The New Hyperspectral Satellite PRISMA: Imagery for Forest Types Discrimination
Abstract
:1. Introduction
1.1. Overview of the PRISMA Mission and Instruments
1.2. Preprocessing Levels of PRISMA Hyperspectral Cubes
- Level0: The L0 product contains raw data in binary files, including instrument and satellite ancillary data, like the cloud cover percentage.
- Level1: The L1 product is a top-of-atmosphere radiance imagery organized as follows: two radiometrically calibrated hyperspectral and panchromatic radiance cubes and two co-registered HYPER and PAN radiance cubes.
- Level2: The L2 product is divided in:
- L2B: Atmospheric correction and geolocation of the L1 product (bottom-of-atmosphere radiance);
- L2C: Atmospheric correction and geolocation of the L1 product (bottom-of-atmosphere reflectance, including aerosol optical thickness and water vapor map);
- L2D: Geocoding (orthorectification) of L2C.
2. Materials and Methods
2.1. Study Areas
2.2. Reference Data
2.3. Remotely Sensed Data
2.4. Methods
- M-Statistic [45] (M): measures the difference of the distributional peaks of the reflectance values and is calculated as follows:
- Bhattacharyya distance [46] (B): measures the degree of dissimilarity between any two probability distributions, and is calculated as follows:
- The Jeffries–Matusita distance [47] (JM distance): the JM distance is a function of separability that directly relates to the probability of how good a resultant classification will be. It is calculated as a function of the Bhattacharyya distance:
- 4.
- Transformed divergence [48,49] (TD): is a maximum likelihood approach that provides a covariance weighted distance between the class means to determine whether spectral signatures were separable:
3. Results
4. Discussion
5. Conclusions
- Hyperspectral data were effective in discriminating forest types in both study areas and nomenclature system levels (average normalized separability higher than 0.50 for four out of six classes in Area 1, and nine out of 10 class pairs in Area 2). Only in Area 1 for the third level of nomenclature system the Sentinel-2 MSI was comparable with the PRISMA sensor.
- The SWIR spectral zone resulted as the most suitable for forest type discrimination. Other remarkable zones were the blue channel (in Area 1) for the broadleaf–coniferous class pair, the red-edge and the NIR-plateau (in Area 2) for most of the considered class pairs. Sentinel-2 relies primarily on the red-edge region (b6, b7) in separating the forest classes.
- The PRISMA sensor improved the separation between coniferous and broadleaves by 50% in Area 1 and 30% in Area 2. At the fourth level, the average separability of was 120% higher in Area 1 and 84% in Area 2.
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Sensor | Spatial Resolution (m) | Number of Bands | Swath (km) | Spectral Range (nm) | Spectral Resolution | Launch |
---|---|---|---|---|---|---|
Hyperion, EO-1 (USA) | 30 | 196 | 7.5 | 427–2395 | 10 | 2000 |
CHRIS, PROBA (ESA) | 25 | 19 | 17.5 | 200–1050 | 1.25–11 | 2001 |
HyspIRI VSWIR (USA) | 60 | 210 | 145 | 380–2500 | 10 | 2020 |
EnMAP HSI (Germany) | 30 | 200 | 30 | 420–1030 | 5–10 | Not launched yet |
TianGong-1 (China) | 10 (VNIR) 20 (SWIR) | 128 | 10 | 400–2500 | 10 (VNIR) 23 (SWIR) | 2011 |
HISUI (Japan) | 30 | 185 | 30 | 400–2500 | 10 (VNIR) 12.5 (SWIR) | 2019 |
SHALOM (Italy–Israel) | 10 | 275 | 30 | 400–2500 | 10 | 2021 |
HypXIM (France) | 8 | 210 | 145–600 | 400–2500 | 10 | 2022 |
PRISMA (Italy | 30 | 240 | 30 | 400–2500 | 10 | 2019 |
Orbit altitude reference | 615 km |
Swath/Field of view | 30 km/2.77° |
Ground Sample Distance | Hyperspectral: 30 m |
PAN: 5 m | |
Spatial pixels | Hyperspectral: 1000 |
PAN: 6000 | |
Pixel size | Hyperspectral: 30 × 30 μm |
PAN: 6.5 × 6.5 μm | |
