Long-term Satellite NDVI Data Sets: Evaluating Their Ability to Detect Ecosystem Functional Changes in South America

Abstract

In the last decades, South American ecosystems underwent important functional modifications due to climate alterations and direct human intervention on land use and land cover. Among remotely sensed data sets, NOAA-AVHRR “Normalized Difference Vegetation Index” (NDVI) represents one of the most powerful tools to evaluate these changes thanks to their extended temporal coverage. In this paper we explored the possibilities and limitations of three commonly used NOAA-AVHRR NDVI series (PAL, GIMMS and FASIR) to detect ecosystem functional changes in the South American continent. We performed pixel-based linear regressions for four NDVI variables (average annual, maximum annual, minimum annual and intra-annual coefficient of variation) for the 1982-1999 period and (1) analyzed the convergences and divergences of significant multi-annual trends identified across all series, (2) explored the degree of aggregation of the trends using the O-ring statistic, and (3) evaluated observed trends using independent information on ecosystem functional changes in five focal regions. Several differences arose in terms of the patterns of change (the sign, localization and total number of pixels with changes). FASIR presented the highest proportion of changing pixels (32.7%) and GIMMS the lowest (16.2%). PAL and FASIR data sets showed the highest agreement, with a convergence of detected trends on 71.2% of the pixels. Even though positive and negative changes showed substantial spatial aggregation, important differences in the scale of aggregation emerged among the series, with GIMMS showing the smaller scale (≤11 pixels). The independent evaluations suggest higher accuracy in the detection of ecosystem changes among PAL and FASIR series than with GIMMS, as they detected trends that match expected shifts. In fact, this last series eliminated most of the long term patterns over the continent. For example, in the “Eastern Paraguay” and “Uruguay River margins” focal regions, the extensive changes due to land use and land cover change expansion were detected by PAL and FASIR, but completely ignored by GIMMS. Although the technical explanation of the differences remains unclear and needs further exploration, we found that the evaluation of this type of remote sensing tools should not only be focused at the level of assumptions (i.e. physical or mathematical aspects of image processing), but also at the level of results (i.e. contrasting observed patterns with independent proofs of change). We finally present the online collaborative initiative “Land ecosystem change utility for South America”, which facilitates this type of evaluations and helps to identify the most important functional changes of the continent.

How Normalized Difference Vegetation Index (NDVI) Trendsfrom Advanced Very High Resolution Radiometer (AVHRR) and Système Probatoire d’Observation de la Terre VEGETATION (SPOT VGT) Time Series Differ in Agricultural Areas: An Inner Mongolian Case Study

“…While comparisons based on the raw time series indicate congruence between AVHRR GIMMS and other NDVI archives, more recent studies based on trend parameters derived from these archives indicate that inconsistencies exist between trends [52,119,120]. We here demonstrated that considerable discrepancies in trend magnitude and direction exist between AVHRR GIMMS and SPOT VGT-based NDVI products (r = 0.28).…”

Analysis of the Phenology in the Mongolian Plateau by Inter-Comparison of Global Vegetation Datasets

“…However, there is better consistency in AVHRR and SPOT from the whole analysis than results on other regions, like the Tibetan Plateau, the Iberian Peninsula and South America [13,14,50,51]. Therefore, we merged AVHRR (1982)(1983)(1984)(1985)(1986)(1987)(1988)(1989)(1990)(1991)(1992)(1993)(1994)(1995)(1996)(1997) and SPOT (1998SPOT ( -2012 datasets to generate a long time series over 30 years for later analysis.…”

“…Inner Mongolia may have benefited from the ecological projects implemented by the Chinese government, for instance, Grain for Green [51], the Natural Forest Conversion Program [57] and the Beijing-Tianjin Sand Source Control Program [50,58]. Previous studies reported that SOS advanced by 5.2 days in the Northern Hemisphere in the early period (1982)(1983)(1984)(1985)(1986)(1987)(1988)(1989)(1990)(1991)(1992)(1993)(1994)(1995)(1996)(1997)(1998)(1999), but only by 0.2 days in the later period (2000-2008) [62].…”

“…There is a slightly enhanced trend in Inner Mongolia NDVI (R = 0.249, p = 0.005). This may be benefited from the ecological projects by the Chinese government, for instance, Grain for Green [51], the Natural Forest Conversion Program [57] and the Beijing-Tianjin Sand Source Control Program [50,58].…”

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 third generation GIMMS NDVI is a 15-day Maximum Value Composite that was acquired from seven different NOAA satellites (7,9,11,14,16,17,18), which have been processed using an adaptive Empirical Mode Decomposition. NDVI3g is appropriate for long-term studies of land surface trends in vegetation, seasonality and coupling between climate variability and vegetation over the last three decades [31,32].…”