@inbook{01472c2d7bf14d039206b67f51a96624,
title = "Assessment of vegetation trends in drylands from time series of earth observation data",
abstract = "This chapter summarizes approaches to the detection of dryland vegetation change and methods for observing spatio-temporal trends from space. An overview of suitable long-term Earth Observation (EO) based datasets for assessment of global dryland vegetation trends is provided and a status map of contemporary greening and browning trends for global drylands is presented. The vegetation metrics suitable for per-pixel temporal trend analysis is discussed, including seasonal parameterisation and the appropriate choice of trend indicators. Recent methods designed to overcome assumptions of long-term linearity in time series analysis (Breaks For Additive Season and Trend(BFAST)) are discussed. Finally, the importance of the spatial scale when performing temporal trend analysis is introduced and a method for image downscaling (Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM)) is presented.",
author = "R. Fensholt and S. Horion and T. Tagesson and A. Ehammer and K. Grogan and F. Tian and S. Huber and J. Verbesselt and S.D. Prince and C.J. Tucker and K. Rasmussen",
year = "2015",
doi = "10.1007/978-3-319-15967-6\_8",
language = "English",
isbn = "9783319159676",
series = "Remote Sensing and Digital Image Processing",
publisher = "Springer International Publishing",
number = "22",
pages = "159--182",
editor = "C. Kuenzer and S. Dech and W. Wagner",
booktitle = "Remote Sensing Time Series : Revealing Land Surface Dynamics",
}