Elevation-dependent warming of land surface temperatures in the Andes assessed using MODIS LST time series (2000–2017)

Jaime Aguilar-Lome, Raúl Espinoza-Villar, Jhan Carlo Espinoza, Joel Rojas-Acuña, Bram Leo Willems, Walter Martín Leyva-Molina

Research output: Contribution to journalArticle

9 Scopus citations

Abstract

© 2019 Elsevier B.V. In this study, we report on the assessment of elevation-dependent warming processes in the Andean region between 7 °S and 20 °S, using Land Surface Temperature (LST). Remotely sensed LST data were obtained from Moderate Resolution Imaging Spectroradiometer (MODIS) sensor in an 8-day composite, at a 1 km resolution, and from 2000 to 2017 during austral winter (June-July-August, JJA). We analysed the relation between mean monthly daytime LST and mean monthly maximum air temperature. This relation is analysed for different types of coverage, obtaining a significant correlation that varies from 0.57 to 0.82 (p < 0.01). However, effects of change in land cover were ruled out by a previous comparative assessment of trends in daytime LST and normalized difference vegetation index (NDVI). The distribution of the winter daytime LST trend was found to be increasing in most areas, while decreasing in only a few areas. This trend shows that winter daytime LST is increasing at an average rate of 1.0 °C/decade. We also found that the winter daytime LST trend has a clear dependence on elevation, with strongest warming effects at higher elevations: 0.50 °C/decade at 1000–1500 masl, and 1.7 °C/decade above 5000 masl. However, the winter nighttime LST trend shows a steady increase with altitude increase. The dependence of rising temperature trends on elevation could have severe implications for water resources and high Andean ecosystems.
Original languageAmerican English
Pages (from-to)119-128
Number of pages10
JournalInternational Journal of Applied Earth Observation and Geoinformation
DOIs
StatePublished - 1 May 2019

Fingerprint Dive into the research topics of 'Elevation-dependent warming of land surface temperatures in the Andes assessed using MODIS LST time series (2000–2017)'. Together they form a unique fingerprint.

  • Cite this