Research

24 pages, 14977 KiB

Open AccessArticle

An Attention-Guided Deep-Learning-Based Network with Bayesian Optimization for Forest Fire Classification and Localization

by Al Mohimanul Islam, Fatiha Binta Masud, Md. Rayhan Ahmed, Anam Ibn Jafar, Jeath Rahmat Ullah, Salekul Islam, Swakkhar Shatabda and A. K. M. Muzahidul Islam

Forests 2023, 14(10), 2080; https://doi.org/10.3390/f14102080 - 18 Oct 2023

Cited by 4 | Viewed by 1306

Abstract

Wildland fires, a natural calamity, pose a significant threat to both human lives and the environment while causing extensive economic damage. As the use of Unmanned Aerial Vehicles (UAVs) with computer vision in disaster management continues to grow, there is a rising need [...] Read more.

Wildland fires, a natural calamity, pose a significant threat to both human lives and the environment while causing extensive economic damage. As the use of Unmanned Aerial Vehicles (UAVs) with computer vision in disaster management continues to grow, there is a rising need for effective wildfire classification and localization. We propose a multi-stream hybrid deep learning model with a dual-stream attention mechanism for classifying wildfires from aerial and territorial images. Our proposed method incorporates a pre-trained EfficientNetB7 and customized Attention Connected Network (ACNet). This approach demonstrates exceptional classification performance on two widely recognized benchmark datasets. Bayesian optimization is employed for the purpose of refining and optimizing the hyperparameters of the model. The proposed model attains 97.45%, 98.20%, 97.10%, and 97.12% as accuracy, precision, recall, and F1-score, respectively, on the FLAME dataset. Moreover, while evaluated on the DeepFire dataset, the model achieves accuracy, precision, recall, and F1-scores of 95.97%, 95.19%, 96.01%, and 95.54%, respectively. The proposed method achieved a TNR of 95.5% and a TPR of 99.3% on the FLAME dataset, as well as a TNR of 94.47% and a TPR of 96.82% on the DeepFire dataset. This performance surpasses numerous state-of-the-art methods. To demonstrate the interpretability of our model, we incorporated the GRAD-CAM technique, which enables us to precisely identify the fire location within the feature map. This finding illustrates the efficacy of the model in accurately categorizing wildfires, even in areas with less fire activity. Full article

(This article belongs to the Special Issue Advances in Forest Fire and Other Detection Systems)

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25 pages, 1477 KiB

Open AccessEditor’s ChoiceArticle

The Wildfire Dataset: Enhancing Deep Learning-Based Forest Fire Detection with a Diverse Evolving Open-Source Dataset Focused on Data Representativeness and a Novel Multi-Task Learning Approach

by Ismail El-Madafri, Marta Peña and Noelia Olmedo-Torre

Forests 2023, 14(9), 1697; https://doi.org/10.3390/f14091697 - 22 Aug 2023

Cited by 1 | Viewed by 4013

Abstract

This study explores the potential of RGB image data for forest fire detection using deep learning models, evaluating their advantages and limitations, and discussing potential integration within a multi-modal data context. The research introduces a uniquely comprehensive wildfire dataset, capturing a broad array [...] Read more.

This study explores the potential of RGB image data for forest fire detection using deep learning models, evaluating their advantages and limitations, and discussing potential integration within a multi-modal data context. The research introduces a uniquely comprehensive wildfire dataset, capturing a broad array of environmental conditions, forest types, geographical regions, and confounding elements, aiming to reduce high false alarm rates in fire detection systems. To ensure integrity, only public domain images were included, and a detailed description of the dataset’s attributes, URL sources, and image resolutions is provided. The study also introduces a novel multi-task learning approach, integrating multi-class confounding elements within the framework. A pioneering strategy in the field of forest fire detection, this method aims to enhance the model’s discriminatory ability and decrease false positives. When tested against the wildfire dataset, the multi-task learning approach demonstrated significantly superior performance in key metrics and lower false alarm rates compared to traditional binary classification methods. This emphasizes the effectiveness of the proposed methodology and the potential to address confounding elements. Recognizing the need for practical solutions, the study stresses the importance of future work to increase the representativeness of training and testing datasets. The evolving and publicly available wildfire dataset is anticipated to inspire innovative solutions, marking a substantial contribution to the field. Full article

