Forest phenoclusters for Argentina based on vegetation phenology and climate

Posted 08/22/23

To improve broad-scale forest mapping and landscape characterization, we developed an approach that goes beyond simple forest– nonforest classification in areas where the lack of detailed field data so far precluded tree species–level classifications. We applied our method in Argentina, where we mapped and characterized 54 forest phenoclusters with unique combinations of vegetation phenology and climate characteristics, contributing to a sustainable management and conservation of forest landscapes across the country.

Classifying forests at tree species level from remotely sensed data over large areas is challenging, especially when ground-data do not exist. Since the opening of the Landsat archive in 2008, opportunities to improve forest type mapping and classification have increased, making it possible to explore phenological properties across different forest types. The seasonal dynamics of vegetation indices (e.g., enhanced vegetation index, EVI) are well correlated with the seasonal dynamics in photosynthetically active leaf area and are a good proxy for phenological stages. In addition to being helpful for individual forest type and tree species classification, phenology is linked to landscape resources because vegetation phenology determines food availability for a wide range of forest species.

To improve broad-scale forest mapping and landscape characterization, we developed an approach that can categorize forests based on both land surface phenology and climate characteristics, the forest phenoclusters. We calculated land surface phenology metrics based on EVI Sentinel-2 and EVI Landsat 8 combined annual time series. We also derived land surface temperature (LST) from Band 10 of the thermal infrared sensor (TIRS) of Landsat 8 and used precipitation from the WorldClim dataset. We then performed stratified X-means classification followed by hierarchical clustering. We applied the methodology in Argentina (2.8 million km2), which has a wide variety of forests, from rainforests to cold-temperate forests. We characterized the forest phenoclusters based on land surface phenology and climate characteristics, as well as based on strong regional expert knowledge.

We identified 54 forest phenoclusters across Argentina (Figure 1), each with unique combinations of vegetation phenology and climate characteristics. The resulting map is a valuable source of novel and ecologically relevant information applicable to management and conservation of biodiversity, for example, for stratifying biodiversity assessments, supporting wildlife habitat mapping and to improve landscape planning, including development of new reserves strategies. Additionally, using our method, it is possible to estimate phenoclusters at a variety of scales, which makes them useful for a variety of modeling applications. Specifically, forest phenoclusters could be an input data set to benefit species distribution modeling greatly over large areas with low data availability, such as for tapirs (Tapirus terrestris) and jaguars (Panthera onca) that occupy several ecoregions in Argentina.