Analysis of cloud condition on Sentinel-2 MSI and Landsat-8 OLI images of a public supply lake in Belém-Pará-Brazil

Dutra, Vítor Abner Borges; Tavares, Paulo Amador; Ribeiro, Hebe Morganne Campos

doi:10.4136/ambi-agua.2375

Cited by 5 publications

(3 citation statements)

References 25 publications

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“…Recent research in many parts of the world involving water quality have made use of data from Landsat-8 (Liu, Wang, 2019, González-Márquez et al, 2018, Mushtaq, Nee Lala, 2017, Sentinel-2 (Karaoui et al, 2019, Potes et al, 2018 or both (Dutra et al, 2019, Yadav et al, 2019, Watanabe et al, 2018. Table 1 gives a list of some of these authors with the sensor they used and the parameters they analysed.…”

Section: Introductionmentioning

confidence: 99%

Estimation of Water Quality in a Reservoir From Sentinel-2 Msi and Landsat-8 Oli Sensors

Pizani

Maillard

Ferreira

et al. 2020

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. The low operational cost of using freely available remote sensing data is a strong incentive for water agencies to complement their field campaigns and produce spatially distributed maps of some water quality parameters. The objective of this study is to compare the performance of Sentinel-2 MSI and Landsat-8 OLI sensors to produce multiple regression models of water quality parameters in a hydroelectric reservoir in Brazil. Physical-chemistry water quality parameters were measured in loco using sensors and also analysed in laboratory from water samples collected simultaneously. The date of sampling corresponded to the almost simultaneous overflight of Sentinel-2B and Landsat-8 satellites which provided a means to perform a fair comparison of the two sensors. Four optically active parameters were considered: chlorophyll-a, Secchi disk depth, turbidity and temperature (the latter using Landsat-8 TIR sensor). Other six optically non-active parameters were also considered. The multiple regression models used the spectral reflectance bands from both sensors (separately) as predictors. The reflectance values were based on averaging kernels of 30 m and 90 m. Stepwise variable selection combined with a priori knowledge based on other studies were used to optimize the choice of predictors. With the exception of temperature, the other optically active parameters yielded strong regression models from both the Sentinel and Landsat sensors, all with r2 > 0.75. The models for the optically non-active parameters produced less striking results with r2 as low as 0.03 (temperature) and as high or better than > 0.8 (pH and Dissolved oxygen).

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Section: Introductionmentioning

confidence: 99%

Estimation of Water Quality in a Reservoir From Sentinel-2 Msi and Landsat-8 Oli Sensors

Pizani

Maillard

Ferreira

et al. 2020

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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“…In one previous analysis in the study area, Tavares, Dutra, et al, 41 found that the Normalised Difference Vegetation Index (NDVI) could dissociate different types of vegetation better than other straight vegetation indices, such as the Normalised Difference Water Index (NDWI). For this reason, we adopted the NDVI to interpret the vegetation classes.…”

Section: Vegetation Classes and Radiometric Indicesmentioning

confidence: 97%

Anthropogenic environmental pressures in urban conservation units: a case study in Belém, Brazilian eastern Amazon

Dutra¹,

Tavares²,

Lima³

et al. 2022

IJH

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Over 70% of the Amazon inhabitants live in cities. Moreover, the contest between urban and green areas is historically known. In this scenario, the green areas in the Eastern Amazon cities are now beneath constant pressure and some of its ecosystem services can be reduced because of environmental stress. Therefore, this study aimed to investigate environmental stressors in two urban conservation units in the Brazilian Eastern Amazon. To understand how the urbanisation affected both the eutrophication process and forest resilience in the study area, we investigated the literature about the urban sprawl in Belém city and analysed, through remote sensing techniques, the responses for vegetation and built-up indices in the last 30 years. The results showed that the city expansion had not considered sustainable criteria, which ultimately intensified the anthropic eutrophication of the Bolonha Lake. Despite anthropogenic and climate pressures in the last decades over the green areas of Belém, the forest within the park showed no signal of reduced resilience. Lastly, joint efforts are necessary to improve wastewater treatment in the Metropolitan Region of Belém, so the water quality of the reservoirs improve, and its macrophytes blooming reduce gradually.

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“…These indices utilize the reflectance and absorptive properties of snow in visible, NIR, and SWIR bands [29,35,[38][39][40][42][43][44][45][46][47].Although these indices have been frequently used for snow cover mapping with various multispectral satellite datasets and proven their significance in extracting snow cover in different terrain conditions, water and cloud are two major impurities that pose as a challenge in extracting snow pixels from space-borne multispectral sensors. A series of studies have been conducted for developing a masking technique or improved indices [48][49][50][51] for removing the impact of cloud pixels, but there are a sufficient number of studies [29,37,[52][53][54][55][56] that have shown that water pixels are always misunderstood as snow when extracted with these indices. Thus, there is an additional requirement of a filtering technique for precise delineation of snow cover with available index-based techniques.In this study, we propose a new snow cover index named the snow water index (SWI).…”

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confidence: 99%

Development and Evaluation of a New “Snow Water Index (SWI)” for Accurate Snow Cover Delineation

2019

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The current study started by examining the three most established snow indices, namely the NDSI (normalized difference snow index), S3, and NDSII-1 (normalized difference snow and ice index), based on their capabilities to differentiate snow pixels from cloud, debris, vegetation, and water pixels. Furthermore, considering the limitations of these indices, a new spectral index called the snow water index (SWI) is proposed. SWI uses spectral characteristics of the visible, SWIR (shortwave infrared), and NIR (near infrared) bands to achieve significant contrast between snow/ice pixels and other pixels including water bodies. A three-step accuracy assessment technique established the dominance of SWI over NDSI, S3, and NDSII-1. In the first step, image thresholding using standard value (>0), individual index theory (fixed threshold), histogram, and GCPs (ground control points) derived threshold were used to assess the performance of the selected indices. In the second step, comparisons of the spectral separation of features in the individual band were made from the field spectral observations collected using a spectroradiometer. In the third step, GCPs collected using field surveys were used to derive the user’s accuracy, producer’s accuracy, overall accuracy, and kappa coefficient for each index. The SWI threshold varied between 0.21 to 0.25 in all of the selected observations from both ablation and accumulation time. Spectral separability plots justify the SWI’s capability of extraction and removal of the most critical water pixels in integration with other impure classes from snow/ice pixels. GCP enabled accuracy assessment resulted in a maximum overall accuracy (0.93) and kappa statistics (0.947) value for the SWI. Thus, the results of the accuracy assessment justified the supremacy of the SWI over other indices. The study revealed that SWI demonstrates a considerably higher correlation with actual snow/ice cover and is prominent for spatio-temporal snow cover studies globally.

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Analysis of cloud condition on Sentinel-2 MSI and Landsat-8 OLI images of a public supply lake in Belém-Pará-Brazil

Cited by 5 publications

References 25 publications

Estimation of Water Quality in a Reservoir From Sentinel-2 Msi and Landsat-8 Oli Sensors

Estimation of Water Quality in a Reservoir From Sentinel-2 Msi and Landsat-8 Oli Sensors

Anthropogenic environmental pressures in urban conservation units: a case study in Belém, Brazilian eastern Amazon

Development and Evaluation of a New “Snow Water Index (SWI)” for Accurate Snow Cover Delineation

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