Beyond Alerts: spatiotemporal Trade-offs in near-real-time Detection Systems for Forest Disturbance in the Brazilian Amazon

Abstract. The Amazon rainforest faces threats from anthropogenic disturbances, which also increase greenhouse gas emissions and contribute to global climate change. In 2004, a system to detect disturbance for the Brazilian Legal Amazon (BLA) was created to mitigate forest loss. The system, Detection of Deforestation in Real Time (Deter), from the National Institute for Space Research (INPE), alerts to seven types of anthropogenic forest disturbances through the visual interpretation of optical satellite imagery from CBERS- 4, CBERS-4A and Amazônia-1. Many near-real-time systems currently generate alerts using automated algorithms, primarily leveraging SAR sensors to compensate for the absence of cloud-free images over tropical forests. Deter uses spatial patterns to identify types of disturbances, minimising commission errors, while most algorithms prioritise the temporal dimension for early-stage detections. Discrepancies in space and time across systems and disturbance types, such as omissions, delays, and mismatches, are linked to the selection of sensor technologies, forest masks, and algorithm strategies. Forest disturbances detected between 2020 and 2024 for the entire Brazilian Amazon Biome were extracted from the systems: Deter, Prodes, MapBiomas, SAD, RADD, GLAD, LUCA and TropiSCO. Based on this dataset, we conducted an exploratory analysis revealing agreement and disagreement between detection systems regarding five classes of disturbances (clear-cut, selective logging degradation, fire scars, mining and windthrow). The results emphasise the importance of systems that consider the trade-off between spatial and temporal context to detect different disturbance types, similar to Deter, but using automated near-real-time alert approaches.