Valid time-series analyses of satellite data to obtain statistical inference about spatiotemporal trends at global scales

(a) Objectives and Benefits As remote sensing has matured, there is a growing number of datasets that have both broad spatial extent and repeated observations over decades. These datasets provide unprecedented ability to detect broad-scale changes in the world through time, and to forecast changes into the future. However, rigorously testing for patterns in these datasets, and confidently making forecasts, require a solid statistical foundation that is currently missing. The challenge presented by remotely sensed data is the same as its remarkable value: remotely sensed datasets consist of potentially millions of time series that are non-randomly distributed in space. We propose to develop new statistical tools to analyze large, remotely sensed datasets that will give statistical rigor to conclusions about patterns of change and statistical confidence to forecasts of future change. Our focus is providing statistical tests for regional-scale hypotheses using pixel-scale data, thereby harnessing the statistical power contained within all of the information in remotely sensed time series. (b) Proposed Work and Methodology We will develop both a framework and software tools for incorporating spatial correlation (non-independence) into analyses of remotely sensed time-series data. Our framework will apply to different types of time-series models that are currently used to analyze pixel-level or small-scale data including continuous changes (e.g., directional trends) and abrupt changes (e.g., breakpoint analyses). Specifically, we will address: i. Patterns in annual trends in time series. Our tools will identify where there are significant time trends. ii. Causes of trends. Our tools will examine which variables explain observed changes best (e.g., climate, elevation, human population, etc.). iii. Within-year patterns of seasonal trends and phenological events. Forecasts for future change are only useful if they include an uncertainty in the forecasts. Thus, statistical models are necessary. Our statistical approach is based on models that, once fit to data, can be used for forecasting. These forecasts will use the spatio-temporal correlations estimated from the data and therefore can account implicitly for regional differences in the past time series that are likely to be perpetuated into the future, even if the underlying drivers for these changes are unknown. We will test our algorithms with AVHRR/GIMMS3g, MODIS, AMSR-E, JAXA/JASMES, and Landsat data at global to regional scales to provide a proof-of-concept, demonstrate the feasibility, and highlight the value of our approach to the remote sensing community at large. Our project will make substantial contributions to the AIST Goal of Increasing the Accessibility and Utility of Science Data by providing appropriate statistical tools for large remotely sensed time-series datasets. There are no available, easy-to-apply methods for testing hypotheses explaining regional patterns of past change and predicting future change that can be scaled to the size of remotely sensed datasets. We propose to remedy this, thereby making a major contribution to both remote sensing science and the application of satellite imagery for decision making. Our proposal fits under the AIST Core Topic of Data-Centric Technologies. (c) Period of Performance Our project will span two years. (d) Entry and Planned Exit TRL Our proposal will enter at Software TRL 2 and exit at TRL 5.

Advance Earth system science knowledge through the identification, development, and demonstration of innovative information systems technologies

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