Monitoring for Changes in Spring Phenology at Both Temporal and Spatial Scales Based on MODIS LST Data in South Korea

• Published in: Remote sensing (Basel, Switzerland) Vol. 12; no. 20; p. 3282
• Main Authors: Lim, Chi Hong, Jung, Song Hie, Kim, A Reum, Kim, Nam Shin, Lee, Chang Seok
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.10.2020
• Subjects: AGDD; Air temperature; Climate change; Environmental changes; EVI; Flowers & plants; Hypotheses; Land surface temperature; Meteorological data; MODIS; MODIS LST; Mongolian oak; Oak; Phenology; Plant reproduction; Remote sensing; Rural areas; Satellite imagery; Seasons; Spectroradiometers; Spring (season); Urban areas; Urban heat islands; Urbanization; Vegetation; Vegetation index; Weather stations; South Korea
• ISSN: 2072-4292; 2072-4292
• DOI: 10.3390/rs12203282

This study aims to monitor spatiotemporal changes of spring phenology using the green-up start dates based on the accumulated growing degree days (AGDD) and the enhanced vegetation index (EVI), which were deducted from moderate resolution imaging spectroradiometer (MODIS) land surface temperature (LST) data. The green-up start dates were extracted from the MODIS-derived AGDD and EVI for 30 Mongolian oak (Quercus mongolica Fisch.) stands throughout South Korea. The relationship between green-up day of year needed to reach the AGDD threshold (DoYAGDD) and air temperature was closely maintained in data in both MODIS image interpretation and from 93 meteorological stations. Leaf green-up dates of Mongolian oak based on the AGDD threshold obtained from the records measured at five meteorological stations during the last century showed the same trend as the result of cherry observed visibly. Extrapolating the results, the spring onset of Mongolian oak and cherry has become earlier (14.5 ± 4.3 and 10.7 ± 3.6 days, respectively) with the rise of air temperature over the last century. The temperature in urban areas was consistently higher than that in the forest and the rural areas and the result was reflected on the vegetation phenology. Our study expanded the scale of the study on spring vegetation phenology spatiotemporally by combining satellite images with meteorological data. We expect our findings could be used to predict long-term changes in ecosystems due to climate change.