Dimensionless Structural Ecology from Monocular Gaussian Splatting: A Proposal for Habitat Characterization Using the MacroscopeVR Archive

@techreport{laboratory2026dimensionless, author = {Laboratory, Michael P. Hamilton, Canemah Nature, Ph.D.}, title = {Dimensionless Structural Ecology from Monocular Gaussian Splatting: A Proposal for Habitat Characterization Using the MacroscopeVR Archive}, institution = {Canemah Nature Laboratory}, year = {2026}, number = {CNL-TN-2026-030}, month = {march}, url = {https://canemah.org/archive/document.php?id=CNL-TN-2026-030}, abstract = {MacroscopeVR maintains an archive of 11,425 three-dimensional Gaussian splat reconstructions derived from 457 equirectangular panoramas captured at 33 ecological research stations across North America and Costa Rica. Each reconstruction was generated by Apple's SHARP monocular Gaussian splatting framework, which infers approximately 1.18 million three-dimensional ellipsoids from a single perspective photograph. Because SHARP operates monocularly, each reconstruction inhabits an arbitrary, uncalibrated coordinate system — absolute distances cannot be recovered without external reference. This paper argues that this apparent limitation is, for certain ecological questions, irrelevant. We propose a suite of dimensionless structural descriptors — eigenvalue-derived morphological ratios, color distribution statistics, opacity heterogeneity measures, and scale distribution parameters — that characterize the structural state of a habitat without requiring metric calibration. These descriptors are computed per grid cell from the filtered Gaussian population and assembled into a multi-dimensional structural state vector. Because the MacroscopeVR archive includes seasonal time series (up to four seasons) at multiple stations spanning diverse biomes, we propose validation against known phenological patterns: deciduous leaf flush and senescence, grassland green-up and dormancy, and tropical evergreenness. If dimensionless structural descriptors track established phenological signals, they constitute a new class of rapid ecological characterization — one that any observer with a consumer 360° camera can contribute to, paralleling iNaturalist's democratization of species identification but applied to habitat structure rather than taxonomy. We outline the proposed descriptor suite, the computational pipeline, validation strategy, and the ecological hypotheses to be tested.} }