@inbook{5aead0d9a321401199872e9f6f14d0e4,
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 = "Rasmus Fensholt and Stephanie Horion and Torbern Tagesson and Andrea Ehammer and Kenneth Grogan and Feng Tian and Silvia Huber and Jan Verbesselt and Prince, {Stephen D.} and Tucker, {Compton J.} and Kjeld Rasmussen",
year = "2015",
month = jan,
day = "1",
doi = "10.1007/978-3-319-15967-6_8",
language = "English",
series = "Remote Sensing and Digital Image Processing",
publisher = "Springer",
pages = "159--182",
booktitle = "Remote Sensing and Digital Image Processing",
address = "Germany",
}