IN040-08
STAREPandas – a dataframe based STARE interface

Tuesday, 15 December 2020: 19:21
Virtual
Niklas Fabian Griessbaum, University of California Santa Barbara, Bren School, Santa Barbara, CA, United States, James Frew, University of California, Santa Barbara, CA, United States, Michael Lee Rilee, Organization Not Listed, Washington, DC, United States, Kwo-Sen Kuo, NASA Goddard SFC, Greenbelt, MD, United States and James H R Gallagher, OPeNDAP, Inc., Butte, MT, United States
Abstract:
The Spatio-Temporal Adaptive Resolution Encoding (STARE) is a geospatiotemporal indexing and referencing scheme based on a hierarchical triangular mesh (HTM) which recursively divides the Earth's surface into a set of quad-trees, allowing triangular regions ("trixels") as small as 0.01 m2 to be identified with a single integer value. The nesting properties of STARE trixels are an elegant solution for aligning multi-resolution Earth science data.

STAREPandas is a Python library built on top of GeoPandas that allows users to perform STARE based spatial operations and relation tests on sets of geospatial features that would otherwise require a STARE-extended spatial database or geographic information system. In contrast to GeoPandas’ GeoDataFrames, where geometries are represented as well known text, STAREPandas’ STAREDataFrames represent geometries as STARE index values corresponding to trixels of variable size and resolution. Polygons are represented as sets of trixels that cover the corresponding polygon, while points are represented as individual trixels at the HTM tree’s leaf resolution. Features such as sensor fields-of-view (FOVs) can be represented as individual trixels at a resolution corresponding to the FOV’s spatial extent.

Using GeoPandas’ input/output (I/O) capabilities, STAREPandas is able to read and convert most vector-based data formats to their STARE representation. STAREPandas further extends the GeoPandas I/O functionality to load raster formats of remote sensing granules and tiles such as MOD09 or MOD09GA. The STARE representations can here either be generated during loading or read from a pre-generated STARE “sidecar” file.

STAREPandas exposes STARE functionality using pendants to the familiar GeoPandas API, lowering technical hurdles for users to carry out STARE based geospatial analysis. Exploiting GeoPandas' rich I/O capabilities, STAREDataFrames are also a convenient pivot format at the beginning and end of a data processing pipeline.

<< Previous Abstract | Next Abstract >>