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

Amici, S.; Spiller, Dario; Ansalone, Luigi; Miller, Lee M.

doi:10.1029/2022jg007055

Cited by 3 publications

(2 citation statements)

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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%

“…Our earlier studies have demonstrated the potential for artificial intelligence, on-board computing, and satellite constellations to serve as interconnected components for future services that should aid in monitoring and prompt responses to wildfire events. The quality of the information that can be extracted from PRISMA HS imagery was investigated in [29], where analytical methodologies were proposed to locate wildfires and estimate the temperature of active fire pixels. At the same time, we showed the possibility of implementing Trusted Autonomous Satellite Operations [30][31][32] by utilizing artificial intelligence [33,34] on-board satellites with astrionics for data processing [35][36][37].…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Sicily Italymentioning

confidence: 99%

Section: Introductionmentioning

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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Prisma-Based Advanced Prototype Products: An Overview

Tricomi,

Acito,

Aiello

et al. 2023

IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium

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Autonomous Satellite Wildfire Detection Using Hyperspectral Imagery and Neural Networks: A Case Study on Australian Wildfire

et al. 2023

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One of the United Nations (UN) Sustainable Development Goals is climate action (SDG-13), and wildfire is among the catastrophic events that both impact climate change and are aggravated by it. In Australia and other countries, large-scale wildfires have dramatically grown in frequency and size in recent years. These fires threaten the world’s forests and urban woods, cause enormous environmental and property damage, and quite often result in fatalities. As a result of their increasing frequency, there is an ongoing debate over how to handle catastrophic wildfires and mitigate their social, economic, and environmental repercussions. Effective prevention, early warning, and response strategies must be well-planned and carefully coordinated to minimise harmful consequences to people and the environment. Rapid advancements in remote sensing technologies such as ground-based, aerial surveillance vehicle-based, and satellite-based systems have been used for efficient wildfire surveillance. This study focuses on the application of space-borne technology for very accurate fire detection under challenging conditions. Due to the significant advances in artificial intelligence (AI) techniques in recent years, numerous studies have previously been conducted to examine how AI might be applied in various situations. As a result of its special physical and operational requirements, spaceflight has emerged as one of the most challenging application fields. This work contains a feasibility study as well as a model and scenario prototype for a satellite AI system. With the intention of swiftly generating alerts and enabling immediate actions, the detection of wildfires has been studied with reference to the Australian events that occurred in December 2019. Convolutional neural networks (CNNs) were developed, trained, and used from the ground up to detect wildfires while also adjusting their complexity to meet onboard implementation requirements for trusted autonomous satellite operations (TASO). The capability of a 1-dimensional convolution neural network (1-DCNN) to classify wildfires is demonstrated in this research and the results are assessed against those reported in the literature. In order to enable autonomous onboard data processing, various hardware accelerators were considered and evaluated for onboard implementation. The trained model was then implemented in the following: Intel Movidius NCS-2 and Nvidia Jetson Nano and Nvidia Jetson TX2. Using the selected onboard hardware, the developed model was then put into practice and analysis was carried out. The results were positive and in favour of using the technology that has been proposed for onboard data processing to enable TASO on future missions. The findings indicate that data processing onboard can be very beneficial in disaster management and climate change mitigation by facilitating the generation of timely alerts for users and by enabling rapid and appropriate responses.

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

Cited by 3 publications

References 76 publications

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

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

Prisma-Based Advanced Prototype Products: An Overview

Autonomous Satellite Wildfire Detection Using Hyperspectral Imagery and Neural Networks: A Case Study on Australian Wildfire

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