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

Standard

Assessment of vegetation trends in drylands from time series of earth observation data. / Fensholt, Rasmus; Horion, Stephanie; Tagesson, Torbern; Ehammer, Andrea; Grogan, Kenneth; Tian, Feng; Huber, Silvia; Verbesselt, Jan; Prince, Stephen D.; Tucker, Compton J.; Rasmussen, Kjeld.

Remote Sensing and Digital Image Processing. ed. / Claudia Kuenzer; Stefan Dech; Wolfgang Wagner. Springer, 2015. p. 159-182 (Remote Sensing and Digital Image Processing, Vol. 22).

Research output: Chapter in Book/Report/Conference proceeding › Book chapter › Research › peer-review

Harvard

Fensholt, R, Horion, S, Tagesson, T, Ehammer, A, Grogan, K, Tian, F, Huber, S, Verbesselt, J, Prince, SD, Tucker, CJ & Rasmussen, K 2015, Assessment of vegetation trends in drylands from time series of earth observation data. in C Kuenzer, S Dech & W Wagner (eds), Remote Sensing and Digital Image Processing. Springer, Remote Sensing and Digital Image Processing, vol. 22, pp. 159-182. https://doi.org/10.1007/978-3-319-15967-6_8

APA

Fensholt, R., Horion, S., Tagesson, T., Ehammer, A., Grogan, K., Tian, F., Huber, S., Verbesselt, J., Prince, S. D., Tucker, C. J., & Rasmussen, K. (2015). Assessment of vegetation trends in drylands from time series of earth observation data. In C. Kuenzer, S. Dech, & W. Wagner (Eds.), Remote Sensing and Digital Image Processing (pp. 159-182). Springer. Remote Sensing and Digital Image Processing Vol. 22 https://doi.org/10.1007/978-3-319-15967-6_8

Vancouver

Fensholt R, Horion S, Tagesson T, Ehammer A, Grogan K, Tian F et al. Assessment of vegetation trends in drylands from time series of earth observation data. In Kuenzer C, Dech S, Wagner W, editors, Remote Sensing and Digital Image Processing. Springer. 2015. p. 159-182. (Remote Sensing and Digital Image Processing, Vol. 22). https://doi.org/10.1007/978-3-319-15967-6_8

Author

Fensholt, Rasmus ; Horion, Stephanie ; Tagesson, Torbern ; Ehammer, Andrea ; Grogan, Kenneth ; Tian, Feng ; Huber, Silvia ; Verbesselt, Jan ; Prince, Stephen D. ; Tucker, Compton J. ; Rasmussen, Kjeld. / Assessment of vegetation trends in drylands from time series of earth observation data. Remote Sensing and Digital Image Processing. editor / Claudia Kuenzer ; Stefan Dech ; Wolfgang Wagner. Springer, 2015. pp. 159-182 (Remote Sensing and Digital Image Processing, Vol. 22).

Bibtex

@inbook{36033862682740658858816fd47c242f,
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",
doi = "10.1007/978-3-319-15967-6_8",
language = "English",
series = "Remote Sensing and Digital Image Processing",
publisher = "Springer",
pages = "159--182",
editor = "Kuenzer, {Claudia } and Dech, {Stefan } and Wolfgang Wagner",
booktitle = "Remote Sensing and Digital Image Processing",
address = "Switzerland",

}

RIS

TY - CHAP

T1 - Assessment of vegetation trends in drylands from time series of earth observation data

AU - Fensholt, Rasmus

AU - Horion, Stephanie

AU - Tagesson, Torbern

AU - Ehammer, Andrea

AU - Grogan, Kenneth

AU - Tian, Feng

AU - Huber, Silvia

AU - Verbesselt, Jan

AU - Prince, Stephen D.

AU - Tucker, Compton J.

AU - Rasmussen, Kjeld

PY - 2015

Y1 - 2015

N2 - 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.

AB - 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.

U2 - 10.1007/978-3-319-15967-6_8

DO - 10.1007/978-3-319-15967-6_8

M3 - Book chapter

AN - SCOPUS:84979982345

T3 - Remote Sensing and Digital Image Processing

SP - 159

EP - 182

BT - Remote Sensing and Digital Image Processing

A2 - Kuenzer, Claudia

A2 - Dech, Stefan

A2 - Wagner, Wolfgang

PB - Springer

ER -

ID: 239904472