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SAR data for tropical forest disturbance alerts in French Guiana: Benefit over optical imagery
Remote Sensing of Environment ( IF 13.5 ) Pub Date : 2021-01-01 , DOI: 10.1016/j.rse.2020.112159
Marie Ballère , Alexandre Bouvet , Stéphane Mermoz , Thuy Le Toan , Thierry Koleck , Caroline Bedeau , Mathilde André , Elodie Forestier , Pierre-Louis Frison , Cédric Lardeux

Abstract French Guiana forests cover 8 million hectares. With 98% of emerged land covered by forests, French Guiana is the area with the highest proportion of forest cover in the world. These forests are home to an exceptionally rich and diverse wealth of biodiversity that is both vulnerable and under threat due to high levels of pressure from human activity. As part of the French territory, French Guiana benefits from determined and continuous national efforts in the preservation of biodiversity and the environmental functionalities of ecosystems. The loss and fragmentation of forest cover caused by gold mining (legal and illegal), smallholder agriculture and forest exploitation, are considered as small-scale disturbances, although representing strong effects to vulnerable natural habitats, landscapes, and local populations. To monitor forest management programs and combat illegal deforestation and forest opening near-real time alerts system based on remote sensing data are required. For this large territory under frequent cloud cover, Synthetic-Aperture Radar (SAR) data appear to be the best adapted. In this paper, a method for forest alerts in a near-real time context based on Sentinel-1 data over the whole of French Guiana (83,534 km2) was developed and evaluated. The assessment was conducted for 2 years between 2016 and 2018 and includes comparisons with reference data provided by French Guiana forest organizations and comparisons with the existing University of Maryland Global Land Analysis and Discovery Forest Alerts datasets based on Landsat data. The reference datasets include 1,867 plots covering 2,124.5 ha of gold mining, smallholder agriculture and forest exploitation. The validation results showed high user accuracies (96.2%) and producer accuracies (81.5%) for forest loss detection, with the latter much higher than for optical forest alerts (36.4%). The forest alerts maps were also compared in terms of detection timing, showing systematic temporal delays of up to one year in the optical method compared to the SAR method. These results highlight the benefits of SAR over optical imagery for forest alerts detection in French Guiana. Finally, the potential of the SAR method applied to tropical forests is discussed. The SAR-based map of this study is available on http://cesbiomass.net/ .

中文翻译:

法属圭亚那热带森林干扰警报的 SAR 数据:优于光学图像

摘要 法属圭亚那森林占地 800 万公顷。法属圭亚那 98% 的出土土地被森林覆盖,是世界上森林覆盖率最高的地区。这些森林拥有极其丰富多样的生物多样性财富,由于人类活动的高度压力,这些生物多样性既脆弱又受到威胁。作为法国领土的一部分,法属圭亚那受益于国家在保护生物多样性和生态系统环境功能方面的坚定和持续努力。由金矿开采(合法和非法)、小农农业和森林开发造成的森林覆盖损失和破碎被视为小规模干扰,尽管对脆弱的自然栖息地、景观和当地人口产生了强烈影响。为了监测森林管理计划和打击非法砍伐森林和森林开放,需要基于遥感数据的近实时警报系统。对于云层频繁覆盖下的大片区域,合成孔径雷达 (SAR) 数据似乎是最适合的。在本文中,开发和评估了一种基于 Sentinel-1 数据在整个法属圭亚那(83,534 平方公里)的近实时环境中的森林警报方法。该评估在 2016 年至 2018 年期间进行了 2 年,包括与法属圭亚那森林组织提供的参考数据进行比较,以及与基于 Landsat 数据的现有马里兰大学全球土地分析和发现森林警报数据集的比较。参考数据集包括 1,867 个地块,覆盖了 2,124.5 公顷的金矿,小农农业和森林开发。验证结果显示,森林损失检测的用户准确度 (96.2%) 和生产者准确度 (81.5%) 都很高,后者远高于光学森林警报 (36.4%)。森林警报地图还在检测时间方面进行了比较,显示与 SAR 方法相比,光学方法的系统时间延迟长达一年。这些结果突出了 SAR 在法属圭亚那森林警报检测中优于光学图像的优势。最后,讨论了 SAR 方法应用于热带森林的潜力。本研究基于 SAR 的地图可在 http://cesbiomass.net/ 上获得。后者远高于光学森林警报(36.4%)。森林警报地图还在检测时间方面进行了比较,显示与 SAR 方法相比,光学方法的系统时间延迟长达一年。这些结果突出了 SAR 在法属圭亚那森林警报检测中优于光学图像的优势。最后,讨论了 SAR 方法应用于热带森林的潜力。本研究基于 SAR 的地图可在 http://cesbiomass.net/ 上获得。后者远高于光学森林警报(36.4%)。森林警报地图还在检测时间方面进行了比较,显示与 SAR 方法相比,光学方法的系统时间延迟长达一年。这些结果突出了 SAR 在法属圭亚那森林警报检测中优于光学图像的优势。最后,讨论了 SAR 方法应用于热带森林的潜力。本研究基于 SAR 的地图可在 http://cesbiomass.net/ 上获得。讨论了 SAR 方法应用于热带森林的潜力。本研究基于 SAR 的地图可在 http://cesbiomass.net/ 上获得。讨论了 SAR 方法应用于热带森林的潜力。本研究基于 SAR 的地图可在 http://cesbiomass.net/ 上获得。
更新日期:2021-01-01
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