Near Real-Time Change Detection System Using Sentinel-2 and Machine Learning: A Test for Mexican and Colombian Forests-猫眼课题宝

DOI：

10.3390/rs14030707

发表时间：

2022-02

期刊：

Remote. Sens.

影响因子：

通讯作者：

中科院分区：

其他

文献类型：

作者：

A. M. Pacheco-Pascagaza;Y. Gou;V. Louis;J. Roberts;P. Rodríguez-Veiga;P. C. Bispo;F. Espírito-Santo;C. Robb;C. Upton;G. Galindo;E. Cabrera;Indira Paola Pachón Cendales;M. Castillo-Santiago;Oswaldo Carrillo Negrete;Carmen Meneses;Marco Iñiguez;H. Balzter

在格拉斯哥举行的COP 26上，100多个国家的政府承诺到2030年结束森林砍伐，覆盖全球90%以上的森林，这需要更有效的森林监测系统。森林覆盖损失的近实时变化检测使森林土地所有者、政府机构和当地社区能够比每年发布的专题地图更及时地监测自然和人为干扰。NRT森林砍伐警报有助于建立更及时的森林库存，并对无证采伐作出快速反应。哥白尼哨兵-2号卫星每五天提供一次全球范围的10米分辨率的多光谱光学/近红外波长的实用地球观测数据。所获取的数据量需要云计算或高性能计算，用于持续监测系统和自动化系统，以及时处理，分析和交付信息。在这里，我们提出了一个基于Sentinel-2的NRT变化检测系统，评估其在两个研究地点的性能，墨西哥的Manantlán和哥伦比亚的卡塔赫纳德尔查拉，并评估2018年发生的森林变化。使用非常高分辨率的PlanetScope（~3 m）和RapidEye（~5 m）数据进行的独立验证表明，所提出的NRT变化检测系统可以准确地检测森林覆盖损失（> 87%），其他植被损失（> 76%）和其他植被增益（> 71%）。此外，拟议的NRT变化检测系统的设计是使用现场数据进行调谐。因此，它可以扩展到更大的区域，整个国家甚至大陆。

The commitment by over 100 governments covering over 90% of the world’s forests at the COP26 in Glasgow to end deforestation by 2030 requires more effective forest monitoring systems. The near real-time (NRT) change detection of forest cover loss enables forest landowners, government agencies and local communities to monitor natural and anthropogenic disturbances in a much timelier fashion than the thematic maps that are released every year. NRT deforestation alerts enable the establishment of more up-to-date forest inventories and rapid responses to unlicensed logging. The Copernicus Sentinel-2 satellites provide operational Earth observation (EO) data from multi-spectral optical/near-infrared wavelengths every five days at a global scale and at 10 m resolution. The amount of acquired data requires cloud computing or high-performance computing for ongoing monitoring systems and an automated system for processing, analyzing and delivering the information promptly. Here, we present a Sentinel-2-based NRT change detection system, assess its performance over two study sites, Manantlán in Mexico and Cartagena del Chairá in Colombia, and evaluate the forest changes that occurred in 2018. An independent validation with very high-resolution PlanetScope (~3 m) and RapidEye (~5 m) data suggests that the proposed NRT change detection system can accurately detect forest cover loss (> 87%), other vegetation loss (> 76%) and other vegetation gain (> 71%). Furthermore, the proposed NRT change detection system is designed to be attuned using in situ data. Therefore, it is scalable to larger regions, entire countries and even continents.

Near Real-Time Change Detection System Using Sentinel-2 and Machine Learning: A Test for Mexican and Colombian Forests

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