Spectral range | VNIR: 400–1010 nm (66 bands) |
SWIR: 920–2500 nm (173 bands) | |
PAN: 400–700 nm | |
Spectral sampling interval (SSI) | ≤12 nm |
Spectral width | ≤12 nm |
Spectral calibration accuracy | ±0.1 nm |
Radiometric quantization | 12 bit |
VNIR Signal to noise ratio (SNR) | >200:1 |
SWIR SNR | >100:1 |
PAN SNR | >240:1 |
Absolute radiometric accuracy | Better than 5% |
Third Level | Description | Fourth Level | Description |
---|---|---|---|
3.1.1 | Broadleaf | 3.1.1.1 | Deciduous evergreen |
3.1.1.2 | Deciduous broadleaf | ||
3.1.1.5 | Azonal formation | ||
3.1.2 | Coniferous | 3.1.2.1 | Mediterranean coniferous |
3.1.2.2 | Mountain coniferous |
Class Pair | PRISMA | Sentinel-2 | |||||||||
---|---|---|---|---|---|---|---|---|---|---|---|
Wavelength | B | JM | M | TD | Wavelength | B | JM | M | TD | ||
Area 1 | 3112_3111 | 48 | 0.81 | 1.11 | 1.27 | 1.11 | 782 | 0.44 | 0.71 | 0.93 | 0.73 |
3115_3111 | 236 | 2.55 | 140 | 0.18 | 2.00 | 2202 | 0.47 | 0.75 | 0.43 | 1.47 | |
3115_3112 | 464 | 2.11 | 1.36 | 0.22 | 2.00 | 2202 | 0.63 | 0.93 | 0.39 | 1.88 | |
3121_3111 | 696 | 0.90 | 1.19 | 0.03 | 2.00 | 1613 | 0.15 | 0.27 | 0.05 | 0.37 | |
3121_3112 | 989 | 0.77 | 1.07 | 1.24 | 1.07 | 864 | 0.57 | 0.87 | 1.07 | 0.90 | |
3121_3115 | 1156 | 3.79 | 1.42 | 0.18 | 2.00 | 782 | 0.32 | 0.55 | 0.69 | 0.72 | |
Area 2 | 3112_3111 | 100 | 1.00 | 1.27 | 0.22 | 2.00 | 1613 | 0.20 | 0.36 | 0.63 | 0.36 |
3115_3111 | 375 | 2.07 | 1.35 | 0.24 | 2.00 | 664 | 0.36 | 0.60 | 0.05 | 1.09 | |
3115_3112 | 561 | 1.71 | 1.224 | 0.10 | 2.00 | 664 | 0.51 | 0.80 | 0.03 | 1.62 | |
3121_3111 | 791 | 2.41 | 1.40 | 0.20 | 2.00 | 740 | 0.31 | 0.54 | 0.79 | 0.54 | |
3121_3112 | 1021 | 2.21 | 1.38 | 0.11 | 2.00 | 740 | 0.85 | 1.15 | 1.30 | 1.15 | |
3121_3115 | 1190 | 0.41 | 0.67 | 0.89 | 0.68 | 782 | 0.27 | 0.47 | 0.73 | 0.48 | |
3121_3122 | 1480 | 1.25 | 1.23 | 0.15 | 2.00 | 1613 | 0.30 | 0.52 | 0.77 | 0.53 | |
3122_3111 | 1985 | 2.10 | 1.35 | 0.24 | 2.00 | 740 | 0.61 | 0.91 | 1.10 | 0.92 | |
3122_3112 | 2171 | 1.75 | 1.25 | 0.09 | 2.00 | 740 | 1.35 | 1.28 | 1.64 | 1.48 | |
3122_3115 | 2380 | 0.88 | 1.17 | 1.33 | 1.18 | 782 | 0.58 | 0.88 | 1.07 | 0.93 |
Class 2 | |||||
---|---|---|---|---|---|
Area 1 | Area 2 | ||||
311 | 312 | 311 | 312 | ||
Class 1 | 311 | / | 864 | / | 782 |
312 | 450 | / | 1841 | / |
Class 2 | |||||||||
---|---|---|---|---|---|---|---|---|---|
Area 1 | Area 2 | ||||||||
3111 | 3112 | 3121 | 3122 | 3112 | 3115 | 3121 | 3122 | ||
Class 1 | 3111 | / | 782 | 664 | / | 1613 | 703 | 740 | 740 |
3112 | 814 | / | 864 | / | / | 740 | 740 | 740 | |
3115 | 443 | 428 | 782 | / | 1373 | / | 782 | 782 | |
3121 | 443 | 1029 | / | / | 1373 | 731 | / | 1613 | |
3122 | / | / | / | / | 1373 | 1142 | 1361 | / |
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Vangi, E.; D’Amico, G.; Francini, S.; Giannetti, F.; Lasserre, B.; Marchetti, M.; Chirici, G. The New Hyperspectral Satellite PRISMA: Imagery for Forest Types Discrimination. Sensors 2021, 21, 1182. https://doi.org/10.3390/s21041182
Vangi E, D’Amico G, Francini S, Giannetti F, Lasserre B, Marchetti M, Chirici G. The New Hyperspectral Satellite PRISMA: Imagery for Forest Types Discrimination. Sensors. 2021; 21(4):1182. https://doi.org/10.3390/s21041182
Chicago/Turabian StyleVangi, Elia, Giovanni D’Amico, Saverio Francini, Francesca Giannetti, Bruno Lasserre, Marco Marchetti, and Gherardo Chirici. 2021. "The New Hyperspectral Satellite PRISMA: Imagery for Forest Types Discrimination" Sensors 21, no. 4: 1182. https://doi.org/10.3390/s21041182
APA StyleVangi, E., D’Amico, G., Francini, S., Giannetti, F., Lasserre, B., Marchetti, M., & Chirici, G. (2021). The New Hyperspectral Satellite PRISMA: Imagery for Forest Types Discrimination. Sensors, 21(4), 1182. https://doi.org/10.3390/s21041182