ANALYSIS AND DETECTION OF WILDFIRES BY USING PRISMA HYPERSPECTRAL IMAGERY

Abstract. This paper deals with the analysis and detection of wildfires by using PRISMA imagery. Precursore IperSpettrale della Missione Applicativa (Hyperspectral Precursor of the Application Mission, PRISMA) is a new hyperspectral mission by ASI (Agenzia Spaziale Italiana, Italian Space Agency) launched in 2019. This mission provides hyperspectral images with a spectral range of 0.4–2.5 µm and an average spectral resolution less than 10 nm. In this work, we used the PRISMA hypercube acquired during the Australian bushfires of December 2019 in New South Wales. The analysis of the image is presented considering the unique amount of information contained in the continuous spectral signature of the hypercube. The Carbon dioxide Continuum-Interpolated Band Ratio (CO2 CIBR), Hyperspectral Fire Detection Index (HFDI), and Normalized Burn Index (NBR) will be used to analyze the informative content of the image, along with the analysis of some specific visible, near-infrared and shortwave-infrared bands. A multiclass classification is presented by using a I-dimensional convolutional neural network (CNN), and the results will be compared with the ones given by a support vector machine classifier reported in literature. Finally, some preliminary results related to wildfire temperature estimation are presented.

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Exploring PRISMA Scene for Fire Detection: Case Study of 2019 Bushfires in Ben Halls Gap National Park, NSW, Australia

Remote Sensing ◽

10.3390/rs13081410 ◽

2021 ◽

Vol 13 (8) ◽

pp. 1410

Author(s):

Stefania Amici ◽

Alessandro Piscini

Keyword(s):

Fire Detection ◽

Machine Learning Algorithms ◽

Support Vector ◽

Spectral Features ◽

Visual Interpretation ◽

Band Ratio ◽

Detection Techniques ◽

Space Agency ◽

South Wales ◽

Italian Space Agency

Precursore IperSpettrale della Missione Applicativa (Hyperspectral Precursor of the Application Mission, PRISMA) is a new hyperspectral mission by the ASI (Agenzia Spaziale Italiana, Italian Space Agency) mission launched in 2019 to measure the unique spectral features of diverse materials including vegetation and forest disturbances. In this study, we explored the potential use of this new sensor PRISMA for active wildfire characterization. We used the PRISMA hypercube acquired during the Australian bushfires of 2019 in New South Wales to test three detection techniques that take advantage of the unique spectral features of biomass burning in the spectral range measured by PRISMA. The three methods—the CO2-CIBR (continuum interpolated band ratio), HFDI (hyperspectral fire detection index) and AKBD (advanced K band difference)—were adapted to the PRISMA sensor’s characteristics and evaluated in terms of performance. Classification techniques based on machine learning algorithms (support vector machine, SVM) were used in combination with the visual interpretation of a panchromatic sharpened PRISMA image for validation. Preliminary analysis showed a good overall performance of the instrument in terms of radiance. We observed that the presence of the striping effect in the data can influence the performance of the indices. Both the CIBR and HFDI adapted for PRISMA were able to produce a detection rate spanning between 0.13561 and 0.81598 for CO2-CIBR and that between 0.36171 and 0.88431 depending on the chosen band combination. The potassium emission index turned out to be inadequate for locating flaming in our data, possibly due to multiple factors such as striping noise and the spectral resolution (12 nm) of the PRISMA band centered at the potassium emission.

