Assessment of vegetation trends in drylands from time series of earth observation data
Research output: Chapter in Book/Report/Conference proceeding › Book chapter › Research › peer-review
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.
Original language | English |
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Title of host publication | Remote Sensing and Digital Image Processing |
Editors | Claudia Kuenzer, Stefan Dech, Wolfgang Wagner |
Number of pages | 24 |
Publisher | Springer |
Publication date | 2015 |
Pages | 159-182 |
DOIs | |
Publication status | Published - 2015 |
Series | Remote Sensing and Digital Image Processing |
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Volume | 22 |
ISSN | 1567-3200 |
ID: 239904472