首页> 外文期刊>Journal of Environmental Science and Technology >Applicability of RapidEye Satellite Imagery in Mapping Mangrove Vegetation Species at Matang Mangrove Forest Reserve, Perak, Malaysia
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Applicability of RapidEye Satellite Imagery in Mapping Mangrove Vegetation Species at Matang Mangrove Forest Reserve, Perak, Malaysia

机译:RapidEye卫星影像在马来西亚霹雳州Matang红树林森林保护区的红树林植被物种制图中的适用性

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Mangroves are typically made up of salt tolerant species of vegetation with great diversity of flora and fauna which is mainly found in tropical and subtropical climate country. However, these forest ecosystems have been declining over time due to the various kinds of direct and indirect pressure. Thus, there is increasing need and efforts to monitor and assess this ecosystem for better conservation and management. In this study, multispectral RapidEye satellite image was analysed to identify the mangrove vegetation species within the Matang Mangrove Forest Reserve in Perak, Malaysia. The Maximum Likelihood Classifier (MLC) was used to classify the mangrove vegetation species with integration of Normalized Difference Vegetation Index (NDVI) using NDVIRed and NDVIRed Edge data. Eleven species of mangrove vegetation were found within the study area including from the genus of Rhizophora , Avicennia , Bruguiera , Sonneratia and Xylocarpus . The overall classification accuracy assessment of RapidEye multispectral image integrated with NDVIRed Edge was higher at 87.5% with overall kappa statistics recorded of 0.85 compared to with employment of NDVIRed at 85% and kappa statistics of 0.80. The results indicated the applicability of red edge band in the RapidEye satellite imagery in combination with ancillary and field data to classify the mangrove species within the study area. It also helps for better management and conservation process to ensure the sustainability of these valuable resources.
机译:红树林通常由耐盐的植物组成,具有动植物多样性,主要分布在热带和亚热带气候国家。但是,由于各种直接和间接压力,这些森林生态系统一直在下降。因此,为了更好地进行保护和管理,越来越需要和努力来监测和评估该生态系统。在这项研究中,分析了多光谱RapidEye卫星图像,以识别马来西亚霹雳州Matang红树林森林保护区内的红树林植被物种。利用NDVI Red 和NDVI Red Edge 数据,通过最大似然分类器(MLC)对红树林植被物种进行归一化差异植被指数(NDVI)集成。在研究区域内发现了11种红树林植被,包括来自根茎,Avicennia,Bruguiera,Sonneratia和Xylocarpus的植物。结合NDVI Red Edge 的RapidEye多光谱图像的整体分类准确度评估为87.5%,记录的总体Kappa统计数据为0.85,而使用NDVI Red 的总体分类准确度为85% kappa统计数据为0.80。结果表明,RapidEye卫星图像中的红色边缘带与辅助和野外数据相结合,可用于对研究区域内的红树林物种进行分类。它还有助于更好的管理和保护过程,以确保这些宝贵资源的可持续性。

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