(This article belongs to the Special Issue Advances in Forest Fire and Other Detection Systems)

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15 pages, 29029 KiB

Open AccessArticle

Modeling Fire Boundary Formation Based on Machine Learning in Liangshan, China

by Yiqing Xu, Yanyan Sun, Fuquan Zhang and Hanyuan Jiang

Forests 2023, 14(7), 1458; https://doi.org/10.3390/f14071458 - 16 Jul 2023

Cited by 2 | Viewed by 986

Abstract

Forest fires create burned and unburned areas on a spatial scale, with the boundary between these areas known as the fire boundary. Following an analysis of forest fire boundaries in the northern region of Yangyuan County, located in the Liangshan Yi Autonomous Prefecture [...] Read more.

Forest fires create burned and unburned areas on a spatial scale, with the boundary between these areas known as the fire boundary. Following an analysis of forest fire boundaries in the northern region of Yangyuan County, located in the Liangshan Yi Autonomous Prefecture of Sichuan Province, China, several key factors influencing the formation of fire boundaries were identified. These factors include the topography, vegetation, climate, and human activity. To explore the impact of these factors in different spaces on potential results, we varied the distances between matched sample points and built six fire environment models with different sampling distances. We constructed a matched case-control conditional light gradient boosting machine (MCC CLightGBM) to model these environment models and analyzed the factors influencing fire boundary formation and the spatial locations of the predicted boundaries. Our results show that the MCC CLightGBM model performs better when points on the selected boundaries are paired with points within the burned areas, specifically between 120 m and 480 m away from the boundaries. By using the MCC CLightGBM model to predict the probability of boundary formation under six environmental models at different distances, we found that fire boundaries are most likely to form near roads and populated areas. Boundary formation is also influenced by areas with significant topographic relief. It should be noted explicitly that this conclusion is only applicable to this study region and has not been validated for other different regions. Finally, the matched case-control conditional random forest (MCC CRF) model was constructed for comparison experiments. The MCC CLightGBM model demonstrates potential in predicting fire boundaries and fills a gap in research on fire boundary predictions in this area which can be useful in future forest fire management, allowing for a quick and intuitive assessment of where a fire has stopped. Full article

(This article belongs to the Special Issue Advances in Forest Fire and Other Detection Systems)

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18 pages, 10411 KiB

Open AccessArticle

Modeling Wildfire Initial Attack Success Rate Based on Machine Learning in Liangshan, China

by Yiqing Xu, Kaiwen Zhou and Fuquan Zhang

Forests 2023, 14(4), 740; https://doi.org/10.3390/f14040740 - 04 Apr 2023

Cited by 1 | Viewed by 1189

Abstract

The initial attack is a critical phase in firefighting efforts, where the first batch of resources are deployed to prevent the spread of the fire. This study aimed to analyze and understand the factors that impact the success of the initial attack, and [...] Read more.

The initial attack is a critical phase in firefighting efforts, where the first batch of resources are deployed to prevent the spread of the fire. This study aimed to analyze and understand the factors that impact the success of the initial attack, and used three machine learning models—logistic regression, XGBoost, and artificial neural network—to simulate the success rate of the initial attack in a specific region. The performance of each machine learning model was evaluated based on accuracy, AUC (Area Under the Curve), and F1 Score, with the results showing that the XGBoost model performed the best. In addition, the study also considered the impact of weather conditions on the initial attack success rate by dividing the scenario into normal weather and extreme weather conditions. This information can be useful for forest fire managers as they plan resource allocation, with the goal of improving the success rate of the initial attack in the area. Full article

(This article belongs to the Special Issue Advances in Forest Fire and Other Detection Systems)

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17 pages, 16317 KiB

Open AccessArticle

Design and Experimental Investigation of a Thermoelectric Conversion Device with Power Management for Forest Fire Monitoring

by Latai Ga, Yuqi Zhang, Daochun Xu and Wenbin Li

Forests 2023, 14(3), 451; https://doi.org/10.3390/f14030451 - 22 Feb 2023

Cited by 2 | Viewed by 1348

Abstract

Forest fires have long been a significant global problem. How to reliably accomplish the early warning and real-time monitoring of forest fires has become a pressing issue in order to limit the damage caused by forest fires. A novel technological approach for forest [...] Read more.

