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Remote Sens. 2010, 2(5), 1348-1363; doi:10.3390/rs2051348
Article

Evaluating Potential of MODIS-based Indices in Determining “Snow Gone” Stage over Forest-dominant Regions

1
, 1,*  and 2
Received: 25 February 2010; in revised form: 28 April 2010 / Accepted: 6 May 2010 / Published: 11 May 2010
(This article belongs to the Special Issue Multi-Temporal Remote Sensing)
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Abstract: “Snow gone” (SGN) stage is one of the critical variables that describe the start of the official forest fire season in the Canadian Province of Alberta. In this paper, our objective is to evaluate the potential of MODIS-based indices for determining the SGN stage. Those included: (i) enhanced vegetation index (EVI), (ii) normalized difference water index (NDWI) using the shortwave infrared (SWIR) spectral bands centered at 1.64 µm (NDWI1.64µm) and at 2.13 µm (NDWI2.13µm), and (iii) normalized difference snow index (NDSI). These were calculated using the 500 m 8-day gridded MODIS-based composites of surface reflectance data (i.e., MOD09A1 v.005) for the period 2006–08. We performed a qualitative evaluation of these indices over two forest fire prone natural subregions in Alberta (i.e., central mixedwood and lower boreal highlands). In the process, we generated and compared the natural subregion-specific lookout tower sites average: (i) temporal trends for each of the indices, and (ii) SGN stage using the ground-based observations available from Alberta Sustainable Resource Development. The EVI-values were found to have large uncertainty at the onset of the spring and unable to predict the SGN stages precisely. In terms of NDSI, it showed earlier prediction capabilities. On the contrary, both of the NDWI’s showed distinct pattern (i.e., reached a minimum value before started to increase again during the spring) in relation to observed SGN stages. Thus further analysis was carried out to determine the best predictor by comparing the NDWI’s predicted SGN stages with the ground-based observations at all of the individual lookout tower sites (approximately 120 in total) across the study area. It revealed that NDWI2.13µm demonstrated better prediction capabilities (i.e., on an average approximately 90% of the observations fell within ±2 periods or ±16 days of deviation) in comparison to NDWI1.64µm (i.e., on an average approximately 73% of the observations fell within ±2 periods or ±16 days of deviation).
Keywords: enhanced vegetation index; normalized difference snow index; normalized difference water index; natural subregions; forest enhanced vegetation index; normalized difference snow index; normalized difference water index; natural subregions; forest
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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MDPI and ACS Style

Sekhon, N.S.; Hassan, Q.K.; Sleep, R.W. Evaluating Potential of MODIS-based Indices in Determining “Snow Gone” Stage over Forest-dominant Regions. Remote Sens. 2010, 2, 1348-1363.

AMA Style

Sekhon NS, Hassan QK, Sleep RW. Evaluating Potential of MODIS-based Indices in Determining “Snow Gone” Stage over Forest-dominant Regions. Remote Sensing. 2010; 2(5):1348-1363.

Chicago/Turabian Style

Sekhon, Navdeep S.; Hassan, Quazi K.; Sleep, Robert W. 2010. "Evaluating Potential of MODIS-based Indices in Determining “Snow Gone” Stage over Forest-dominant Regions." Remote Sens. 2, no. 5: 1348-1363.


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