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NEW ALGORITHM FOR SEAGRASS BIOMASS ESTIMATION

机译:海草生物质估计的新算法

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The seagrass biomass study in tropical region is rarely found which make it difficult to refer the previous and existing research. Besides, the seagrass biomass map by field survey is expensive and time consuming. Remote sensing technique was used to estimate seagrass biomass over large areas to avoid costly andtime consuming. However, the existing studies werebased on purely empirical based model where the in-situ measurements by quadrat were applied. In this paper, we introduced a new approach for seagrass biomass estimation using information of percentage coverage, dry weight, wet weight and cubical model. The cubical model is important in order to measure the density of the seagrass using the current height of the seagrass species exist. In this study, we demonstrate the concept of derivation of seagrass biomass estimation algorithm using cubical model. This algorithm is sensitive for detrimental changes, thereby offers indicator for changes in marine ecology.
机译:在热带地区的海草生物量研究很少被发现,这使得难以提及之前和现有的研究。此外,海草生物量地图通过现场调查昂贵且耗时。遥感技术用于估算大面积的海草生物量,以避免昂贵随机消耗。然而,现有的研究对纯粹的基于经验基于的模型,其中施加了Quadrat的原位测量。在本文中,我们使用百分比覆盖率,干重,湿重和立方模型来介绍了一种新的海草生物量估计方法。立方体模型对于使用海草物种的当前高度来测量海草的密度。在本研究中,我们展示了使用立方模型的海草生物量估计算法推导的概念。该算法对于有害变化是敏感的,因此提供了用于海洋生态学的变化的指示。

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