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.

中文翻译：

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使用最低成本路径和前后控制影响方法确定森林景观中道路建设年份的自动化程序

靠近道路是亚马逊流域森林砍伐的主要决定因素之一。确定道路建设年份 (CYR) 对于提高对道路建设驱动因素的了解以及预测道路网络的扩张及其对生态系统的影响至关重要。虽然最近的人工智能方法已成功用于道路提取，但它们通常依赖于高空间分辨率图像，因此无法采用它们来确定旧道路的 CYR。在本文中，我们开发了一种新方法来自动确定 CYR 的过程，该方法依赖于当前道路网络的大致位置以及基于 Landsat 计划的数十年遥感图像的暴露土壤比例的时间序列。从这些输入开始，我们的方法依靠最小成本路径算法来共同配准道路网络，并依靠前后控制影响设计来规避估计暴露土壤量中固有的图像到图像的变化。我们在巴西亚马逊 Transamazon 高速公路 (BR-230) 周围 357 000 km ²的区域（包括 36 240 个路段）演示了这种方法。通过将使用我们的方法估计的 CYR 与基于 Landsat 图像时间序列观测到的 CYR 进行比较来评估该方法的可靠性。这项练习揭示了估计的 CYR 和观察到的 CYR 之间的密切对应关系（$r_{\text{皮尔逊}}=0.77$）。最后，我们展示了如何使用这些数据来评估保护区（PA）在降低每年道路建设率方面的有效性，从而评估其未来退化的脆弱性。特别是，我们发现，与可持续利用保护区相比，该地区的整体保护保护区通常更能有效地减少道路网络的扩张。