Mapping of Post-Wildfire Burned Area Using a Hybrid Algorithm and Satellite Data: The Case of the Camp Fire Wildfire in California, USA

Estimating the Pre-Historical Volcanic Eruption in the Hatangang River Volcanic Field: Experimental and Simulation Study

The volcanic landforms associated with fluvial topography in the Hantangang River Volcanic Field (HRVF) have geoheritage value. The Hantangang basalt geological landform stretches along 110 km of the paleoriver channel of the Hantangang River. Since the eruption that formed this basalt occurred from 0.15 to 0.51 Ma, estimating the eruption in the HRVF that originated from two source vents in North Korea (Orisan Mountain and the 680 m peak) is challenging due to the limited recorded data for this eruption. In this study, we estimated this prehistorical eruption using 3D printing of a terrain model and Q-LavHA simulation. The results from the experiment were further analyzed using findings from an artificial neural network (ANN) and support vector machine (SVM) to classify the experimental lava area. The SVM classification results showed higher accuracy and efficiency in the computational process than the ANN algorithm. Results from the single eruptive vent scenario showed that the experiment had a higher accuracy than the Q-LavHA simulation. Further analysis of multiple vent scenarios in the Q-LavHA simulation has improved the accuracy compared with the single eruptive vent scenarios.

DSMNN-Net: A Deep Siamese Morphological Neural Network Model for Burned Area Mapping Using Multispectral Sentinel-2 and Hyperspectral PRISMA Images

Wildfires are one of the most destructive natural disasters that can affect our environment, with significant effects also on wildlife. Recently, climate change and human activities have resulted in higher frequencies of wildfires throughout the world. Timely and accurate detection of the burned areas can help to make decisions for their management. Remote sensing satellite imagery can have a key role in mapping burned areas due to its wide coverage, high-resolution data collection, and low capture times. However, although many studies have reported on burned area mapping based on remote sensing imagery in recent decades, accurate burned area mapping remains a major challenge due to the complexity of the background and the diversity of the burned areas. This paper presents a novel framework for burned area mapping based on Deep Siamese Morphological Neural Network (DSMNN-Net) and heterogeneous datasets. The DSMNN-Net framework is based on change detection through proposing a pre/post-fire method that is compatible with heterogeneous remote sensing datasets. The proposed network combines multiscale convolution layers and morphological layers (erosion and dilation) to generate deep features. To evaluate the performance of the method proposed here, two case study areas in Australian forests were selected. The framework used can better detect burned areas compared to other state-of-the-art burned area mapping procedures, with a performance of >98% for overall accuracy index, and a kappa coefficient of >0.9, using multispectral Sentinel-2 and hyperspectral PRISMA image datasets. The analyses of the two datasets illustrate that the DSMNN-Net is sufficiently valid and robust for burned area mapping, and especially for complex areas.

Grazing in California's Mediterranean Multi-Firescapes

The California landscape is layered and multifunctional, both historically and spatially. Currently, wildfire size, frequency, and intensity are without precedent, at great cost to human health, property, and lives. We review the contemporary firescape, the indigenous landscape that shaped pre-contact California's vegetation, the post-contact landscape that led us to our current situation, and the re-imagined grazing-scape that offers potential relief. Vegetation has been profoundly altered by the loss of Indigenous management, introduction of non-native species, implantation of inappropriate, militarized, forest management from western Europe, and climate change, creating novel ecosystems almost always more susceptible to wildfire than before. Vegetation flourishes during the mild wet winters of a Mediterranean climate and dries to a crisp in hot, completely dry, summers. Livestock grazing can break up continuous fuels, reduce rangeland fuels annually, and suppress brush encroachment, yet it is not promoted by federal or state forestry and fire-fighting agencies. Agencies, especially when it comes to fire, operate largely under a command and control model, while ranchers are a diverse group not generally subject to agency regulations, with a culture of autonomy in decision-making and a unit of production that is mobile. Concerns about potential loss of control have limited prescribed burning despite landowner and manager enthusiasm. Agriculture and active management in general are much neglected as an approach to developing fire-resistant landscape configurations, yet such interventions are essential. Prescribed burning facilitates grazing; grazing facilitates prescribed burning; both can reduce fuels. Leaving nature “to itself” absent recognizing that California's ecosystems have been irrecoverably altered has become a disaster of enormous proportions. We recommend the development of a database of the effects and uses of prescribed fire and grazing in different vegetation types and regions throughout the state, and suggest linking to existing databases when possible. At present, livestock grazing is California's most widespread vegetation management activity, and if purposefully applied to fuel management has great potential to do more.