Forest fires have long been a significant global problem. How to reliably accomplish the early warning and real-time monitoring of forest fires has become a pressing issue in order to limit the damage caused by forest fires. A novel technological approach for forest fire monitoring has been made possible by the quick development of wireless sensor network (WSN) technology. Currently, batteries are the primary source of power for WSNs used in forests, but frequent battery replacement will compromise the network for monitoring. As a result, the power supply is the key limit to its application in forest areas. This paper puts forward the thermoelectric conversion based on the Seebeck effect. Its notable feature is to convert heat energy into electric energy through the temperature difference between shallow soil and air. In the process of testing the device, the maximum voltage was 803.36 mV. At the same time, a power management system (PMS) for a thermoelectric conversion device was designed. The main feature of this system is that there is no need for an external control module. In the laboratory test, the minimum input power of this system was 200 mV. When the load resistance was 8 KΩ, the output power was 0.55 mW, and the maximum efficiency could reach 65.38% when the input was 500 mV, which fulfills the requirements of low cost and high reliability, providing a feasible solution for solving the energy limitation problem of WSNs in a forest area. Full article

(This article belongs to the Special Issue Advances in Forest Fire and Other Detection Systems)

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16 pages, 6311 KiB

Open AccessArticle

Multi-Scale Forest Fire Recognition Model Based on Improved YOLOv5s

by Gong Chen, Hang Zhou, Zhongyuan Li, Yucheng Gao, Di Bai, Renjie Xu and Haifeng Lin

Forests 2023, 14(2), 315; https://doi.org/10.3390/f14020315 - 06 Feb 2023

Cited by 14 | Viewed by 2354

Abstract

The frequent occurrence of forest fires causes irreparable damage to the environment and the economy. Therefore, the accurate detection of forest fires is particularly important. Due to the various shapes and textures of flames and the large variation in the target scales, traditional [...] Read more.

The frequent occurrence of forest fires causes irreparable damage to the environment and the economy. Therefore, the accurate detection of forest fires is particularly important. Due to the various shapes and textures of flames and the large variation in the target scales, traditional forest fire detection methods have high false alarm rates and poor adaptability, which results in severe limitations. To address the problem of the low detection accuracy caused by the multi-scale characteristics and changeable morphology of forest fires, this paper proposes YOLOv5s-CCAB, an improved multi-scale forest fire detection model based on YOLOv5s. Firstly, coordinate attention (CA) was added to YOLOv5s in order to adjust the network to focus more on the forest fire features. Secondly, Contextual Transformer (CoT) was introduced into the backbone network, and a CoT3 module was built to reduce the number of parameters while improving the detection of forest fires and the ability to capture global dependencies in forest fire images. Then, changes were made to Complete-Intersection-Over-Union (CIoU) Loss function to improve the network’s detection accuracy for forest fire targets. Finally, the Bi-directional Feature Pyramid Network (BiFPN) was constructed at the neck to provide the model with a more effective fusion capability for the extracted forest fire features. The experimental results based on the constructed multi-scale forest fire dataset show that YOLOv5s-CCAB increases AP@0.5 by 6.2% to 87.7%, and the FPS reaches 36.6. This indicates that YOLOv5s-CCAB has a high detection accuracy and speed. The method can provide a reference for the real-time, accurate detection of multi-scale forest fires. Full article

(This article belongs to the Special Issue Advances in Forest Fire and Other Detection Systems)

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17 pages, 16803 KiB

Open AccessEditor’s ChoiceArticle

A Small-Target Forest Fire Smoke Detection Model Based on Deformable Transformer for End-to-End Object Detection

by Jingwen Huang, Jiashun Zhou, Huizhou Yang, Yunfei Liu and Han Liu

Forests 2023, 14(1), 162; https://doi.org/10.3390/f14010162 - 16 Jan 2023

Cited by 13 | Viewed by 3182

Abstract

Forest fires have continually endangered personal safety and social property. To reduce the occurrences of forest fires, it is essential to detect forest fire smoke accurately and quickly. Traditional forest fire smoke detection based on convolutional neural networks (CNNs) needs many hand-designed components [...] Read more.

