Mapping Periodic Patterns of Global Vegetation Based on Spectral Analysis of NDVI Time Series

Abstract

Vegetation seasonality assessment through remote sensing data is crucial to understand ecosystem responses to climatic variations and human activities at large-scales. Whereas the study of the timing of phenological events showed significant advances, their recurrence patterns at different periodicities has not been widely study, especially at global scale. In this work, we describe vegetation oscillations by a novel quantitative approach based on the spectral analysis of Normalized Difference Vegetation Index (NDVI) time series. A new set of global periodicity indicators permitted to identify different seasonal patterns regarding the intra-annual cycles (the number, amplitude, and stability) and to evaluate the existence of pluri-annual cycles, even in those regions with noisy or low NDVI. Most of vegetated land surface (93.18%) showed one intra-annual cycle whereas double and triple cycles were found in 5.58% of the land surface, mainly in tropical and arid regions along with agricultural areas. In only 1.24% of the pixels, the seasonality was not statistically significant. The highest values of amplitude and stability were found at high latitudes in the northern hemisphere whereas lowest values corresponded to tropical and arid regions, with the latter showing more pluri-annual cycles. The indicator maps compiled in this work provide highly relevant and practical information to advance in assessing global vegetation dynamics in the context of global change.

Spatio-Temporal Changes and Driving Forces of Vegetation Coverage on the Loess Plateau of Northern Shaanxi

“…The Normalized Difference Vegetation Index (NDVI), as an important index reflecting the study of vegetation growth and spatio-temporal change, is closely related to vegetation coverage, patterns, biomass, and photosynthesis, and is also a vital indicator for monitoring land degradation [6]. Therefore, most scholars have extensively used NDVI to study global or regional vegetation cover changes and the driving forces [7][8][9].…”

Combining Remote Sensing and Species Distribution Modelling to Assess Pinus hartwegii Response to Climate Change and Land Use from Izta-Popo National Park, Mexico

“…However, its use to validate SDM at higher scales (e.g., 1-km 2 resolution) is rare, as identifying species from remote sensing data is not straightforward. First experiences to identify individual tree species confirmed the high value of RSD, thanks to the increasing availability of higher-resolution images at high frequency [28,87,88]. Parallel efforts to generate ground data [50] provide fundamental knowledge to get better accuracy on RSD-based maps [22].…”

“…Multi-spectral biophysical estimates of vegetation have been used to map large areas of forests [24] or to assist forest surveys for stratification and post-stratification field sampling [25]. The continuous advances on multiand hyper-spectral approaches and techniques to obtain biophysical estimates at higher temporal and spatial resolution [26][27][28] in parallel increasing quality and accessibility of in situ observations [29] open new horizons in biodiversity studies [30]. However, few studies integrate RSD with SDM, and usually they are limited to the inclusion of RSD estimates as explanatory variables to calibrate SDM; this handicaps the possibility to project such models under future scenarios due to the absence of RSD estimates in future conditions [31][32][33].…”

Multiple UAV Flights across the Growing Season Can Characterize Fine Scale Phenological Heterogeneity within and among Vegetation Functional Groups

“…Multi-temporal analyses based on remote sensing are increasingly used to monitor ecosystems and their dynamics over multiple time steps and improve the thematic resolution of remote sensing products. For example, analyses of multiple remotely sensed images from within the same year are used to improve classification accuracy [16][17][18], measure species composition and coverage in shrublands [19], classify crop types [20], examine yields and performance [21,22], differentiate tree species in an urban environment [23], identify plants with C3 versus C4 photosynthetic pathways [24], detect invasive species [25], and examine the number of annual green-up cycles across ecosystems [26]. Importantly, examining continuous patterns can reveal new dynamics of a system across seasons and over multiple years [26][27][28].…”