Vegetation scenario of Indian part of Ganga Delta: a change analysis using Sentinel-1 time series data on Google earth engine platform
Vegetation scenario of Indian part of Ganga Delta: a change analysis using Sentinel-1 time series data on Google earth engine platform
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DOI：
10.1007/s42797-021-00041-1
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发表时间：
2021-08
期刊：
影响因子：
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通讯作者：
B. Halder;Jatisankar Bandyopadhyay
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文献类型：
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作者：
B. Halder;Jatisankar Bandyopadhyay
快速的城市化进程、气候变化、地球运动和地貌结构变化是环境退化的过程。人口压力也是特定地区森林砍伐或植被退化的影响因素之一。由于这些原因，印度的森林和植被逐渐遭到破坏。温室气体 (GHG)、土壤湿度、土壤侵蚀、含氧量、边坡稳定性和滑坡均受植被控制。在印度，森林地区有多种类型。遥感应用领域广泛，提高了各个部门的准确性。其中包括全球森林丧失、地球表面变化、气候变化情景、水资源等等。超级气旋“阿利亚”之后，恒河三角洲主要受到影响，出现巨大的洪水淹没和植被损失。 Google Earth Engine (GEE) 是一个基于云的平台，使用具有行星尺度分析功能的高空卫星数据来检测地球观测。使用多年合成孔径雷达（SAR）Sentinel-1 C波段GRD数据来识别植被受损区域。 Sentinel-1 哥白尼图像的时间序列分析可在 Google Earth Engine (GEE) 上使用。使用双偏振强度Sentinel-1 C波段数据和算法对2015/2017年和2017/2020年之间的时间序列进行检测分析。由此产生的总植被退化平均值为1.5759（2015/2017年数据）、-0.4840（2017/2020年），标准差为2.8503（2015/2017年数据）、2.2387（2017/2020年数据）。大数据涵盖了 Google 地球引擎 (GEE) 中的总面积植被退化情况。恒河三角洲南部地区几十年来正面临着巨大的变化，红树林也受到影响。
Rapid urbanization process, climate change, earth movement and geomorphological structure change were courses environmental degradation. Population pressure also one of the issuing factors for deforestation or vegetation degradation in a particular region. Gradually Indian forest and vegetated area were damaged due to those reasons. Greenhouse Gas (GHG), Soil moisture, soil erosion, oxygen level, slope stability and landslide are controlled by vegetation. In India, there were many types of the forest region.. Remote sensing is a wide area of application improving accuracy result in various sectors. These include global forest loss, earth surface change, climate change scenario, water resource and many more. After super cyclone ‘Alia’, the Ganga delta is mostly affected the huge flood inundation and vegetation loses are identified. Google Earth Engine (GEE) is a cloud-based platform to detect earth observation using high spatial satellite data with planetary-scale analysis capabilities. Multi-year Synthetic Aperture Radar (SAR) Sentinel-1 C-band GRD data was used to identify vegetation damaged area. Time series analysis of Sentinel-1 Copernicus imagery was used available on Google Earth Engine (GEE). Dual polarization intensity Sentinel-1 C-band data and algorithm were used to detect the time series analysis between 2015/2017 and 2017/2020 year. Resulting total vegetation degradation mean are 1.5759 (2015/2017 data), −0.4840 (2017/2020) and standard deviation are 2.8503 (2015/2017 data), 2.2387 (2017/2020 data). The big-data are covering total areal vegetation degradation in Google Earth Engine (GEE). Southern parts of Ganga-delta are facing huge change in some decades, also effected in the mangrove forest.