Forest Fire Susceptibility Prediction Based on Machine Learning Models with Resampling Algorithms on Remote Sensing Data

This study predicts forest fire susceptibility in Chaloos Rood watershed in Iran using three machine learning (ML) models—multivariate adaptive regression splines (MARS), support vector machine (SVM), and boosted regression tree (BRT). The study utilizes 14 set of fire predictors derived from vegetation indices, climatic variables, environmental factors, and topographical features. To assess the suitability of the models and estimating the variance and bias of estimation, the training dataset obtained from the Natural Resources Directorate of Mazandaran province was subjected to resampling using cross validation (CV), bootstrap, and optimism bootstrap techniques. Using variance inflation factor (VIF), weight indicating the strength of the spatial relationship of the predictors to fire occurrence was assigned to each contributing variable. Subsequently, the models were trained and validated using the receiver operating characteristics (ROC) area under the curve (AUC) curve. Results of the model validation based on the resampling techniques (non, 5- and 10-fold CV, bootstrap and optimism bootstrap) produced AUC values of 0.78, 0.88, 0.90, 0.86 and 0.83 for the MARS model; 0.82, 0.82, 0.89, 0.87, 0.84 for the SVM and 0.87, 0.90, 0.90, 0.90, 0.91 for the BRT model. Across the individual model, the 10-fold CV performed best in MARS and SVM with AUC values of 0.90 and 0.89. Overall, the BRT outperformed the other models in all ramification with highest AUC value of 0.91 using optimism bootstrap resampling algorithm. Generally, the resampling process enhanced the prediction performance of all the models.

Effects of canopy midstory management and fuel moisture on wildfire behavior

Increasing trends in wildfire severity can partly be attributed to fire exclusion in the past century which led to higher fuel accumulation. Mechanical thinning and prescribed burns are effective techniques to manage fuel loads and to establish a higher degree of control over future fire risk, while restoring fire prone landscapes to their natural states of succession. However, given the complexity of interactions between fine scale fuel heterogeneity and wind, it is difficult to assess the success of thinning operations and prescribed burns. The present work addresses this issue systematically by simulating a simple fire line and propagating through a vegetative environment where the midstory has been cleared in different degrees, leading to a canopy with almost no midstory, another with a sparse midstory and another with a dense midstory. The simulations are conducted for these three canopies under two different conditions, where the fuel moisture is high and where it is low. These six sets of simulations show widely different fire behavior, in terms of fire intensity, spread rate and consumption. To understand the physical mechanisms that lead to these differences, detailed analyses are conducted to look at wind patterns, mean flow and turbulent fluxes of momentum and energy. The analyses also lead to improved understanding of processes leading to high intensity crowning behavior in presence of a dense midstory. Moreover, this work highlights the importance of considering fine scale fuel heterogeneity, seasonality, wind effects and the associated fire-canopy-atmosphere interactions while considering prescribed burns and forest management operations.

Monitoring Wildfires in the Northeastern Peruvian Amazon Using Landsat-8 and Sentinel-2 Imagery in the GEE Platform

During the latest decades, the Amazon has experienced a great loss of vegetation cover, in many cases as a direct consequence of wildfires, which became a problem at local, national, and global scales, leading to economic, social, and environmental impacts. Hence, this study is committed to developing a routine for monitoring fires in the vegetation cover relying on recent multitemporal data (2017–2019) of Landsat-8 and Sentinel-2 imagery using the cloud-based Google Earth Engine (GEE) platform. In order to assess the burnt areas (BA), spectral indices were employed, such as the Normalized Burn Ratio (NBR), Normalized Burn Ratio 2 (NBR2), and Mid-Infrared Burn Index (MIRBI). All these indices were applied for BA assessment according to appropriate thresholds. Additionally, to reduce confusion between burnt areas and other land cover classes, further indices were used, like those considering the temporal differences between pre and post-fire conditions: differential Mid-Infrared Burn Index (dMIRBI), differential Normalized Burn Ratio (dNBR), differential Normalized Burn Ratio 2 (dNBR2), and differential Near-Infrared (dNIR). The calculated BA by Sentinel-2 was larger during the three-year investigation span (16.55, 78.50, and 67.19 km2) and of greater detail (detected small areas) than the BA extracted by Landsat-8 (16.39, 6.24, and 32.93 km2). The routine for monitoring wildfires presented in this work is based on a sequence of decision rules. This enables the detection and monitoring of burnt vegetation cover and has been originally applied to an experiment in the northeastern Peruvian Amazon. The results obtained by the two satellites imagery are compared in terms of accuracy metrics and level of detail (size of BA patches). The accuracy for Landsat-8 and Sentinel-2 in 2017, 2018, and 2019 varied from 82.7–91.4% to 94.5–98.5%, respectively.