  The Black Grouse (Lyrurus tetrix) is an emblematic alpine species with high
conservation importance. The population size of these mountain bird tends to
decline on the reference sites and shows differences according to changes in
local landscape characteristics. Habitat changes are at the centre of the
identified pressures impacting part or all of its life cycle, according to
experts. Hence, an approach to monitor population dynamics, is trough modelling
the favourable habitats of Black Grouse breeding (nesting sites). Then,
coupling modelling with multi-source remote sensing data (medium and very high
spatial resolution), allowed the implementation of a spatial distribution model
of the species. Indeed, the extraction of variables from remote sensing helped
to describe the area studied at appropriate spatial and temporal scales:
horizontal and vertical structure (heterogeneity), functioning (vegetation
indices), phenology (seasonal or inter-annual dynamics) and biodiversity. An
annual time series of radiometric indices (NDVI, NDWI, BI {\ldots}) from
Sentinel-2 has made it possible to generate Dynamic Habitat Indices (DHIs) to
derive phenological indications on the nature and dynamics of natural habitats.
In addition, very high resolution images (SPOT6) provided access to the fine
structure of natural habitats, i.e. the vertical and horizontal organisation by
states identified as elementary (mineral, herbaceous, low and high woody).
Indeed, one of the essential limiting factors for brood rearing is the presence
of a well-developed herbaceous or ericaceous stratum in the northern Alps and
larch forests in the southern region. A deep learning model was used to
classify elementary strata. Finally, Biomod2 R platform, using an ensemble
approach, was applied to model, the favourable habitat of Black Grouse
reproduction. Of all the models, Random Forest and Extreme Boosted Gradient are
the best performing, with TSS and ROC scores close to 1. For the SDM, we
selected only Random Forest models (ensemble modelling) because of their low
susceptibility to overfitting and coherent predictions (after comparing model
predictions).In this ensemble model, the most important explanatory variables
are altitude, the proportion of heathland, and the DHI (NDVI Max and NDWI Max).
Results from the habitat model can be used as an operational tool for
monitoring forest landscape shifts and changes. In addition, to delimiting
potential areas to protect the species habitat, which constitute a valuable
decision-making tool for conservation management of mountain open forest.
