An automated procedure to determine construction year of roads in forested landscapes using a least-cost path and a Before-After Control-Impact approach

Proximity to roads is one of the main determinants of deforestation in the Amazon basin. Determining the construction year of roads (CYR) is critical to improve the understanding of the drivers of road construction and to enable predictions of the expansion of the road network and its consequent impact on ecosystems. While recent artificial intelligence approaches have been successfully used for road extraction, they have typically relied on high spatial-resolution imagery, precluding their adoption for the determination of CYR for older roads. In this article, we developed a new approach to automate the process of determining CYR that relies on the approximate position of the current road network and a time-series of the proportion of exposed soil based on the multidecadal remote sensing imagery from the Landsat program. Starting with these inputs, our methodology relies on the Least Cost Path algorithm to co-register the road network and on a Before-After Control-Impact design to circumvent the inherent image-to-image variability in the estimated amount of exposed soil. We demonstrate this approach for a 357 000 km² area around the Transamazon highway (BR-230) in the Brazilian Amazon, encompassing 36 240 road segments. The reliability of this approach is assessed by comparing the estimated CYR using our approach to the observed CYR based on a time-series of Landsat images. This exercise reveals a close correspondence between the estimated and observed CYR ($r_{\text{Pearson}}=0.77$). Finally, we show how these data can be used to assess the effectiveness of protected areas (PAs) in reducing the yearly rate of road construction and thus their vulnerability to future degradation. In particular, we find that integral protection PAs in this region were generally more effective in reducing the expansion of the road network when compared to sustainable use PAs.