Publications

Wang, JM; Zhang, XY; Rodman, K (2021). Land cover composition, climate, and topography drive land surface phenology in a recently burned landscape: An application of machine learning in phenological modeling. AGRICULTURAL AND FOREST METEOROLOGY, 304, 108432.

Abstract
Land surface phenology (LSP) characterizes the seasonal dynamics of vegetation communities that compose individual satellite pixels and its interannual and spatial variations have been widely associated with climate. However, increasing evidence shows an effect of land cover composition within a pixel on LSP, but it remains unclear the extent of impacts relative to other drivers. To fill this gap, this study quantitatively assessed the contributions of land cover composition, climate, and topography on the spatial and interannual variation in LSP throughout the 2002 Ponil Complex Fire in New Mexico, USA, using a machine learning approach of Boosted Regression Trees (BRT). As the fire mainly converted ponderosa pine and Douglas-fir (evergreen tree) to soil ground and Gambel Oak (deciduous shrub), we computed both the proportion of tree cover to all vegetation cover (PTV) and vegetation fractional cover (VFC) as the metrics of land cover composition from high-resolution images in 2018 and from MODIS growing season greenness from 2001-2018. Start (SOS) and end (EOS) of growing season were derived from 500-m MODIS data from 2001-2018 and 30-m Harmonized Landsat Sentinel-2 data in 2018. BRT models showed that PTV was the most important predictor of spatial variations in SOS and EOS in 2018, despite the different contributions (20.3% - 42.9%) at 30-m and 500-m spatial scales. Although the growing degree days (28.6%) and the first freeze date (19.6%) were the most important predictors of interannual variations in SOS and EOS from 2001-2018, respectively, VFC also presented an important contribution for SOS (8.4%) and EOS (12.2%). This study demonstrates the utility of machine learning in modeling phenology and highlights the essential role of land cover composition in understanding the spatial and interannual variations of LSP that have been widely associated with topography and climate.

DOI:
10.1016/j.agrformet.2021.108432

ISSN:
0168-1923