The New Hyperspectral Satellite PRISMA: Imagery for Forest Types Discrimination

Vangi, Elia; D’Amico, Giovanni; Francini, Saverio; Giannetti, Francesca; Lasserre, Bruno; Marchetti, Marco; Chirici, Gherardo

doi:10.3390/s21041182

Cited by 94 publications

(61 citation statements)

References 44 publications

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“…The increasingly available aquatic remote sensing missions with enhanced spectral capabilities encourage the development of novel approaches to estimate water quality parameters such as PC. Missions include the current PRecursore IperSpettrale della Missione Applicativa (PRISMA) hyperspectral mission and the upcoming Environmental Mapping and Analysis Program (EnMAP), Plankton, Aerosol, Cloud, ocean Ecosystem (PACE), FLuorescence Explorer (FLEX), and surface biology and geology (SBG) satellites [34]- [37]. Hyperspectral data facilitate the analysis of a reflectance curve obtained in the visible and near-infrared (VNIR) in aquatic applications.…”

Section: Introductionmentioning

confidence: 99%

Impact of Spectral Resolution on Quantifying Cyanobacteria in Lakes and Reservoirs: A Machine-Learning Assessment

Zolfaghari

Pahlevan

Binding

et al. 2022

IEEE Trans. Geosci. Remote Sensing

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Cyanobacterial harmful algal blooms are an increasing threat to coastal and inland waters. These blooms can be detected using optical radiometers due to the presence of phycocyanin (PC) pigments. The spectral resolution of bestavailable multispectral sensors limits their ability to diagnostically detect PC in the presence of other photosynthetic pigments. To assess the role of spectral resolution in the determination of PC, a large (N = 905) database of colocated in situ radiometric spectra and PC are employed. We first examine the performance of selected widely used machine-learning (ML) models against that of benchmark algorithms for hyperspectral remote sensing reflectance (R rs ) spectra resampled to the spectral configuration of the Hyperspectral Imager for the Coastal Ocean (HICO) with a full-width at half-maximum (FWHM) of < 6 nm. Results show that the multilayer perceptron (MLP) neural network applied to HICO spectral configurations (median errors < 65%)

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Section: Introductionmentioning

confidence: 99%

Impact of Spectral Resolution on Quantifying Cyanobacteria in Lakes and Reservoirs: A Machine-Learning Assessment

Zolfaghari

Pahlevan

Binding

et al. 2022

IEEE Trans. Geosci. Remote Sensing

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“…Differently from Sentinel, PRISMA satellite (hyperspectral precursor of the application mission) is a recently introduced earth observation system launched by ASI (Italian Space Agency), equipped with an innovative electro-optic hyperspectral sensor capable of chemical scene characterization [28]. The sensor high spectral resolution allows for environmental analysis, ranging from geological mapping, vegetation analysis, and pollutant diffusion [29][30][31].…”

Section: Limitatons Of Field Methodsmentioning

confidence: 99%

Multitemporal Mapping of Post-Fire Land Cover Using Multiplatform PRISMA Hyperspectral and Sentinel-UAV Multispectral Data: Insights from Case Studies in Portugal and Italy

Lazzeri

Frodella

Rossi

et al. 2021

Sensors

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Wildfires have affected global forests and the Mediterranean area with increasing recurrency and intensity in the last years, with climate change resulting in reduced precipitations and higher temperatures. To assess the impact of wildfires on the environment, burned area mapping has become progressively more relevant. Initially carried out via field sketches, the advent of satellite remote sensing opened new possibilities, reducing the cost uncertainty and safety of the previous techniques. In the present study an experimental methodology was adopted to test the potential of advanced remote sensing techniques such as multispectral Sentinel-2, PRISMA hyperspectral satellite, and UAV (unmanned aerial vehicle) remotely-sensed data for the multitemporal mapping of burned areas by soil–vegetation recovery analysis in two test sites in Portugal and Italy. In case study one, innovative multiplatform data classification was performed with the correlation between Sentinel-2 RBR (relativized burn ratio) fire severity classes and the scene hyperspectral signature, performed with a pixel-by-pixel comparison leading to a converging classification. In the adopted methodology, RBR burned area analysis and vegetation recovery was tested for accordance with biophysical vegetation parameters (LAI, fCover, and fAPAR). In case study two, a UAV-sensed NDVI index was adopted for high-resolution mapping data collection. At a large scale, the Sentinel-2 RBR index proved to be efficient for burned area analysis, from both fire severity and vegetation recovery phenomena perspectives. Despite the elapsed time between the event and the acquisition, PRISMA hyperspectral converging classification based on Sentinel-2 was able to detect and discriminate different spectral signatures corresponding to different fire severity classes. At a slope scale, the UAV platform proved to be an effective tool for mapping and characterizing the burned area, giving clear advantage with respect to filed GPS mapping. Results highlighted that UAV platforms, if equipped with a hyperspectral sensor and used in a synergistic approach with PRISMA, would create a useful tool for satellite acquired data scene classification, allowing for the acquisition of a ground truth.

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“…IUTI is based on a systematic unaligned sampling design with a 0.5 km × 0.5 km grid cells. A complete description on IUTI can be found in Vangi et al (2021) [55].…”

Section: Validation Datasetmentioning

confidence: 99%

Estimating VAIA Windstorm Damaged Forest Area in Italy Using Time Series Sentinel-2 Imagery and Continuous Change Detection Algorithms

Giannetti

Pecchi

Travaglini

et al. 2021

Forests

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Mapping forest disturbances is an essential component of forest monitoring systems both to support local decisions and for international reporting. Between the 28 and 29 October 2018, the VAIA storm hit the Northeast regions of Italy with wind gusts exceeding 200 km h−1. The forests in these regions have been seriously damaged. Over 490 Municipalities in six administrative Regions in Northern Italy registered forest damages caused by VAIA, that destroyed or intensely damaged forest stands spread over an area of 67,000 km2. The present work tested the use of two continuous change detection algorithms, i.e., the Bayesian estimator of abrupt change, seasonal change, and trend (BEAST) and the continuous change detection and classification (CCDC) to map and estimate forest windstorm damage area using a normalized burned ration (NBR) time series calculated on three years Sentinel-2 (S2) images collection (i.e., January 2017–October 2019). We analyzed the accuracy of the maps and the damaged forest area using a probability-based stratified estimation within 12 months after the storm with an independent validation dataset. The results showed that close to the storm (i.e., 1 to 6 months November 2018–March 2019) it is not possible to obtain accurate results independently of the algorithm used, while accurate results were observed between 7 and 12 months from the storm (i.e., May 2019 – October 2019) in terms of Standard Error (SE), percentage SE (SE%), overall accuracy (OA), producer accuracy (PA), user accuracy (UA), and gmean for both BEAST and CCDC (SE< 3725.3 ha , SE% < 9.69 , OA > 89.7, PA and UA > 0.87, gmean > 0.83).

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The New Hyperspectral Satellite PRISMA: Imagery for Forest Types Discrimination

Cited by 94 publications

References 44 publications

Impact of Spectral Resolution on Quantifying Cyanobacteria in Lakes and Reservoirs: A Machine-Learning Assessment

Impact of Spectral Resolution on Quantifying Cyanobacteria in Lakes and Reservoirs: A Machine-Learning Assessment

Multitemporal Mapping of Post-Fire Land Cover Using Multiplatform PRISMA Hyperspectral and Sentinel-UAV Multispectral Data: Insights from Case Studies in Portugal and Italy

Estimating VAIA Windstorm Damaged Forest Area in Italy Using Time Series Sentinel-2 Imagery and Continuous Change Detection Algorithms

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