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Yang S, Huang Q, Yu M. Advancements in remote sensing for active fire detection: A review of datasets and methods. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 943:173273. [PMID: 38823698 DOI: 10.1016/j.scitotenv.2024.173273] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/10/2024] [Revised: 04/06/2024] [Accepted: 05/13/2024] [Indexed: 06/03/2024]
Abstract
This study comprehensively and critically reviews active fire detection advancements in remote sensing from 1975 to the present, focusing on two main perspectives: datasets and corresponding instruments, and detection algorithms. The study highlights the increasing role of machine learning, particularly deep learning techniques, in active fire detection. Looking forward, the review outlines current challenges and future research opportunities in remote sensing for active fire detection. These include exploring data quality management and multi-modal learning, developing spatiotemporally explicit models, investigating self-supervised learning models, improving explainable and interpretable models, integrating physical-process based models with machine learning, and building digital twins to replicate wildfire dynamics and perform what-if scenario analysis. The review aims to serve as a valuable resource for informing natural resource management and enhancing environmental protection efforts through the application of remote sensing technology.
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Affiliation(s)
- Songxi Yang
- Spatial Computing and Data Mining Lab, Department of Geography, University of Wisconsin-Madison, Madison 53705, WI, USA
| | - Qunying Huang
- Spatial Computing and Data Mining Lab, Department of Geography, University of Wisconsin-Madison, Madison 53705, WI, USA.
| | - Manzhu Yu
- Department of Geography, Pennsylvania State University, University Park, 16802, PA, USA
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Zhou B, Niu R, Yang S, Yang J, Zhao W. Multisource working condition recognition via nonlinear kernel learning and p-Laplacian manifold learning. Heliyon 2024; 10:e26436. [PMID: 38449626 PMCID: PMC10915344 DOI: 10.1016/j.heliyon.2024.e26436] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2023] [Revised: 02/12/2024] [Accepted: 02/13/2024] [Indexed: 03/08/2024] Open
Abstract
Effectively utilizing information from multiple sources and fewer labeled operating condition samples from a sucker-rod pumping system for oil production can improve the recognition effects and engineering practicability. Nevertheless, this is a challenging energy environment scientific application research subject, and therefore, this study proposes an operating state recognition scheme that relies on multisource nonlinear kernel learning and p-Laplacian high-order manifold regularization logistic regress. Specifically, three measured features are selected and extracted, i.e., wellhead temperature signal, electrical power signal, and ground dynamometer cards, based on mechanism analysis, expert experience, and prior knowledge. Finally, we establish the operating condition recognition model to recognize by the multisource p-Laplacian regularization kernel logistic regress algorithm. The experimental data are derived from 60 wells of a common high-pressure and low-permeability thin oil reservoir block of an oil field in China. The corresponding trials highlight that our scheme outperforms traditional recognition methods by exploiting single-source and multiple-feature data. In the context of fewer labeled samples, the proposed method has a greater recognition effect, engineering practicability, and better model robustness than the existing schemes based on other high-order manifold learning, verifying our method's effectiveness.