Forest fires have continually endangered personal safety and social property. To reduce the occurrences of forest fires, it is essential to detect forest fire smoke accurately and quickly. Traditional forest fire smoke detection based on convolutional neural networks (CNNs) needs many hand-designed components and shows poor ability to detect small and inconspicuous smoke in complex forest scenes. Therefore, we propose an improved early forest fire smoke detection model based on deformable transformer for end-to-end object detection (deformable DETR). We use deformable DETR as a baseline containing the best sparse spatial sampling for smoke with deformable convolution and relation modeling capability of the transformer. We integrate a Multi-scale Context Contrasted Local Feature module (MCCL) and a Dense Pyramid Pooling module (DPPM) into the feature extraction module for perceiving features of small or inconspicuous smoke. To improve detection accuracy and reduce false and missed detections, we propose an iterative bounding box combination method to generate precise bounding boxes which can cover the entire smoke object. In addition, we evaluate the proposed approach using a quantitative and qualitative self-made forest fire smoke dataset, which includes forest fire smoke images of different scales. Extensive experiments show that our improved model’s forest fire smoke detection accuracy is significantly higher than that of the mainstream models. Compared with deformable DETR, our model shows better performance with improvement of mAP (mean average precision) by 4.2%, AP_S (AP for small objects) by 5.1%, and other metrics by 2% to 3%. Our model is adequate for early forest fire smoke detection with high detection accuracy of different-scale smoke objects. Full article

(This article belongs to the Special Issue Advances in Forest Fire and Other Detection Systems)

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12 pages, 4704 KiB

Open AccessArticle

An Event-Response Tree-Based Resource Scheduling Method for Wildfire Fighting

by Kaiwen Zhou and Fuquan Zhang

Forests 2023, 14(1), 102; https://doi.org/10.3390/f14010102 - 05 Jan 2023

Cited by 2 | Viewed by 1494

Abstract

Dispatching firefighting resources effectively plays a vital role in wildfire management. To control the fire in a timely manner, resources should be dispatched in an effective and reasonable way. Moreover, the relationship between various resource-dispatching processes should be intuitive for firefighters to make [...] Read more.

Dispatching firefighting resources effectively plays a vital role in wildfire management. To control the fire in a timely manner, resources should be dispatched in an effective and reasonable way. Moreover, the relationship between various resource-dispatching processes should be intuitive for firefighters to make decisions. In this paper, we propose a novel event-response tree-based model to dispatch different kinds of firefighting resources based on the fire suppression index (SI), which evaluates the effect of fire suppression by considering the time, cost, and effect of dispatching resources. To validate the proposed method, we compared it with the widely used mixed-integer programming (MIP) by using the historical fire data of Nanjing Laoshan National Forest Park. The results showed that the E-R tree-based resource scheduling can effectively schedule resources as well as the MIP model. Moreover, the relationship between various resource-dispatching processes in the proposed model is clear and intuitive for firefighters to make decisions. Full article

(This article belongs to the Special Issue Advances in Forest Fire and Other Detection Systems)

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17 pages, 15858 KiB

Open AccessArticle

Modeling Forest Fire Spread Using Machine Learning-Based Cellular Automata in a GIS Environment

by Yiqing Xu, Dianjing Li, Hao Ma, Rong Lin and Fuquan Zhang

Forests 2022, 13(12), 1974; https://doi.org/10.3390/f13121974 - 22 Nov 2022

Cited by 6 | Viewed by 5109

Abstract

The quantitative simulation of forest fire spread is of great significance for designing rapid risk management approaches and implementing effective fire fighting strategies. A cellular automaton (CA) is well suited to the dynamic simulation of the spatiotemporal evolution of complex systems, and it [...] Read more.

