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

Amici, S.; Piscini, Alessandro

doi:10.3390/rs13081410

Cited by 23 publications

(6 citation statements)

References 30 publications

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“…These two bands were found to provide the best performance using AVIRIS spectral data (Dennison & Roberts, 2009; Matheson & Dennison, 2012). The HFDI was shown to be valuable as a detection index from space as well (Amici & Piscini, 2021; Waigl et al., 2019). In this study the PRISMA wavelengths 2,061 and 2,428 nm were chosen as the closest to the Dennison's best choice.…”

Section: Methodsmentioning

confidence: 99%

Wildfires Temperature Estimation by Complementary Use of Hyperspectral PRISMA and Thermal (ECOSTRESS & L8)

et al. 2022

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This paper deals with detection and temperature analysis and of wildfires 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 400–2,500 nm and an average spectral resolution less than 12 nm and a spatial resolution of 30 m/pixel. This study focuses on the wildfire temperature estimation over the Bootleg Fire, US 2021. The analysis starts by considering the Hyperspectral Fire Detection Index (HFDI) which is used to analyze the informative content of the images, along with the analysis of some specific visible, near‐infrared and shortwave‐infrared bands. This first analysis is used as input to perform a temperature estimation of the areas with active wildfire. Surface temperature is retrieved using PRISMA radiance and a linear mixing model based on two background components (vegetation and burn scar) and two active fire components. PRISMA temperatures are compared with LST (Land Surface Temperature) products from NASA's ECOSTRESS and Landsat 8 which imaged the Bootleg Fire before and after PRISMA. A critical discussion of the results obtained with PRISMA is presented, followed by the advantages and limitation of the proposed approach.

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Section: Methodsmentioning

confidence: 99%

Wildfires Temperature Estimation by Complementary Use of Hyperspectral PRISMA and Thermal (ECOSTRESS & L8)

et al. 2022

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“…PRISMA acquisition was planned under very urgent acquisition (within the project "Progetto per Sviluppo di prodotti iperspettrali prototipali evoluti" Rif: CMM-PRO-18-013, funded by Italian Space Agency) aiming to map the fire front on the region, but the large fire was suppressed at the time of PRISMA acquisition (Figure 4). However, three hot spots were detected by using the Hyperspectral Fire Detection Index (HFDI) [29,43,44], and verified during the manual inspection of the pixels spectrum, by using both the NIR-SWIR color composition and spectral behaviour of active fires' reflectance. The presence of clouds and water are evident in this area, which has been utilized for training the CNN in detecting and distinguishing between these two categories; meanwhile, the three hot spots corresponding to small pastoral fires were excluded from the dataset; they were utilized to validate the generalization ability assessment process, i.e., to verify if the CNN trained on Australia fires can also successfully detect the small fires in Sicily.…”

Section: Sicily Italymentioning

confidence: 99%

Wildfire Detection Using Convolutional Neural Networks and PRISMA Hyperspectral Imagery: A Spatial-Spectral Analysis

Spiller,

Carbone,

Amici

et al. 2023

Remote Sensing

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The exacerbation of wildfires, attributed to the effects of climate change, presents substantial risks to ecological systems, infrastructure, and human well-being. In the context of the Sustainable Development Goals (SDGs), particularly those related to climate action, prioritizing the assessment and management of the occurrence and intensity of extensive wildfires is of utmost importance. In recent times, there has been a significant increase in the frequency and severity of widespread wildfires worldwide, affecting several locations, including Australia, Italy, and the United States of America. The presence of complex phenomena marked by limited predictability leads to significant negative impacts on biodiversity and human lives. The utilization of satellite-derived data with neural networks, such as convolutional neural networks (CNNs), is a potentially advantageous approach for augmenting the monitoring capabilities of wildfires. This research examines the generalization capability of four neural network models, namely the fully connected (FC), one-dimensional (1D) CNN, two-dimensional (2D) CNN, and three-dimensional (3D) CNN model. Each model’s performance, as measured by accuracy, recall, and F1 scores, is assessed through K-fold cross-validation. Subsequently, T-statistics and p-values are computed based on these metrics to conduct a statistical comparison among the different models, allowing us to quantify the degree of similarity or dissimilarity between them. By using training data from Australia and Sicily, the performances of the trained model are evaluated on the test dataset from Oregon. The results are promising, with cross-validation on the training dataset producing mean precision, recall, and F1 scores ranging between approximately 0.97 and 0.98. Especially, the fully connected model has superior generalization capabilities, whilst the 3D CNN offers more refined and less distorted classifications. However, certain issues, such as false fire detection and confusion between smoke and shadows, persist. The aforementioned methodologies offer significant perspectives on the capabilities of neural network technologies in supporting the detection and management of wildfires. These approaches address the crucial matter of domain transferability and the associated dependability of predictions in new regions. This study makes a valuable contribution to the ongoing efforts in climate change by assisting in monitoring and managing wildfires.

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“…The literature on the use of PRISMA data, particularly in the field of land cover classification, is still limited. Most of the research shows the potential of PRISMA data for specific purposes, such as forest conservation with wildfire fuel mapping [28] or fire detection [29], geological applications [30], cryospheric applications [31], urban surface detection [32], and mapping methane point emissions [33]. There are also interesting studies in the agricultural field dealing with specific crop or vegetation type discrimination [34][35][36][37].…”

Section: Introductionmentioning

confidence: 99%

Using PRISMA Hyperspectral Data for Land Cover Classification with Artificial Intelligence Support

Delogu,

Caputi,

Perretta

et al. 2023

Sustainability

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Hyperspectral satellite missions, such as PRISMA of the Italian Space Agency (ASI), have opened up new research opportunities. Using PRISMA data in land cover classification has yet to be fully explored, and it is the main focus of this paper. Historically, the main purposes of remote sensing have been to identify land cover types, to detect changes, and to determine the vegetation status of forest canopies or agricultural crops. The ability to achieve these goals can be improved by increasing spectral resolution. At the same time, improved AI algorithms open up new classification possibilities. This paper compares three supervised classification techniques for agricultural crop recognition using PRISMA data: random forest (RF), artificial neural network (ANN), and convolutional neural network (CNN). The study was carried out over an area of 900 km2 in the province of Caserta, Italy. The PRISMA HDF5 file, pre-processed by the ASI at the reflectance level (L2d), was converted to GeoTiff using a custom Python script to facilitate its management in Qgis. The Qgis plugin AVHYAS was used for classification tests. The results show that CNN gives better results in terms of overall accuracy (0.973), K coefficient (0.968), and F1 score (0.842).

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

Cited by 23 publications

References 30 publications

Wildfires Temperature Estimation by Complementary Use of Hyperspectral PRISMA and Thermal (ECOSTRESS & L8)

Wildfires Temperature Estimation by Complementary Use of Hyperspectral PRISMA and Thermal (ECOSTRESS & L8)

Wildfire Detection Using Convolutional Neural Networks and PRISMA Hyperspectral Imagery: A Spatial-Spectral Analysis

Using PRISMA Hyperspectral Data for Land Cover Classification with Artificial Intelligence Support

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