Vegetation Health and Forest Canopy Density Monitoring in The Sundarban Region Using Remote Sensing and GIS

IF 0.3
Soma Mitra, Samarjit Naskar, Dr. Saikat Basu
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Abstract

The present study explores vegetation health and forest canopy density in the Sundarbans region using Landsat-8 images. This work analyzes changes in vegetation health using two vegetation indices, the Normalized Difference Vegetation Index (NDVI) and Forest Canopy Density (FCD) values of the Sundarbans, from 2014 to 2020. NDVI, comprising two bands, Red and Near-infrared (NIR), shows a declining trend during the period. Two NDVI land cover classification maps for 2014 and 2020 are produced, and the interest area is divided into five classes: Scanty, Low, Medium, and Densely Vegetated Regions and Water Bodies. A single-band linear gradient pseudo-color is used to assess the land cover difference between 2020 and 2014, showing marked changes in densely vegetative areas. The NDVI difference marks the coastal regions with a higher depletion rate of vegetation than the regions away from the seacoasts. FCD has been taken to compare the results of NDVI with it. FCD consists of another four models: AVI (advanced vegetative index), BI (Bare soil index), SSI (scaled shadow index), and TI (thermal index). FCD is also called crown cover or canopy coverage, which refers to the portion of an area in the field covered by the crown of trees. 2014 and 2015 FCD maps are produced with a single band linear gradient pseudocolor with five land cover classifications: bare soil, Bare Soil, Shrubs, Low, Medium, and Highly vegetated regions. Both maps bear a significant resemblance to NDVI land classification maps. Further, the FCD values of the two maps are scaled between 1 and 100, and the area of each class is calculated. To check the veracity of the NDVI and FCD analysis, a Deep Neural Network (DNN) model has been developed to classify each year’s image taken from Google Earth Engine (GEE). It classifies each year’s image with 99% accuracy. The calculation of the area of each class emphasizes the rapid decline of densely wooded vegetation. Almost 80% of the highly forested zone has been diminished and has become part of the medium-forested region. Area inflation in medium-forested regions corroborates the same. The study also analyzes the migration of vegetation density, i.e., where and how many areas are unchanged, growing, or deforested.
利用遥感和地理信息系统监测孙达尔班地区的植被健康和林冠密度
本研究利用 Landsat-8 图像探索孙德尔本斯地区的植被健康和林冠密度。本研究利用归一化植被指数(NDVI)和孙德尔本斯森林冠层密度(FCD)值这两个植被指数,分析了 2014 年至 2020 年孙德尔本斯植被健康的变化。归一化差异植被指数包括红外和近红外两个波段,在此期间呈下降趋势。绘制了 2014 年和 2020 年的两幅 NDVI 土地覆被分类图,并将相关区域划分为五个等级:稀疏植被区、低植被区、中等植被区、茂密植被区和水体。采用单波段线性梯度伪彩色评估 2020 年与 2014 年的土地覆被差异,显示植被茂密地区的显著变化。净植被指数差异标志着沿海地区的植被损耗率高于远离海岸的地区。FCD 被用来与 NDVI 的结果进行比较。FCD 由另外四个模型组成:AVI(高级植被指数)、BI(裸土指数)、SSI(缩放阴影指数)和 TI(热指数)。FCD 也称为树冠覆盖率或树冠覆盖率,指的是田野中树冠覆盖的部分区域。2014 年和 2015 年的 FCD 地图采用单波段线性梯度伪彩色,有五种土地覆被分类:裸土、裸土、灌木、低植被、中植被和高植被区域。这两张地图与 NDVI 土地分类图非常相似。此外,两张地图的 FCD 值在 1 到 100 之间缩放,并计算出每个等级的面积。为了检验 NDVI 和 FCD 分析的真实性,我们开发了一个深度神经网络(DNN)模型,用于对从谷歌地球引擎(GEE)获取的每年图像进行分类。该模型对每年图像进行分类的准确率为 99%。通过计算每个等级的面积,可以看出密林植被在迅速减少。近 80% 的高森林覆盖率地区已经缩小,成为中等森林覆盖率地区的一部分。中度林区的面积膨胀也证实了这一点。研究还分析了植被密度的迁移情况,即哪些地方以及有多少地区的植被没有变化、正在增长或遭到砍伐。
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来源期刊
International Journal of Next-Generation Computing
International Journal of Next-Generation Computing COMPUTER SCIENCE, THEORY & METHODS-
自引率
66.70%
发文量
60
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