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Geostatistical approach to assess mangrove spatial variability: a bi-decadal scenario over Raigarh coast of Maharashtra

机译:评估红树林空间变异性的地质统计方法:马哈拉施特拉raigarh海岸的双层情景

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The present study aims at assessing the bi-decadal scenario related to the spatial variability of Mangroves over Raigarh district by adopting Geostatistical approach. Landsat TM/OLI data for the year 2000 and 2019 were employed for the study. Methodology adopted, involve computation and plotting of Semi-variogram (in 'R') with NDVI data, which was extracted for 28 homogeneous mangrove patches. These patches, based on similarity of Semi-variogram components, were further classified into clusters. Semi-variogram pattern for the year 2000 revealed a low range and high sill, indicating greater variation in the dataset that was correlated for a shorter distance. While in the year 2019, the Semi-variogram obtained for the same mangrove patches, exhibited high range and low sill. This clearly indicates a high spatial correlation associated with relatively low variation within the data. During the year 2019, some patches have shown evidences of exponential growth of mangroves that were represented by higher NDVI values, which were more similar, correlated and dependent. Overall rise in NDVI value indicates that the health status of mangroves has increased in 2019 along with natural expansion of mangrove colony. Over bi-decadal time span, mangrove ecology has also improved corresponding to higher NIR reflectance. The present research can be implemented to mangrove management and conservation purpose as this technique reveals the spatial dependency in the dataset.
机译:本研究旨在通过地质统计学方法评估与Raigarh地区红树林空间变异性相关的双十年期情景。该研究采用了2000年和2019年的陆地卫星TM/OLI数据。所采用的方法包括利用NDVI数据计算和绘制半变异函数(在“R”中),该数据是针对28个同质红树林斑块提取的。基于半变异函数成分的相似性,这些斑块被进一步分类为聚类。2000年的半变异函数模式显示了一个低范围和高门槛,这表明数据集中的较大变异与较短距离相关。而在2019年,相同红树林斑块获得的半变异函数显示出高范围和低门槛。这清楚地表明,与数据内相对较低的变化相关的高空间相关性。2019年期间,一些斑块显示红树林呈指数级增长,表现为NDVI值更高,更相似、更相关、更依赖。NDVI值的总体上升表明,随着红树林群落的自然扩张,2019年红树林的健康状况有所改善。在两个十年的时间跨度内,红树林生态也因较高的近红外反射率而得到改善。本研究可用于红树林管理和保护目的,因为该技术揭示了数据集中的空间依赖性。

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