scholarly journals DEVELOPMENT OF REMOTE SENSING METHODS FOR NATURAL FIRE PREVENTION

2021 ◽  
pp. 149-161
Author(s):  
Almaz T. Gizatullin ◽  
Keyword(s):  
Remote Sensing ◽  
Neural Networks ◽  
Simulation Models ◽  
Fire Spread ◽  
Simulation Modelling ◽  
Fire Danger ◽  
Fire Prevention ◽  
Natural Fire ◽  
The Subject ◽  
Remote Sensing Methods

The study deals with remote sensing methods for natural fire prevention, provides analysis and systematization on the subject. It traces the historical development and demonstrates the diversity of the methods. The main development stages and their characteristics were identified taking into account the increasing number of the sources and types of remote sensing and deepening knowledge of the subject. Fire interpretation includes fundamentally different processes of ignition and fire spread. The concepts of fire danger and its factors were introduced, the ways for their selection and application in the methods were analyzed. The source data for the methods were defined: satellite imagery of various resolutions (Landsat, Sentinel, MODIS/Terra-Aqua, AVHRR/NOAA, etc.), UAV images, lidar data, as well as technologies to process those. The study demonstrates that the most commonly used are traditional methods of geoinformation analysis, simulation modelling and neural networks. The methods were described, features of their implementation were identified. The description includes specific examples of fire danger assessment methods based on GIS, simulation models of fire spread, fire prevention methods based on neural networks and their application for territories of different spatial levels – global, regional and local.

scholarly journals Development of natural fire prevention method based on remote sensing data: case study of Krasnoyarsk region forests

2021 ◽  
Vol 27 (2) ◽  
pp. 340-354
Author(s):  
Almaz Gizatullin
Keyword(s):  
Remote Sensing ◽  
Remote Sensing Data ◽  
Fire Danger ◽  
Fire Prevention ◽  
Natural Fire ◽  
Sensing Data ◽  
Krasnoyarsk Region ◽  
Prevention Method ◽  
Fire Conditions

The stages of development of natural fire prevention method based on remote sensing data were considered. The case study is focused on Krasnoyarsk region forests. There was a rationale for selecting a study area on the basis of statistical fire data (FIRMS thermal hot spots 2016–2018) and a variety of fire conditions. The fire assessment was founded on the most informative fire factors—surface temperature, vegetation cover inhomogenuity and man-made load, which are derived by the natural-fire characteristics of the territory. These factors were evaluated by measuring parameters closed to them, respectively—radiobrightness temperature based on thermal emission, vegetation index NDVI and integral indicator of distance to settlements and roads. Materials from the Terra/Aqua, Sentinel-3, Landsat-8, Sentinel-2 satellites and Open Street Maps vector map layers were used as data sources. With use of statistical data, the relationship between above parameters and the present fire danger of Krasnoyarsk region was analyzed. Based on the results, we obtained different by forest rayon and fire season month correlation coefficients that described the contribution of individual factors to a fire danger, and threshold values of parameters for preventing fires. Then a sequence of stages of analytical and synthetic fire danger assessment as a study method was built. Validation of the method was performed in the most fire dangerous and representative in terms of fire conditions area in the south-west of the Krasnoyarsk Territory from April 1 to May 10, 2019. It showed sufficient accuracy (65 %) and reliability (58 %) of fire forecast.

Development of the preventive natural fire danger assessment algorithm using remote sensing data

2019 ◽  
Vol 943 (1) ◽  
pp. 102-109 ◽  
Author(s):  
A.T. Gizatullin ◽  
N.A. Alekseenko ◽  
V.S. Moiseeva
Keyword(s):  
Remote Sensing ◽  
General Structure ◽  
Remote Sensing Data ◽  
Fire Danger ◽  
Filling Process ◽  
Program Code ◽  
Natural Fire ◽  
Sensing Data ◽  
Danger Assessment ◽  
Current State

This article is devoted to the development of an algorithm for the preventive assessment of the fire danger of natural areas using remote sensing data (the preventive natural fire danger assessment algorithm). The problems of the current state of the remote sensing materials use for fires researches as a justification for the need of the algorithm are considered. A review of existing methods and algorithms of natural fire danger assessment is done. The algorithm development includes description of the general structure and the content filling process of different algorithm components. The algorithm is a stages sequence of remote sensing data processing and analysis in terms of fire danger. As a result of algorithm, the fire danger assessment of the observed territory is formed. A special feature of the algorithm is its preventiveness, universality (applicability for any territory), practical automatability (the ability to represent in the form of a program code for the processing of RSD) and flexibility (the ability to add and branch the sequence). In the end, general conclusions and recommendations on the use of the algorithm are given.