The quantitative simulation of forest fire spread is of great significance for designing rapid risk management approaches and implementing effective fire fighting strategies. A cellular automaton (CA) is well suited to the dynamic simulation of the spatiotemporal evolution of complex systems, and it is therefore used to model the complex process of forest fire spread. However, the process of forest fire spread is linked with a variety of mutually influencing factors, which are too complex to analyze using conventional approaches. Here, we propose a new method for modeling fire spread, namely LSSVM-CA, in which least squares support vector machines (LSSVM) is combined with a three-dimensional forest fire CA framework. In this approach, the effects of adjacent wind on the law of fire spread are considered and analyzed. The LSSVM is utilized to derive the complex state transformation rules for fire spread by training with a dataset based on actual local data. To validate the proposed model, the forest fire spread area simulated by LSSVM-CA and the actual extracted forest fire spread area were subjected to cross-comparison. The results show that LSSVM-CA performs well in simulating the spread of forest fire and determining the probability of forest fire. Full article

(This article belongs to the Special Issue Advances in Forest Fire and Other Detection Systems)

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15 pages, 5114 KiB

Open AccessArticle

A UAV-Based Forest Fire Patrol Path Planning Strategy

by Yiqing Xu, Jiaming Li and Fuquan Zhang

Forests 2022, 13(11), 1952; https://doi.org/10.3390/f13111952 - 18 Nov 2022

Cited by 5 | Viewed by 1820

Abstract

The application of UAVs in forest fire monitoring has attracted increasing attention. When a UAV carries out forest fire monitoring cruises in a large area of the forest, one of the main problems is planning an appropriate cruise path so that the UAV [...] Read more.

The application of UAVs in forest fire monitoring has attracted increasing attention. When a UAV carries out forest fire monitoring cruises in a large area of the forest, one of the main problems is planning an appropriate cruise path so that the UAV can start from the starting point, cruise the entire area with little detour, and return to the initial position within its maximum cruise distance. In this paper, we propose a flight path planning method for UAV forest fire monitoring based on a forest fire risk map. According to the forest fire risk level, the method uses the ring self-organizing mapping (RSOM) algorithm to plan a corresponding flight path. In addition, since it is difficult for a single UAV to complete a single full-path cruise task in a large area within its maximum cruise time, a multi-UAV cruise scheme is proposed. First, the Gaussian mixture clustering algorithm is used to cluster the study area and divide it into several subareas. In combination with the RSOM algorithm, the corresponding path is planned for each UAV. A simulation with an actual dataset showed that the proposed method solves the problem of UAV patrol path planning for forest fire monitoring and can complete the task within a reasonable time. Full article

(This article belongs to the Special Issue Advances in Forest Fire and Other Detection Systems)

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30 pages, 14996 KiB

Open AccessArticle

Study on the Moisture Content Diagnosis Method of Living Trees Based on WASN and CTWGAN-GP-L

by Yin Wu, Nengfei Yang and Yanyi Liu

Forests 2022, 13(11), 1879; https://doi.org/10.3390/f13111879 - 09 Nov 2022

Viewed by 1464

Abstract

Water is an important component of tree cells, so the study of moisture content diagnostic methods for live standing trees not only provides help for production management in agriculture, forestry and animal husbandry but also provides technical guidance for plant physiology. With the [...] Read more.

