4.7 Article

Rhythm of change of trend-cycles of vegetation dynamics as an early warning indicator for land management

期刊

ECOLOGICAL INDICATORS
卷 126, 期 -, 页码 -

出版社

ELSEVIER
DOI: 10.1016/j.ecolind.2021.107663

关键词

Adaptive management; NDVI; Patagonia; Rangelands; Time series analysis

资金

  1. Instituto Nacional de Tecnologia Agropecuaria (INTA)
  2. Consejo Federal de Ciencia y Tecnologia (Cofecyt) [PFIP2017NQN, EX-2018-40803603-APN-DDYGDMCT]

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The use of satellite-derived vegetation indices time series is important for land degradation assessment and monitoring. However, there is a lack of information on future vegetation dynamics. Trend-cycle as an indicator can provide information on long-term movements and changes in direction, helping to estimate the direction and speed of change in vegetation productivity cycles.
The use of time series of vegetation indices obtained from satellites has become a highly relevant source of data in studies of land degradation assessment and monitoring. However, information about future vegetation dynamics, which is key for early warnings oriented at land management decisions, is still lacking. Trend-cycle was recently proposed as an indicator that represents a smoothed version of a seasonally adjusted data series, which provides information on long-term movements (trend) while including changes in direction underlying the series (cycles). The aim was to estimate the direction and speed of change of the Normalized Difference Vegetation Index (NDVI) trend-cycles as a complementary information of the rhythm of change between cyclic phases of vegetation productivity. In particular, we estimate the first and second derivative of the end-point of the trendcycle function, as a measure of the direction the function is going and the speed of change, respectively. The potential advantage of our proposal is the fast processing for large areas and its sensitivity to capturing shifts in temporal dynamics for short time series data. This information can be used as a proxy to build scenarios of the future behaviour of vegetation dynamics, which is a relevant issue to move forward in the development of early warning tools for adaptive land management.

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