Combining Field Observations and Remote Sensing to Forecast Fine Fuel Loads

Ensley-Field, Mira W.; Shriver, Robert K.; Law, S. Edward; Adler, Peter B.

doi:10.1016/j.rama.2023.04.008

Cited by 6 publications

(2 citation statements)

References 32 publications

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“…Although many studies have focused on the canopy fuels of forests, ground-level fuels present a unique challenge due to their relative inaccessibility to direct detection via remote sensing technologies. Numerous scholars have attempted to integrate satellite remote sensing technology for estimating forest fine fuel [23][24][25][26] and have used this technology to create forest combustible load maps, which have become integral components of comprehensive fire management policies in some regions [27]. Accurately estimating ground-level combustible materials using remote sensing satellites remains a formidable challenge.…”

Section: Introductionmentioning

confidence: 99%

Estimating Fine Fuel Load Using Sentinel-2A Imagery and Machine Learning: A Case Study in the Mountainous Forests of Changsha, China

Deng,

Yan,

Jiang

et al. 2023

Remote Sensing

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Fine fuel load (FFL) is a crucial variable influencing the occurrence of wildfire. Accurate knowledge of the distribution of FFL in mountainous forests is essential for ongoing wildfire risk management and the stability of mountain ecosystems. Traditional methods of estimating forest fuel load typically involve ground surveys combined with remote sensing, which can be costly and inefficient. Therefore, low-cost, large-scale FFL estimation remains challenging. In this study, Sentinel-2A satellite imagery from the Changsha forest region was used as the data source. Firstly, different feature variables were constructed based on false-color (B843), true-color (B432), four-band (B8432) combinations, and the Normalized Difference Water Index (NDWI). Subsequently, a machine learning approach based on random convolution was employed to estimate FFL. This study also included accuracy assessments of the estimation results and the creation of FFL maps for the study area. The results showed that the FFL estimation based on the B8432 band combination achieved the highest accuracy, with RMSE and R2 values of 5.847 t·hm−2 and 0.656, respectively. FFL estimation results based on false-color imagery followed, with true-color imagery and NDWI index-based estimation results exhibiting lower accuracy. This study offers critical FFL insights using random convolution techniques applied to Sentinel-2A imagery, enhancing the ability to monitor and manage forest fuel conditions effectively, thereby facilitating more informed regional wildfire risk management strategies.

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Section: Introductionmentioning

confidence: 99%

Estimating Fine Fuel Load Using Sentinel-2A Imagery and Machine Learning: A Case Study in the Mountainous Forests of Changsha, China

Deng,

Yan,

Jiang

et al. 2023

Remote Sensing

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“…This methodology was applied to map fuel types in northeastern Greece. Additionally, Ensley-Field et al [15] developed a fuel model that considers the fuel load from the previous year and utilizes productivity estimates derived from early spring remotely sensed data to predict fuel load at specific locations. D'Este et al [16] conducted a study on the estimation of fine dead fuel load.…”

Section: Introductionmentioning

confidence: 99%

Fuel Type Mapping Using a CNN-Based Remote Sensing Approach: A Case Study in Sardinia

Carbone,

Spiller,

Laneve

2023

Fire

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Accurate fuel mapping is crucial for effectively determining wildfire risk and implementing management strategies. The primary challenge in fuel type mapping lies in the need to develop accurate and efficient methods for identifying and categorizing the various combustible materials present in an area, often on a large scale. In response to this need, this paper presents a comprehensive approach that combines remote sensing data and Convolutional Neural Network (CNN) to discriminate between fire behavior fuel models. In particular, a CNN-based classification approach that leverages Sentinel-2 imagery is exploited to accurately classify fuel types into seven preliminary main classes (broadleaf, conifers, shrubs, grass, bare soil, urban areas, and water bodies). To further refine the fuel mapping results, subclasses were generated from the seven principles by using biomass and bioclimatic maps. These additional maps provide complementary information about vegetation density and climatic conditions, respectively. By incorporating this information, we align our fuel type classification with the widely used Standard Scott and Burgan (2005) fuel classification system. The results are highly promising, showcasing excellent CNN training performance with all three metrics—accuracy, recall, and F1 score—achieving an impressive 0.99%. Notably, the network exhibits exceptional accuracy in a test case conducted in the southern region of Sardinia, successfully identifying Burnable classes in previously unseen pixels: broadleaf at 0.99%, conifer at 0.79%, shrub at 0.76%, and grass at 0.84%. The proposed approach presents a valuable tool for enhancing fire management, contributing to more effective wildfire prevention and mitigation efforts. Thus, this tool could be leveraged by fire management agencies, policymakers, and researchers to improve the determination of wildfire risk and management.

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Bayesian Models for Weather Prediction: Using Remote Sensing Data to Improve Forecast Accuracy

Nair,

Ezhilarasan

2024

Lecture Notes in Networks and Systems

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Combining Field Observations and Remote Sensing to Forecast Fine Fuel Loads

Cited by 6 publications

References 32 publications

Estimating Fine Fuel Load Using Sentinel-2A Imagery and Machine Learning: A Case Study in the Mountainous Forests of Changsha, China

Estimating Fine Fuel Load Using Sentinel-2A Imagery and Machine Learning: A Case Study in the Mountainous Forests of Changsha, China

Fuel Type Mapping Using a CNN-Based Remote Sensing Approach: A Case Study in Sardinia

Bayesian Models for Weather Prediction: Using Remote Sensing Data to Improve Forecast Accuracy

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