Use of satellite images for forest fires in area determination and monitoring

2009 ◽  
Author(s):  
Mulayim Gure ◽  
Mehmet Emin Ozel ◽  
H. Hulya Yildirim ◽  
Muzaffer Ozdemir
Keyword(s):  
Forest Fires ◽  
Satellite Images ◽  
Area Determination

scholarly journals Low-Cost Solution for Assessment of Urban Flash Flood Impacts Using Sentinel-2 Satellite Images and Fuzzy Analytic Hierarchy Process: A Case Study of Ras Ghareb City, Egypt

2019 ◽  
Vol 2019 ◽  
pp. 1-15 ◽  
Author(s):  
Mohammed Sadek ◽  
Xuxiang Li
Keyword(s):  
Natural Hazards ◽  
Forest Fires ◽  
Satellite Images ◽  
Flash Flood ◽  
Low Cost ◽  
Regional Scale ◽  
Warning System ◽  
Geographical Information ◽  
Hierarchy Process ◽  
Sentinel 2

Natural hazards are indeed counted as the most critical challenges facing our world, represented in floods, earthquakes, volcanoes, hurricanes, and forest fires. Among these natural hazards, the flash flood is regarded the most frequent. In this work, we utilized two Sentinel-2 satellite images, before and after the flash flood, SRTM and photos captured by using a helicopter. This paper aims at three prime objectives. Firstly, the flood influence is determined on the city of Ras Ghareb, Egypt, based on analyzing free satellite data (Sentinel-2 images). Secondly, fuzzy the analytical hierarchy process (FAHP) method and a geographical information system (GIS) are integrated for flood risk analysis and evaluation in the flood-prone area. Finally, such a flood vulnerability map is used as an index to assist the decision-makers prepare for probable flooding. FAHP is preferable as it can cater to the uncertainties in data and analysis. As a result, FAHP is appropriate to determine the flood-vulnerable area in cities especially due to the matching with the most destroyed areas identified by the change detection between the two Sentinel-2 images. Then, the decision-maker can depend on Sentinel-2 images to estimate the flood influence through a regional scale or applying the FAHP on cities susceptible to flash floods in case of unavailable satellite images to contribute in establishing an early warning system enough to the evacuation of the risky areas.

scholarly journals High resolution fire hazard index based on satellite images

2018 ◽  
pp. 29-41 ◽  
Author(s):  
Giovanni Laneve ◽  
Lorenzo Fusilli
Keyword(s):  
Forest Fires ◽  
Satellite Images ◽  
Emergency Services ◽  
Fire Hazard ◽  
Hazard Index ◽  
Mediterranean Area ◽  
Information Support ◽  
Burnt Area ◽  
Radar Imagery ◽  
Information Products

In December 2015, after 3 year of activity, the FP7 project PREFER (Space-based Information Support for Prevention and REcovery of Forest Fires Emergency in the MediteRranean Area) came to an end. The project was designed to respond to the need to improve the use of satellite images in applications related to the emergency services, in particular, to forest fires. The project aimed at developing, validating and demonstrating information products based on optical and SAR (Synthetic Aperture Radar) imagery for supporting the prevention of forest fires and the recovery/damage assessment of burnt area. The present paper presents an improved version of one of the products developed under the PREFER project, which is the Daily Fire Hazard Index (DFHI).

scholarly journals Forest Fire Area Detection by Using Landsat-8 and Sentinel-2 Satellite Images: A Case Study in Mugla, Turkey

2020 ◽  
Author(s):  
Bahadir Kurnaz ◽  
Caglar Bayik ◽  
Saygin Abdikan
Keyword(s):  
Forest Fire ◽  
Forest Fires ◽  
Satellite Images ◽  
Near Infrared ◽  
Kappa Statistics ◽  
Landsat 8 ◽  
Important Place ◽  
Burn Scar ◽  
Object Based ◽  
Sentinel 2

