Web-GIS platform for forest fire danger prediction in Ukraine

Prospects of RS technologies

N. V. Baranovskiy, M. V. Zharikova

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

There are many different statistical and empirical methods of forest fire danger use at present time. All systems have not physical basis. Last decade deterministic-probabilistic method is rapidly developed in Tomsk Polytechnic University. Forest sites classification is one way to estimate forest fire danger. We used this method in present work. Forest fire danger estimation depends on forest vegetation condition, forest fire retrospective, precipitation and air temperature. In fact, we use modified Nesterov Criterion. Lightning activity is under consideration as a high temperature source in present work. We use Web-GIS platform for program realization of this method. The program realization of the fire danger assessment system is the Web-oriented geoinformation system developed by the Django platform in the programming language Python. The GeoDjango framework was used for realization of cartographic functions. We suggest using of Terra/Aqua MODIS products for hot spot monitoring. Typical territory for forest fire danger estimation is Proletarskoe forestry of Kherson region (Ukraine).

Original languageEnglish
Title of host publicationRemote Sensing of Clouds and the Atmosphere XXI
PublisherSPIE
Volume10001
ISBN (Electronic)9781510604063
DOIs
Publication statusPublished - 2016
EventRemote Sensing of Clouds and the Atmosphere XXI - Edinburgh, United Kingdom
Duration: 28 Sep 201629 Sep 2016

Conference

ConferenceRemote Sensing of Clouds and the Atmosphere XXI
CountryUnited Kingdom
CityEdinburgh
Period28.9.1629.9.16

Fingerprint

WebGIS
Ukraine
forest fires
Forest Fire
Geographic information systems
hazards
Fires
platforms
Prediction
predictions
Lightning
programming languages
Forestry
Python
MODIS
Probabilistic Methods
MODIS (radiometry)
lightning
Vegetation
Hot Spot

Keywords

  • assessment
  • danger
  • forest fire
  • remote sensing
  • Web-GIS

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering

Cite this

Baranovskiy, N. V., & Zharikova, M. V. (2016). Web-GIS platform for forest fire danger prediction in Ukraine: Prospects of RS technologies. In Remote Sensing of Clouds and the Atmosphere XXI (Vol. 10001). [100010Y] SPIE. https://doi.org/10.1117/12.2241670

Web-GIS platform for forest fire danger prediction in Ukraine : Prospects of RS technologies. / Baranovskiy, N. V.; Zharikova, M. V.

Remote Sensing of Clouds and the Atmosphere XXI. Vol. 10001 SPIE, 2016. 100010Y.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Baranovskiy, NV & Zharikova, MV 2016, Web-GIS platform for forest fire danger prediction in Ukraine: Prospects of RS technologies. in Remote Sensing of Clouds and the Atmosphere XXI. vol. 10001, 100010Y, SPIE, Remote Sensing of Clouds and the Atmosphere XXI, Edinburgh, United Kingdom, 28.9.16. https://doi.org/10.1117/12.2241670
Baranovskiy NV, Zharikova MV. Web-GIS platform for forest fire danger prediction in Ukraine: Prospects of RS technologies. In Remote Sensing of Clouds and the Atmosphere XXI. Vol. 10001. SPIE. 2016. 100010Y https://doi.org/10.1117/12.2241670
Baranovskiy, N. V. ; Zharikova, M. V. / Web-GIS platform for forest fire danger prediction in Ukraine : Prospects of RS technologies. Remote Sensing of Clouds and the Atmosphere XXI. Vol. 10001 SPIE, 2016.
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