Water is an important component of tree cells, so the study of moisture content diagnostic methods for live standing trees not only provides help for production management in agriculture, forestry and animal husbandry but also provides technical guidance for plant physiology. With the booming development of deep learning in recent years, the generative adversarial network (GAN) provides a method to solve the problem of insufficient manual sample collection and tedious and time-consuming labeling. In this paper, we design and implement a wireless acoustic sensor network (WASN)-based wood moisture content diagnosis system with the main objective of nondestructively detecting the water content of live tree trunks. Firstly, the WASN nodes sample the acoustic emission signals of tree trunk bark at high speed then calculate the characteristic parameters and transmit them wirelessly to the gateway; secondly, the Conditional Tabular Wasserstein GAN-Gradient Penalty-L (CTWGAN-GP-L) algorithm is used to expand the 900 sets of offline samples to 1800 sets of feature parameters to improve the recognition accuracy of the model, and the quality of the generated data is also evaluated using various evaluation metrics. Moreover, the optimal combination of features is selected from the expanded mixed data set by the random forest algorithm, and the moisture content recognition model is established by the LightGBM algorithm (GSCV-LGB) optimized by the grid search and cross-validation algorithm; finally, real-time long-term online monitoring and diagnosis can be performed. The system was tested on six tree species: Magnolia (Magnoliaceae), Zelkova (Ulmaceae), Triangle Maple (Aceraceae), Zhejiang Nan (Lauraceae), Ginkgo (Ginkgoaceae), and Yunnan Pine (Pinaceae). The results showed that the diagnostic accuracy was at least 97.4%, and the designed WASN model is fully capable of long-term deployment for observing tree transpiration. Full article

(This article belongs to the Special Issue Advances in Forest Fire and Other Detection Systems)

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16 pages, 2157 KiB

Open AccessArticle

A Coverage Optimization Algorithm for the Wireless Sensor Network with Random Deployment by Using an Improved Flower Pollination Algorithm

by Wanguo Jiao, Rui Tang and Yun Xu

Forests 2022, 13(10), 1690; https://doi.org/10.3390/f13101690 - 14 Oct 2022

Cited by 6 | Viewed by 1251

Abstract

Due to complex terrain and harsh environments, sensor nodes are often randomly scattered in the monitoring area, which may cause coverage holes or network disconnection. Current works move some sensor nodes to certain places to address this problem. However, these works cannot guarantee [...] Read more.

Due to complex terrain and harsh environments, sensor nodes are often randomly scattered in the monitoring area, which may cause coverage holes or network disconnection. Current works move some sensor nodes to certain places to address this problem. However, these works cannot guarantee the coverage and connectivity simultaneously and have larger moving cost in energy. In this paper, we propose a coverage optimization strategy based on the flower pollination algorithm (FPA). First, to solve the shortcomings of the classical FPA in convergence and accuracy, an improved FPA is proposed. Then, the network deployment optimization problem is modeled as a multi-objective optimization problem that guarantees the coverage of target points and the connectivity of the network while minimizing the energy consumption of sensor nodes’ moving. The sensor nodes are selected and moved to the proper position by utilizing the improved FPA to minimize the energy consumed by the sensors’ motion and guarantee the coverage and connectivity. Test results show that the improved FPA has good convergence speed and accuracy compared with other evolutionary algorithms. Simulation results demonstrate that the proposed algorithm can guarantee network connectivity and satisfy the coverage requirement while minimizing the energy consumption of the sensor movement. Consequently, more energy of the sensor node can be used to collect and transmit sensed data. These results indicate that our algorithm can prolong network lifetime and improve monitoring quality in fields such as forest monitoring. Full article

(This article belongs to the Special Issue Advances in Forest Fire and Other Detection Systems)

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15 pages, 6091 KiB

Open AccessEditor’s ChoiceArticle

A Small Target Forest Fire Detection Model Based on YOLOv5 Improvement

by Zhenyang Xue, Haifeng Lin and Fang Wang

Forests 2022, 13(8), 1332; https://doi.org/10.3390/f13081332 - 20 Aug 2022

Cited by 54 | Viewed by 6173

Abstract

Forest fires are highly unpredictable and extremely destructive. Traditional methods of manual inspection, sensor-based detection, satellite remote sensing and computer vision detection all have their obvious limitations. Deep learning techniques can learn and adaptively extract features of forest fires. However, the small size [...] Read more.

