these tools do not account for the spatial and/or temporal autocorrelation inherent to the majority of environmental phenomena. This can lead to biased estimates and erroneous identification of relationships between parameters. Developing statistical tools that explicitly account for spatial or temporal autocorrelation avoids such errors. In addition, using models that quantify and account for spatial and/or temporal correlation can decrease the uncertainty associated with model predictions because the spatial or temporal information footprint of available data can be assessed and used to inform the model.
Spatial statistics tools can be used to combine data collected from different instruments with differing resolutions, and to reduce uncertainties associated with data interpolation, among other things. This talk emphasized that principles of spatial statistics can address many of the challenges in geoscience. The simplest examples of spatial statistics are interpolating data and generating realizations (i.e., equally likely scenarios) of a given process given sparse data. These principles can be applied to data at any scale. On a global scale, for example, data from the Orbiting Carbon Observatory, a satellite designed to measure carbon dioxide (CO2) from space to improve our understanding of global CO2 concentrations, would have contained large gaps due to the satellite track and the presence of clouds and aerosols. Methods based on geostatistics are being developed to generate estimates of the global distribution of CO2 based on such data, by first characterizing the degree of spatial variability in the CO2 observations, and using this information to estimate CO2 for portions of the globe that are not measured. On a local scale, similar principles have been applied to a project that assesses areas of low oxygen in Lake Erie. It is difficult to quantify the extent of the Lake Erie dead zone and how it varies from year to year because the in situ measurements are sparse. Therefore, new statistical techniques were developed to identify remote sensing variables that are good predictors of the dissolved oxygen concentration, and these variables are then merged in a geostatistical framework with available in situ data to estimate the spatial extent of hypoxia, and how it varies across years. Results show that fusing the in situ and remotely sensed data yields a more realistic distribution of the extent of hypoxia, with lower associated uncertainties, when compared with the results using only the in situ data.
Spatial aggregation, averaging, and linear interpolation are often using to merge data collected from multiple remote sensing instruments. Such approaches, however, do not yield at optimal estimate at the target scale of analysis, and estimated values can be influenced by samples in neighboring pixels. This problem is exacerbated when remote sensing data are “re-sampled” multiple times. Spatial statistical tools applied to measurements from one or more instruments that may have different