**5. Conclusions**

This study has conducted an experimentation on the forest-savanna vegetation, with the goal of assessing dynamics, assuming anomalies and predicting boundaries. On Google Earth Engine platform and using Sentinel2-A satellite images of seven consecutive dry seasons, from 2015 to 2016 to 2021–2022, twelve spectral indices were selected according to their different phenological and physiological assessment of the vegetation, and other natural features to be discriminate. Using the processing of change vector analysis, CVA, it was successfully observed that each index brings a substantial information, to better assess increase or decrease patterns of the vegetation cover. Further, proposing the averaged moving standard deviation index, aMSDI, to face potential issues of simple MSDI, the scale of spatial trends appraisal was found identical between the same interval **[0–0.083]** for all pixel window sizes, while keeping spatial trends as specific as they are for each selected CVA. As confirmation, all *p* � *values* ¼ **0**, *z* � *scores >* **2***:***5** there is a high clustering between anomaly pixels, whereas low adjusted R<sup>2</sup> among each analytical index aMSDI and MSAVI2 ones validate the performance of the model. Besides, three main trends of vegetation emerged, i.e., moist broadleaf forest in the south, grassland mixed to savanna in the core and savanna mixed to shrubland in the north, based on CART, RF and SVM classifiers performed on thresholded, PCA regrouped and stacked bands, with **88.7%** OA and **0.86** KC. Finally, taking all the nine aMSDI as entries, a paired cross-correlation mapping helped to identify same general trends for high and low values. Whereas, the application of simple linear combination, SLC, highlighted the important spots of anomalies distribution in the northern part of the subset towards Sahel and desert, but less concentrated in the southern part towards moist forest area. Because the forestsavanna anisotropy with latitudes remains questionable depending on the scale of the study, it can be inferred from the study that, although aMSDI method shows capabilities in semi-dry areas, the choice of indices to use is the responsibility of the author. Whereas, a strong correlation remains to be investigated between the seasonal anomalies and the causes or drivers.

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