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Identification of peat type and humification by laboratory VNIR/SWIR hyperspectral imaging of peat profiles with focus on fen-bog transition in aapa mires

Plant and Soil ◽

10.1007/s11104-020-04775-y ◽

2020 ◽

Author(s):

L. Granlund ◽

M. Keinänen ◽

T. Tahvanainen

Keyword(s):

Hyperspectral Imaging ◽

Near Infrared ◽

Support Vector ◽

High Resolution Imaging ◽

Factors Affecting ◽

Vector Machines ◽

Resolution Imaging ◽

Shortwave Infrared ◽

Type Classification ◽

Wide Potential

Abstract Aims Hyperspectral imaging (HSI) has high potential for analysing peat cores, but methodologies are deficient. We aimed for robust peat type classification and humification estimation. We also explored other factors affecting peat spectral properties. Methods We used two laboratory setups: VNIR (visible to near-infrared) and SWIR (shortwave infrared) for high resolution imaging of intact peat profiles with fen-bog transitions. Peat types were classified with support vector machines, indices were developed for von Post estimation, and K-means clustering was used to analyse stratigraphic patterns in peat quality. With separate experiments, we studied spectral effects of drying and oxidation. Results Despite major effects, oxidation and water content did not impede robust HSI classification. The accuracy between Carex peat and Sphagnum peat in validation was 80% with VNIR and 81% with SWIR data. The spectral humification indices had accuracies of 82% with VNIR and 56%. Stratigraphic HSI patterns revealed that 36% of peat layer shifts were inclined by over 20 degrees. Spectral indices were used to extrapolate visualisations of element concentrations. Conclusions HSI provided reliable information of basic peat quality and was useful in visual mapping, that can guide sampling for other analyses. HSI can manage large amounts of samples to widen the scope of detailed analysis beyond single profiles and it has wide potential in peat research beyond the exploratory scope of this paper. We were able to confirm the capacity of HSI to reveal shifts of peat quality, connected to ecosystem-scale change.

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Detection of Insect Damage in Green Coffee Beans Using VIS-NIR Hyperspectral Imaging

Remote Sensing ◽

10.3390/rs12152348 ◽

2020 ◽

Vol 12 (15) ◽

pp. 2348

Author(s):

Shih-Yu Chen ◽

Chuan-Yu Chang ◽

Cheng-Syue Ou ◽

Chou-Tien Lien

Keyword(s):

Energy Minimization ◽

Near Infrared ◽

Principal Component ◽

Detection Algorithm ◽

Band Selection ◽

Spectral Signature ◽

Support Vector ◽

Insect Damage ◽

Selection Methods ◽

Coffee Beans

The defective beans of coffee are categorized into black beans, fermented beans, moldy beans, insect damaged beans, parchment beans, and broken beans, and insect damaged beans are the most frequently seen type. In the past, coffee beans were manually screened and eye strain would induce misrecognition. This paper used a push-broom visible-near infrared (VIS-NIR) hyperspectral sensor to obtain the images of coffee beans, and further developed a hyperspectral insect damage detection algorithm (HIDDA), which can automatically detect insect damaged beans using only a few bands and one spectral signature. First, by taking advantage of the constrained energy minimization (CEM) developed band selection methods, constrained energy minimization-constrained band dependence minimization (CEM-BDM), minimum variance band prioritization (MinV-BP), maximal variance-based bp (MaxV-BP), sequential forward CTBS (SF-CTBS), sequential backward CTBS (SB-CTBS), and principal component analysis (PCA) were used to select the bands, and then two classifier methods were further proposed. One combined CEM with support vector machine (SVM) for classification, while the other used convolutional neural networks (CNN) and deep learning for classification where six band selection methods were then analyzed. The experiments collected 1139 beans and 20 images, and the results demonstrated that only three bands are really need to achieve 95% of accuracy and 90% of kappa coefficient. These findings show that 850–950 nm is an important wavelength range for accurately identifying insect damaged beans, and HIDDA can indeed detect insect damaged beans with only one spectral signature, which will provide an advantage in the process of practical application and commercialization in the future.

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Online Detection of Watercore Apples by Vis/NIR Full-Transmittance Spectroscopy Coupled with ANOVA Method

Foods ◽

10.3390/foods10122983 ◽

2021 ◽

Vol 10 (12) ◽

pp. 2983

Author(s):

Yifei Zhang ◽

Xuhai Yang ◽

Zhonglei Cai ◽

Shuxiang Fan ◽

Haiyun Zhang ◽

...

