Characterizing Brazilian Atlantic Forest Restoration Outcomes with Geospatial AlphaEarth Embeddings

TL;DR

This study uses geospatial AlphaEarth embeddings to evaluate early success in restoring the Brazilian Atlantic Forest, addressing limitations of traditional remote sensing methods.

Contribution

Introduces a 'Reference Trajectory Embedding' concept to quantify restoration success via satellite data, revealing clusters and change vectors in embedding space.

Findings

Embedding space shows distinct clusters by land use types.

Cosine similarity can measure restoration progress.

Embeddings require fine-tuning for detailed site metadata prediction.

Abstract

The Atlantic Forest in Brazil is a critical biodiversity hotspot, yet less than 12-15% of its original cover remains. Although monitoring forest restoration on a large scale is essential, traditional methods are limited by the impracticality of on-the-ground reporting on such a scale and by the saturation of remote-sensing indices such as NDVI. Furthermore, reforestation is a gradual process as opposed to the rapid spectral changes caused by deforestation. In this study, we examine 1,729 restoration sites in S\~ao Paulo, using satellite embeddings from the AlphaEarth Foundation's model to evaluate their effectiveness in characterising early restoration success. We introduce the concept of a 'Reference Trajectory Embedding', defining a metric of restoration success based on cosine similarity to reference sites of mature secondary forest. We observe distinct clusters in embedding space according to different land use and land cover (LULC) types, and we can identify sites with clear change vectors. However, the signal can be noisy, and embeddings may require further fine-tuning to capture and predict site metadata beyond LULC.