Wildland fires are one of the most dangerous natural risks, causing significant economic damage and loss of lives worldwide. Every year, millions of hectares are lost, and experts warn that the frequency and severity of wildfires will increase in the coming years due to climate change. To mitigate these hazards, numerous deep learning models were developed to detect and map wildland fires, estimate their severity, and predict their spread. In this paper, we provide a comprehensive review of recent deep learning techniques for detecting, mapping, and predicting wildland fires using satellite remote sensing data. We begin by introducing remote sensing satellite systems and their use in wildfire monitoring. Next, we review the deep learning methods employed for these tasks, including fire detection and mapping, severity estimation, and spread prediction. We further present the popular datasets used in these studies. Finally, we address the challenges faced by these models to accurately predict wildfire behaviors, and suggest future directions for developing reliable and robust wildland fire models.

/pdf/deep-learning-approaches-for-wildland-fires-using-satellite-17h44i9y.pdf

Deep Learning Approaches for Wildland Fires Using Satellite Remote Sensing Data: Detection, Mapping, and Prediction

Prediction of forest fire occurrence in China under climate change scenarios

The alterations in vegetation cover in the Three North Protection Forest Project area influence its ecological and environmental management. It helps to study soil erosion, environmental change, and ecosystem protection to provide basic data support. Based on the Google Earth Engine cloud platform, this paper selects MODIS 3Q1 data from 2000–2020 and uses the image element dichotomous model to estimate the fractional vegetation cover (FVC) of the Three North Protection Forest Project area, evaluates the stability and temporal and spatial variation of FVC, investigates the coupling relationship between the FVC and temperature and rainfall through evaluation indexes such as the slope of inter-annual variation and partial correlation coefficient, and then analyzes the influence of land use changes on the FVC. The results show that the fractional vegetation cover of the Three North Protection Forest Project area as a whole has increased significantly over the past 20 years, the medium and high vegetation coverage areas have reached 36.4%, the high vegetation FVC has increased from 20.66% in 2000 to 21.59% in 2020, and the vegetation activity is increasing. The vegetation cover is significantly affected by the topographic effect, with the vegetation cover improving relatively well at slopes between 5–13° and elevations ranging from 2000–2500 m. The overall positive correlation between vegetation and temperature and vegetation and precipitation in the study area was 54.08% and 70.04%, respectively, and precipitation was the key factor influencing vegetation growth in the research region. Human activities have a stronger effect on vegetation construction than destruction, and this study contributes to the evaluation of the benefits of the Three North Protection Forest Project and the understanding of human influence on environmental change.

/pdf/spatial-and-temporal-changes-in-vegetation-cover-in-the-2wl3rnio.pdf

Spatial and Temporal Changes in Vegetation Cover in the Three North Protection Forest Project Area Supported by GEE Cloud Platform

The main scope of the study is to evaluate the prognostic accuracy of a one-dimensional convolutional neural network model (1D-CNN), in flood susceptibility assessment, in a selected test site on the island of Euboea, Greece. Logistic regression (LR), Naïve Bayes (NB), gradient boosting (GB), and a deep learning neural network (DLNN) model are the benchmark models used to compare their performance with that of a 1D-CNN model. Remote sensing (RS) techniques are used to collect the necessary flood related data, whereas thirteen flash-flood-related variables were used as predictive variables, such as elevation, slope, plan curvature, profile curvature, topographic wetness index, lithology, silt content, sand content, clay content, distance to faults, and distance to river network. The Weight of Evidence method was applied to calculate the correlation among the flood-related variables and to assign a weight value to each variable class. Regression analysis and multi-collinearity analysis were used to assess collinearity among the flood-related variables, whereas the Shapley Additive explanations method was used to rank the features by importance. The evaluation process involved estimating the predictive ability of all models via classification accuracy, sensitivity, specificity, and area under the success and predictive rate curves (AUC). The outcomes of the analysis confirmed that the 1D-CNN provided a higher accuracy (0.924), followed by LR (0.904) and DLNN (0.899). Overall, 1D-CNNs can be useful tools for analyzing flood susceptibility using remote sensing data, with high accuracy predictions.