Abstract Background: Forests have an extremely important place in the ecosystem in terms of ensuring social and environmental balance. The biggest danger for forests that have this importance is forest fires due to various reasons. It is extremely important to estimate the formation and behavior characteristics of fires in terms of combating forest fires. Using the satellite images obtained with the developing technology for this purpose provides great convenience in the detection of the fire areas and the severity of the fire affected. In this study, forest fire that occurred in the Zeytinköy region of Muğla province was investigated using remotely sensed images. According to the reference data provided by the General Directorate of Forestry (GDF), 425 hectares of area was destroyed by fire. In this study, it is aimed to extract burn scar by applying seven vegetation indexes on Sentinel-2 and Landsat-8 satellite images. Additionally, forest fire areas have been determined with the object-based classification technique. Results: As a result of the study, when the obtained results are compared with the values obtained from GDF, it is determined that object based analysis of Sentinel-2 provided the highest accuracy with 98.36% overall accuracy and 0.976 kappa statistics. Comparing the results of spectral indices of Sentinel-2 and Landsat-8, Sentinel-2 resulted better results in all indices. Among the indices RdNBR and dNDVI obtained better results than other indices with Sentinel-2 and Landsat-8, respectively. Conclusions: In general, it has been determined that Sentinel-2 data is more suitable than Landsat-8 satellite images for determining Turkish red pine forest fired areas. Red and near infrared based images can be used for rapid mapping of fired areas. The results also indicated that the indices provided by multi-temporal Sentinel-2 data can assist forest management for rapid monitoring of fire scars and also for evolution of reforestation after fire.

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

scholarly journals Multitemporal Analysis of Land Use Changes and Their Effect on the Landscape of the Jerte Valley (Spain) by Remote Sensing

Agronomy ◽  
2021 ◽  
Vol 11 (8) ◽  
pp. 1470
Author(s):  
Yolanda Sánchez Sánchez ◽  
Antonio Martínez Graña ◽  
Fernando Santos-Francés ◽  
Joan Leandro Reyes Ramos ◽  
Marco Criado Nicolás
Keyword(s):  
Land Use ◽  
Forest Fires ◽  
Satellite Images ◽  
Land Use Changes ◽  
Environmental Parameters ◽  
Land Uses ◽  
Environmental Disturbances ◽  
Management Measures ◽  
Landscape Quality ◽  
Landscape Units

In recent years, the interest of institutions in land use has increased, creating the need to determine the changes in use through spatial-temporal and statistical analysis. This study analyzes the changes over the last 40 years, based on a cartography of landscape units obtained from the study of geo-environmental parameters in the Jerte Valley (Spain) with satellite images, Landsat 5 and 7. Subsequently, through the analysis of spatial patterns and diversity and fragmentation indices, and with the Fragstat software, the landscape was characterized from 1994 to the present. The results show that wooded areas decreased slightly, crops increased in altitude and major environmental disturbances (mainly forest fires) negatively affected the environmental mosaic. Land uses affect the landscape by developing larger tesserae (+5 ha), which are less fragmented (−0.15), but more isolated (0.12). This study demonstrates that landscape metrics can be used to understand changes in spatial pattern, help in decision making to implement appropriate management measures in the conservation of traditional land uses, and allow the maintenance of connecting areas between fragments to avoid the loss of natural corridors to increase landscape quality.

scholarly journals ANALYSIS OF EFFICIENCY OF FIRE DANGER INDICES IN FOREST FIRE PREDICTION

2017 ◽  
Vol 41 (2) ◽  
Author(s):  
Fillipe Tamiozzo Pereira Torres ◽  
Gumercindo Souza Lima ◽  
Sebastião Venâncio Martins ◽  
Sebastião Renato Valverde
Keyword(s):  
Forest Fires ◽  
Satellite Images ◽  
Skill Score ◽  
Fire Danger ◽  
Fire Weather ◽  
Allocation Of Resources ◽  
Weather Index ◽  
Fire Weather Index ◽  
The City ◽  
Fire Danger Indices

