“sen2r”: An R toolbox for automatically downloading and preprocessing Sentinel-2 satellite data

Ranghetti, Luigi; Boschetti, Mirco; Nutini, Francesco; Busetto, Lorenzo

doi:10.1016/j.cageo.2020.104473

Cited by 105 publications

(56 citation statements)

References 28 publications

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“…The S2 multispectral instrument (MSI) measures the Earth's reflected radiance in 13 spectral bands from VIS/NIR to SWIR with a spatial resolution ranging from 10 m to 60 m. The study case was built on S2 data collected for post-event assessments (after flooding occurrences at ROI_1 and ROI_2 and immediately after the rice field survey for ROI_3). Level-2A S2 images were downloaded and preprocessed with a sen2r toolbox [33]. The details of the preprocessing operations are described in [29].…”

Section: Dates # Ground Truth Pixels For (S)mentioning

confidence: 99%

“…Preprocessing consisted of clipping images to our area of interest and masking clouds using the scene classification (SC) product; pixels classified as high and medium cloud probability were masked out, while pixels belonging to different classes were retained to avoid masking-out water pixels. For ROI_3, a BOA image was not available at the desired dates of the field survey in the Copernicus archive, so it was necessary to download the top of atmosphere Level-1C products and apply atmospheric correction by using the Sen2Cor algorithm of the sen2r toolbox library [33].…”

Section: Dates # Ground Truth Pixels For (S)mentioning

confidence: 99%

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Knowledge and Data-Driven Mapping of Environmental Status Indicators from Remote Sensing and VGI

et al. 2020

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The paper proposes a transparent approach for mapping the status of environmental phenomena from multisource information based on both soft computing and machine learning. It is transparent, intended as human understandable as far as the employed criteria, and both knowledge and data-driven. It exploits remote sensing experts’ interpretations to define the contributing factors from which partial evidence of the environmental status are computed by processing multispectral images. Furthermore, it computes an environmental status indicator (ESI) map by aggregating the partial evidence degrees through a learning mechanism, exploiting volunteered geographic information (VGI). The approach is capable of capturing the specificities of local context, as well as to cope with the subjectivity of experts’ interpretations. The proposal is applied to map the status of standing water areas (i.e., water bodies and rivers and human-driven or natural hazard flooding) using multispectral optical images by ESA Sentinel-2 sources. VGI comprises georeferenced observations created both in situ by agronomists using a mobile application and by photointerpreters interacting with a geographic information system (GIS) using several information layers. Results of the validation experiments were performed in three areas of Northern Italy characterized by distinct ecosystems. The proposal showed better performances than traditional methods based on single spectral indexes.

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Section: Dates # Ground Truth Pixels For (S)mentioning

confidence: 99%

Section: Dates # Ground Truth Pixels For (S)mentioning

confidence: 99%

Knowledge and Data-Driven Mapping of Environmental Status Indicators from Remote Sensing and VGI

et al. 2020

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“…Spatial data on settlements and roads were obtained from satellite images available in Google Earth. As a surrogate of productivity index, we extracted satellite-derived normalized difference vegetation index (hereafter NDVI, an estimator of vegetation biomass) per each camera seasonally, by obtaining different spectral indexes from the Sentinel-2 satellite platform using the R-package “sen2R” ( 61 ). Previously, we created shapefiles in ArcGIS® to represent the study area extension polygons using the “gdal” geospatial toolbox.…”

Section: Methodsmentioning

confidence: 99%

Domestic Dogs and Wild Foxes Interactions in a Wildlife-Domestic Interface of North-Central Chile: Implications for Multi-Host Pathogen Transmission