Forest fires are highly unpredictable and extremely destructive. Traditional methods of manual inspection, sensor-based detection, satellite remote sensing and computer vision detection all have their obvious limitations. Deep learning techniques can learn and adaptively extract features of forest fires. However, the small size of the forest fire target in the long-range-captured forest fire images causes the model to fail to learn effective information. To solve this problem, we propose an improved forest fire small-target detection model based on YOLOv5. This model requires cameras as sensors for detecting forest fires in practical applications. First, we improved the Backbone layer of YOLOv5 and adjust the original Spatial Pyramid Pooling-Fast (SPPF) module of YOLOv5 to the Spatial Pyramid Pooling-Fast-Plus (SPPFP) module for a better focus on the global information of small forest fire targets. Then, we added the Convolutional Block Attention Module (CBAM) attention module to improve the identifiability of small forest fire targets. Second, the Neck layer of YOLOv5 was improved by adding a very-small-target detection layer and adjusting the Path Aggregation Network (PANet) to the Bi-directional Feature Pyramid Network (BiFPN). Finally, since the initial small-target forest fire dataset is a small sample dataset, a migration learning strategy was used for training. Experimental results on an initial small-target forest fire dataset produced by us show that the improved structure in this paper improves mAP@0.5 by 10.1%. This demonstrates that the performance of our proposed model has been effectively improved and has some application prospects. Full article

(This article belongs to the Special Issue Advances in Forest Fire and Other Detection Systems)

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25 pages, 8502 KiB

Open AccessArticle

Simulating Wind Disturbances over Rubber Trees with Phenotypic Trait Analysis Using Terrestrial Laser Scanning

by Bo Zhang, Xiangjun Wang, Xingyue Yuan, Feng An, Huaiqing Zhang, Lijun Zhou, Jiangong Shi and Ting Yun

Forests 2022, 13(8), 1298; https://doi.org/10.3390/f13081298 - 15 Aug 2022

Cited by 10 | Viewed by 1738

Abstract

Hurricanes often devastate trees throughout coastal China; accordingly, developing a method to quantitatively evaluate the changes in tree phenotypic characteristics under continuous strong winds is of great significance for guiding forest cultivation practices and mitigating wind hazards. For this research, we built a [...] Read more.

Hurricanes often devastate trees throughout coastal China; accordingly, developing a method to quantitatively evaluate the changes in tree phenotypic characteristics under continuous strong winds is of great significance for guiding forest cultivation practices and mitigating wind hazards. For this research, we built a lifting steel truss carrying a large forced draft fan near a rubber plantation on Hainan Island, and we aligned three selected small rubber trees in a row in front of the fan (with separation distances from the forced draft fan outlet of approximately 1.3, 3.3, and 5.3 m) to explore the susceptibility of rubber trees to the mechanical loading of hurricane-level winds. By adjusting the power of the forced draft fan, four wind speeds were emitted: 0 m/s, 10.5 m/s, 13.5 m/s, and 17.5 m/s. Meanwhile, point clouds of the three rubber trees under different continuous wind speeds were acquired using two terrestrial laser scanners. Computer algorithms were applied to derive the key parameters of the three rubber trees, namely, the zenith and azimuth angles of each leaf, effective leaf area index (LAI), windward area of each tree, volume of the tree canopy, and trunk tilt angle, from these point clouds under all four wind speeds. The results show that by increasing the wind speed from 0 m/s to 17.5 m/s, the leaf zenith angles of the three rubber trees were unimodally distributed with the peak concentrated at 0°, while the leaf azimuth angles were bimodally distributed with the peaks concentrated at 0° and 360°. The effective LAI values of the three trees increased from 2.97, 4.77, and 3.63 (no wind) to 3.84, 5.9, and 4.29 (wind speed of 17.5 m/s), respectively, due to a decrease in the vertical crown projection area caused by the compression of the tree canopy. We also found that the effective LAI, windward area, and canopy volume of the third rubber tree (the tree farthest from the forced draft fan) varied less than those of the other two trees, reflecting the attenuation of the wind speed by the crowns of the two trees closer to the fan. The experimental results also indicate that the joint use of light detection and ranging (LiDAR) data with computer graphics algorithms to analyse the dynamic changes in tree phenotypic characteristics during the passage of a hurricane is promising, enabling the development of a novel strategy for mitigating wind hazards. The proposed method with the designed device capable of producing an adjustable wind speed also has the potential to study the impacts of wind damage under various forest conditions by further modifying the tree spacing and tree species. Full article

(This article belongs to the Special Issue Advances in Forest Fire and Other Detection Systems)

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