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

Can Infrared Spectroscopy Be Used to Measure Change in Potassium Nitrate Concentration as a Proxy for Soil Particle Movement?

Displacement of soil particles caused by erosion influences soil condition and fertility. To date, the cesium 137 isotope (137Cs) technique is most commonly used for soil particle tracing. However when large areas are considered, the expensive soil sampling and analysis present an obstacle. Infrared spectral measurements would provide a solution, however the small concentrations of the isotope do not influence the spectral signal sufficiently. Potassium (K) has similar electrical, chemical and physical properties as Cs. Our hypothesis is that it can be used as possible replacement in soil particle tracing. Soils differing in texture were sampled for the study. Laboratory soil chemical analyses and spectral sensitivity analyses were carried out to identify the wavelength range related to K concentration. Different concentrations of K fertilizer were added to soils with varying texture properties in order to establish spectral characteristics of the absorption feature associated with the element. Changes in position of absorption feature center were observed at wavelengths between 2,450 and 2,470 nm, depending on the amount of fertilizer applied. Other absorption feature parameters (absorption band depth, width and area) were also found to change with K concentration with coefficient of determination between 0.85 and 0.99. Tracing soil particles using K fertilizer and infrared spectral response is considered suitable for soils with sandy and sandy silt texture. It is a new approach that can potentially grow to a technique for rapid monitoring of soil particle movement over large areas