Spatioformer: A Geo-encoded Transformer for Large-Scale Plant Species Richness Prediction

TL;DR

This paper introduces Spatioformer, a novel transformer-based model with geolocation encoding, to improve large-scale plant species richness prediction from satellite data, demonstrating superior performance across Australia.

Contribution

The study presents Spatioformer, integrating geolocation encoding with transformers, to effectively model location-dependent plant richness, advancing large-scale biodiversity mapping from remote sensing data.

Findings

Spatioformer outperforms existing models in predicting plant richness.

Richness maps reveal spatiotemporal dynamics of Australian plant diversity.

Uncertainty regions highlight areas needing further in-situ surveys.

Abstract

Earth observation data have shown promise in predicting species richness of vascular plants ($\alpha$-diversity), but extending this approach to large spatial scales is challenging because geographically distant regions may exhibit different compositions of plant species ($\beta$-diversity), resulting in a location-dependent relationship between richness and spectral measurements. In order to handle such geolocation dependency, we propose \textit{Spatioformer}, where a novel geolocation encoder is coupled with the transformer model to encode geolocation context into remote sensing imagery. The Spatioformer model compares favourably to state-of-the-art models in richness predictions on a large-scale ground-truth richness dataset (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 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.