ABSTRACT Despite the existence of different fire danger indices, the use of an inefficient index can lead to making wrong decisions on the appropriate procedures for preventing and fighting forest fires, while a trusted prediction index can help the most quantification and allocation of resources for prevention. Thereat, the objective of this study is to analyze the efficiency of Fire Weather Index (FWI), Logarithmic of Telicyn Index, Nesterov Index, cumulative indexes of precipitation - evaporation (P-EVAP) and evaporation / precipitation (EVAP/P), Monte Alegre Index (FMA) and Monte Alegre Changed Index (FMA+) in the prediction of forest fires for the city of Viçosa (MG). The indices were compared using the method known as Skill Score (SS) taking into account the days that the indexes pointed to the risk of events with focus fire identified by satellite images on the 01/01/2005 to 31/12/2014 period. According to the results, the Logarithm of Telicyn Index (0.53257) as the most efficient for the study area, followed by the indices EVAP/P (0.46553), P-EVAP (0.43724), Nesterov (0.40445), FWI (0.39213), FMA+(0.34595) and FMA (0.28982).

Beetles sniff out forest fires

Nature ◽  
1999 ◽  
Author(s):  
Henry Gee
Keyword(s):  
Forest Fires

The machine training in problems of satellite images’s processing

Metrologiya ◽  
2020 ◽  
pp. 15-37
Author(s):  
L. P. Bass ◽  
Yu. A. Plastinin ◽  
I. Yu. Skryabysheva
Keyword(s):  
Remote Sensing ◽  
Neural Networks ◽  
Computer Vision ◽  
Satellite Images ◽  
Vision Systems ◽  
Earth Remote Sensing ◽  
Practical Applications ◽  
Convolution Neural Networks ◽  
Computer Vision Systems ◽  
Trained Neural Network

Use of the technical (computer) vision systems for Earth remote sensing is considered. An overview of software and hardware used in computer vision systems for processing satellite images is submitted. Algorithmic methods of the data processing with use of the trained neural network are described. Examples of the algorithmic processing of satellite images by means of artificial convolution neural networks are given. Ways of accuracy increase of satellite images recognition are defined. Practical applications of convolution neural networks onboard microsatellites for Earth remote sensing are presented.

Multitemporal distribution analysis of Dodonaea viscosa (L.) Jacq. by remote sensing in Durango, Mexico

2020 ◽  
Vol 13 (8) ◽  
Author(s):  
Marco, A. Márquez-Linares ◽  
Jonathan G. Escobar--Flores ◽  
Sarahi Sandoval- Espinosa ◽  
Gustavo Pérez-Verdín
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Design Methodology ◽  
Supervised Classification ◽  
Satellite Images ◽  
Ground Cover ◽  
Distribution Analysis ◽  
Dodonaea Viscosa ◽  
Artificial Neural ◽  
Landsat Satellite

Objective: to determine the distribution of D. viscosa in the vicinity of the Guadalupe Victoria Dam in Durango, Mexico, for the years 1990, 2010 and 2017.Design/Methodology/Approach: Landsat satellite images were processed in order to carry out supervised classifications using an artificial neural network. Images from the years 1990, 2010 and 2017 were used to estimate ground cover of D. viscosa, pastures, crops, shrubs, and oak forest. This data was used to calculate the expansion of D. viscosa in the study area.Results/Study Limitations/Implications: the supervised classification with the artificial neural network was optimal after 400 iterations, obtaining the best overall precision of 84.5 % for 2017. This contrasted with the year 1990, when overall accuracy was low at 45 % due to less training sites (fewer than 100) recorded for each of the land cover classes.Findings/Conclusions: in 1990, D. viscosa was found on only five hectares, while by 2017 it had increased to 147 hectares. If the disturbance caused by overgrazing continues, and based on the distribution of D. viscosa, it is likely that in a few years it will have the ability to invade half the study area, occupying agricultural, forested, and shrub areas

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