Wyniki wyszukiwania - BazTech

1

Estimation of Chlorophyll-a, TSM and salinity in mangrove dominated tropical estuarine areas of Hooghly River, North East Coast of Bay of Bengal, India using sentinel-3 data

Mondal Ismail, De Aakash, Nandi Subhanil, Thakur Sandeep, Raman Mini, Jose Felix, De Tarun Kumar

Acta Geophysica

|

2024

|

Vol. 72, no. 1

303--322

EN

The study aims to assess variations in spatio-temporal characteristics of water quality parameters from three tropical estuaries, namely Muri-Ganga, Saptamukhi, and Hooghly, in the western portion of the Indian Sundarbans. Reliable retrieval of near-surface concentration of water quality parameters such as Chlorophyll-a, SST & TSM from diverse aquatic ecosystems with broad ranges of tropical requirements has always remained a complex issue. In this study, application of Case 2 Regional Colour Correction (C2RCC) processor has been tested for its accuracy across different bio-optical regimes in both inland and coastal waters. Satellite images for the same period were also collected and analysed using the C2RCC processing sequence to retrieve parameters like the depth of water, surface reflectance, water temperature, inherent optical properties (IOPs), chlorophyll-a, salinity, total suspended matter (TSM), etc., using the SNAP software. In situ sampling from specific locations within these estuaries and water quality analysis were conducted for the period 2017-2019. The OLCI retrieved datasets were compared and corroborated with field survey datasets. It was observed that the highest amount of TSM was recorded at Diamond Harbour during the 2018 pre-monsoon season (301.40 mg/L field-based value and 308.54 mg/L estimated value). Similarly, chlorophyll-a had higher concentrations throughout the monsoon season (3.03 mg m-3, (field survey), and 2.96 mg m-3, (estimated) at Fraserganj and Sagar south points. A very good correlation was observed for all seasons for Chl-a (r = 0.829) and TSM (r = 0.924) between the OLCI data and in situ measurements. Higher correlation and significant ‘r’ values highlight the importance of having both field-based as well as remotely-sensed information in understanding any dynamic system in a sustained manner. Results also confirm that the water quality model using OLCI Chl-a and TSM products outperforms conventional techniques. The study demonstrates the efficacy of using Sentinel 3 OCLI data for shallow marine and estuarine remote sensing applications, especially for monitoring TSM and Chl-a concentrations.

2

Assessing the multi-decadal shoreline dynamics along the Purba Medinipur Balasore coastal stretch, India by integrating remote sensing and statistical methods SK

Hossain Ariful, Mondal Ismail, Thakur Sandeep, Linh Nguyen Thi Thuy, Anh Duong Tran

Acta Geophysica

|

2022

|

Vol. 70, no 4

1701--1715

EN

Monitoring decadal shoreline change is essential to understand the influence of coastal processes on the coastline. The shoreline is constantly shaped by natural and anthropogenic factors, and so, it is critical to understand decadal trends. The prediction of future shoreline positions is a must for effective long-term coastal zone management. This study was conducted along a 90-km-stretch of the coastline from the mouth of the Haldi River (Purba Medinipur) in the Northeast to the Subarnarekha estuary (Balasore) in the Southwest. The primary objectives of the study were to analyze the decadal shoreline migration using the End Point Rate (EPR) method and then predict future shoreline change prediction using the Kalman Filter method. Shoreline positions were digitized after extracting the shorelines using Principal Component Analysis (PCA) from Multi-temporal (1990, 2000, 2010, and 2020) and Multisensor (Landsat TM, ETM+, and OLI) satellite data. A total of 887 transects were cast to compute change statistics of the time series shoreline. It was observed that the average shoreline change rate was −8.41 m/year in the periods of 1990–2000 and 2000–2010, and −8.80 m/year from 2010 to 2020. Accretion along this coastal stretch is caused by the growth of morphological features such as sand bars, beaches, and dunes. We also found that erosion occurred from 1990 to 2000 along the coastline of Bhograi, Ramnagar-I, Ramnagar-II, a few parts of Contai-I, Khejuri-I, and the Nandigram-I coastal block. Accretion mostly occurred due to Land reclamation in the Northern portion of Bhograi, Contai-1 blocks and Nandigram- I block from 2000 to 2010 and 2010 to2020. Root mean square error (RMSE) and Regression Coefficient values were computed for the future shoreline prediction of 2031 and 2041. The calculated RMSE value of±4.7 m and value of 0.97 shows a good relationship between the actual and predicted coastline of 2020. This study concludes that the coastline of Purba Medinipur-Balasore experienced severe erosion and needs management action and also proves the efficiency of the Digital Shoreline Analysis System (DSAS) tool for decadal analysis and prediction of shoreline change. The findings of this study may help the coastal planners, environmentalists, and coastal managers in preparing both short-term and long-term coastal zone management plans.

3

Monitoring forest landcover changes in the Eastern Sundarban of Bangladesh from 1989 to 2019

Kumar Mithun, Mondal Ismail, Pham Quoc Bao

Acta Geophysica

|

2021

|

Vol. 69, no. 2

561--577

EN

The present study aims to estimate areal extent of the mangrove forest cover in the eastern Sundarban of Bangladesh from 1989 to 2019 to understand mangrove dynamic over the last 30 years. Freely available Landsat TM of 1989, 2014, and L8 OLI imagery were used to generate land use/land cover (LU/LC) map for the study area using maximum likelihood (MaxLike) algorithm. Results of previous investigations among diferent scientists and researchers were used to develop a conceptual background and also included in this paper to fnd out the causes that relate to forest cover change in the study area. Study results show that the vegetation cover of Sharankhola range in Sundarban has decreased by 0.44% over last 30 years (from 1989 to 2019). Water body has increased (1.30%) with the decrease in vegetation cover. Classifed map of 2014 and 2019 shows that 2.66% vegetation cover of the study area was lost in 2014 based on 1989 while 2.22% vegetation cover was gained in 2019 based on 2014. The overall accuracy of Landsat TM (1989), TM (2014), and L8 OLI (2019) were 80%, 82.85%, and 84.28%, respectively. Its accuracy would increase if it is supplemented by extensive ground verifcation data and hybrid satellite data of diferent spectral and spatial resolution.