scholarly journals Application of SAR Interferometry Using ALOS-2 PALSAR-2 Data as Precise Method to Identify Degraded Peatland Areas Related to Forest Fire

Geosciences ◽  
2019 ◽  
Vol 9 (11) ◽  
pp. 484
Author(s):  
Joko Widodo ◽  
Albertus Sulaiman ◽  
Awaluddin Awaluddin ◽  
Agung Riyadi ◽  
Mohammad Nasucha ◽  
...  
Keyword(s):  
Monitoring System ◽  
Forest Fire ◽  
Forest Fires ◽  
Research Result ◽  
Sar Interferometry ◽  
Depth Data ◽  
Combination Methods ◽  
X Band ◽  
Precision Level ◽  
Land Deformation

Deforestation in peatland areas such as Kalimantan, Indonesia has been going on for decades. The deforestation has indirectly increased peatlands to become degraded and flammable. The Synthetic Aperture Radar (SAR) interferometry approach for identification of degraded peatlands can be performed using ALOS-2 PALSAR-2 data by converting land deformation data generated from SAR interferometry analysis into water table (WT) depth data using Wosten models. Peatlands with WT depth conditions of more than 40 cm are classified as degraded peatlands which are flammable. By using fire data from previous studies, this research confirms that identification of degraded peatlands using SAR interferometry approach by ALOS-2 PALSAR-2 is more reliable with high precision related to forest fires, with a precision level of 88% compared to 5% precision level using the WT depth monitoring system that has been installed in Central Kalimantan. The highest wavelength of ALOS-2 PALSAR-2 (L-Band) data can resolve the limitation due to temporal and volumetric decorrelation, compared to C-Band and X-Band satellite data. The combination methods of SAR interferometry approach and the real-time WT depth monitoring system to identify degraded peatlands can be more efficient, faster, and accurate. The advantage of this research result shows that SAR interferometry analysis can reach blank spot areas that are not covered by the observation station of WT depth monitoring system. It also gives a benefit as a guide to select precise locations of observation stations related to degraded peatland and forest fire.

scholarly journals Detection and Monitoring a Forest Fire using GSM Module

2021 ◽  
Vol 9 (VII) ◽  
pp. 1914-1922
Author(s):  
Patil N S
Keyword(s):  
Internet Of Things ◽  
Monitoring System ◽  
Forest Fire ◽  
Forest Fires ◽  
Environmental Variables ◽  
Residential Area ◽  
Sensor Data ◽  
Simulation Program ◽  
Fire Detector ◽  
The Given

In the present arena, wildlife and forest departments are facing the problem of movement of animals from forest area to residential area. The number of trees has reduced drastically from the forest that creates an unhealthy environment for animals to survive in the forest. It has been found in a survey that 80% losses are caused due to fire. This could have been avoided if the fire was detected in the early stages. This project proposes a system for tracking and alarming for the protection of trees against forest fires. Nowadays IOT (Internet of Things) devices and sensors allow the monitoring of different environmental variables, such as temperature, humidity, moisture etc. Arduino platform based IOT enabled fire detector and monitoring system is the solution to this problem. In this project we have built fire detector using ESP32 which is interfaced with a fire sensor and a buzzer. In order to implement this project, we will be using GSM which is used to provide the final SMS to the user through the given number in the simulation program. The sensor data is displayed on LCD. Whenever a fire occurs, the system automatically senses and alerts the user by sending an alert to an app installed on user’s android mobile.

Automatic Monitoring System for Forest Fire Based on Wireless Sensor Network

2013 ◽  
Vol 694-697 ◽  
pp. 1211-1214 ◽  
Author(s):  
Bi Hua Zhu ◽  
Da Qing Zhu
Keyword(s):  
Wireless Sensor Network ◽  
Data Acquisition ◽  
Sensor Network ◽  
Monitoring System ◽  
Forest Fire ◽  
Forest Fires ◽  
Living Environment ◽  
Wireless Sensor ◽  
Sensor Technology ◽  
Long Distance

The forest is considered as a precious and indispensable nature resource, but forest fire which can destroy forest resource safety and threaten human-living environment is considered as one of the severest disasters. How to monitor and collect information of forest fire at any time, it is a difficult problem for Forest Fire Prevention Departments to urgently solve. With the development of sensor technology, MEMS and wireless communications, wireless sensor network (WSN) has wide application in all kinds of fields. In order to prevent forest fire occurrence, this paper designs a monitoring system for forest fires based on wireless sensor network and GPRS network. The system gives the hardware design of wireless sensor nodes and software implementations, and chooses CC2530 to achieve the process of data acquisition and transmission, then sends the data through GPRS module to the remote monitoring center. By means of WSN and GPRS network, the system accomplishes data acquisition and long distance transmission.

