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
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
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DOI：
10.1016/j.foreco.2020.118007
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发表时间：
2020-04
影响因子：
3.7
通讯作者：
U. Pimple;D. Simonetti;Isabella Hinks;J. Oszwald;U. Berger;S. Pungkul;Kumron Leadprathom;Tamanai Pravinvongvuthi;Pasin Maprasoap;V. Gond
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文献类型：
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作者：
U. Pimple;D. Simonetti;Isabella Hinks;J. Oszwald;U. Berger;S. Pungkul;Kumron Leadprathom;Tamanai Pravinvongvuthi;Pasin Maprasoap;V. Gond
最近，人们重新关注红树林的生态系统服务，如固碳或海岸保护，因此，越来越需要开发工具，对包括恢复或自然再生林在内的红树林土地覆盖的动态进行有效和自动监测。在沿海地区，基于卫星的时间序列分析可能会受到大气污染的限制，如烟雾、云及其阴影。在这里，我们提出了一个“自动再生监测算法”（阿尔马），使用谷歌地球引擎（GEE），根据Landsat的年际中位数复合材料从1987年到2019年与30米的空间分辨率。物种和结构多样性进行了评估，使用样条清单。基于陆地卫星的归一化差分红外指数（NDII）和信息从样地清单获得的天然和恢复的红树林的特点进行了评估。阿尔马利用卫星数据确定了恢复项目的起始年份、恢复后所需的稳定期以及2019年的林龄。从实地调查数据中获得的信息与使用阿尔马获得的结果相关联。经过28年的恢复，在研究地点的红树林由红树科的单一栽培，而未受干扰的和自然再生的红树林有更大的物种多样性。然而，恢复后的红树林被发现达到邻近天然红树林的高度。恢复后达到稳定的NDII值（类似于天然林）所需的时间为7至13年。仔细评估NDII上升趋势对阿尔马的业绩至关重要。然而，这里提出的应用程序表明，该系统可以用来评估小型和大型的康复项目。这项研究的结果提供了宝贵的基线信息的网站评估和比较与其他恢复红树林在泰国。由于技术潜力，我们相信，阿尔马系统是适合调查红树林覆盖动态的变化，在一般情况下，包括增益（如这里所介绍的），但也红树林的损失，由于干扰，如退化或森林枯死。
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.