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Affiliation(s)
- Bin Zhou
- School of Computer Science and Technology, Shandong University of Technology, Zibo, China
| | - Rui Niu
- College of Geosciences and Engineering, North China University of Water Resources and Electric Power, Zhengzhou, China
| | - Shuo Yang
- School of Computer Science and Technology, Shandong University of Technology, Zibo, China
| | - Jianguo Yang
- School of Mechanical Engineering, Shandong University of Technology, Zibo, China
| | - Weiwei Zhao
- School of Computer Science and Technology, Shandong University of Technology, Zibo, China
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Goparaju L, Pillutla RCP, Venkata SBK. Assessment of forest fire emissions in Uttarakhand State, India, using Open Geospatial data and Google Earth Engine. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2023; 30:100873-100891. [PMID: 37642912 DOI: 10.1007/s11356-023-29311-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/02/2023] [Accepted: 08/08/2023] [Indexed: 08/31/2023]
Abstract
In the recent past, forest fires have increased due to the changing climate pattern. It is necessary to analyse and quantify various gaseous emissions so as to mitigate their harmful effects on air pollution. Satellite remote sensing data provides an opportunity to study the greenhouse gases in the atmosphere. The multispectral sensor of the Tropospheric Monitoring Instrument (Sentinel-5) is capable of recording the reflectance of wavelengths vital for measuring the atmospheric concentrations of methane, formaldehyde, aerosol, carbon monoxide, etc., at a spatial resolution of 0.01°. The present study utilized the Google Earth Engine (GEE) platform to study the emissions caused by forest fires in four districts of Uttarakhand State of India, which witnessed unprecedented fires in April-May 2021. All the datasets were ingested in GEE, which has the capability to analyse large datasets without the need to download them. The pre-fire period chosen was September 2020; the fire period was February-May 2021, and the post-fire period was June 2021. The variables chosen were aerosol absorbing index (AAI), carbon monoxide (CO) and nitrogen dioxide (NO2). The climate parameter temperature (Moderate Resolution Imaging Spectroradiometer Land Surface Temperature) and precipitation (from Climate Hazards Group InfraRed Precipitation (CHIRPS) Pentad) were also studied for the period mentioned. The results indicate a different trend for emissions in each district. For AAI, maximum emissions were noted in district Nainital followed by Almora, Tehri Garhwal and Garhwal. For CO emissions, the most affected district was Almora followed by Nainital, Garhwal and Tehri Garhwal. For NO2 emissions, the most affected district was Garhwal, followed by Nainital, Tehri Garhwal and Almora. Delta Normalized Burn Ratio was computed from Sentinel data (difference of pre-fire and post-fire images) to assess the burnt area severity. The Delta Normalized Burn Ratio values observed that the district with the most burnt area is Garhwal, followed by Nainital, Almora and Tehri Garhwal. The elevated temperatures and scanty rainfall patterns regulated the intensity and duration of forest fire. Monitoring the gaseous emissions as a consequence of forest fire in the GEE platform is much easier and more convenient at a regional level. Such data is much needed for mitigation measures to be implemented in time.
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Affiliation(s)
- Laxmi Goparaju
- Vindhyan Ecology and Natural History Foundation, 36/30, Shivpuri Colony, Station Road, Mirzapur-231001, Uttar Pradesh, India.
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Dehghani-Dehcheshmeh S, Akhoondzadeh M, Homayouni S. Oil spills detection from SAR Earth observations based on a hybrid CNN transformer networks. MARINE POLLUTION BULLETIN 2023; 190:114834. [PMID: 36934487 DOI: 10.1016/j.marpolbul.2023.114834] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/03/2023] [Revised: 03/08/2023] [Accepted: 03/10/2023] [Indexed: 06/18/2023]
Abstract
Oil spills are the main threats to marine and coastal environments. Due to the increase in the marine transportation and shipping industry, oil spills have increased in recent years. Moreover, the rapid spread of oil spills in open waters seriously affects the fragile marine ecosystem and creates environmental concerns. Effective monitoring, quick identification, and estimation of the volume of oil spills are the first and most crucial steps for a successful cleanup operation and crisis management. Remote Sensing observations, especially from Synthetic Aperture Radar (SAR) sensors, are a very suitable choice for this purpose due to their ability to collect data regardless of the weather and illumination conditions and over far and large areas of the Earth. Owing to the relatively complex nature of SAR observations, machine learning (ML) based algorithms play an important role in accurately detecting and monitoring oil spills and can significantly help experts in faster and more accurate detection. This paper uses SAR images from ESA's Copernicus Sentinel-1 satellite to detect and locate oil spills in open waters under different environmental conditions. To this end, a deep learning framework has been presented to identify oil spills automatically. The SAR images were segmented into two classes, the oil slick and the background, using convolutional neural networks (CNN) and vision transformers (ViT). Various scenarios for the proposed architecture were designed by placing ViT networks in different parts of the CNN backbone. An extensive dataset of oil spill events in various regions across the globe was used to train and assess the performance of the proposed framework. After the detection performance assessments, the F1-score values for the standard DeepLabV3+, FC-DenseNet, and U-Net networks were 75.08 %, 73.94 %, and 60.85, respectively. In the combined networks models (combination of CNN and ViT), the best F1-score results were obtained as 78.48 %. Our results showed that these hybrid models could improve detection accuracy and have a high ability to distinguish oil spill borders even in noisy images. Evaluation metrics are increased in all the combined networks compared to the original CNN networks.