Keyword(s):

Near Infrared ◽

Support Vector ◽

Detection Accuracy ◽

Success Rates ◽

Online Detection ◽

Physiological Disorder ◽

Full Spectrum ◽

Band Ratio ◽

Discrimination Model ◽

Svm Model

Watercore is an internal physiological disorder affecting the quality and price of apples. Rapid and non-destructive detection of watercore is of great significance to improve the commercial value of apples. In this study, the visible and near infrared (Vis/NIR) full-transmittance spectroscopy combined with analysis of variance (ANOVA) method was used for online detection of watercore apples. At the speed of 0.5 m/s, the effects of three different orientations (O1, O2, and O3) on the discrimination results of watercore apples were evaluated, respectively. It was found that O3 orientation was the most suitable for detecting watercore apples. One-way ANOVA was used to select the characteristic wavelengths. The least squares-support vector machine (LS-SVM) model with two characteristic wavelengths obtained good performance with the success rates of 96.87% and 100% for watercore and healthy apples, respectively. In addition, full-spectrum data was also utilized to determine the optimal two-band ratio for the discrimination of watercore apples by ANOVA method. Study showed that the threshold discrimination model established based on O3 orientation had the same detection accuracy as the optimal LS-SVM model for samples in the prediction set. Overall, full-transmittance spectroscopy combined with the ANOVA method was feasible to online detect watercore apples, and the threshold discrimination model based on two-band ratio showed great potential for detection of watercore apples.

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First Evaluation of PRISMA Level 1 Data for Water Applications

Sensors ◽

10.3390/s20164553 ◽

2020 ◽

Vol 20 (16) ◽

pp. 4553 ◽

Cited By ~ 3

Author(s):

Claudia Giardino ◽

Mariano Bresciani ◽

Federica Braga ◽

Alice Fabbretto ◽

Nicola Ghirardi ◽

...

Keyword(s):

Near Infrared ◽

In Situ Measurements ◽

In Situ Data ◽

Space Agency ◽

Wide Range ◽

Level 1 ◽

Italian Space Agency ◽

Mean Square Difference ◽

Sentinel 2

This study presents a first assessment of the Top-Of-Atmosphere (TOA) radiances measured in the visible and near-infrared (VNIR) wavelengths from PRISMA (PRecursore IperSpettrale della Missione Applicativa), the new hyperspectral satellite sensor of the Italian Space Agency in orbit since March 2019. In particular, the radiometrically calibrated PRISMA Level 1 TOA radiances were compared to the TOA radiances simulated with a radiative transfer code, starting from in situ measurements of water reflectance. In situ data were obtained from a set of fixed position autonomous radiometers covering a wide range of water types, encompassing coastal and inland waters. A total of nine match-ups between PRISMA and in situ measurements distributed from July 2019 to June 2020 were analysed. Recognising the role of Sentinel-2 for inland and coastal waters applications, the TOA radiances measured from concurrent Sentinel-2 observations were added to the comparison. The results overall demonstrated that PRISMA VNIR sensor is providing TOA radiances with the same magnitude and shape of those in situ simulated (spectral angle difference, SA, between 0.80 and 3.39; root mean square difference, RMSD, between 0.98 and 4.76 [mW m−2 sr−1 nm−1]), with slightly larger differences at shorter wavelengths. The PRISMA TOA radiances were also found very similar to Sentinel-2 data (RMSD < 3.78 [mW m−2 sr−1 nm−1]), and encourage a synergic use of both sensors for aquatic applications. Further analyses with a higher number of match-ups between PRISMA, in situ and Sentinel-2 data are however recommended to fully characterize the on-orbit calibration of PRISMA for its exploitation in aquatic ecosystem mapping.

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Ground-Based Corroboration of GOES-17 Fire Detection Capabilities During Ignition of the Kincade Fire

Journal of Operational Meteorology ◽

10.15191/nwajom.2020.0808 ◽

2020 ◽

pp. 105-110

Author(s):

T. Todd Lindley ◽

Alexander B. Zwink ◽

Chad M. Gravelle ◽

Christopher C. Schmidt ◽

Cynthia K. Palmer ◽

...

