Phases or regimes? Revisiting NDVI trends as proxies for land degradation

Abstract

One of the main challenges in land degradation assessment is that a rigorous and systematic approach to addressing its complex dynamics is still missing. The development and application of operative tools at regional and global scales remain a challenge. Land degradation is usually defined as a long‐term decline in ecosystem function and productivity. Due to its temporal and spatial resolution as well as data availability, the use of time series of spectral vegetation indexes obtained from satellite sensors has become frequent in recent studies in this field. Slope of linear trends of the normalized difference vegetation index is usually considered an accurate indicator and is widely used as a proxy for land degradation. Yet this method is built on a number of simplifying conceptual and methodological assumptions that prevent capturing more complex dynamics, such as cyclic or periodic behaviors. Our aim was to examine the limitations associated with using linear normalized difference vegetation index trends as proxies for land degradation by comparing outcomes with an alternative methodological procedure based on wavelet autoregressive methods. We explored these issues in 5 case studies from Africa and South America. We observed that trend explained a marginal portion of total temporal variability, whereas monotonic functions, such as linear trends, were unable to capture dynamics that were non‐unidirectional, resulting in misinterpretation of actual trends. Wavelet autoregressive method results were encouraging as a step towards the application of more accurate methods to provide sound scientific information of land degradation and restoration.

Forest tree species distribution for Europe 2000-2020: mapping potential and realized distributions using spatiotemporal Machine Learning

“…7 and 8). Importance of NDVI is well known since it is one of the most used proxies in vegetation studies such as biodiversity estimation (Madonsela et al, 2017;He et al, 2009), net primary productivity (Schloss et al, 1999) and land degradation (Easdale et al, 2018), phenology (Fawcett et al, 2021) and species composition changes (Wang et al, 2021). NDVI incorporates information from the red and the near-infrared (NIR) portion of the electromagnetic spectrum (see Table 1).…”

Forest tree species distribution for Europe 2000-2020: mapping potential and realized distributions using spatiotemporal Machine Learning

“…The inclusion of Landsat data and derived spectral indices increases predictive performances and contains more detailed information on species distribution ranges. Importance of NDVI is well known since it is one of the most used proxies in vegetation studies such as biodiversity estimation (Madonsela et al, 2017;He et al, 2009), net primary productivity (Schloss et al, 1999) and land degradation (Easdale et al, 2018), phenology (Fawcett et al, 2021) and species composition changes (Wang et al, 2021). NDVI incorporates information from the red and the near-infrared (NIR) portion of the electromagnetic spectrum.…”

Spatial analysis to inform the mitigation hierarchy

“…Beyond data such as the human footprint or forest landscape integrity index, which are coarse resolution, global‐scale datasets with a number of limitations, there are numerous other approaches to scope areas that could benefit from actions to improve condition and lend themselves as offset sites. For example, vegetation productivity indices (e.g., NDVI) can be used to monitor ecosystem degradation (Easdale et al, 2018), and there are now user‐friendly GUIs for quickly assessing such indices using cloud computing (Langner et al, 2018). Alternatively, the IUCN Restoration Opportunities Assessment Methodology provides a flexible and affordable framework to rapidly identify suitable restoration areas, but it is crucial that the indicators used in such approaches are linked to the objectives of restoration efforts (IUCN and World Resources Institute, 2014; Worthington & Spalding, 2020).…”