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

Remote Sensing

Navdeep S. Sekhon¹  Quazi K. Hassan¹ 

[1] Department of Geomatics Engineering, Schulich School of Engineering, University of Calgary, 2500 University Dr. NW Calgary, Alberta, T2N 1N4, Canada; E-Mail:

关键词: enhanced vegetation index;    normalized difference snow index;    normalized difference water index;    natural subregions;    forest;   

DOI  :  10.3390/rs2051348

来源: mdpi

【 摘 要 】

“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 (NDWI_1.64µm) and at 2.13 µm (NDWI_2.13µm), 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 NDWI_2.13µm 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 NDWI_1.64µm (i.e., on an average approximately 73% of the observations fell within ±2 periods or ±16 days of deviation).

【 授权许可】

CC BY   
© 2010 by the authors; licensee MDPI, Basel, Switzerland.