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

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 . 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.

Tendências temporais de índices de vegetação nos campos do Pampa do Brasil e do Uruguai

“…Em todos os casos, houve pequena variabilidade interna, observada nos baixos valores de desviospadrão médios estimados. Os sinais negativos de r e τ corroboram os resultados de Jong et al (2011), que calcularam tendências monotônicas negativas globais ao utilizar dados de índices de vegetação derivados do "advanced very high resolution radiometer" (AVHRR) do produto "global inventory modeling and mapping studies" (Gimms), na série temporal de 1981-2006. Constatou-se nítido sinal de diminuição do vigor vegetativo, associado ao campo de solos rasos. Nesses locais, a pecuária extensiva tem avançado substancialmente sobre os campos nos últimos anos (Paruelo et al, 2006), e o sobrepastoreio pode estar impactando negativamente a área.…”

“…Estudos já apontaram a resposta da vegetação aos efeitos cumulativos da ocorrência destes fenômenos nos mesmos campos (Jong et al, 2011).…”

Attribution of divergent northern vegetation growth responses to lengthening non-frozen seasons using satellite optical-NIR and microwave remote sensing

“…This method is insensitive to outliers, which are a challenge to all remote-sensing-based datasets (Alvera-Azcárate et al 2012). Similar non-parametric approaches have been successfully used for environmental trend studies on a grid-cell-wise basis (AlcarazSegura et al 2010;de Jong et al 2011;Pouliot, Latifovic, and Olthof 2009).…”

Towards an Improved Environmental Understanding of Land Surface Dynamics in Ukraine Based on Multi-Source Remote Sensing Time-Series Datasets from 1982 to 2013

“…The validation of these trends is not straightforward because the field observations are not temporally dense and mostly were restricted to a few-time steps. In addition, the scale compatibility was another issue, as the datasets might be not representative for an 8 km grid [53]. To overcome this, we compared our results with the results of the previous studies [1,[4][5][6][7][8][9] and investigated the validity of the results by local experts.…”