1 of 54

GIS & RS APPLICATIONS ON FOREST FIRES���MR. PA. RAAJAN BAALU�DEPUTY COMMANDANT, NDRF-04BN��MR. A. VIJAY�ASSISTANT PROFESSOR, DEPT. OF CIVIL ENGG,�SRM INSTITUTE OF SCIENCE AND TECHNOLOGY

2 of 54

OUTLINE

  • Why remote sensing is useful
  • Current methods & problems with them
  • Future of remote sensing
    • LIDAR
    • Landsat
    • AVHRR
    • ASTER
    • Hyperspectral satellites

3 of 54

WHY?

  • Human population
  • Environmental planning
  • More cost/time efficient than current methods
  • Better understanding
  • Detailed mapping
  • Improved accuracy

4 of 54

CURRENT METHODS

  • Aerial photography
  • Field measurements and mapping
  • Passive remote sensing
  • Medium spatial resolution multi-spectral satellite

5 of 54

6 of 54

PASSIVE SENSORS

  • Effectiveness
  • Can’t see understory
  • Depends on intended application

7 of 54

AERIAL PHOTOGRAPHY PROBLEMS

  • Limited number of bands
  • Limited coverage
  • Time consuming
  • Can’t take photos as often
  • Development cost
  • Difficulty assessing fuel
    • subjective

8 of 54

PROBLEMS IN THE FIELD

  • Time consuming
  • Accessibility issues
  • Subjective
  • Costly
    • Human and instrument
  • Low updating frequency

9 of 54

10 of 54

11 of 54

MEDIUM RESOLUTION

  • Superficial observations
  • Reflectance
  • Rely on field obs

12 of 54

LIDAR

  • Light Detection and Ranging
  • Penetrability
  • Accuracy
  • Data computed
  • Applications

13 of 54

LANDSAT

  • Multispectral
    • visible and mid-infrared
  • High resolution
  • Surface/Canopy characteristics
  • Vegetation categories
  • Recalibrate

​

​

14 of 54

15 of 54

16 of 54

AVHRR

  • Advanced Very High Resolution Radiometer
  • Originally Met. Satellite
  • Multispectral
    • visible and thermal infrared
  • Long-term monitoring
  • Remote and isolated areas
  • Restricted

​

17 of 54

18 of 54

19 of 54

ASTER

  • Multispectral
    • visible and near-infrared telescope
  • Vegetation
  • Mapping fuel characteristics
  • Quantitative accuracy

​

​

​

20 of 54

21 of 54

HYPERSPECTRAL

  • Directly related analysis
  • Map vegetation
  • Species mapping
  • Vegetation classification
  • Preventative measure
  • Limited spatial coverage

​

22 of 54

23 of 54

Frequency of SATELLITE DATA

24 of 54

Spectral Bands

25 of 54

Satellite

​

Description

​

Sentinel-1

Maritime and land monitoring emergency response climate change

Sentinel-2

Land-cover maps land-change detection maps vegetation monitoring monitoring of burnt areas

Sentinel-3

Surface topography observations ocean and land surface colour observation and monitoring. The Sentinel-3 OLCI instrument ensures the continuity of Envisat Meris

Sentinel-5P

Monitoring the concentration of carbon monoxide (CO) nitrogen dioxide (NO2) and ozone (O3) in the air. Monitoring the UV aerosol index (AER_AI) and various geophysical parameters of clouds (CLOUD)

ESAs archive of Landsat 5/7 and 8

vegetation monitoring land use land cover maps change monitoring global coverage of Landsat 8 - Envisat Meris and old data

Proba-V

The observation of land cover vegetation growth climate impact assessment water resource management agricultural monitoring and food security estimates inland water resource monitoring and tracking the steady spread of deserts and deforestation.

MODIS

Monitoring of land clouds ocean colour at a global scale (by ESA)

GIBS

Global image browser service with over 600 satellites made available by NASA

26 of 54

Overlay based on Common Geographic Location

27 of 54

Anatomy of a GIS Database:

Vector Layers

Attribute Tables

Raster

Layers

28 of 54

Topography

Terrain, aspect, exposure, accessibility

Weather

Temperature, rainfall, humidity, wind

​

People

Current and Past management practices, forest resources use, fire use, policies and regulations, etc.

