Remote Sensing of Boreal Spring Phenology
Application of MODIS data over Canadian Boreal Region
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Vegetation phenology is vital in understanding various forestry related activities. Here, the objectives were to determine the spatial dynamics of two phenological stages in the spring season [i.e. snow gone (SGN), and conifer needle flushing (CNF)] in the Canadian province of Alberta during 2006-08. In the first phase, the potential of MODerate-resolution Imaging Spectroradiometer (MODIS)-based indices [i.e., enhanced vegetation index (EVI), normalized difference water index (NDWI), and normalized difference snow index (NDSI)] were evaluated in determining the SGN stages. It revealed that NDW...
Vegetation phenology is vital in understanding various forestry related activities. Here, the objectives were to determine the spatial dynamics of two phenological stages in the spring season [i.e. snow gone (SGN), and conifer needle flushing (CNF)] in the Canadian province of Alberta during 2006-08. In the first phase, the potential of MODerate-resolution Imaging Spectroradiometer (MODIS)-based indices [i.e., enhanced vegetation index (EVI), normalized difference water index (NDWI), and normalized difference snow index (NDSI)] were evaluated in determining the SGN stages. It revealed that NDWI at 2.13µm demonstrated best prediction capabilities for SGN (i.e., on an average ~65.6% of the cases fell within ±1 period or ±8 days of deviation). In the second phase of delineating CNF, the logical 'OR' combination between the thresholds of NDWI at 2.13µm (i.e., 0.525) and accumulated growing degree days (AGDD of 200 degree days) were found to be generating the best results (i.e., on anaverage ~68.7% of the cases fell within ±1 period of deviation). Overall, the outcomes demonstrated its effectiveness in delineating the boreal spring phenological stages at a spatial resolution of 500m.
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