scholarly journals Optimum Sensors Allocation for a Forest Fires Monitoring System

Forests ◽  
2021 ◽  
Vol 12 (4) ◽  
pp. 453
Author(s):  
Beatriz Flamia Azevedo ◽  
Thadeu Brito ◽  
José Lima ◽  
Ana I. Pereira
Keyword(s):  
Monitoring System ◽  
Forest Fire ◽  
Forest Fires ◽  
Fire Hazard ◽  
Wireless Sensor ◽  
Sensor Coverage ◽  
Forest Density ◽  
Optimisation Model ◽  
Forest Fire Hazard ◽  
Fire Ignition

Every year forest fires destroy millions of hectares of land worldwide. Detecting forest fire ignition in the early stages is fundamental to avoid forest fires catastrophes. In this approach, Wireless Sensor Network is explored to develop a monitoring system to send alert to authorities when a fire ignition is detected. The study of sensors allocation is essential in this type of monitoring system since its performance is directly related to the position of the sensors, which also defines the coverage region. In this paper, a mathematical model is proposed to solve the sensor allocation problem. This model considers the sensor coverage limitation, the distance, and the forest density interference in the sensor reach. A Genetic Algorithm is implemented to solve the optimisation model and minimise the forest fire hazard. The results obtained are promising since the algorithm could allocate the sensor avoiding overlaps and minimising the total fire hazard value for both regions considered.

scholarly journals Forest Farm Fire Drone Monitoring System Based on Deep Learning and Unmanned Aerial Vehicle Imagery

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Shaoxiong Zheng ◽  
Weixing Wang ◽  
Zeqian Liu ◽  
Zepeng Wu
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Monitoring System ◽  
Forest Fire ◽  
Forest Fires ◽  
Operating Cost ◽  
Operating Efficiency ◽  
Rescue Operation ◽  
Fire Monitoring ◽  
Forest Farm

Forest fires represent one of the main problems threatening forest sustainability. Therefore, an early prevention system of forest fire is urgently needed. To address the problem of forest farm fire monitoring, this paper proposes a forest fire monitoring system based on drones and deep learning. The proposed system aims to solve the shortcomings of traditional forest fire monitoring systems, such as blind spots, poor real-time performance, expensive operational costs, and large resource consumption. The image processing techniques are used to determine whether the frame returned by a drone contains fire. This process is accomplished in real time, and the resultant information is used to decide whether a rescue operation is needed. The proposed method has simple operations, high operating efficiency, and low operating cost. The experimental results indicate that the relative accuracy of the proposed algorithm is 81.97%. In addition, the proposed technique provides a digital ability to monitor forest fires in real time effectively. Thus, it can assist in avoiding fire-related disasters and can significantly reduce the labor and other costs of forest fire disaster prevention and suppression.

Network-centric approach to prevention and elimination of forest fires based on integration information resources and systems

2020 ◽  
Vol 89 ◽  
pp. 98-109
Author(s):  
S. A. Barkovsky ◽  
◽  
A. V. Spiridonov ◽  
A. I. Ovsyanik ◽  
V. L. Semikov ◽  
...  
Keyword(s):  
Monitoring System ◽  
Forest Fire ◽  
Forest Fires ◽  
Information Resources ◽  
Fire Monitoring ◽  
Digital Maps ◽  
Emergency Situations ◽  
Operational Information ◽  
Current Situation Analysis ◽  
Forest Fire Prevention

Introduction. The article analyzes the use of modern information resources and systems in extinguishing wildfires. The approach uses a network-centric system for modern resources and systems integration. Goals and objectives. The purpose of the study is to identify and improve methods and tools for assessing forest fire prevention based on the integration of information resources and systems. Methods. Information-based methods and systems that allow simultaneous analysis of multi-dimensional data using digital maps, simplify the prediction and assessment of the complex effects of forest fires are considered, make it possible to quickly identify the anomalies and take the necessary measures to eliminate them. Results and discussion. Based on the results of the analysis, a generalized structure of the forest fire monitoring system was developed using a network-centric approach. Conclusion. A network-centric monitoring system combines all information resources and systems at all levels and directions. Early monitoring of the current situation, analysis of current operational information about identified thermal points, and modeling of possible scenarios for the development of the situation allows the most effective approach to solving problems of protecting the population and territory from the consequences of forest fires. Combining the advantages of individual modern technologies into a single distributed network-centric system makes it possible to effectively implement the elimination of the consequences of all types of emergencies. Key words: network-centric system, forest fires, forecasting and prevention of emergency situations, information resources and systems.

scholarly journals Improvement of forest fire monitoring system by expanding information and technological possibilities of modern quadcopters

