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

Publikation: Bidrag til bog/antologi/rapport › Bidrag til bog/antologi › Forskning › fagfællebedømt

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

Publikation: Bidrag til bog/antologi/rapport › Bidrag til bog/antologi › Forskning › fagfællebedømt

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. i C Kuenzer, S Dech & W Wagner (red), Remote Sensing and Digital Image Processing. Springer, Remote Sensing and Digital Image Processing, bind 22, s. 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. I C. Kuenzer, S. Dech, & W. Wagner (red.), Remote Sensing and Digital Image Processing (s. 159-182). Springer. Remote Sensing and Digital Image Processing Bind 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 o.a. Assessment of vegetation trends in drylands from time series of earth observation data. I Kuenzer C, Dech S, Wagner W, red., Remote Sensing and Digital Image Processing. Springer. 2015. s. 159-182. (Remote Sensing and Digital Image Processing, Bind 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. red. / Claudia Kuenzer ; Stefan Dech ; Wolfgang Wagner. Springer, 2015. s. 159-182 (Remote Sensing and Digital Image Processing, Bind 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