quazi k. hassan & navdeep s. sekhon

Earth Observation for Environment Laboratory, Dept. of Geomatics Engineering

Application of Remote Sensing-based Indices in Determining "Snow Gone"Stage over Forest-dominant Regions

Quazi K. Hassan&

Navdeep S. Sekhon


Outline

1. Introduction 2. Objectives

3. Study area and data used

4. Methodology

5. Results & discussion

6. Concluding remarks

7. Acknowledgements

8. Q & A


http://atlas.nrcan.gc.ca

Introduction (1)


Boreal Phenology• The phenological events of boreal forest are

broadly classified into the four categories namely:

i.snow stages;ii.understory stages;iii.deciduous stages; and iv.conifer stages.

Introduction (2)


“Snow gone” (SGN) can be defined as the date when 25% or less of the area surrounding a site is covered by snow.

In practice, the SGN stage is critical for determining the onset of the forest fire season.

In Alberta, the Provincial Government acquires SGN stage at ~120 lookout tower sites across the landscape. It has two limitations:

(i) As it is based on visual observation, and thus the results potentially may vary from person to person; and

(ii) it fails to address spatial variability as the lookout tower network provides only point type information over spatial areas of a few hundreds of hectares.

Introduction (3)


One option to address these concerns is to employ remote sensing-based techniques, which have already been proven as an effective method for delineating forestry-related variables at the landscape level.

Thus, we intend to explore the potential of remote sensing-based techniques for determining the SGN stage over the forest-dominant regions in Alberta.

Remote sensing is the acquisition of information about the earth surface in the form of images using electromagnetic radiation without having any physical contact.

In general, the reflective portion of the electromagnetic radiation (i.e., in between 0.4-3.0 µm) is used to understand SGN and other periodic changes.

Introduction (4)


The physical characteristics of the vegetation cover (e.g., greenness, water content, reflectance etc.) are different during various phenological stages

Temporal trends of vegetation indices capturing a physical characteristic(s) of vegetation cover reflect the changes caused by phenological transformations

The most commonly used remote sensing-based indices in phenological studies are: Normalized difference vegetation index (NDVI) and enhanced

vegetation index (EVI); both are a measure of greenness Normalized difference water index (NDWI: a measure of water

content in the vegetation) Normalized difference snow index (NDSI: a measure of snow on

the ground)

Introduction (5)


REDNIR

REDNIRNDVI

15.765.2

Re

Re

BluedNIR

dNIREVI

13.2

13.2 13.2

matSWIRNIR

matSWIRNIRmNDWI

m1.64at

m1.64at

SWIRGreen

SWIRGreenNDSI

is the reflectance from the respective spectral band

Modified from Zarco-Tejada et al. 2002

0.4 0.8 1.2 1.6 2.0 2.4Wavelength, µm

50

40

30

20

10

0

%

Ref

lect

ance

Blue Green Red NIR SWIR 0.469 0.555 0.645 0.858 1.24 1.64 2.13

Introduction (6)


i. Perform a qualitative evaluation of four MODIS-based indices (i.e., EVI, NDWI1.64μm, NDWI2.13μm, and NDSI) in predicting the SGN stages;

ii. Compare the SGN values predicted by the efficient indices as determined in objective (i) with the observations available at lookout tower sites across the landscape; and

iii. Generate a SGN map using the best predictor as determined in objective (ii) to discuss the spatial variability over the entire Province of Alberta.

Objectives


Central Mixedwood (25.4%)*Dry Mixedwood (13.43%)*Northern Mixedwood (3.5%)*Boreal Sub-Arctic (1.76%)*Peace-Athabasca Delta (0.83%)Lower Boreal Highlands (8.5%)*Alpine (2.03%)*Sub-Alpine (3.9%)*Montane (1.4%)*Upper Foothills (3.44%)*Lower Foothills (6.94%)*Athabasca Plain (2.04%)*Kazan Uplands (1.43%)*Foothills Parkland (0.57%)Peace River Parkland (0.48%)Central Parkland (8.1%)Dry Mixedgrass (6.99%)Foothills Fescue (2.12%)Northern Fescue (2.26%)Mixedgrass (3%)Upper Boreal Highlands (1.84%)*

Lookout tower sites in year 2006 (115)N

E

Study Area and Data Used (1)


Five hundred fifty two scenes of the MODIS-based 8-day composites of surface reflectance data (i.e., MOD09A1 v.005) at a 500 m resolution for the years 2006–2008 were obtained from NASA.

