Remotely Sensed High-Resolution Global Cloud Dynamics for Predicting Ecosystem and Biodiversity Distributions

Abstract

Cloud cover can influence numerous important ecological processes, including reproduction, growth, survival, and behavior, yet our assessment of its importance at the appropriate spatial scales has remained remarkably limited. If captured over a large extent yet at sufficiently fine spatial grain, cloud cover dynamics may provide key information for delineating a variety of habitat types and predicting species distributions. Here, we develop new near-global, fine-grain (≈1 km) monthly cloud frequencies from 15 y of twice-daily Moderate Resolution Imaging Spectroradiometer (MODIS) satellite images that expose spatiotemporal cloud cover dynamics of previously undocumented global complexity. We demonstrate that cloud cover varies strongly in its geographic heterogeneity and that the direct, observation-based nature of cloud-derived metrics can improve predictions of habitats, ecosystem, and species distributions with reduced spatial autocorrelation compared to commonly used interpolated climate data. These findings support the fundamental role of remote sensing as an effective lens through which to understand and globally monitor the fine-grain spatial variability of key biodiversity and ecosystem properties.

The HD(CP)<sup>2</sup> Observational Prototype Experiment (HOPE) – an overview

“…Uncertainties in the characterization of clouds and precipitation have manifold consequences on virtually all non-atmospheric climate components from ocean mixed-layer stability to vegetation variability, to net mass balance of ice sheets (Wilson and Jetz, 2016).…”

Leveraging Multi-Sensor Time Series Datasets to Map Short- and Long-Term Tropical Forest Disturbances in the Colombian Andes

“…MAIAC's cloud detection technique yields a noise reduction by a factor of 3-10 compared to standard MODIS surface reflectance products (MYD09, MYD09GA) and derived composites (MYD09A1, MCD43A4, and MYD13A2-Vegetation Index) without the conventional assumption of Lambertian land surface behavior [19,20]. The improvements offered by MAIAC increase the number of viable clear-sky observations by a factor of 2-5 [19], which is especially valuable for monitoring vegetation dynamics in the persistently clouded tropics [42,43]. We used 8-day composite Normalized Difference Vegetation Index (NDVI) observations at 1-km resolution from January 2001 to June 2014 as input for the BFAST analysis.…”

“…2017, 9, 179 4 of 17 improvements offered by MAIAC increase the number of viable clear-sky observations by a factor of 2-5 [19], which is especially valuable for monitoring vegetation dynamics in the persistently clouded tropics [42,43]. We used 8-day composite Normalized Difference Vegetation Index (NDVI) observations at 1-km resolution from January 2001 to June 2014 as input for the BFAST analysis.…”

The Next Decade of Big Data in Ecosystem Science

“…Cross-scale inference in space or time remains a challenge for developing predictive capacity in ecosystem science (Soranno and others 2014; Mouquet and others 2015; Petchey and others 2015; Price and Schmitz 2016). Recent ecological examples highlighting the utility of big data for answering questions at relevant spatial scales include a study that examined species distributions and habitat boundaries through highresolution, large-scale, long-term cloud cover data using remote sensing (Wilson and Jetz 2016). Combining this data product with existing knowledge about the role of cloud cover in key life history characteristics of animal species (for example, growth, reproduction, and behavior) offers a way to combine high-frequency, highvolume data of high veracity to better estimate habitat transitions and species distributions across a large spatial extent.…”