Keyword(s):

Near Infrared ◽

Wildland Fire ◽

Remote Sensing Data ◽

Fire Detection ◽

Surveillance Video ◽

Camera Network ◽

Detection Characteristic ◽

Detection Capabilities ◽

Fire Initiation ◽

Shortwave Infrared

Corroboration of Geostationary Operational Environmental Satellite-17 (GOES-17) wildland fire detection capabilities occurred during the 24 October 2019 (evening of 23 October LST) ignition of the Kincade Fire in northern California. Post-analysis of remote sensing data compared to observations by the ALERTWildfire fire surveillance video system suggests that the emerging Kincade Fire hotspot was visually evident in GOES17 shortwave infrared imagery 52 s after the initial near-infrared heat source detected by the ground-based camera network. GOES-17 Advanced Baseline Imager Fire Detection Characteristic algorithms registered the fire 5 min after ignition. These observations represent the first documented comparative dataset between fire initiation and satellite detection, and thus provide context for GOES-16/17 wildland fire detections.

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PRISMA hyperspectral satellite mission: first data on snow in the Alps

10.5194/egusphere-egu2020-19825 ◽

2020 ◽

Author(s):

Biagio Di Mauro ◽

Roberto Garzonio ◽

Gabriele Bramati ◽

Sergio Cogliati ◽

Edoardo Cremonese ◽

...

Keyword(s):

Grain Size ◽

Near Infrared ◽

Snow Water Equivalent ◽

Satellite Observation ◽

Hyperspectral Data ◽

European Alps ◽

Spectral Evolution ◽

Satellite Mission ◽

Space Agency ◽

Italian Space Agency

On the 22nd of March 2019, PRISMA (PRecursore IperSpettrale della Missione Applicativa) mission has been launched by the Italian Space Agency (ASI). Since then, the spacecraft has been collecting on demand hyperspectral data of the Earth surface. The imaging spectrometer features 239 bands covering the visible, near infrared and shortwave infrared wavelengths (400-2500 nm) with a spectral resolution <12nm. PRISMA acquires hyperspectral images with a spatial resolution of 30m and a swath of 30 km.The satellite mission is still in the initial commissioning phase. During this period, the acquisition of field spectroscopy data contemporary to satellite observation is fundamental. With the aim of calibrating and validating PRISMA observations on snow fields, we organized field campaigns at a high altitude (2160 m) experimental site (Torgnon, Aosta Valley) in the European Alps. During these campaigns, we measured spectral reflectance of snow with a Spectral Evolution spectrometer (350-2500 nm), snow grain size, and snow density. Among different instruments operating at the site (e.g. net radiometer, webcam, sensors for snow depth, snow water equivalent, snow surface temperature etc.), we recently installed an unattended spectrometer acquiring continuous measurements of snow reflectance. This instrument covers part of the visible and near infrared spectral range (400-900 nm) and it was used to analyze the daily evolution of snow reflectance during the snow season.In this contribution, we present a preliminary comparison between field and satellite hyperspectral reflectance data of snow. This comparison is fundamental for the future development of algorithms for the estimation of snow physical variables (snow grain size, snow albedo, and concentration of impurities) from satellite hyperspectral data.

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Two Different Portables Visible-Near Infrared and Shortwave Infrared Region for On-Tree Measurement of Soluble Solid Content of Marian Plum Fruit

Engineering Journal ◽

10.4186/ej.2020.24.5.227 ◽

2020 ◽

Vol 24 (5) ◽

pp. 227-236

Author(s):

Jetsada Posom ◽

Navavit Soonnamtiang ◽

Patcharapong Kotethum ◽

Pakhpoom Konjun ◽

Panmanas Sirisomboon ◽

...