et al. 2021

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Domestic dogs (Canis familiaris) often cohabite at interfaces shared by humans and wildlife, interacting with wild canids as predators, prey, competitors and reservoirs of several multi-host pathogens, such as canid-borne micro and macro parasites that could impact on wildlife, livestock and public health. However, spatio-temporal patterns of indirect interactions as promoters of pathogen transfer between domestic and wild canids are largely unknown. In this study, we used camera traps to describe the activity patterns and habitat use of dogs, chilla (Lycalopex griseus) and culpeo (Lycalopex culpaeus) foxes and identify the local-scale factors that may affect the frequency of dog-fox interactions through an anthropization gradient of the Coquimbo region, Chile. We assessed local-scale variables that may predict the number of interactions between dogs and foxes, and compared the time interval between dog-culpeo and dog-chilla interactions. Our findings suggested that closeness to urbanized zones predicts the frequency of indirect interactions between dogs and foxes. We found higher number of dog-fox interactions (60 interactions) at a periurban site adjacent to two coastal towns (Tongoy and Guanaqueros), compared to other two more undisturbed sites (12 interactions) increasingly distanced from urbanized areas. We showed that dogs interacted more frequently with chilla foxes (57 interactions) than with culpeo foxes (15 interactions), and the first interaction type occurred almost exclusively at the periurban site, where dogs and chillas were more frequently detected than in the other sites. We detected a marked temporal segregation between dogs and foxes, but dog-chilla interactions resulted in shorter time intervals (2.5 median days) compared to dog-culpeo interactions (7.6 median days), suggesting a higher potential risk of pathogen spillover between the first species pairing. Based on previous studies, we suggest periurban zones may constitute a potential focus of pathogen exposure between dog and fox populations in the study area. Our research contributes to improving the knowledge on the spatio-temporal patterns of interspecific contact between invasive and native carnivores within the context of multi-host pathogen dynamics. Our outcomes will inform theoretical epidemiological models designed to predict and minimize the contact risk between domestic and threatened species, guiding effective control strategies at the wildlife-domestic interface.

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“…Although the “base” year for our analysis was 2019, we used three Sentinel-2 images from May 2018, as data from this part of the season was missing in 2019 due to heavy cloud cover. Sentinel-2 Bottom-of-Atmosphere products were downloaded using the sen2r package in R [ 54 ]. The study site contained four Sentinel-2 tiles, (34UFA, 34UFV, 34UEA, and 34UFV).…”

Section: Methodsmentioning

confidence: 99%

Evaluating the effect of stand properties and site conditions on the forest reflectance from Sentinel-2 time series

Grabska

Socha

2021

PLoS ONE

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Forest stand reflectance at the canopy level results from various factors, such as vegetation chemical properties, leaf morphology, canopy structure, and tree sizes. These factors are dependent on the species, age, and health statuses of trees, as well as the site conditions. Sentinel-2 imagery with the high spatial, spectral, and temporal resolution, has enabled analysis of the relationships between vegetation properties and their spectral responses at large spatial scales. A comprehensive study of these relationships is needed to understand the drivers of vegetation spectral patterns and is essential from the point of view of remote sensing data interpretation. Our study aimed to quantify the site and forest parameters affecting forest stands reflectance. The analysis was conducted for common beech-, silver fir- and Scots pine-dominated stands in a mountainous area of the Polish Carpathians. The effect of stands and site properties on reflectance in different parts of the growing season was captured using the dense time series provided by Sentinel-2 from 2018–2019. The results indicate that the reflectance of common beech stands is mainly influenced by elevation, particularly during spring and autumn. Other factors influencing beech stand reflectance include the share of the broadleaved understory, aspect, and, during summer, the age of stands. The reflectance of coniferous species, i.e., Scots pine and silver fir, is mainly influenced by the age and stand properties, namely the crown closure and stand density. The age is a primary driver for silver fir stands reflectance changes, while the stand properties have a large impact on Scots pine stands reflectance. Also, the understory influences Scots pine stands reflectance, while there appears to be no impact on silver fir stands. The influence of the abovementioned factors is highly diverse, depending on the used band and time of the season.

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“sen2r”: An R toolbox for automatically downloading and preprocessing Sentinel-2 satellite data

Cited by 105 publications

References 28 publications

Knowledge and Data-Driven Mapping of Environmental Status Indicators from Remote Sensing and VGI

Knowledge and Data-Driven Mapping of Environmental Status Indicators from Remote Sensing and VGI

Domestic Dogs and Wild Foxes Interactions in a Wildlife-Domestic Interface of North-Central Chile: Implications for Multi-Host Pathogen Transmission

Evaluating the effect of stand properties and site conditions on the forest reflectance from Sentinel-2 time series

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