scholarly journals Damage-Map Estimation Using UAV Images and Deep Learning Algorithms for Disaster Management System

2020 ◽  
Vol 12 (24) ◽  
pp. 4169
Author(s):  
Dai Quoc Tran ◽  
Minsoo Park ◽  
Daekyo Jung ◽  
Seunghee Park
Keyword(s):  
Deep Learning ◽  
Forest Fire ◽  
Forest Fires ◽  
Learning Algorithms ◽  
Burnt Area ◽  
New Approach ◽  
The Republic ◽  
Uav Images ◽  
Burnt Areas ◽  
Damage Map

Estimating the damaged area after a forest fire is important for responding to this natural catastrophe. With the support of aerial remote sensing, typically with unmanned aerial vehicles (UAVs), the aerial imagery of forest-fire areas can be easily obtained; however, retrieving the burnt area from the image is still a challenge. We implemented a new approach for segmenting burnt areas from UAV images using deep learning algorithms. First, the data were collected from a forest fire in Andong, the Republic of Korea, in April 2020. Then, the proposed two-patch-level deep-learning models were implemented. A patch-level 1 network was trained using the UNet++ architecture. The output prediction of this network was used as a position input for the second network, which used UNet. It took the reference position from the first network as its input and refined the results. Finally, the final performance of our proposed method was compared with a state-of-the-art image-segmentation algorithm to prove its robustness. Comparative research on the loss functions was also performed. Our proposed approach demonstrated its effectiveness in extracting burnt areas from UAV images and can contribute to estimating maps showing the areas damaged by forest fires.

scholarly journals Socioeconomic Impacts of Forest Fires upon Portugal: An Analysis for the Agricultural and Forestry Sectors

2019 ◽  
Vol 11 (2) ◽  
pp. 374 ◽  
Author(s):  
Vítor Martinho
Keyword(s):  
Forest Fire ◽  
Forest Fires ◽  
Returns To Scale ◽  
Increasing Returns ◽  
Burnt Area ◽  
Socioeconomic Impacts ◽  
Increasing Returns To Scale ◽  
Fire Activity ◽  
Burnt Areas ◽  
The Impact

Recent forest fire activity has resulted in several consequences across different geographic locations where both natural and socioeconomic conditions have promoted a favorable context for what has happened in recent years in a number of countries, including Portugal. As a result, it would be interesting to examine the implications of forest fire activity in terms of the socioeconomic dynamics and performance of the agroforestry sectors in the context of those verified in the Portuguese municipalities. For this purpose, data from Statistics on Portugal was considered for output and employment from the business sector related to agricultural and forestry activities, which were disaggregated at the municipality level, for the period 2008–2015. Data for the burnt area was also considered in order to assess the impact of forest fires. The data was analyzed using econometric models in panel data based on the Keynesian (Kaldor laws) and convergence (conditional approaches) theories. The results from the Keynesian approaches show that there are signs of increasing returns to scale in the Portuguese agroforestry sectors, where the burnt area increased employment growth in agricultural activities and decreased employment in the forestry sector. Forest fires seem to create favorable conditions for agricultural employment in Portuguese municipalities and the inverse occurs for forestry employment. Additionally, some signs of convergence were identified between Portuguese municipalities for agroforestry output and employment, as well for the burnt areas. However, signs of divergence (increasing returns to scale) from the Keynesian models seem to be stronger. On the other hand, the evidence of beta convergence for the burnt areas are stronger than those verified for other variables, showing that the impacts from forest fires are more transversal across the whole country (however not enough to have sigma convergence).

Wildfires and the Canadian Forest Fire Weather Index system for the Daxing'anling region of China

2011 ◽  
Vol 20 (8) ◽  
pp. 963 ◽  
Author(s):  
Xiaorui Tian ◽  
Douglas J. McRae ◽  
Jizhong Jin ◽  
Lifu Shu ◽  
Fengjun Zhao ◽  
...  
Keyword(s):  
Forest Fire ◽  
Forest Fires ◽  
Coniferous Forest ◽  
Northern China ◽  
Fire Danger ◽  
Fire Season ◽  
Fire Weather ◽  
Burnt Area ◽  
Weather Index ◽  
Fire Weather Index

The Canadian Forest Fire Weather Index (FWI) system was evaluated for the Daxing'anling region of northern China for the 1987–2006 fire seasons. The FWI system reflected the regional fire danger and could be effectively used there in wildfire management. The various FWI system components were classified into classes (i.e. low to extreme) for fire conditions found in the region. A total of 81.1% of the fires occurred in the high, very high and extreme fire danger classes, in which 73.9% of the fires occurred in the spring (0.1, 9.5, 33.3 and 33.1% in March, April, May and June). Large wildfires greater than 200 ha in area (16.7% of the total) burnt 99.2% of the total burnt area. Lightning was the main ignition source for 57.1% of the total fires. Result show that forest fires mainly occurred in deciduous coniferous forest (61.3%), grass (23.9%) and deciduous broad leaved forest (8.0%). A bimodal fire season was detected, with peaks in May and October. The components of FWI system were good indicators of fire danger in the Daxing'anling region of China and could be used to build a working fire danger rating system for the region.