Applying a 1D Convolutional Neural Network in Flood Susceptibility Assessments—The Case of the Island of Euboea, Greece

Assessing and predicting forest fires has long been an arduous task. Nowadays, the rapid advancement of artificial intelligence and machine learning technologies have provided a novel solution to forest fire occurrence assessment and prediction. In this research, we developed a novel hybrid machine-learning-technique algorithm to improve forest fire prediction based on random forest (RF), gradient-boosting decision tree (GBDT), support vector machine (SVM), and other machine learning models. The dataset we employed was satellite fire point data from 2010 to 2018 from the Chinese Department of Fire Prevention. The efficacy and performance of our methods were examined by validating the model fit and predictive capability. The results showed that the ensemble model LR (logistic regression)-RF-SVM-GBDT outperformed the single RFSVMGBDT model and the LR-RF-GBDT integrated framework, displaying higher accuracy and greater robustness. We believe that our newly developed hybrid machine-learning algorithm has the potential to improve the accuracy of predicting forest fire occurrences, thus enabling more efficient firefighting efforts and saving time and resources.

https://www.mdpi.com/1999-4907/14/4/704/pdf?version=1680096859

An Ensemble Model for Forest Fire Occurrence Mapping in China

Assessment of China’s forest fire occurrence with deep learning, geographic information and multisource data

Aiming at the application of the geographical conditions monitoring data in the results of the third national land resource survey, this paper proposes a comparison and fusion model of the third national land resource survey and the geographical conditions monitoring based on regression analysis. This study analyses the reasons for the differences between the two groups of data, and formulates the fusion rules, and takes Jieshou city as the study area to carry out experiments. We randomly selected sample areas for fusion experiments to obtain statistical data, consider the relationship between the fused cultivated land area (Y) and the cultivated land overlapping area ( x1 ), and the cultivated land difference area ( x2 ), and established a regression equation. The model can solve the problem of the difference between the data of the third national land resource survey and the geographical conditions monitoring, and realize the effective integration of cultivated land types. Based on the regression analysis method, the regression equation of cultivated land area after the integration of Jieshou city is obtained. The model proposed in this paper can not only effectively connect the third national land resource survey data with the geographical conditions monitoring data, but also scientifically and accurately predict the area of cultivated land in Jieshou city. The research results can provide technical support for the distribution of cultivated land quality monitoring points, land environmental protection, land management, etc.

Data fusion of the third national land resource survey and the geographical conditions monitoring based on regression analysis

Pine wilt disease (PWD) is a great danger, due to two aspects: no effective cure and fast dissemination. One key to the prevention and treatment of pine wilt disease is the early detection of infected wood. Subsequently, appropriate treatment can be applied to limit the further spread of pine wilt disease. In this work, a UAV (Unmanned Aerial Vehicle) with a RGB (Red, Green, Blue) camera was employed as it provided high-quality images of pine trees in a timely manner. Seven flights were performed above seven sample plots in northwestern Beijing, China. Then, raw images captured by the UAV were further pre-processed, classified, annotated, and formed the research datasets. In the formal analysis, improved YOLOv5 frameworks that integrated four attention mechanism modules, i.e., SE (Squeeze-and-Excitation), CA (Coordinate Attention), ECA (Efficient Channel Attention), and CBAM (Convolutional Block Attention Module), were developed. Each of them had been shown to improve the overall identification rate of infected trees at different ranges. The CA module was found to have the best performance, with an accuracy of 92.6%, a 3.3% improvement over the original YOLOv5s model. Meanwhile, the recognition speed was improved by 20 frames/second compared to the original YOLOv5s model. The comprehensive performance could well support the need for rapid detection of pine wilt disease. The overall framework proposed by this work shows a fast response to the spread of PWD. In addition, it requires a small amount of financial resources, which determines the duplication of this method for forestry operators.

/pdf/identification-of-pine-wilt-disease-infected-wood-using-uav-2rx56o7w.pdf

Identification of Pine Wilt Disease Infected Wood Using UAV RGB Imagery and Improved YOLOv5 Models Integrated with Attention Mechanisms

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Assessment of China’s forest fire occurrence with deep learning, geographic information and multisource data

Data fusion of the third national land resource survey and the geographical conditions monitoring based on regression analysis

Identification of Pine Wilt Disease Infected Wood Using UAV RGB Imagery and Improved YOLOv5 Models Integrated with Attention Mechanisms