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Detecting changes in understorey and canopy vegetation cycles in West Central Alberta using a fusion of Landsat and MODIS
Author(s) -
McClelland Cameron J. R.,
Coops Nicholas C.,
Berman Ethan E.,
Kearney Sean P.,
Nielsen Scott E.,
Burton A. Cole,
Stenhouse Gordon B.
Publication year - 2020
Publication title -
applied vegetation science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.096
H-Index - 64
eISSN - 1654-109X
pISSN - 1402-2001
DOI - 10.1111/avsc.12466
Subject(s) - vegetation (pathology) , moderate resolution imaging spectroradiometer , environmental science , enhanced vegetation index , remote sensing , normalized difference vegetation index , physical geography , satellite , satellite imagery , canopy , leaf area index , geography , vegetation index , ecology , medicine , pathology , biology , engineering , aerospace engineering , archaeology
Aims To model regional vegetation cycles through data fusion methods for creating a 30‐m daily vegetation product from 2000 to 2018 and to analyze annual vegetation trends over this time period. Location The Yellowhead Bear Management Area, a 31,180‐km 2 area in west central Alberta, Canada. Methods In this paper, we use Dynamic Time Warping (DTW) as a data fusion technique to combine Landsat 5, 7 and 8 satellite data and Moderate Resolution Image Spectroradiometer (MODIS) Aqua and Terra imagery, to quantify daily vegetation using Enhanced Vegetation Index at a 30‐m resolution, for the years 2000–2018. We validated this approach, entitled DRIVE (Daily Remote Inference of VEgetation), using imagery acquired from a network of ground cameras. Results When DRIVE was compared to start and end of season dates (SOS and EOS respectively) derived from ground cameras, correlations were r = 0.73 at SOS and r = 0.85 at EOS with a mean absolute error of 7.17 days at SOS and 10.76 days at EOS. Results showed that DRIVE accurately increased spatial and temporal resolution of remote‐sensing data. We demonstrated that SOS is advancing at a maximum rate of 0.78 days per year temporally over the 18‐year time period for varying elevation gradients and land cover classes over the region. Conclusions With DRIVE, we demonstrate the utility of DTW in quantifying vegetation cycles over a large heterogeneous region and determining how changing climate is affecting regional vegetation. DRIVE may prove to be an important method to determine how carbon sequestration is varying within fine‐scale individual plant communities in response to changing climate and likely will be beneficial to wildlife movement and habitat selection studies examining the varying response of wildlife species to changing vegetation cycles under shifting climatic conditions.