Comparison of Functional and Structural Biodiversity Using Sentinel-2 and Airborne Lidar Data in Agroforestry Systems

Biodiversity plays a critical role in maintaining the health and stability of ecosystems. Biodiversity monitoring has traditionally been labor-intensive, prompting a shift towards remote sensing techniques for efficient and large-scale approaches. In this research, we explore the use of Sentinel-2 satellite data and airborne LiDAR data to evaluate and compare functional and structural biodiversity in agroforestry areas within two distinct ecoregions, namely the Montane forests ecoregion and the Victoria Basin forest-savanna mosaic ecoregion in Columbia and Tanzania, respectively. The aim of the study is to compare functional diversity and structural diversity across varying spatial scales and land cover types including trees, cropland and grassland, thereby addressing the correlation and divergence between functional and structural diversity in different ecological contexts. Our methodology involves integrating airborne LiDAR data to assess structural diversity and Sentinel-2 data to estimate functional diversity based on the proxies of three key functional traits, leaf chlorophyll content (CHL), leaf anthocyanin content (ANTH), and specific leaf area (SLA). We developed two novel functional diversity indices, ShannonF and GiniF, which are modified versions of the well-established Shannon index and Gini index. These novel indices effectively incorporate both functional richness and evenness into their calculations. Our results indicated a significant correlation between our proposed ShannonF index and Shannon index derived from LiDAR, with stronger correlations at larger spatial scales. This study demonstrated that trees exhibit higher biodiversity than grassland and cropland across both study areas, with particularly high biodiversity in Colombia’s Montane forests ecoregion. These findings underscore the potential of integrating satellite and airborne LiDAR data for comprehensive biodiversity assessment in agroforestry systems, offering valuable insights for global ecosystem management and conservation efforts.