Application of artificial neural networks and logistic regression to the prediction of forest fire danger in Galicia using MODIS data

2012 ◽  
Vol 21 (8) ◽  
pp. 1025 ◽  
Author(s):  
Mar Bisquert ◽  
Eduardo Caselles ◽  
Juan Manuel Sánchez ◽  
Vicente Caselles
Keyword(s):  
Remote Sensing ◽  
Neural Networks ◽  
Logistic Regression ◽  
Artificial Neural Networks ◽  
Surface Temperature ◽  
Forest Fire ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Fire Danger ◽  
Forest Fire Danger

Fire danger models are a very useful tool for the prevention and extinction of forest fires. Some inputs of these models, such as vegetation status and temperature, can be obtained from remote sensing images, which offer higher spatial and temporal resolution than direct ground measures. In this paper, we focus on the Galicia region (north-west of Spain), and MODIS (Moderate Resolution Imaging Spectroradiometer) images are used to monitor vegetation status and to obtain land surface temperature as essential inputs in forest fire danger models. In this work, we tested the potential of artificial neural networks and logistic regression to estimate forest fire danger from remote sensing and fire history data. Remote sensing inputs used were the land surface temperature and the Enhanced Vegetation Index. A classification into three levels of fire danger was established. Fire danger maps based on this classification will facilitate fire prevention and extinction tasks.

scholarly journals A Fire Danger Index for the early detection of areas vulnerable to wildfires in the Eastern Mediterranean region

2020 ◽  
Vol 5 (2) ◽  
Author(s):  
Panteleimon Xofis ◽  
Georgios Tsiourlis ◽  
Pavlos Konstantinidis
Keyword(s):  
Remote Sensing ◽  
Field Data ◽  
Detection System ◽  
Fire Behavior ◽  
Fire Detection ◽  
Fire Danger ◽  
Detection Accuracy ◽  
Anthropogenic Influence ◽  
Fire Detection System ◽  
Remote Sensing Methods

Abstract Wildfires continue to be a major factor of disturbance to Mediterranean ecosystems, and are often associated with significant losses of properties and human lives. Fast fire detection and suppression within the first few minutes after ignition are crucial to successfully managing wildfires and preventing their potentially catastrophic consequences. In this study, remote-sensing methods and data were integrated wih fire behavior simulation and field data to develop a Fire Danger Index (FDI) that can be used to detect the areas most vulnerable to wildfires. This FDI will be integrated into an automatic fire detection system that utilizes optical and thermal land cameras and an unmanned aerial vehicle. The FDI was calculated for a nature reserve in Southern Greece based on fire behavior, pyric history, and anthropogenic influence. Fire behavior was estimated using the FlamMap fire simulation model, while the fuel types to include in the model were determined using state-of-the-art remote-sensing methods and field data. The pyric history was represented by point data on fire occurrences over a period of 40 years. The anthropogenic influence was estimated based on an inverse relationship of this influence with the Euclidean distance from roads and settlements. The calculated FDI demonstrated that a large part of the reserve, including its most ecologically important ecosystems, is highly vulnerable to wildfires. Integrating the FDI into the automatic fire detection system is expected to significantly improve its detection accuracy.

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.

Application of Remote Sensing in Agriculture

2014 ◽  
Vol 13 (1) ◽  
Author(s):  
Jan Piekarczyk
Keyword(s):  
Remote Sensing ◽  
Precision Agriculture ◽  
Crop Production ◽  
Satellite Images ◽  
Crop Yields ◽  
Spatial Scales ◽  
Strong Relationship ◽  
Physical Parameters ◽  
Agricultural Crop ◽  
Remote Sensing Methods

AbstractWith increasing intensity of agricultural crop production increases the need to obtain information about environmental conditions in which this production takes place. Remote sensing methods, including satellite images, airborne photographs and ground-based spectral measurements can greatly simplify the monitoring of crop development and decision-making to optimize inputs on agricultural production and reduce its harmful effects on the environment. One of the earliest uses of remote sensing in agriculture is crop identification and their acreage estimation. Satellite data acquired for this purpose are necessary to ensure food security and the proper functioning of agricultural markets at national and global scales. Due to strong relationship between plant bio-physical parameters and the amount of electromagnetic radiation reflected (in certain ranges of the spectrum) from plants and then registered by sensors it is possible to predict crop yields. Other applications of remote sensing are intensively developed in the framework of so-called precision agriculture, in small spatial scales including individual fields. Data from ground-based measurements as well as from airborne or satellite images are used to develop yield and soil maps which can be used to determine the doses of irrigation and fertilization and to take decisions on the use of pesticides.

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