Header

UZH-Logo

Maintenance Infos

Analysis of monotonic greening and browning trends from global NDVI time-series


de Jong, Rogier; de Bruin, S; de Wit, A; Schaepman, M E; Dent, D L (2011). Analysis of monotonic greening and browning trends from global NDVI time-series. Remote Sensing of Environment, 115(2):692 - 702.

Abstract

Remotely sensed vegetation indices are widely used to detect greening and browning trends; especially the global coverage of time-series normalized difference vegetation index (NDVI) data which are available from 1981. Seasonality and serial auto-correlation in the data have previously been dealt with by integrating the data to annual values; as an alternative to reducing the temporal resolution, we apply harmonic analyses and non-parametric trend tests to the GIMMS NDVI dataset (1981-2006). Using the complete dataset, greening and browning trends were analyzed using a linear model corrected for seasonality by subtracting the seasonal component, and a seasonal non-parametric model. In a third approach, phenological shift and variation in length of growing season were accounted for by analyzing the time-series using vegetation development stages rather than calendar days. Results differed substantially between the models, even though the input data were the same. Prominent regional greening trends identified by several other studies were confirmed but the models were inconsistent in areas with weak trends. The linear model using data corrected for seasonality showed similar trend slopes to those described in previous work using linear models on yearly mean values. The non-parametric models demonstrated the significant influence of variations in phenology; accounting for these variations should yield more robust trend analyses and better understanding of vegetation trends.

Abstract

Remotely sensed vegetation indices are widely used to detect greening and browning trends; especially the global coverage of time-series normalized difference vegetation index (NDVI) data which are available from 1981. Seasonality and serial auto-correlation in the data have previously been dealt with by integrating the data to annual values; as an alternative to reducing the temporal resolution, we apply harmonic analyses and non-parametric trend tests to the GIMMS NDVI dataset (1981-2006). Using the complete dataset, greening and browning trends were analyzed using a linear model corrected for seasonality by subtracting the seasonal component, and a seasonal non-parametric model. In a third approach, phenological shift and variation in length of growing season were accounted for by analyzing the time-series using vegetation development stages rather than calendar days. Results differed substantially between the models, even though the input data were the same. Prominent regional greening trends identified by several other studies were confirmed but the models were inconsistent in areas with weak trends. The linear model using data corrected for seasonality showed similar trend slopes to those described in previous work using linear models on yearly mean values. The non-parametric models demonstrated the significant influence of variations in phenology; accounting for these variations should yield more robust trend analyses and better understanding of vegetation trends.

Statistics

Citations

148 citations in Web of Science®
178 citations in Scopus®
Google Scholar™

Altmetrics

Downloads

180 downloads since deposited on 08 Mar 2011
83 downloads since 12 months
Detailed statistics

Additional indexing

Item Type:Journal Article, refereed, original work
Communities & Collections:07 Faculty of Science > Institute of Geography
Dewey Decimal Classification:910 Geography & travel
Uncontrolled Keywords:Seasonal Mann-Kendall
Language:English
Date:February 2011
Deposited On:08 Mar 2011 16:17
Last Modified:05 Apr 2016 14:34
Publisher:Elsevier
ISSN:0034-4257
Publisher DOI:https://doi.org/10.1016/j.rse.2010.10.011

Download

Download PDF  'Analysis of monotonic greening and browning trends from global NDVI time-series'.
Preview
Content: Accepted Version
Filetype: PDF
Size: 986kB
View at publisher