Remote Sensing for Year-Round Biodiversity Estimates: In Support of Monitoring During Extreme Events

This paper contains a discussion of application of multi-season data in forest biodiversity estimates from remote sensing. Current approaches rely only on summertime data, which is insufficient for multi-season, accurate, real-time assessment. Given the dynamic pace of development, wildfire activity, and other landscapescale changes to ecosystems, methods that are sufficiently robust for year-round monitoring applications are critical. Methods surveyed include Rao's Q Diversity Index, Coefficient of Variation, NDVI, Enhanced Vegetation Index, Tasseled Cap Transformation, and Shannon's H Index. Remote sensing data sources include LiDAR and open-source satellite data. Excluding measures of greenness in situations where it is inappropriate for the mix of species and climate, this comparison will investigate the efficacy of tree species diversity indicators at seasonal and annual time scales, in both deciduous and boreal forest contexts. Different classifications of forests will respond uniquely to each approach, and the intricacies of these results are recorded to ensure appropriate implementation in the future. The reliability of each indicator is determined through comparison to previously verified values - either in-situ derived data or remote sensing estimates from established combinations of methods and seasons. Ultimately, the objective is to understand whether current approaches suffice to characterize canopy-level biodiversity throughout the year, propose potential modifications to improvecharacterization outside the summer season, and introduce other practical or theoretical considerations that may be relevant to ecologists and other biodiversity researchers and professionals.