 2015 and 2016 and 2016–2017 (Change1, **Figure 5a**), from 61,609 km2 to 22,030 km<sup>2</sup> , i.e., 58% of the total change for the study period (**Figure 5b**). While, GVMI indicates an important increase between 2019 and 2020 and 2020–2021 (Change 5, **Figure 5a**), from 1439 km<sup>2</sup> to 41,215 km<sup>2</sup> , i.e., 69.4% of the total change for the study period (**Figure 5b**). Then all changes were complementary between the two magnitudes, except for IREIP1 in the last period, 2020–2021/2021– 2022, which the decrease was unsignificant (4km<sup>2</sup> ) compared to the increase (**Figure 5a**&**b**). Above all, the magnitudes of change were compared in terms of hierarchy tree, to measure indices that better assess the spatial progression or regression. For the decrease magnitudes, LWCI assessed regression for 4/5 intervals, except during the period 2018–2019 to 2019–2020 (Change 4, **Figure 5a**), which recorded a recover followed by another regression. While for the increase magnitudes, mCRIG gave the most accurate assessment of a spatial progression for 5/5 intervals, which is the overall best score magnitudes. These statements justify their dominant position in the tree-map (**Figure 5c**).

### **3.2 Trends and intensity of anomalies**

#### *3.2.1 Contribution of aMSDI and spatial autoregression significance*

The outputs of the aMSDI were all obtained in the same interval, **[0–0.083]** for each CVA-band, with a maximum value confirmed above **10<sup>6</sup>**, in every pixel window size. It should be noticed that the binarized inputs do not interrupt the spatial gradient of the phenomena described and the values showed a continuum of intensities. Therefore, although the results are unitless, the lowest value, **[0]**, predict no potential issues, and the highest value, **[0.083]**, predict a critical intensity of the targeted degradation or anomaly. Globaly, the spatial distribution of seasonal degradation/ anomaly evolves in the south-north direction (Additional material 5 a-d).

From the spatial autocorrelation model synthesized in **Table 4**, the expected Moran's I index and p-values are identical for all, **0.000648** and **0**, respectively, while variance is almost the same for all, **0.000457** or **0.000458**. Besides, final Moran's I index varies between **0.81** for MSAVI2 aMSDI of increase CVA, and **0.95** for *Dynamics, Anomalies and Boundaries of the Forest-Savanna Transition: A Novel Remote… DOI: http://dx.doi.org/10.5772/intechopen.105074*


#### **Table 4.**

*Spatial autocorrelation model report. N = 1544 for all variables and parameters.*

PVR; whereas *z*-scores are all above **2.5**, i.e., between **37.9** for MSAVI2 aMSDI of increase CVA, and **44.3** for PVR. Therefore, the spatial patterns of every highlighted vegetation's distribution issue by each aMSDI, described an important clustering of pixels with same and closest values at a confidence level of **99%**, stating that the phenomenon is far from randomness as wished (ESRI, 2014) (Additional material 6). Added to this, the proposed model of aMSDI has been significantly stable around all parameters, while its values scale is easy to compare and interpret among outputs of different analytical spectral indices.

### *3.2.2 Spatial convergences of anomalies and non-spatial autoregression analysis*

First outputs of the cross-correlation mapping algorithm informed on the spatial relationship between each MSAVI2's aMSDI and individual aMSDI of analytical indices. About the MSAVI2 decrease CVA, the highest positive correlation of aMSDI was with PVR (**R = 0.4**; **R2 = 0.16**; **RMSE = 0.026**), while PBI (**R = -0.23**; **R2 = 0.05**; **RMSE = 0.027**) and MARI (**R = -0.22**; **R<sup>2</sup> = 0.05**; **RMSE = 0.027**) were found totally decorrelated (**Figure 6**). As such as, the most noticeable anomaly impacting the forest-savannah vegetation cover is the decrease of vigor and other subsequent characteristics highlighted by PVR and close algorithms. Less issues might be caused by biochemical composition including anthocyanin. Concerning the MSAVI2 increase CVA, the highest positive correlation of aMSDI was established with LWCI (**R = 0.53**; **R2 = 0.28**; **RMSE = 0.016**), at the opposite of decorrelation to MARI (**R = -0.56**; **R2 = 0.32**; **RMSE = 0.015**) and IREIP1 (**R = -0.55**; **R2 = 0.31**; **RMSE = 0.015**) (**Figure 6**). With its particular distribution on the East area, LWCI express that water content influence to the increase of vegetation, while anthocyanin and canopy chlorophyll have no influence.

