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A history of the rehabilitation of mangroves and an assessment of their diversity and structure using Landsat annual composites (1987–2019) and transect plot inventories
Forest Ecology and Management ( IF 3.7 ) Pub Date : 2020-04-01 , DOI: 10.1016/j.foreco.2020.118007
Uday Pimple , Dario Simonetti , Isabella Hinks , Johan Oszwald , Uta Berger , Sukan Pungkul , Kumron Leadprathom , Tamanai Pravinvongvuthi , Pasin Maprasoap , Valery Gond

Abstract Recently, there has been renewed interest in the ecosystem services of mangroves such as carbon sequestration or coastal protection, and consequently, the development of tools providing an effective and automatic monitoring of the dynamics of mangrove land coverage including rehabilitated or naturally regenerated forest stand is increasingly demanded. Satellite-based time series analysis in coastal areas can be limited by atmospheric contaminations, such as haze, and clouds and their shadows. Here, we present an “automatic regrowth monitoring algorithm” (ARMA) using the Google Earth Engine (GEE), based on Landsat interannual median composites from 1987 to 2019 with 30 m spatial resolution. The species and structural diversity were assessed using transect plot inventories. The Landsat-based normalized difference infrared index (NDII) and information obtained from plot inventories were used to assess the characteristics of the natural and rehabilitated mangrove forests. The ARMA identified the starting year of the rehabilitation project using the satellite data, the required stability period after the rehabilitation, and the stand age in the year 2019. The information obtained from the field survey data were linked to the results obtained using the ARMA. After 28 years, the rehabilitated mangroves at the study site consist of monocultures of Rhizophoraceae, while the undisturbed and naturally regenerated mangroves had greater species diversity. Nevertheless, the rehabilitated mangroves were found to reach the height of the adjacent natural mangroves. The period required to reach a stable NDII value (similar to natural stands) after rehabilitation ranged from 7 to 13 years. The careful assessment of the NDII upward trend was crucial for the performance of the ARMA. The application presented here shows, however, that the system can be used to evaluate both small- and large-scale rehabilitation projects. The results of this study provide valuable baseline information for the site assessed and for its comparison with other rehabilitated mangroves in Thailand. Due to the technical potential, we are convinced that the ARMA system is suited to investigate changes in mangrove coverage dynamics, in general, including gain (as presented here), but also mangrove losses, due to disturbances such as degradation or forest diebacks.

中文翻译:

红树林恢复历史以及使用 Landsat 年度综合数据(1987-2019)和样地清单评估红树林的多样性和结构

摘要 最近,人们重新关注红树林的生态系统服务,如碳固存或海岸保护,因此,开发能够有效和自动监测红树林土地覆盖动态的工具,包括恢复或自然再生的林分。要求越来越高。沿海地区基于卫星的时间序列分析可能会受到大气污染(如雾霾、云层及其阴影)的限制。在这里,我们基于 1987 年至 2019 年具有 30 m 空间分辨率的 Landsat 年际中值复合材料,使用 Google Earth Engine (GEE) 提出了一种“自动再生监测算法”(ARMA)。物种和结构多样性使用样地清单进行评估。基于 Landsat 的归一化差异红外指数 (NDII) 和从地块清单中获得的信息用于评估天然和恢复红树林的特征。ARMA 使用卫星数据确定了修复项目的开始年份、修复后所需的稳定期以及 2019 年的林龄。 从现场调查数据中获得的信息与使用 ARMA 获得的结果相关联。28年后,研究地点恢复的红树林由单一栽培的根茎科植物组成,而未受干扰和自然再生的红树林具有更大的物种多样性。尽管如此,修复后的红树林已达到邻近天然红树林的高度。恢复后达到稳定的 NDII 值(类似于自然林分)所需的时间为 7 至 13 年。对 NDII 上升趋势的仔细评估对于 ARMA 的表现至关重要。然而,此处介绍的应用程序表明,该系统可用于评估小型和大型修复项目。这项研究的结果为评估的地点以及与泰国其他恢复红树林的比较提供了宝贵的基线信息。由于技术潜力,我们确信 ARMA 系统适合调查红树林覆盖动态的变化,一般包括增益(如此处所述),但也包括由于退化或森林枯死等干扰引起的红树林损失。对 NDII 上升趋势的仔细评估对于 ARMA 的表现至关重要。然而,此处介绍的应用程序表明,该系统可用于评估小型和大型修复项目。这项研究的结果为评估的地点以及与泰国其他恢复红树林的比较提供了宝贵的基线信息。由于技术潜力,我们确信 ARMA 系统适合调查红树林覆盖动态的变化,一般包括增益(如此处所述),但也包括由于退化或森林枯死等干扰引起的红树林损失。对 NDII 上升趋势的仔细评估对于 ARMA 的表现至关重要。然而,此处介绍的应用程序表明,该系统可用于评估小型和大型修复项目。这项研究的结果为评估的地点以及与泰国其他恢复红树林的比较提供了宝贵的基线信息。由于技术潜力,我们确信 ARMA 系统适合调查红树林覆盖动态的变化,一般包括增益(如此处所述),但也包括由于退化或森林枯死等干扰引起的红树林损失。该系统可用于评估小型和大型修复项目。这项研究的结果为评估的地点以及与泰国其他恢复红树林的比较提供了宝贵的基线信息。由于技术潜力,我们确信 ARMA 系统适合研究红树林覆盖动态的变化,一般包括增益(如此处所述),但也包括由于退化或森林枯死等干扰引起的红树林损失。该系统可用于评估小型和大型修复项目。这项研究的结果为评估的地点以及与泰国其他恢复红树林的比较提供了宝贵的基线信息。由于技术潜力,我们确信 ARMA 系统适合调查红树林覆盖动态的变化,一般包括增益(如此处所述),但也包括由于退化或森林枯死等干扰引起的红树林损失。
更新日期:2020-04-01
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