Browsing by Author "Harikesh Singh"

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A Comprehensive Review of Empirical and Dynamic Wildfire Simulators and Machine Learning Techniques used for the Prediction of Wildfire in Australia
(Springer Science and Business Media B.V., 2025) Harikesh Singh; Kenneth Li Minn Ang; Dipak Paudyal; Mauricio A. Acuna; Prashant Kumar Srivastava; Sanjeev Kumar Srivastava
Wildfires pose significant environmental threats in Australia, impacting ecosystems, human lives, and property. This review article provides a comprehensive analysis of various empirical and dynamic wildfire simulators alongside machine learning (ML) techniques employed for wildfire prediction in Australia. The study examines the effectiveness of traditional empirical methods, dynamic physical models, and advanced ML algorithms in forecasting wildfire spread and behaviour. Key simulators discussed include PHOENIX Rapidfire, SPARK, AUSTRALIS, REDEYE, and IGNITE, each evaluated for their inputs, models, and outputs. Additionally, the application of ML methods such as artificial neural networks, logistic regression, decision trees, and support vector machines is explored, highlighting their predictive capabilities and limitations. The integration of these advanced techniques is essential for enhancing the accuracy of wildfire predictions, enabling better preparedness and response strategies. This review aims to inform future research and development in wildfire prediction and management, ultimately contributing to more effective fire mitigation efforts in Australia and beyond. © The Author(s) 2025.
Mapping and monitoring of vegetation regeneration and fuel under major transmission power lines through image and photogrammetric analysis of drone-derived data
(Taylor and Francis Ltd., 2023) Joshua Sos; Kim Penglase; Tom Lewis; Prashant K. Srivastava; Harikesh Singh; Sanjeev K. Srivastava
The use of drones and remote sensing in combination with geospatial analysis is a cost-efficient way to monitor energy distribution networks, especially those in fire-prone areas. This study investigated the use of image and photogrammetric analysis together with segmentation algorithms to assess vegetation height and volume in power line corridors in Southeast Queensland, Australia. Various fuel reduction techniques, including mega-mulching, spot sprays and cool mosaic burns, were implemented, and drone-generated models were employed to evaluate their effectiveness. The fuel hazard reduction and regrowth in terms of vegetation height and volume were recorded and analysed. Importantly, the study demonstrates a robust correlation (R 2 = 0.9073; df = 1,16; F = 156; p <.001) between field observations and drone-derived models, affirming the efficacy of this method in assessing fuel heights. This validation suggests that the approach could represent a viable, cost-efficient option for future monitoring and management of energy distribution networks in fire-prone areas. © 2023 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.
Role of air and light in sclerotial development and basidiospore formation in Sclerotium rolfsii
(2010) Sudarshan Maurya; Udai Singh; Rashmi Singh; Amitabh Singh; Harikesh Singh
Sclerotium rolfsii is one of the devastating soil-borne phytopathogens which causes severe loss at the time of seedling development. It also causes leaf spots in several crops and wild plants. Petri plates, containing potato dextrose agar medium, were inoculated with S. rolfsii. Two-third area of three, 50% area of three and 100% area of other three plates were sealed with cellophane tape. The other three plates were not sealed. All the plates were incubated at 27±2°C. Two sets of such plates were prepared. One set was incubated in light whereas the other set in the dark. There was no significant difference in mycelial growth and number of sclerotia among them but significant difference was observed when compared to the control, i.e. the plates which were not sealed. Sclerotium and basidiospore formation were directly influenced by air as completely sealed plates failed to produce sclerotia and basidiospores. Basidiospores were produced abundantly in the light and in the dark conditions in unsealed plates only on Cyperus rotundus rhizome meal agar medium.
Tracking Post-Fire Vegetation Regrowth and Burned Areas Using Bitemporal Sentinel-1 SAR Data: A Google Earth Engine Approach in Heath Vegetation of Mooloolah River National Park, Queensland, Australia
(Multidisciplinary Digital Publishing Institute (MDPI), 2025) Harikesh Singh; Prashant Kumar Srivastava; Rajendra B. Prasad; Sanjeev Kumar Srivastava
This study utilizes the unique capabilities of Sentinel-1 C-band synthetic aperture radar (SAR) data to map post-fire burned areas and monitor vegetation recovery in a heath-dominated Queensland National Park. Sentinel-1 SAR data were used due to their cloud-penetrating capability and frequent revisit times. Using Google Earth Engine (GEE), a bitemporal ratio analysis was applied to SAR data from post-fire periods between 2021 and 2023. SAR backscatter changes over time captured fire impacts and subsequent vegetation regrowth. This differentiation was further enhanced with k-means clustering. Validation was supported by Sentinel-2 dNBR and official fire history records. The dNBR provided a quantitative assessment of burn severity and was used alongside the fire history data to evaluate the accuracy of the burned area classification. While Sentinel-2 false-colour composite (FCC) imagery was generated for visualisation and interpretation purposes, the primary validation relied on dNBR and QPWS fire history records. The results highlighted significant vegetation regrowth, with some areas returning to near pre-fire biomass levels by March 2023. This approach demonstrates the sensitivity of Sentinel-1 SAR, especially in VV polarization, for detecting subtle changes in vegetation, providing a cost-effective method for post-fire ecosystem monitoring and informing ecological management strategies amid increasing wildfire events. © 2025 by the authors.
Trending and emerging prospects of physics-based and ML-based wildfire spread models: a comprehensive review
(Springer Nature, 2024) Harikesh Singh; Li-Minn Ang; Tom Lewis; Dipak Paudyal; Mauricio Acuna; Prashant Kumar Srivastava; Sanjeev Kumar Srivastava
The significant threat of wildfires to forest ecology and biodiversity, particularly in tropical and subtropical regions, underscores the necessity for advanced predictive models amidst shifting climate patterns. There is a need to evaluate and enhance wildfire prediction methods, focusing on their application during extended periods of intense heat and drought. This study reviews various wildfire modelling approaches, including traditional physical, semi-empirical, numerical, and emerging machine learning (ML)-based models. We critically assess these models’ capabilities in predicting fire susceptibility and post-ignition spread, highlighting their strengths and limitations. Our findings indicate that while traditional models provide foundational insights, they often fall short in dynamically estimating parameters and predicting ignition events. Cellular automata models, despite their potential, face challenges in data integration and computational demands. Conversely, ML models demonstrate superior efficiency and accuracy by leveraging diverse datasets, though they encounter interpretability issues. This review recommends hybrid modelling approaches that integrate multiple methods to harness their combined strengths. By incorporating data assimilation techniques with dynamic forecasting models, the predictive capabilities of ML-based predictions can be significantly enhanced. This review underscores the necessity for continued refinement of these models to ensure their reliability in real-world applications, ultimately contributing to more effective wildfire mitigation and management strategies. Future research should focus on improving hybrid models and exploring new data integration methods to advance predictive capabilities. © The Author(s) 2024.