The multi-regression performed between each analytical aMSDI and simultaneously both MSAVI2's aMSDI on **3** � **3**-pixel moving window, were consistent but specifics by case. Generally, the relationships were all highly significant (all *p* ¼ **0**). The corrected Akaike Information Coefficient (AICc) vary between �**7795**

#### **Figure 6.**

*Cross-correlation maps between aMSDIs of MSAVI2 and the selected aMSDI for each index.*


#### **Figure 7.**

*OLS regression results synthesis (Eqs. (5) and (6)). All estimates have, p = 0 and n = 1544. Values in parentheses indicate standard error estimates for regression parameters. MSAVI\_Decr/MSAVI\_Incr = values of aMSDI for decreasing or increasing patterns of the second modified soil-adjusted vegetation index; R*<sup>2</sup> <sup>¼</sup> *adjusted correlation coefficient; RMSE = root mean square error; ΔAICc = indicates the corrected Akaike information criterion, thus the spatial model improvement over the non-spatial form.*

and � **6642**, for IREIP1 and PVR respectively; the intercept values are in between [**0.005–0.03**]; the regression coefficient of each MSAVI2 is more significant with indices potentially spotting high and low anomalies at the same areas, but both negatives for LWCI and MARI (**Figure 7**). The adjusted correlation coefficients were low (all *R***<sup>2</sup> 0***:***19**) and those of aMSDI sharing identical patterns with MSAVI2 (PVR, PBI *Dynamics, Anomalies and Boundaries of the Forest-Savanna Transition: A Novel Remote… DOI: http://dx.doi.org/10.5772/intechopen.105074*

#### **Figure 8.**

*Anomaly trends and spots. The synthesized correlation maps on the first row shows patchwork of high correlation in the medium area for the* **3 3***-pixel moving window, but two large continuous spots of convergence in the northeast and south-west according to* **5 5** *- and* **7 7***-pixel moving windows. The SLC outputs on the second row definitely separate high values from low values correlations.*

and GVMI) recorded a normal probability distribution of standardized residuals around **0**, whereas other are skewed to the right. In both cases, there is a dominant peak, marking the separation among affected and non-affected areas, or between covered and uncovered areas. All the graphs of residuals show less randomness and more clustering per class of standard deviation (Additional material 6).

Spatial synthesis of the cross-correlation mapping algorithm confirms the distribution of patterns and global trends. The combined correlation among the nine indices' aMSDI were found strong at different degrees, for both highest and lowest values, then indicating convergences of significant or non-significant anomalies at the Southwest and North-East of the area (**Figure 8**). For the three windows of calculation, they were clearly separated from predicted vegetation statuses, i.e., highly and lowly affected, soil, built-up and water features (**Figure 8**). From the cross-correlation map synthesis, the simple linear combination (SLC) outputs for each window discriminated positive from negative spatial trends. Then, the largest spots of extremely severe anomalies are located in the south-west, south-east and north-east areas, well separated on **5 5** and **7 7**-pixel moving windows. Subsequently at the finest **3 3**-pixel moving window, the spots of anomalies are continuously concentrated in the south-west to north-east direction (**Figure 8**).

#### **3.3 Spatial extent and predicted delineation**

The repeated sampling of vegetation gave spectral curves with at least three pairs of same trends for vegetation (**Figure 9a**). As spatial evidence, the patterns of ML were identical for the three algorithms, with three distinguished classes of vegetation, well separated from soil/built-up and water (**Figure 9b**). The three classifiers performed with a high and identical OA of **88.7%**, for a KC of **0.86** (**Figure 9b**). These measures confirm the spectral agreement between classified and multispectral image reflectance of the five land features. When confronting PA and UA, that also recorded

#### **Figure 9.**

*Spectral curves (A), spatial patterns (B) and statistics (C) of the three axes of vegetation on LULC. In details: (A) CART; (B) RF; (C) SVM: (D) stacked axes of vegetation with soil (NDSoDI) and water (NWI) features for 2021–2022; E) Sentinel2-a image of 2021–2022.*

identical values for all classifiers per class (**Figure 9b**), it can be assumed that regrouping indices thresholded images is a good option to identify the forest-savanna transition and interpenetrated vegetation. Presumably and based on the world ecoregions map as well as the empirical vegetation density, the three classes of vegetation can be identified as follow in south-north latitudes direction (Olson et al., 2001): *i) first ax matches tropical and sub-tropical moist broadleaf forests*; *ii) second ax is dominated by grasslands, mixed with savannas*; *iii) third ax mixes savannas and shrublands*.

Concerning the vegetation types, discrimination, and extent, RF and SVM gave the exact areas for the three classes, CART agreed with them for the second and third axes, whereas the stacked derivative only agreed with all the algorithms concerning the third ax, i.e., **3807.3 km<sup>2</sup>** (**Figure 9c**). The discrepancies especially with the stacked image could be explained by the soil features footprint that is different between the NDSoDI displayed and classified image, as well as the urge overlapping noticed earlier between second and third axes. In fact, the interpenetration of the predicted grassland/savanna (axe2) and savanna/shrubland (axe3), which might better reflect the transiting vegetation behavior targeted along the study, raises on the other hand assumptions on the accurate assessment of areas. Their UA (grassland/ Savanna = **73.3%** and savanna/shrubland = **94.4%**) then support this assumption (**Figure 9b**).

*Dynamics, Anomalies and Boundaries of the Forest-Savanna Transition: A Novel Remote… DOI: http://dx.doi.org/10.5772/intechopen.105074*