scholarly journals THE IMPACT OF FOREST FIRE ON FOREST COVER TYPES IN MONGOLIA

2020 ◽  
Vol XLIII-B3-2020 ◽  
pp. 693-698
Author(s):  
N.-E. Geserbaatar ◽  
E. Nasanbat ◽  
O. Lkhamjav
Keyword(s):  
Forest Fire ◽  
Forest Fires ◽  
Forest Cover ◽  
Forest Type ◽  
Landsat Imagery ◽  
Object Oriented ◽  
Burnt Area ◽  
Current Classification ◽  
The Impact ◽  
Forest Cover Types

Abstract. The objective of this study was the impact of forest fire on forest cover types. This study has identified non-forest and forest area that has seven forest class are included with cedar, pine, larch, birch, birch-pine mixed, birch-larch mixed and cedar-larch mixed, additionally, remote sensing imagery is applied. In contrast, Landsat imagery has been used several classification approaches. Moreover, the current classification has developments in segmentation and object-oriented techniques offer the suitable analysis to classify satellite data. In the object-oriented classification approach, images cluster to homogenous area as forest types by suitable parameters in some level. The accuracy analysis revealed that overall accuracy showed a good accuracy of determination (86.33 percent in 2000 and 93.75 percent in 2011) with regard to identify of the forest cover and type. Furthermore, these results suggest that the Landsat TM and ETM+ data can reliable detect the forest type based upon the segmentation and object-oriented techniques. In generally, our study area is high-risky region to forest fires. It is higher influence to forest cover and tree species and other ecosystems. Overall, wildfire of impact results showed that 25239 ha of forests were changed to burnt area and 52603 ha forests were changed to grassland.

scholarly journals INDIVIDUAL BANANA TREE CROWN DELINEATION USING UNMANNED AERIAL VEHICLE (UAV) IMAGES

2021 ◽  
Vol XLIII-B3-2021 ◽  
pp. 581-585
Author(s):  
S. Kuikel ◽  
B. Upadhyay ◽  
D. Aryal ◽  
S. Bista ◽  
B. Awasthi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Tree Crown ◽  
Individual Tree ◽  
Banana Tree ◽  
Crown Delineation ◽  
Uav Images

Abstract. Individual Tree Crown (ITC) delineation from aerial imageries plays an important role in forestry management and precision farming. Several conventional as well as machine learning and deep learning algorithms have been recently used in ITC detection purpose. In this paper, we present Convolutional Neural Network (CNN) and Support Vector Machine (SVM) as the deep learning and machine learning algorithms along with conventional methods of classification such as Object Based Image Analysis (OBIA) and Nearest Neighborhood (NN) classification for banana tree delineation. The comparison was done based by considering two cases; Firstly, every single classifier was compared by feeding the image with height information to see the effect of height in banana tree delineation. Secondly, individual classifiers were compared quantitatively and qualitatively based on five metrices i.e., Overall Accuracy, Recall, Precision, F-Score, and Intersection Over Union (IoU) and best classifier was determined. The result shows that there are no significant differences in the metrices when height information was fed as there were banana tree of almost similar height in the farm. The result as discussed in quantitative and qualitative analysis showed that the CNN algorithm out performed SVM, OBIA and NN techniques for crown delineation in term of performance measures.

scholarly journals Das Konzept «Waldbrand 2020» des Kantons Tessin

2019 ◽  
Vol 170 (5) ◽  
pp. 242-250
Author(s):  
Aron Ghiringhelli ◽  
Gianni Boris Pezzatti ◽  
Marco Conedera
Keyword(s):  
Forest Fire ◽  
Forest Fires ◽  
Forest Restoration ◽  
Fire Regime ◽  
Forest Service ◽  
Management Approach ◽  
Fire Control ◽  
Burnt Area ◽  
Forest Protection ◽  
Forest Fire Management

