Spatioformer: A geo-encoded transformer for large-scale plant species richness prediction

Abstract: Earth observation (EO) data have shown promise in predicting species richness of vascular plants (α-diversity), but extending this approach to large spatial scales is challenging because geographically distant regions may exhibit different compositions of plant species (β-diversity), resulting in a location-dependent relationship between richness and spectral measurements. In order to handle such geolocation dependence, we propose Spatioformer, where a novel geolocation encoder is coupled with the transformer model to encode geolocation context into remote sensing imagery. The Spatioformer model compares favorably to state-of-the-art models in richness predictions on a large-scale ground-truth richness dataset harmonized Australian vegetation plot (HAVPlot) that consists of 68 170 in situ richness samples covering diverse landscapes across Australia. The results demonstrate that geolocational information is advantageous in predicting species richness from satellite observations over large spatial scales. With Spatioformer, plant species richness maps over Australia are compiled from the Landsat archive for the years from 2015 to 2023. The richness maps produced in this study reveal the spatiotemporal dynamics of plant species richness in Australia, providing supporting evidence to inform effective planning and policy development for plant diversity conservation. Regions of high richness prediction uncertainties are identified, highlighting the need for future in situ surveys to be conducted in these areas to enhance the prediction accuracy.

Recommended citation: Y. Guo, K. Mokany, S. R. Levick, J. Yang, and P. Moghadam (2025). Spatioformer: A geo-encoded transformer for large-scale plant species richness prediction. IEEE Transactions on Geoscience and Remote Sensing, 63, 4403216.
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