For a particular 8-day period, there were 4 scenes required to produce the entire extent of Alberta. Thus, for each of the years, there were forty six 8-day periodical images spanning from January 1 to December 31.

In addition, we also acquired ground-based observations of SGN at ~120 lookout tower sites during 2006-2008. There data were then converted into period to match with the satellite data as follows:

Study Area and Data Used (2)


MODIS–based 8-day composites of surface reflectance at 500 m resolution

(i.e. MOD09A1); Four scenes per period

Natural subregion map of Alberta

Mosiacking of the four scenes

Calculating remote sensing-based indices of:

EVI NDSINDWI1.64µm NDWI2.13µm

Qualitative evaluation of the indices over the natural subregions of interest• Extracting the temporal dynamics for each of the indices at all of the lookout tower sites; • Generating natural subregion-specific average temporal dynamics; • Comparing with the average ground-based observations of SGN; and• Determining the efficient indices in predicting SGN

Averaging the ground observed SGN day for natural subregions of:(i) central mixwood, and(ii) lower boreal highlands.

Determining the best index in predicting SGN periods

Converting the DOY of SGN into no. of periods of MODIS-based indices

Generating the SGN map for the entire study area

Ground-based observations of “snow gone” (SGN) day at

lookout tower sites (in DOY)

Methodology


Temporal dynamics of averaged values forEVI, NDWI1.64μm, NDWI2.13μm, and NDSI for central mixedwood(i.e., occupies ~25.5% of the province) during 2006–08. The average snow gone day from ground-based observations for the same area is shown bythe dotted vertical line.

Results & Discussion (1)


Temporal dynamics of averaged values forEVI, NDWI1.64μm, NDWI2.13μm, and NDSI for lower boreal highlands(i.e., occupies ~8.5% of the province) during 2006–08. The average snow gone day from ground-based observations for the same area is shown bythe dotted vertical line.



An example SGN map generated using NDWI2.13μm (the best predictor) for the year 2008.

It revealed that approximately 56% of the times the SGN stages fell in the rangeof 121–136 DOY.



Here we evaluated the potential of four MODIS-based indices (i.e., EVI, NDWI1.64μm, NDWI2.13μm, and NDSI) for determining SGN stages in Alberta.

A qualitative evaluation over two forest fire prone natural subregions demonstrated that both of the NDWI’s had better capabilities with

compare to EVI and NDSI.

Our quantitative analysis revealed that the NDWI2.13μm could better predict the SGN stages in comparison with NDWI1.64μm.

Thus, it will potentially be incorporated in the framework of forest fire management in the Province of Alberta.

Concluding Remarks


Recommendation & Further Research

We have generated SGN maps at 500 m spatial resolution using 8-day composites of MODIS data. Thus, it would be important to enhance:

temporal resolution to 2-4 days; and spatial resolution to 250 m.

Despite good results, we recommend to quantify the applicability of the described approach before implementing over other biomes/regions in Canada or elsewhere in the world.


Acknowledgements


References

Sekhon, N.S., Hassan, Q.K., and Sleep, R.W. 2010. Evaluating potential of MODIS-based indices in determining "snow gone" stage over forest-dominant regions. Remote Sensing, 2, 1348-1363.

Zarco –Tejada, P.J., Rueda, C.A., and Ustin, S.L. 2003. Water content estimation in vegetation with MODIS reflectance data and model inversion methods. Remote Sensing of Environment, 85, 109-124.


Thank You.

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quazi k. hassan & navdeep s. sekhon

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