2021 ◽  
pp. 33-46
Author(s):  
Olena Husak ◽  
Volodymyr Husak
Keyword(s):  
Information Technology ◽  
Early Detection ◽  
Unmanned Aerial Vehicles ◽  
Monitoring System ◽  
Forest Fire ◽  
Fire Protection ◽  
Forest Fires ◽  
High Tech ◽  
Fire Monitoring ◽  
Aerial Vehicles

The article proposes a solution to an important problem — the development of an information technology based on expanding the functionality of non-specialized unmanned aerial vehicles (drones) for early detection of forest fires. The proposed information technology is designed to increase the effectiveness of monitoring forest fires. Тhe existing level of information technology does not fully settle the issue of reliable fire protection of forests. Today, there is a contradiction between the high cost of developing high-tech fire-fighting equipment and lack of its efficiency. The elimination of this contradiction will be facilitated by the involvement of additional non-technical and technical resources in the information technology of early detection of forest fire hotspots. The results of the analysis of the use of modern drones prove that the involvement of unmanned aerial vehicles significantly increases the efficiency of many types of monitoring and they can successfully be used to solve the problems of early detection of forest fire hotspots. The results of experiments are presented, which were carried out both for a series of digital images and for video.

scholarly journals A Collaborative Region Detection and Grading Framework for Forest Fire Smoke Using Weakly Supervised Fine Segmentation and Lightweight Faster-RCNN

Forests ◽  
2021 ◽  
Vol 12 (6) ◽  
pp. 768
Author(s):  
Jin Pan ◽  
Xiaoming Ou ◽  
Liang Xu
Keyword(s):  
Forest Fire ◽  
Forest Fires ◽  
Learning Strategy ◽  
Early Stage ◽  
Fire Detection ◽  
Surveillance Systems ◽  
Region Detection ◽  
Decision Network ◽  
Fire Smoke ◽  
Weakly Supervised

Forest fires are serious disasters that affect countries all over the world. With the progress of image processing, numerous image-based surveillance systems for fires have been installed in forests. The rapid and accurate detection and grading of fire smoke can provide useful information, which helps humans to quickly control and reduce forest losses. Currently, convolutional neural networks (CNN) have yielded excellent performance in image recognition. Previous studies mostly paid attention to CNN-based image classification for fire detection. However, the research of CNN-based region detection and grading of fire is extremely scarce due to a challenging task which locates and segments fire regions using image-level annotations instead of inaccessible pixel-level labels. This paper presents a novel collaborative region detection and grading framework for fire smoke using a weakly supervised fine segmentation and a lightweight Faster R-CNN. The multi-task framework can simultaneously implement the early-stage alarm, region detection, classification, and grading of fire smoke. To provide an accurate segmentation on image-level, we propose the weakly supervised fine segmentation method, which consists of a segmentation network and a decision network. We aggregate image-level information, instead of expensive pixel-level labels, from all training images into the segmentation network, which simultaneously locates and segments fire smoke regions. To train the segmentation network using only image-level annotations, we propose a two-stage weakly supervised learning strategy, in which a novel weakly supervised loss is proposed to roughly detect the region of fire smoke, and a new region-refining segmentation algorithm is further used to accurately identify this region. The decision network incorporating a residual spatial attention module is utilized to predict the category of forest fire smoke. To reduce the complexity of the Faster R-CNN, we first introduced a knowledge distillation technique to compress the structure of this model. To grade forest fire smoke, we used a 3-input/1-output fuzzy system to evaluate the severity level. We evaluated the proposed approach using a developed fire smoke dataset, which included five different scenes varying by the fire smoke level. The proposed method exhibited competitive performance compared to state-of-the-art methods.

scholarly journals Short-Term Effects of Fire Severity on Vegetation Based on Sentinel-2 Satellite Data

2021 ◽  
Vol 13 (1) ◽  
pp. 432
Author(s):  
Aru Han ◽  
Song Qing ◽  
Yongbin Bao ◽  
Li Na ◽  
Yuhai Bao ◽  
...  
Keyword(s):  
Forest Fire ◽  
Forest Fires ◽  
Vegetation Change ◽  
Fire Severity ◽  
Dynamic Change ◽  
Burned Area ◽  
Fire Event ◽  
Vegetation Dynamic ◽  
Area Index ◽  
Sentinel 2