The “forest fire 2020” program of Canton Ticino The Canton of Ticino has a long-lasting experience in facing forest fires. As a result, a tradition in forest fire documentation and analysis exists and the forest fire management approach is continuously reviewed and improved with the aim to preserve the forest protection functions and to keep the mountain areas safe for the inhabitants. The fire regime has been reduced in Ticino since the seventies of last century thanks to improvement of the firefighting organization and fire control techniques (e.g. systematic use of helicopters for the aerial fire control) and the possibility of declaring a total fire ban in the open. However the demand in terms of protection of human lives and goods of the modern society is raising and as consequence of the climate change fire risk may increase in the future. For this reason two years ago the forest service of Canton Ticino developed the “forest fire 2020” program, in collaboration with the cantonal fire brigades association and the federal research Institute WSL. The program consists of four interdependent activity modules, which are 1) prevention, 2) organizational and technical measures, 3) firefighting and control, 4) burnt area restoration. The forest service is responsible for the fire-danger rating, the fire-ban release, the mentoring of local authorities in forest management questions and for planning pre-suppression facilities (e.g. water points for helicopters). It is also responsible for defining the mission rules for aerial firefighting, for collecting the data for the statistics, and for planning the post-fire forest restoration measures. The fire brigades are in charge of the firefighting tasks, by first intervening with the urban fire brigades and in case of need requiring the support of specialized forest-fire brigades. During the firefighting actions the forest service takes a consulting role. The first two years of implementation confirmed the suitability of the “forest fire 2020” program. Potential improvements have been however detected and are under implementation, such as the completion of the pre-suppression infrastructures, a better coordination between aerial and terrestrial firefighting and the strengthening of the specialized forest-fire brigades.

scholarly journals Fire Drivers Affecting Forest Fire Occurrences in the Tropical Mixed Broad-leaved Forests of Nepal

2021 ◽  
pp. 84-99
Author(s):  
Krishna Bahadur Bhujel ◽  
Rejina Maskey Byanju ◽  
Ambika P. Gautam ◽  
Ramesh Prasad Sapkota ◽  
Udhab Raj Khadka
Keyword(s):  
Forest Fire ◽  
Forest Fires ◽  
Public Perception ◽  
Sustainable Forest Management ◽  
Forest Fire Management ◽  
Lower Elevation ◽  
Sal Forest ◽  
Burnt Areas ◽  
The Hill ◽  
Anthropogenic Drivers

Forest fires triggered by various natural and anthropogenic drivers are increasing and threatening forest ecosystems across the globe. In Nepal, the high value Tropical Mixed Broad-leaved Forests are prone to fire caused by both natural and anthropogenic drivers. Thus, understanding fire drivers and their effect is important for the sustainable forest fire management. However, the preceding studies on forest specific fire drivers and their effect are limited. This research has identified the fire drivers and assessed their effect to fire occurrences in the Tropical Mixed Broad-leaved Forests of Nawalparasi District, Nepal. Fire drivers were identified and prioritized by participatory approaches. The fire incidences and burnt areas were obtained from the MODIS fire data (2001–2017). The results revealed altogether 20 drivers including eight natural and 12 anthropogenic. Based on the public perception and magnitude of forest fire, among the natural drivers, temperature, precipitation, forest fuel, aspect, elevation and slope were the major drivers. Likewise, among the anthropogenic drivers, forest distance from roads and settlements showed significant effect. The natural drivers, ambient temperature >30ºC and annual precipitation <2400 mm, revealed signi-ficant impacts on forest fire. Likewise, forests situated at lower elevation (<500 m), and southern and eastern aspects were highly vulnerable to fire. Considering anthropogenic drivers, forest lying within 500 m from the roads and settlements were highly vulnerable to fire. Among the forest types, the Hill Sal Forest was more affected. Future strategies should address the major fire drivers, construction of adequate fire lines and conservation ponds for the sustainable forest management.

scholarly journals GIS-Based Forest Fire Susceptibility Zonation with IoT Sensor Network Support, Case Study—Nature Park Golija, Serbia

Sensors ◽  
2021 ◽  
Vol 21 (19) ◽  
pp. 6520
Author(s):  
Ivan Novkovic ◽  
Goran B. Markovic ◽  
Djordje Lukic ◽  
Slavoljub Dragicevic ◽  
Marko Milosevic ◽  
...  
Keyword(s):  
Risk Management ◽  
Sensor Network ◽  
Forest Fire ◽  
Forest Fires ◽  
Fuzzy Ahp ◽  
Fire Risk ◽  
Early Warning Systems ◽  
Environmental Data ◽  
Nature Park ◽  
The Republic

The territory of the Republic of Serbia is vulnerable to various natural disasters, among which forest fires stand out. In relation with climate changes, the number of forest fires in Serbia has been increasing from year to year. Protected natural areas are especially endangered by wildfires. For Nature Park Golija, as the second largest in Serbia, with an area of 75,183 ha, and with MaB Reserve Golija-Studenica on part of its territory (53,804 ha), more attention should be paid in terms of forest fire mitigation. GIS and multi-criteria decision analysis are indispensable when it comes to spatial analysis for the purpose of natural disaster risk management. Index-based and fuzzy AHP methods were used, together with TOPSIS method for forest fire susceptibility zonation. Very high and high forest fire susceptibility zone were recorded on 26.85% (Forest Fire Susceptibility Index) and 25.75% (fuzzy AHP). The additional support for forest fire prevention is realized through an additional Internet of Thing (IoT)-based sensor network that enables the continuous collection of local meteorological and environmental data, which enables low-cost and reliable real-time fire risk assessment and detection and the improved long-term and short-term forest fire susceptibility assessment. Obtained results can be applied for adequate forest fire risk management, improvement of the monitoring, and early warning systems in the Republic of Serbia, but are also important for relevant authorities at national, regional, and local level, which will be able to coordinate and intervene in a case of emergency events.

