Characterizing 32 Years of Shrub Cover Dynamics in Southern Portugal Using Annual Landsat Composites and Machine Learning Regression Modeling


The Landsat archive presents a unique source for mapping and monitoring of shrublands. Still, efficient and accurate mapping approaches are needed that provide shrub cover fraction estimates over space and time. The spectral signal of shrubs is composed of green vegetation and non-photosynthetic vegetation as well as varying fractions of soil, grass, and shadow, which makes a direct mapping of fraction cover challenging. In this study, we mapped 32 years of shrub cover fractions from annual Landsat best observation composites covering a rockrose (genus Cistus) ecosystem in the Baixo Alentejo region of southern Portugal. Fraction mapping was based on a multi-year support vector regression model trained with synthetically mixed data from a multi-annual image spectral library. Resulting fractions maps were validated using reference information derived from high-resolution satellite imagery available for 10 out of 32 years. Fraction maps reproduced the spatial-temporal patterns of shrub cover in the study region very well, with an average mean absolute error over all validation years of 13.7%. For individual validation years, mean absolute errors ranged from 7.7% to 17.1%. Our consistent modeling framework led to a reliable annual shrub cover fraction time-series, which allowed identifying areas of stable shrub cover and areas with different types and intensities of change. Such long-term shrub cover fraction monitoring is of great value for land use assessments and contributes to a thorough understanding and management of Earth’s arid and semi-arid ecosystems.

Remote Sensing of Environment