Delineating monitoring Network in Biodiversity Hotspot based on Land Surface Phenology. The case of Tropical Montane Cloud Forest

Land Surface Phenology (LSP) is the study of the phenology through satellite sensors. It integrates phenological patterns (mainly spatial) and processes (mainly temporal) within heterogeneous biophysical environments across multiple scales. It is a very useful tool for the characterization and monitoring of forests. Tropical montane cloud forest is the most diverse type of vegetation per unit area, since it occupies less than 1% of Mexico but harbours 10% of the country’s plant biodiversity. It is a critical priority for biodiversity conservation, its permanence in the medium and long term is threatened by habitat destruction and climate change. A regional conservation approach, which values all fragments of this type of forest as contributing to regional biodiversity, will be required to conserve plant biodiversity in central Veracruz. This area is one of the Rare forest ecoregions within biodiversity hotspots. Our primary aim was to identify priority zones for stablishing a Tropical montane cloud forest monitoring network in Central Veracruz based on its phenological responses at multiples scales. Our methodology can be applied in other tropical biodiversity zones, even in the absence of adequate cartography. We start from homogeneous and reliable pixels and automatically calculate the number of pheno-regions that exist within this type of vegetation in the study area, based on different LSP pheno-metrics extracted from different MODIS vegetation index time-series (NDVI & EVI) with Timesat and BFAST algorithm. We extract Fraction cover subpixels homogeneus from MODIS and Sentinel 2 LC map with Random Forest classification and success rate analysis curve ensures the reliability of the LC map. We identify 4 statistically different representative pheno-regions through cluster analysis in this type of forest within the study area and we obtained 351 priority areas where a phenological monitoring network could be located.