Developing MODIS-based cloud climatologies to aid species distribution modeling and conservation activities
Published Web Locationhttps://doi.org/10.21425/F58329532
WorldClim (Hijmans et al. 2005) has been the de-facto source of basic climatological analyses for most species distribution modeling research and conservation science applications because of its global coverage and fine (<1 km) spatial resolution. However, it has been recognized since its development that there are limitations in data-poor regions, especially with regard to the precipitation analyses. Here we describe procedures to develop a satellite-based daytime cloudiness climatology that better reflects the variations in vegetation cover in many regions of the globe than do the WorldClim precipitation products. Moderate Resolution Imaging Spectroradiometer (MODIS) imagery from the National Aeronautics and Space Administration (NASA) Terra and Aqua sun-synchronous satellites have recently been used to develop multi-year climatologies of cloudiness. Several procedures exist for developing such climatologies. We first discuss a simple procedure that uses brightness thresholds to identify clouds. We compare these results with those from a more complex procedure: the MODIS Cloud Mask product, recently averaged into climatological products by Wilson and Jetz (2016). We discuss advantages and limitations of both approaches. We also speculate on further work that will be needed to improve the usefulness of these MODIS-based climatologies of cloudiness. Despite limitations of current MODIS-based climatology products, they have the potential to greatly improve our understanding of the distribution of biota across the globe. We show examples from oceanic islands and arid coastlines in the subtropics and tropics where the MODIS products should be of special value in predicting the observed vegetation cover. Some important applications of reliable climatologies based on MODIS imagery products will include 1) helping to restore long-degraded cloud-impacted environments; 2) improving estimations of the spatial distribution of cloud-impacted species; and 3) helping to identify areas for rapid biological assessments. The last application can even benefit from qualitative perusal of the current MODIS climatologies.