Keyword(s):

Near Infrared ◽

Solid Content ◽

Coefficient Of Determination ◽

Support Vector ◽

Soluble Solid Content ◽

Relative Standard ◽

Soluble Solid ◽

Shortwave Infrared ◽

Mean Square Errors

The goal of this study was to predict the soluble solid content (SSC) of on-tree Marian plum fruit using two different wavelength range and algorithm. One of these was the commercial dispersion NIR spectrometer (MicroNIR 1700), providing shortwave infrared (SWIR), while the other was a making diode array spectrometer giving visible-near infrared (Vis-NIR). To search optimal model, the analytical ability of the two wavelength ranges spectrometers coupled with two algorithms: i.e. partial least squares regression (PLSR) and support vector machine regression (SVR), were investigated. Different spectral pre-processing methods were tested. The model providing the lowest root mean square errors of prediction (RMSEP) was selected. Overall, the proposed outcome was that the performance of SWIR was more accurate than Vis-NIR spectrometer, and that both SWIR and Vis-NIR coupled with PLSR algorithm had a higher accuracy than SVR algorithm. The best model for on-tree evaluation SSC was the SWIR constructed using the PLSR algorithm with the spectral pre-processing of the 2nd derivative, providing a coefficient of determination of calibration set (R2) of 0.81, a coefficient of determination of validation set (r2) of 0.76, RMSEP of 0.69 °Brix, and a relative standard error of prediction (RSEP) of 4.43%. The outcome showed that a portable SWIR spectrometer developed with PLSR could be used for monitoring the SSC of individual Marian plum fruit on-tree for quality assurance.

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Two-Dimensional Near-Infrared and Shortwave Infrared Molecular Aggregates: Taking Kasha’s Model to the Next Dimension

10.26434/chemrxiv.7699358.v3 ◽

2019 ◽

Author(s):

Arundhati Deshmukh ◽

Danielle Koppel ◽

Chern Chuang ◽

Danielle Cadena ◽

Jianshu Cao ◽

...

Keyword(s):

Near Infrared ◽

Photophysical Properties ◽

Satellite Telemetry ◽

Transition Dipole Moment ◽

Short Wave ◽

Tissue Imaging ◽

Transition Dipole ◽

Deep Tissue Imaging ◽

Excitonic States ◽

Shortwave Infrared

Technologies which utilize near-infrared (700 – 1000 nm) and short-wave infrared (1000 – 2000 nm) electromagnetic radiation have applications in deep-tissue imaging, telecommunications and satellite telemetry due to low scattering and decreased background signal in this spectral region. However, there are few molecular species, which absorb efficiently beyond 1000 nm. Transition dipole moment coupling (e.g. J-aggregation) allows for redshifted excitonic states and provides a pathway to highly absorptive electronic states in the infrared. We present aggregates of two cyanine dyes whose absorption peaks redshift dramatically upon aggregation in water from ~ 800 nm to 1000 nm and 1050 nm with sheet-like morphologies and high molar absorptivities (e ~ 105 M-1cm-1). To describe this phenomenology, we extend Kasha’s model for J- and H-aggregation to describe the excitonic states of 2-dimensional aggregates whose slip is controlled by steric hindrance in the assembled structure. A consequence of the increased dimensionality is the phenomenon of an intermediate “I-aggregate”, one which redshifts yet displays spectral signatures of band-edge dark states akin to an H-aggregate. We distinguish between H-, I- and J-aggregates by showing the relative position of the bright (absorptive) state within the density of states using temperature dependent spectroscopy. Our results can be used to better design chromophores with predictable and tunable aggregation with new photophysical properties.

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Image fire detection algorithm based on support vector machine

Journal of Computer Applications ◽

10.3724/sp.j.1087.2010.01129 ◽

2010 ◽

Vol 30 (4) ◽

pp. 1129-1131

Author(s):

Na-juan YANG ◽

Hui-qin WANG ◽

Zong-fang MA

Keyword(s):

Support Vector Machine ◽

Fire Detection ◽

Detection Algorithm ◽

Support Vector

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