Cumulative Sum (CuSum) change detection was applied on a Sentinel-1 backscatter time series at a spatial scale of 10 m as part of a conservation program implemented in Acre, Brazil, requiring the monitoring of deforestation activities by participants in the program. This study evaluated the results of CuSum and compared them to those obtained from conventional deforestation products, demonstrating how this method can improve the implementation of such programs. We aimed to map deforestation events with a minimum resolution of 0.1 ha to maximise event detection while minimising false positives, which could lead to unfair penalties for participants. The remarkable detection precision (ranging from 87.3 % to 96.1 %) and short delay of the CuSum algorithm make it suitable for implementing a conservation program, as illustrated in this study. Moreover, this method has the potential to accurately assess the extent of future deforestation. This study contributes to the development of effective deforestation monitoring strategies within the framework of conservation programmes to facilitate improved farming practices and climate change mitigation. This code is available at https://github.com/Pfefer/cusum.