印尼廖内省红树林恢复和恢复项目成功了吗?

A. Darmawan, N. Setyaningrum, Afifuddin, S. Arfah, Muhammad Iqbal Habibie
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引用次数: 0

摘要

红树林不仅具有碳汇的功能,还具有食物来源、野生动物栖息地和海岸保护的功能。然而,在巨大的利益背后,信息和数据仍然相对较少。在印度尼西亚红树林恢复和恢复计划的背景下,有必要研究迄今取得的进展。评估的指标之一是估计一个地区在一定时期内的红树林密度。本研究将使用哨兵2号卫星数据计算2016年和2021年9个重点省份之一的廖内省的红树林密度。红树林密度的估算采用植被指数法,即修正土壤调整植被指数-2 (MSAVI2)、土壤调整植被指数2 (SAVI2)和绿色归一化植被指数-2 (GNDVI2)。该植被指数是电磁波、可见光和近红外(NIR)波反射的经验数学模型算法。从本研究的结果来看,廖内省红树林的恢复和恢复计划正在按计划进行,这可以从密度水平的变化中看出。该算法显示,与2016年相比,2021年非常密集类型的红树林密度变化约为20%。
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Is the Mangrove Restoration and Rehabilitation Program Successful in Riau Province, Indonesia?
Mangroves not only function as carbon sinks but also as food sources, wildlife habitats, and coastal protection. However, behind the enormous benefits, the information and data are still relatively minimal. In the context of the mangrove restoration and rehabilitation program in Indonesia, it is necessary to study the progress that has been achieved so far. One of the indicators assessed is the estimation of mangrove density in an area over a certain period. This study will calculate the density of mangroves in Riau Province, one of 9 priority provinces, using Sentinel 2 satellite data for 2016 and 2021. Estimation of mangrove density is carried out using vegetation indices approach, namely Modified Soil-Adjusted Vegetation Index-2 (MSAVI2), Soil-Adjusted Vegetation Index 2 (SAVI2), and Green Normalized Difference Vegetation Index-2 (GNDVI2). This vegetation index is an empirical mathematical model algorithm of the reflection of electromagnetic, visible, and near-infrared (NIR) waves. From the results of this study, the mangrove restoration and rehabilitation program in Riau Province is going as expected, and it can be seen from the change in the density level. The algorithm shows that the change in mangrove density in 2021 is about 20% for the very dense type compared to 2016.
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