1 / 20

Contribution of environment factors to the temperature distribution

Contribution of environment factors to the temperature distribution according to different resolution levels Test in a small area of Svalbard. DR. Daniel Joly & Dr. Thierry Brossard CNRS, Université de Franche-Comté Besançon, FRANCE. 9th Bi-Annual Circumpolar Remote Sensing Symposium

jenna-booth
Télécharger la présentation

Contribution of environment factors to the temperature distribution

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Contribution of environment factors to the temperature distribution according to different resolution levels Test in a small area of Svalbard DR. Daniel Joly & Dr. Thierry Brossard CNRS, Université de Franche-Comté Besançon, FRANCE 9th Bi-Annual Circumpolar Remote Sensing Symposium May 15-19, 2006 Seward, Alaska, USA

  2. Objectives Climate is organized hierarchically according to scale levels The objective is to identify the scale level for which the contribution of topography and land cover to temperature spatial variation is the highest Temperature, one major climate element in the Arctic, depends on scale levels of landscape structures

  3. Organisation of the presentation • 1. Study area • 2. Data sets: • Temperature measurements • Remote sensed data • DTM • 3. Method • 4. Results

  4. 1. Study arealocalisation Svalbard archipelago Kongsforden area

  5. Study area and localisation of the 53 loggers Fjord s a n d u r plain morainic amphitheater Mountain with two glaciers

  6. Data set 153 temperature loggers - Type: HOBO - Located at 20 cm above the ground

  7. Temperature records • - Record once • every 6 minutes • from 12th of July • until 7 of August • 1999 (27 days) • Daily minima are extracted from the records

  8. Data set 2 Remote sensed images shadow 2 m primary data a scanned infrared aerial photography 20 m primary data SPOT image

  9. Derived data from remote sensed data 2 m primary data PVI (Probability to belong a 100% Vegetated area Index) 20 m primary data NDVI

  10. Data set 3 Didital elevation model < 2 m > 150 m 2 m a GPS DEM 20 m Norsk PolarInstitut DEM

  11. Derived data from DEM: Solar energy < 9.5 kW/J > 10.1 kW/J 2 m primary data 20 m primary data

  12. 3. Method Procedure of windowing From the 2 m primary database, 6 subsests (6 m square to 300 m square windows) are derived From the 20 m primary database, 3 subsests (60 m square to 300 m square windows) are derived

  13. Windowing is applied on the derived files 2 m primary data 20 m primary data 6 VPI values are provided (one for each window) 3 NDVI values are provided (one for each window)

  14. Linear correlation analysis Variable to be explained: - daily minima of temperature (17th of July and 5th of August) Explanatory variables: - solar energy - PVI and NDVI - elevation One coefficient of correlation for each date, each window and each explanatory variable

  15. 4. Results linear correlation analysis applied to Solar energy 0.8 0.6 17th of July 0.4 0.2 Solar energy Calculated from: r 0 2 m primary base -0.2 5th of August -0.4 -0.6 20 m primary base -0.8 6 m 14 m 30 m 60 m 140 m 300 m resolution 1 2 3 4 5 6 window

  16. linear correlation analysis applied tovegetation indices 0.8 0.6 17th of July 0.4 0.2 r 0 -0.2 PVI (2 m primary base) 5th of August -0.4 -0.6 NDVI (20 m primary base) -0.8 6 m 14 m 30 m 60 m 140 m 300 m resolution 1 2 3 4 5 6 window

  17. linear correlation analysis applied toaltitude 0.8 17th of July 0.6 0.4 0.2 r 0 -0.2 PVI (2 m primary base) -0.4 5th of August -0.6 NDVI (20 m primary base) -0.8 resolution 2 m 5 m 10 m 20 m 50 m 100 m window 1 2 3 4 5 6

  18. Temperature map 17th of July 2 m resolution 20 m resolution Temp=f(g1, se2, e1, PVI6, PrxFj) Temp=f(g4, se4, e4, PVI6, PrxFj) g=gradient, se=solar energy, e=elevation, Pvi=probability to belonging a 100% vegetated area Index PrxFj=proximity to the fjord

  19. Temperature map 5th of August 2 m resolution 20 m resolution Temp=f(se4, e1, g3, NDVI6) Temp=f(se4, e4, g4, NDVI6) g=gradient, se=solar energy, e=elevation

  20. Conclusions The highest coefficient on each curve marks the optimum scale level; it varies in value and place according to the variables. The results from the both primary DTM are similar. NDVI (satellite image) provides better results than PVI (Infrared aerial photography). Temperature distribution modelling is optimum when using usual data sources such as satellite images and DTM available for wide areas.

More Related