Detection of mangrove palm coverage at Cu Lao Cham-Hoi An Biospherer reserve using Sentinel-2A data

Khac Dang Vu
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Abstract

The mangrove palm grows at the Thu Bon river’s mouth. Mangrove palm has contributed to the conservation of natural ecosystems, and the biological diversity of Cu Lao Cham – Hoi An Biosphere Reserve, as well as the socio-economic development of Hoi An city. However, in recent year, its coverage has been gradually reduced due to human socio-economic activities. This study has used not only the raw bands of the Sentinel-2A image but also some spectral indices have been calculated via the band ratio for classifying land covers. The principal component analysis (PCA) was applied to these all bands to eliminate redundant and noise contributions in data. Mangrove palm was separated from other land cover categories with an object-based image classification approach using Support Vector Machines (SVMs) algorithm because of its high efficiency, performance, and flexibility. The obtained map indicates that mangrove palm principally distributes on the left bank of Thu Bon River with a total area of 147 ha by 2019. The validation, was realized by comparing the classification results with random samples through the visual interpretation of Google Earth high resolution image. The error assessment shows that the overall accuracy is about 88.67% and the Kappa coefficient reaches 0.84. Although there are some differences related to the spatial resolution of Sentinel-2 images, such obtained results may support authorities in decision-making by providing maps able to suggest necessary responses for the conservation of mangrove palms this coastal area.
利用Sentinel-2A数据探测古老滩海安生物圈保护区红树林棕榈树覆盖率
红树棕榈生长在图邦河的河口。红树林棕为保护古老占-会安生物圈保护区的自然生态系统和生物多样性,以及会安市的社会经济发展做出了贡献。但近年来,由于人类社会经济活动的影响,其覆盖范围逐渐缩小。本研究不仅利用Sentinel-2A影像的原始波段,还利用波段比计算了一些光谱指标,用于土地覆被分类。主成分分析(PCA)应用于所有波段,以消除数据中的冗余和噪声贡献。利用基于目标的支持向量机(svm)图像分类方法,将红树林棕与其他土地覆盖类别进行分类,具有较高的效率、性能和灵活性。获得的地图显示,截至2019年,红树林棕榈主要分布在Thu Bon河左岸,总面积为147 ha。通过Google Earth高分辨率图像的目视解译,将分类结果与随机样本进行对比验证。误差评估表明,总体精度约为88.67%,Kappa系数达到0.84。尽管Sentinel-2图像的空间分辨率存在一些差异,但这些获得的结果可以通过提供能够建议保护该沿海地区红树林棕榈树的必要响应的地图来支持当局的决策。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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