How to Orient and Orthorectify PRISMA Images and Related Issues

Baiocchi, Valerio; Giannone, Francesca; Monti, Felicia

doi:10.3390/rs14091991

Cited by 5 publications

(3 citation statements)

References 25 publications

(30 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To import the hyperspectral scenes and to remove the channel overlap between VNIR and SWIR cubes, the EnMAP Toolbox version 3.11 was used in QGIS 3.28 Firenze (van der Linden et al, 2015; EnMAP-Box Developers, 2019; QGIS Development Team, 2023). However, the geolocation error of PRISMA images is up to 200 m, which means further coregistration is needed (Baiocchi et al, 2022). The co-registration was performed using the image registration workflow in ENVI 5.5.3 (Jin, 2017;L3Harris Geospatial Solutions, 2023).…”

Section: Description and Pre-processing Of Satellite Datamentioning

confidence: 99%

Spectral imaging of grass species in arid ecosystems of Namibia

Bantelmann,

Wyss,

Pius

et al. 2024

Front. Remote Sens.

View full text Add to dashboard Cite

Grasslands across the African continent are under pressure from climate change and human activities, particularly in arid ecosystems. From a remote sensing perspective, these ecosystems have not received much scientific attention, especially in Namibia. To address this knowledge gap, various remote sensing methods were implemented using new generation spaceborne imaging spectrometers amongst others. Therefore, this research provides a first methodological approach aimed at mapping and evaluating the distribution of grasslands within two private nature reserves, namely, the NamibRand Nature Reserve (NRNR) and ProNamib Nature Reserve (PNNR) with surrounding farmlands on the edge of Namib Sand Sea. The multi-sensor approach utilizes Mixture Tuned Matched Filtering (MTMF) and incorporated spectral information collected in the field to analyze grasslands. The research involves a sensor comparison of multispectral Sentinel-2 and PlanetScope data, hyperspectral data from Environmental Mapping and Analysis Programme (EnMAP) and PRecursore IperSpettrale della Missione Applicativa (PRISMA) and an additional data fusion product derived from Sentinel-2 and EnMAP imagery based on a Smoothing Filter-based Intensity Modulation Hypersharpening method (SFIM-HS). Additionally, a unique spectral library of collected field spectra was established and inter-species spectral separability and intra-species spectral homogeneity was analyzed. This library presents newly published spectra of individual species. Due to dry initial conditions, the calculated spectral separability of individual grasses is limited, making only a mean endmember feasible for partial unmixing. The validation results of satellite comparison show that data fusion products (R2 = 0.51 with Normalized Difference Vegetation Index (NDVI); R2 = 0.66 with Soil Adjusted Vegetation Index (SAVI)) are more suitable for mapping arid grasslands than multispectral or hyperspectral data (all R2 < 0.35). More research is required and potential methodological adjustments are discussed to further investigate the spatio-temporal dynamics of arid grasslands and to aid conservation efforts in the Greater Sossusvlei-Namib Landscape in line with the United Nations Decade of Restoration.

show abstract

Section: Description and Pre-processing Of Satellite Datamentioning

confidence: 99%

Spectral imaging of grass species in arid ecosystems of Namibia

Bantelmann,

Wyss,

Pius

et al. 2024

Front. Remote Sens.

View full text Add to dashboard Cite

show abstract

“…Noise in the spectral signature caused by saturated atmospheric water vapor was removed and validated analogous to the AVIRIS-NG data. Since there are known issues regarding the orthorectification of the PRISMA data [30], a geometric correction using ground control points (GCPs) and second order polynomial transformation had to be performed.…”

Section: Prisma Data and Preprocessingmentioning

confidence: 99%

Detection of Solar Photovoltaic Power Plants Using Satellite and Airborne Hyperspectral Imaging

Jörges,

Vidal,

Hank

et al. 2023

Remote Sensing

View full text Add to dashboard Cite

Solar photovoltaic panels (PV) provide great potential to reduce greenhouse gas emissions as a renewable energy technology. The number of solar PV has increased significantly in recent years and is expected to increase even further. Therefore, accurate and global mapping and monitoring of PV modules with remote sensing methods is important for predicting energy production potentials, revealing socio-economic drivers, supporting urban planning, and estimating ecological impacts. Hyperspectral imagery provides crucial information to identify PV modules based on their physical absorption and reflection properties. This study investigated spectral signatures of spaceborne PRISMA data of 30 m low resolution for the first time, as well as airborne AVIRIS-NG data of 5.3 m medium resolution for the detection of solar PV. The study region is located around Irlbach in southern Germany. A physics-based approach using the spectral indices nHI, NSPI, aVNIR, PEP, and VPEP was used for the classification of the hyperspectral images. By validation with a solar PV ground truth dataset of the study area, a user’s accuracy of 70.53% and a producer’s accuracy of 88.06% for the PRISMA hyperspectral data, and a user’s accuracy of 65.94% and a producer’s accuracy of 82.77% for AVIRIS-NG were achieved.

show abstract

“…High-and very-high-resolution images are affected by deformation mainly because of camera distortions and acquisition geometry, and then they must undergo a geometric rectification process to be used for metrical purposes. New satellite platforms dedicated to remote sensing with increasingly technologically advanced sensors onboard may present new acquisition geometries that need to be analyzed to perform proper orientation of new products, a fundamental procedure that is the basis of modern photogrammetry [10].…”

mentioning

confidence: 99%