Fuel

Living and dead vegetation, organic soil material

29 of 54

Grid 5 km × 5 km

Forest Cover

Temperature Data

Relative Humidity

Forest Grid

Forest Type Map

-Vulnerable Forest Types

Drought

Intersect

Mask Out

  • Rainfall area
  • Fire point data to identify grids that are already depleted of fuels
  • High FRP value

- Knowledge base decision based on forest cover density and forest boundary

- Selection of pre-warning alert grids

Pre-Warning Alerts (KML)

30 of 54

31 of 54

  • 7,854 hectares of forest were razed in 4,247 cases of reported fire incidents in the last five years in the State
  • An average of 1,000ha of forest are being engulfed in a fire every year
  • Among all the forest divisions, Theni reported the maximum damage losing 205ha in 98 forest fires. It also suffered one of the biggest tragedies in recent memory in March 2018, when 23 persons were charred to death during a trekking expedition on Kurangani Hills.

Forest Fires and Assessment in the State of Tamilnadu

32 of 54

FIRMS VS SENTINEL

​

33 of 54

FIRMS – MODIS NETWORK

  1. Open Google Earth
  2. Click Add >> Network Link
  3. Paste and Copy URL location
  4. Select Refresh Tab
  5. Set Time-based Refresh to When:Periodically
  6. Adjust the Frequency as needed
  7. Click OK at the bottom
  8. The specified *.kml file will be shown at temporary places and available at temporary places menu

34 of 54

35 of 54

36 of 54

37 of 54

OPEN SOURCE – EO BROWSER

  1. Go to EO Browser — sign up and login (it's free)
  2. Select Sentinel-2 (To monitor vegetation, soil and water cover, inland waterways and coastal areas.)
  3. Narrow down the data collection by limiting cloud coverage to 30%.
  4. Spot Wildfires in the Bandipur/Kurangani Forest range, by choosing appropriate time period.

​

38 of 54

OPEN SOURCE – EO BROWSER

  • BAND
  • Level 1 visualization - R5 G4 B3
  • Level 2 visualization - R4 G3 B2

​

39 of 54

  • MODIS – Moderate Resolution Imaging Spectroradiometer
  • VIIRS – Visible Infrared Imaging Radiometer Suite

​

40 of 54

41 of 54

42 of 54

43 of 54

44 of 54

45 of 54

46 of 54

47 of 54

48 of 54

49 of 54

50 of 54

51 of 54

MAJOR RESOURCES

  • Sentinel EO
  • USGS
  • GloVis
  • EARTHData-NASA
  • EOSDIS – FIRMS (https://firms.modaps.eosdis.nasa.gov)

52 of 54

CONCLUSIONS

  • Better than aerial/ground obs
  • Don’t use alone
  • Need for surface info
  • Helpful in mapping and analyzing
    • both before and after
  • Don’t generalize, need to know underlying process

53 of 54

REFERENCES

  • Remote Sensing Techniques to Assess Active Fire Characteristics and Post-Fire Effects. Lentile, Leigh B. et al., International Journal of Wildland Fire, 2006, 15, 319-345
  • Evaluating ASTER Satellite Imagery and Gradient Modeling for Mapping and Characterizing Wildland Fire Fuels. Falkowski, Michael J. et al., ASPRS Annual Conference Proceedings, May 2004
  • Assessing Fuel Loads using Remote Sensing Technical Report Summary. Roff, A. et al., The University of New South Wales, 2005
  • http://earthobservatory.nasa.gov/Library/GlobalFire/fire_5.html
  • http://www.eduspace.esa.int/subdocument/default.asp?document=353
  • EO Browser/Sentinel
  • Copernicus Sentinel Playground

54 of 54

  • er.vijaystrgis@gmail.com
  • 9952 09 32 33

​

@VjEngr

www.vijaypro.com