Evapotranspiration estimates for two tropical mountain forest using high spatial resolution satellite data

Abstract

ABSTRACT Tropical Mountain Forest (TMF) provides important ecological functions like evapotranspiration (ET) that supplies moisture and energy to the atmosphere. ET observations are scarce and difficult to accomplish particularly in areas of high heterogeneity where TMF are. Remote sensing (RS) allows to quantify and to determine ET spatial variation at the landscape level. Detail imaginary improves high spatial variability retrieval. Thought the greater detail introduces cast shadows by trees which hamper image interpretation. The objective of this study is to characterize ET estimation for the TMF of the southern Ecuadorian Andes by combining meteorological data with high-resolution satellite images. Shadows from high resolution images were masked out by applying focal statistics. The analysis included two meteorological periods typical of the area; a wet period when rain prevails and a dry period when precipitation is more sporadic. The reference evapotranspiration (ET0) was calculated using the FAO-Penman Montheid method by applying data obtained from an automatic weather station. The enhanced vegetation index (EVI) was derived from 2 m resolution WorldView2 satellite images. Results showed a lower ET mean value during the wet period: 1.54 mm day−1 compared to 2.37 mm day−1. Two forest types, differentiated from its structural composition and topographical position (ravine and ridge), marked ET spatial variation. Ravine forest that has a more dense and closed canopy showed higher ET values for both meteorological conditions. A comparison between ET estimations and ET field measurements from a scintillometer device showed a good agreement (coefficient of correlation = 0.89) that proves the validity of the method. This study demonstrates that the application of high spatial resolution improves ET estimation in TMF especially when shadows are removed. Also, emphasizes the importance of analysing spatial heterogeneity to properly assess ecosystem water flux terms.