An important component in improving the quality of forests is to study the interference intensity of forest fires, in order to describe the intensity of the forest fire and the vegetation recovery, and to improve the monitoring ability of the dynamic change of the forest. Using a forest fire event in Bilahe, Inner Monglia in 2017 as a case study, this study extracted the burned area based on the BAIS2 index of Sentinel-2 data for 2016–2018. The leaf area index (LAI) and fractional vegetation cover (FVC), which are more suitable for monitoring vegetation dynamic changes of a burned area, were calculated by comparing the biophysical and spectral indices. The results showed that patterns of change of LAI and FVC of various land cover types were similar post-fire. The LAI and FVC of forest and grassland were high during the pre-fire and post-fire years. During the fire year, from the fire month (May) through the next 4 months (September), the order of areas of different fire severity in terms of values of LAI and FVC was: low > moderate > high severity. During the post fire year, LAI and FVC increased rapidly in areas of different fire severity, and the ranking of areas of different fire severity in terms of values LAI and FVC was consistent with the trend observed during the pre-fire year. The results of this study can improve the understanding of the mechanisms involved in post-fire vegetation change. By using quantitative inversion, the health trajectory of the ecosystem can be rapidly determined, and therefore this method can play an irreplaceable role in the realization of sustainable development in the study area. Therefore, it is of great scientific significance to quantitatively retrieve vegetation variables by remote sensing.

scholarly journals Empirical Research on Climate Warming Risks for Forest Fires: A Case Study of Grade I Forest Fire Danger Zone, Sichuan Province, China

2021 ◽  
Vol 13 (14) ◽  
pp. 7773
Author(s):  
San Wang ◽  
Hongli Li ◽  
Shukui Niu
Keyword(s):  
Wind Speed ◽  
Forest Fire ◽  
Climate Warming ◽  
Forest Fires ◽  
Meteorological Factors ◽  
Sichuan Province ◽  
Danger Zone ◽  
Climatic Regions ◽  
Area Burned ◽  
Forest Fire Danger

The Sichuan province is a key area for forest and grassland fire prevention in China. Forest resources contribute significantly not only to the biological gene pool in the mid latitudes but also in reducing the concentration of greenhouse gases and slowing down global warming. To study and forecast forest fire change trends in a grade I forest fire danger zone in the Sichuan province under climate change, the dynamic impacts of meteorological factors on forest fires in different climatic regions were explored and a model between them was established by using an integral regression in this study. The results showed that the dominant factor behind the area burned was wind speed in three climatic regions, particularly in Ganzi and A’ba with plateau climates. In Ganzi and A’ba, precipitation was mainly responsible for controlling the number of forest fires while it was mainly affected by temperature in Panzhihua and Liangshan with semi-humid subtropical mountain climates. Moreover, the synergistic effect of temperature, precipitation and wind speed was responsible in basin mid-subtropical humid climates with Chengdu as the center and the influence of temperature was slightly higher. The differential forest fire response to meteorological factors was observed in different climatic regions but there was some regularity. The influence of monthly precipitation in the autumn on the area burned in each climatic region was more significant than in other seasons, which verified the hypothesis of a precipitation lag effect. Climate warming and the combined impact of warming effects may lead to more frequent and severe fires.

Associations Between Ambient Particulate Air Pollution and Cognitive Function in Indonesian Children Living in Forest Fire–Prone Provinces

2021 ◽  
pp. 101053952110317
Author(s):  
Bin Jalaludin ◽  
Frances L. Garden ◽  
Agata Chrzanowska ◽  
Budi Haryanto ◽  
Christine T. Cowie ◽  
...  
Keyword(s):  
Air Pollution ◽  
Cognitive Function ◽  
Forest Fire ◽  
Forest Fires ◽  
Regression Models ◽  
Individual Factors ◽  
Aerodynamic Diameter ◽  
Particulate Air Pollution ◽  
Multivariable Models ◽  
Particulate Matter 2.5

Smoke from forest fires can reach hazardous levels for extended periods of time. We aimed to determine if there is an association between particulate matter ≤2.5 µm in aerodynamic diameter (PM2.5) and living in a forest fire–prone province and cognitive function. We used data from the Indonesian Family and Life Survey. Cognitive function was assessed by the Ravens Colored Progressive Matrices (RCPM). We used regression models to estimate associations between PM2.5 and living in a forest fire–prone province and cognitive function. In multivariable models, we found very small positive relationships between PM2.5 levels and RCPM scores (PM2.5 level at year of survey: β = 0.1%; 95% confidence interval [CI] = 0.01% to 0.19%). There were no differences in RCPM scores for children living in forest fire–prone provinces compared with children living in non-forest fire–prone provinces (mean difference = −1.16%, 95% CI = −2.53% to 0.21%). RCPM scores were lower for children who had lived in a forest fire–prone province all their lives compared with children who lived in a non-forest fire–prone province all their life (β = −1.50%; 95% CI = −2.94% to −0.07%). Living in a forest fire–prone province for a prolonged period of time negatively affected cognitive scores after adjusting for individual factors.

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