**1. Introduction**

The global structure and productivity of ecosystems are deeply impacted by joined climate conditions and human drivers, causing general vegetation degradation [1]. The phenomenon has kept increasing in the last four decades and eventually affects whole ecosystem, soil productivity, biological systems, biotic diversity, and other environmental systems' ability to support human needs in concerned areas. Main indicators are the decline in parameters, such as low biomass, less ecological production, fragmentation, or lower canopy cover [2, 3].

Inside the tropics, vegetation is globally sensitive to seasonal and inter-annual variation in precipitations and temperatures. Extremes seasonally, i.e., longer rainy season and shorter dry season in lowest latitudes, versus the reverse phenomenon towards medium latitudes, influence the vegetation distribution with several phenological and physiological adaptations, including cover and status changes [4–6]. Typically, forest colonizes wetter areas, while savannas cover drier areas, with a gradual species distribution such as dense forest, tree savannas, grassy/herbaceous savannas, and isolated desert shrubs or clumps of dry grasses known as steppes. However, transitions are not rigidly determined by climate [7]. There is an extensive overlap between forest and savanna creating a mosaic of landscapes, and most studies on the subject remain widely hypothesized and modeled with controversial results, supported by questionable evidences. Biases include the high species turnover around 1000 mm to 2500 mm rainfall, the (un)stable states of forest and savanna maintained by feedbacks between tree cover and disturbances, and for the satellite-based approaches, the structural (in)difference between trees or grasses layer [8].

These specificities are challenging to spatialize at a point that sub-Saharan African ecosystems have played a key role in the development of remote sensing of vegetation for decades [9–11]. Nowadays, several satellite-based models provide scalable spectral information relevant to vegetation distribution and changes, physiology, and phenology, in broad terms, to monitor and combat land degradation, especially in African drylands [12–15]. As such, numerous spectral indices measure the vegetation parameters [16]. The Normalized Difference Vegetation Index, NDVI, especially, has purposely been widely used [17]. However, some limitations like sensitivity to soil background effects and atmospheric influence as well as values saturation under dense and multilayered canopy, usually alter the NDVI capacity to simultaneously predict senesced vegetation and efficiently discriminate individual anomalies, i.e., growth, vigor, leaf area index, biochemical components (anthocyanin, carotenoids, cellulose, etc.), water content or pigmentation [18–20] with accuracy. Then some previous studies focused on identifying or modeling the direction of change as well as underlying drivers of drylands vegetation [21]. Those models applied to two or more spatially close and interwoven vegetation species, require to implementation of specific processings. To the best of our knowledge, the recent progress in modeling sub-Saharan vegetation transition introduced the term of "bistability" around lower and upper transition boundaries between forest and savanna [8]. This model is based on paleo-ecological evidences (soil, topography) and climatic parameters change and oscillation (rainfall and temperature), that influence (for the firsts) and predispose (for the seconds) these two species to coexistence. With the support of a floristic survey, the ambiguity of mischaracterizing savanna as a degraded forest was clarified at some point, by identifying, the dense forest, the "bistable" forest, the "bistable" savanna, and the proper savanna.

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

This study approaches the forest-savanna transition study, by investigating its different spectral behaviors and their statistical meaning, in a context lacking field data, and from an open-source/open-data perspective. The triple aim is to assess the dynamics, discriminate species disturbance based on their empirical spatial distribution, and predict their extent and boundaries. As such, assuming a blurred boundary and an overlapping spatial gradient between the two species, some phenological and physiological characteristics are considered as separately as possible in terms of anomalies, and further integrated beneath the same model, so as to locate the spots requiring permanent monitoring or sustainable actions, without mischaracterizing punctual changes, factual distribution, and most accurate delineation.