scholarly journals Deterministic-Probabilistic Approach to Predict Lightning-Caused Forest Fires in Mounting Areas

Forecasting ◽  
2021 ◽  
Vol 3 (4) ◽  
pp. 695-715
Author(s):  
Nikolay Baranovskiy
Keyword(s):  
Mathematical Models ◽  
Forest Fire ◽  
Forest Fires ◽  
Lightning Discharge ◽  
Probabilistic Approach ◽  
Lightning Activity ◽  
Cloud To Ground Lightning ◽  
The Republic ◽  
Forest Fire Danger ◽  
Probabilistic Criteria

Forest fires from lightnings create a tense situation in various regions of states with forested areas. It is noted that in mountainous areas this is especially important in view of the geophysical processes of lightning activity. The aim of the study is to develop a deterministic-probabilistic approach to predicting forest fire danger due to lightning activity in mountainous regions. To develop a mathematical model, the main provisions of the theory of probability and mathematical statistics, as well as the general theory of heat transfer, were used. The scientific novelty of the research is due to the complex use of probabilistic criteria and deterministic mathematical models of tree ignition by a cloud-to-ground lightning discharge. The paper presents probabilistic criteria for predicting forest fire danger, taking into account the lightning activity, meteorological data, and forest growth conditions, as well as deterministic mathematical models of ignition of deciduous and coniferous trees by electric current of a cloud-to-ground lightning discharge. The work uses synthetic data on the discharge parameters and characteristics of the forest-covered area, which correspond to the forest fire situation in the Republic of Altay and the Republic of Buryatia (Russian Federation). The dependences of the probability for occurrence of forest fires on various parameters have been obtained.

scholarly journals Comparative study on machine learning algorithms for early fire forest detection system using geodata.

2020 ◽  
Vol 10 (5) ◽  
pp. 5507
Author(s):  
Zouiten Mohammed ◽  
Chaaouan Hanae ◽  
Setti Larbi
Keyword(s):  
Machine Learning ◽  
Forest Fire ◽  
Forest Fires ◽  
Learning Algorithm ◽  
Detection System ◽  
Learning Algorithms ◽  
Spatial Prediction ◽  
Machine Learning Algorithms ◽  
Geographical Information ◽  
Deep Learning Algorithm

Forest fires have caused considerable losses to ecologies, societies and economies worldwide. To minimize these losses and reduce forest fires, modeling and predicting the occurrence of forest fires are meaningful because they can support forest fire prevention and management. In recent years, the convolutional neural network (CNN) has become an important state-of-the-art deep learning algorithm, and its implementation has enriched many fields. Therefore, a competitive spatial prediction model for automatic early detection of wild forest fire using machine learning algorithms can be proposed. This model can help researchers to predict forest fires and identify risk zonas. System using machine learning algorithm on geodata will be able to notify in real time the interested parts and authorities by providing alerts and presenting on maps based on geographical treatments for more efficacity and analyzing of the situation. This research extends the application of machine learning algorithms for early fire forest prediction to detection and representation in geographical information system (GIS) maps.

scholarly journals A COMPARISON BETWEEN OPTICAL AND RADAR SATELLITE IMAGES FOR DETECTING TROPICAL FOREST FIRES IN SOUTH SUMATRA, INDONESIA

2015 ◽  
Vol 11 (2) ◽  
Author(s):  
Heri Sunuprapto ◽  
Yousif Ali Hussin
Keyword(s):  
Forest Fire ◽  
Forest Fires ◽  
Satellite Images ◽  
Forest Degradation ◽  
Remote Sensing And Gis ◽  
Remotely Sensed Data ◽  
Burnt Area ◽  
Forest Condition ◽  
Main Factors ◽  
Radar Satellite

Forest fire in Indonesia is a yearly potential caused for forest degradation. Theinformation available about the main factors that promote the forest fire and informationabout the forest condition after the forest fire are insufficient . This is one of the reasonswhy forest area neglected after they are burned. Remote sensing and GIS are helpful toolsto provide a quick and accurate data acquisition and that can describe the forestcondition after the forest fire. The objectives of this research were to asses the ability ofoptical and radar satellite remotely sensed data to detect, identify and classify forestdamage (burnt area) caused by fire and to develop a spatial model for forest fire hazard.Key words: detection burnt forest, Landsat-TM, ERS, and JERS images

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