scholarly journals Modeling and Multi-Temporal Characterization of Total Suspended Matter by the Combined Use of Sentinel 2-MSI and Landsat 8-OLI Data: The Pertusillo Lake Case Study (Italy)

2020 ◽  
Vol 12 (13) ◽  
pp. 2147 ◽  
Author(s):  
Emanuele Ciancia ◽  
Alessandra Campanelli ◽  
Teodosio Lacava ◽  
Angelo Palombo ◽  
Simone Pascucci ◽  
...  
Keyword(s):  
Suspended Matter ◽  
Total Suspended Matter ◽  
Water Level Fluctuations ◽  
Landsat 8 ◽  
Combined Use ◽  
Multi Temporal ◽  
Water Constituents ◽  
Western Side ◽  
Sentinel 2A ◽  
Regional Authorities

The total suspended matter (TSM) variability plays a crucial role in a lake’s ecological functioning and its biogeochemical cycle. Sentinel-2A MultiSpectral Instrument (MSI) and Landsat 8 Operational Land Instrument (OLI) data offer unique opportunities for investigating certain in-water constituents (e.g., TSM and chlorophyll-a) owing to their spatial resolution (10–60 m). In this framework, we assessed the potential of MSI–OLI combined data in characterizing the multi-temporal (2014–2018) TSM variability in Pertusillo Lake (Basilicata region, Southern Italy). We developed and validated a customized MSI-based TSM model (R2 = 0.81) by exploiting ground measurements acquired during specific measurement campaigns. The model was then exported as OLI data through an intercalibration procedure (R2 = 0.87), allowing for the generation of a TSM multi-temporal MSI–OLI merged dataset. The analysis of the derived multi-year TSM monthly maps showed the influence of hydrological factors on the TSM seasonal dynamics over two sub-regions of the lake, the west and east areas. The western side is more influenced by inflowing rivers and water level fluctuations, the effects of which tend to longitudinally decrease, leading to less sediment within the eastern sub-area. The achieved results can be exploited by regional authorities for better management of inland water quality and monitoring systems.

scholarly journals Greenhouse Crop Identification from Multi-Temporal Multi-Sensor Satellite Imagery Using Object-Based Approach: A Case Study from Almería (Spain)

2018 ◽  
Vol 10 (11) ◽  
pp. 1751 ◽  
Author(s):  
Abderrahim Nemmaoui ◽  
Manuel A. Aguilar ◽  
Fernando J. Aguilar ◽  
Antonio Novelli ◽  
Andrés García Lorca
Keyword(s):  
Time Series ◽  
Binary Classification ◽  
Free Access ◽  
Landsat 8 ◽  
Horticultural Crops ◽  
Command Line Tool ◽  
Multi Temporal ◽  
Tree Models ◽  
Sentinel 2A

A workflow headed up to identify crops growing under plastic-covered greenhouses (PCG) and based on multi-temporal and multi-sensor satellite data is developed in this article. This workflow is made up of four steps: (i) data pre-processing, (ii) PCG segmentation, (iii) binary pre-classification between greenhouses and non-greenhouses, and (iv) classification of horticultural crops under greenhouses regarding two agronomic seasons (autumn and spring). The segmentation stage was carried out by applying a multi-resolution segmentation algorithm on the pre-processed WorldView-2 data. The free access AssesSeg command line tool was used to determine the more suitable multi-resolution algorithm parameters. Two decision tree models mainly based on the Plastic Greenhouse Index were developed to perform greenhouse/non-greenhouse binary classification from Landsat 8 and Sentinel-2A time series, attaining overall accuracies of 92.65% and 93.97%, respectively. With regards to the classification of crops under PCG, pepper in autumn, and melon and watermelon in spring provided the best results (Fβ around 84% and 95%, respectively). Data from the Sentinel-2A time series showed slightly better accuracies than those from Landsat 8.

scholarly journals Forest Type Identification with Random Forest Using Sentinel-1A, Sentinel-2A, Multi-Temporal Landsat-8 and DEM Data

2018 ◽  
Vol 10 (6) ◽  
pp. 946 ◽  
Author(s):  
Yanan Liu ◽  
Weishu Gong ◽  
Xiangyun Hu ◽  
Jianya Gong
Keyword(s):  
Random Forest ◽  
Forest Type ◽  
Landsat 8 ◽  
Multi Temporal ◽  
Sentinel 2A

scholarly journals Combined Use of Landsat-8 and Sentinel-2A Images for Winter Crop Mapping and Winter Wheat Yield Assessment at Regional Scale

2017 ◽  
Vol 3 (2) ◽  
pp. 163-186 ◽  
Author(s):  
Sergii Skakun ◽  
◽  
Eric Vermote ◽  
Jean-Claude Roger ◽  
Belen Franch ◽  
...  
Keyword(s):  
Winter Wheat ◽  
Regional Scale ◽  
Wheat Yield ◽  
Landsat 8 ◽  
Combined Use ◽  
Winter Crop ◽  
Winter Wheat Yield ◽  
Crop Mapping ◽  
Sentinel 2A

scholarly journals Anomalous Reduction of the Total Suspended Matter During the COVID-19 Lockdown in the Hooghly Estuarine System

2021 ◽  
Vol 8 ◽  
Author(s):  
Chiranjivi Jayaram ◽  
Rajdeep Roy ◽  
Neethu Chacko ◽  
Debadatta Swain ◽  
Ramunaidu Punnana ◽  
...  
Keyword(s):  
Suspended Matter ◽  
Anthropogenic Activities ◽  
Positive Influence ◽  
Total Suspended Matter ◽  
Landsat 8 ◽  
Estuarine System ◽  
Carbon Export ◽  
Sediment Removal ◽  
Brick Kilns ◽  
The Impact

The impact of the coronavirus disease 2019 (COVID-19) lockdown in the Hooghly estuarine region, India is assessed using the total suspended matter (TSM) concentration. The estimation of TSM is performed using Landsat-8/operational land imager (OLI), and an intercomparison of TSM load during the pre-lockdown and lockdown periods is done. It is observed that during the lockdown period, TSM reduced by 30–50%. This is a significant observation considering the ecological balance of the region and the fact that it is home to the largest mangroves in the world. This change in suspended matter presumably reflects the influence of reduction in anthropogenic activities owing to the COVID-19 lockdowns, such as industries, closure of shipping activities (through less dredging), and brick kilns (through less sediment removal), which are generally the primary contributors in this region. Even though these observed changes are representative of the positive influence of the COVID-19 lockdown, its implications in estuarine biogeochemistry still remain poorly quantified. The decrease in TSM content may increase light penetration, thereby increasing the primary productivity. In addition, low sediment load reaching the Bay of Bengal could influence the carbon export due to reduction in ballasting effect as reported from this region. In summary, the influence of the COVID-19 lockdown on the biogeochemistry of the aquatic ecosystem appears rather complex than thought earlier and may vary regionally based on local hydrodynamics. The analysis elucidates the complex interplay of regional lockdown and its implication in modulation of local biogeochemistry. However, the relative importance of each process in the Hooghly estuary remains to be fully evaluated.

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