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Affiliation(s)
| | - Mehdi Akhoondzadeh
- School of Surveying and Geospatial Engineering, College of Engineering, University of Tehran, Iran.
| | - Saeid Homayouni
- School of Surveying and Geospatial Engineering, College of Engineering, University of Tehran, Iran; Centre Eau Terre Environnement, Institut National de la Recherche Scientifique, Quebec City, Canada
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Hamza A, Khan MA, ur Rehman S, Albarakati HM, Alroobaea R, Baqasah AM, Alhaisoni M, Masood A. An Integrated Parallel Inner Deep Learning Models Information Fusion With Bayesian Optimization for Land Scene Classification in Satellite Images. IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 2023; 16:9888-9903. [DOI: 10.1109/jstars.2023.3324494] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/25/2024]
Affiliation(s)
- Ameer Hamza
- Department of CS, HITEC University, Taxila, Pakistan
| | | | | | - Hussain Mobarak Albarakati
- Computer Engineering Department, College of Computer and Information Systems, Umm Al-Qura University, Makkah, Saudi Arabia
| | - Roobaea Alroobaea
- Department of Computer Science, College of Computers and Information Technology, Taif University, Taif, Saudi Arabia
| | - Abdullah M. Baqasah
- Department of Information Technology, College of Computers and Information Technology, Taif University, Taif, Saudi Arabia
| | - Majed Alhaisoni
- Computer Sciences Department, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia
| | - Anum Masood
- Department of Physics, Norwegian University of Science and Technology, Trondheim, Norway
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Object Tracking Based on Satellite Videos: A Literature Review. REMOTE SENSING 2022. [DOI: 10.3390/rs14153674] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/27/2023]
Abstract
Video satellites have recently become an attractive method of Earth observation, providing consecutive images of the Earth’s surface for continuous monitoring of specific events. The development of on-board optical and communication systems has enabled the various applications of satellite image sequences. However, satellite video-based target tracking is a challenging research topic in remote sensing due to its relatively low spatial and temporal resolution. Thus, this survey systematically investigates current satellite video-based tracking approaches and benchmark datasets, focusing on five typical tracking applications: traffic target tracking, ship tracking, typhoon tracking, fire tracking, and ice motion tracking. The essential aspects of each tracking target are summarized, such as the tracking architecture, the fundamental characteristics, primary motivations, and contributions. Furthermore, popular visual tracking benchmarks and their respective properties are discussed. Finally, a revised multi-level dataset based on wpafb videos is generated and quantitatively evaluated for future development in the satellite video-based tracking area. In addition, 54.3% of the tracklets with lower ds are selected and renamed as the Easy group, while 27.2% and 18.5% of the tracklets are grouped into the Medium-ds group and the Hard-ds group, respectively.
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Abstract
The construction and expansion of highways aiming to improve the integration of the most isolated regions in Brazil facilitated the access to many inhabited areas in the Amazon biome, but had as a consequence assisted the degradation of many of these regions. Over the last two decades, we have observed in this biome a gradual diversification and intensification of land uses through vegetation loss and an increase in fire associated with deforestation and an increase in grazing areas. We used data from several active fires products derived from 14 different satellites, available on the Brazilian National Institute for Space Research (INPE). We evaluated the influence of highway infrastructure on fire occurrence inside and around Indigenous Lands (IL) located in the Brazilian Amazon biome, from 2008 to 2021. We classified 332 ILs into “cut by highways”, “without highways”, and “with highways in a 10 km buffer”. We performed: (a) the descriptive statistics of the fire occurrence by state, by season, and by type of land use and land cover (LULC) affected by fire; (b) the spatial distribution of the active fire density; and (c) a simple linear regression model between the fire occurrence and the IL area. Our results showed that in total, 16–46% of the fires occurred within the IL in most of the states, while the 10 km buffer was the region most affected by fire. We confirmed that in the last three years there was a significant increase in the number of active fires, representing anomalies in fire occurrence across the studied period. We discussed the result implications and the role of the highway network in environmental degradation inside and around the ILs located in the Brazilian Amazon.
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