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.

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.

scholarly journals Estimation of forest cover restoration damaged by Siberian silkmoth using remote sensing data and maps

2019 ◽  
Vol 75 ◽  
pp. 02005
Author(s):  
Elena Fedotova
Keyword(s):  
Remote Sensing ◽  
Land Cover ◽  
Tree Species ◽  
Forest Cover ◽  
Remote Sensing Data ◽  
Deciduous Forests ◽  
Sensing Data ◽  
Current State

The current state of the land cover has been estimated in the territories where in different years (1885, 1955, 1995) the forests were damaged by Siberian silkmoth. Dark-needle taiga is restored through the change of tree species. In 20 years in areas of dark-needle taiga there are graminoid communities, in 60 years we have deciduous forests there, and in 130 - dark needle forests, but not everywhere.

scholarly journals ANALYSIS OF THE CURRENT STATE OF SHELTERBELTS BY THE REMOTE SENSING DATA: RUSSIA, BELGOROD OBLAST

2020 ◽  
Author(s):  
Anastasiya Narozhnyaya ◽  
◽  
Yury Chendev ◽  
Aleksander Solovyov ◽  
Maria Lebedeva ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Remote Sensing Data ◽  
Belgorod Oblast ◽  
Sensing Data ◽  
Current State

Remote sensing of terrestrial non-photosynthetic vegetation using hyperspectral, multispectral, SAR, and LiDAR data

2015 ◽  
Vol 40 (2) ◽  
pp. 276-304 ◽  
Author(s):  
Zhaoqin Li ◽  
Xulin Guo
Keyword(s):  
Remote Sensing ◽  
Remote Sensing Data ◽  
Wildlife Habitat ◽  
Optical Data ◽  
Lidar Data ◽  
Natural Fire ◽  
Sensing Data ◽  
Advantages And Disadvantages ◽  
Sar Data ◽  
Ecological Importance

Quantifying non-photosynthetic vegetation (NPV) is important for ecosystem management and studies on climate change, ecology, and hydrology because it controls uptake of carbon, water, and nutrients together with frequency and intensity of natural fire, and serves as wildlife habitat. The ecological importance of NPV has driven considerable research on quantitatively estimating NPV in diverse ecosystems including croplands, forests, grasslands, savannah, and shrublands using remote sensing data. However, a comprehensive review is not available. This review highlights the theoretical bases and the critical elements of remote sensing for NPV estimation, and summarizes research on estimating fractional cover of NPV (NPV cover) and biomass using passive optical hyperspectral and multispectral remote sensing data, active synthetic aperture radar (SAR) and light detection and ranging (LiDAR), and integrated multi-sensorial data. We also discuss advantages and disadvantages of optical, LiDAR, and SAR data and pinpoint future direction on NPV estimation using remote sensing data. Currently, most NPV research has been mainly focused on NPV cover, not NPV biomass, using passive optical data, while a few studies have used LiDAR data to quantify NPV biomass in forests and SAR data on NPV estimation in croplands and grasslands. In the future, more efforts should be made to estimate NPV biomass and to investigate the best use of hyperspectral, LiDAR, SAR data, and their integration. The upcoming new optical sensor on Sentinel-2 satellites, Radarsat-2 constellation and NovaSAR, technological innovation in hyperspectral, LiDAR, and SAR, and improvements on methodology for information extraction and combining multi-sensorial data will provide more opportunities for NPV estimation.

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