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

IF 3.9 2区环境科学与生态学 Q1 ECOLOGY

Remote Sensing in Ecology and Conservation Pub Date : 2023-12-21 DOI:10.1002/rse2.376

Denis Valle, Sami W. Rifai, Gabriel C. Carrero, Ana Y. Y. Meiga

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Abstract

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_{Pearson} = 0.77 $$ {r}_{\mathrm{Pearson}}=0.77 $$

). 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.

Abstract Image

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使用最低成本路径和前后控制影响法确定森林景观中道路施工年份的自动程序

靠近公路是亚马逊流域森林砍伐的主要决定因素之一。确定道路的建设年份（CYR）对于更好地了解道路建设的驱动因素、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的图像，因此无法用于确定旧道路的 CYR。在本文中，我们开发了一种新方法来自动确定 CYR，该方法依赖于当前道路网络的大致位置，以及基于 Landsat 计划十年期遥感图像的裸露土壤比例时间序列。从这些输入开始，我们的方法依赖于最小成本路径算法对道路网络进行共同注册，并依赖于控制-影响前后设计来规避估计裸露土壤量中固有的图像间差异。我们对巴西亚马逊地区 Transamazon 高速公路（BR-230）周围 357 000 平方公里的区域（包括 36 240 个路段）演示了这种方法。通过比较使用我们的方法估算出的 CYR 和基于陆地卫星图像时间序列观测到的 CYR，评估了这种方法的可靠性。结果表明，估算的 CYR 与观测到的 CYR 非常接近（rPearson=0.77$${r}_{mathrm{Pearson}}=0.77$$）。最后，我们展示了如何利用这些数据来评估保护区在降低道路建设年增长率方面的有效性，以及保护区在未来退化中的脆弱性。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在减少道路网络扩张方面通常更为有效。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Remote Sensing in Ecology and Conservation Earth and Planetary Sciences-Computers in Earth Sciences

CiteScore

9.80

自引率

5.50%

发文量

审稿时长

18 weeks

期刊介绍： emote Sensing in Ecology and Conservation provides a forum for rapid, peer-reviewed publication of novel, multidisciplinary research at the interface between remote sensing science and ecology and conservation. The journal prioritizes findings that advance the scientific basis of ecology and conservation, promoting the development of remote-sensing based methods relevant to the management of land use and biological systems at all levels, from populations and species to ecosystems and biomes. The journal defines remote sensing in its broadest sense, including data acquisition by hand-held and fixed ground-based sensors, such as camera traps and acoustic recorders, and sensors on airplanes and satellites. The intended journal’s audience includes ecologists, conservation scientists, policy makers, managers of terrestrial and aquatic systems, remote sensing scientists, and students. Remote Sensing in Ecology and Conservation is a fully open access journal from Wiley and the Zoological Society of London. Remote sensing has enormous potential as to provide information on the state of, and pressures on, biological diversity and ecosystem services, at multiple spatial and temporal scales. This new publication provides a forum for multidisciplinary research in remote sensing science, ecological research and conservation science.