npphen: An R-Package for Detecting and Mapping Extreme Vegetation Anomalies Based on Remotely Sensed Phenological Variability

npphen: An R-Package for Detecting and Mapping Extreme Vegetation Anomalies Based on Remotely Sensed Phenological Variability

Monitoring vegetation disturbances using long remote sensing time series is crucial to support environmental management, biodiversity conservation, and adaptation strategies to climate change from global to local scales. However, it is difficult to assess whether a remotely detected vegetation distu...

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Bibliographic Details
Published in:Remote sensing (Basel, Switzerland) Vol. 15; no. 1; p. 73
Main Authors: Chávez, Roberto O., Estay, Sergio A., Lastra, José A., Riquelme, Carlos G., Olea, Matías, Aguayo, Javiera, Decuyper, Mathieu
Format: Journal Article
Language:English
Published: Basel MDPI AG 01-01-2023
Subjects:
Algorithms
Anomalies
Bandwidths
Biodiversity
Climate change
disturbance
EBV
Ecosystems
Environmental management
Expected values
Frequency distribution
GEO-BON
Methods
Monitoring
Phenology
Probabilistic methods
Remote sensing
Satellites
Seasons
Statistical analysis
Time series
Variability
Vegetation
Vegetation changes
Vegetation index
Wildlife conservation
Chile
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Summary:Monitoring vegetation disturbances using long remote sensing time series is crucial to support environmental management, biodiversity conservation, and adaptation strategies to climate change from global to local scales. However, it is difficult to assess whether a remotely detected vegetation disturbance is critical or not, since available operational remote sensing methods deliver only maps of the vegetation anomalies but not maps of how “uncommon” or “extreme” the detected anomalies are based on the available records of the reference period. In this technical note, we present a new release of the probabilistic method and its implementation, the npphen R package, designed to detect not only vegetation anomalies from remotely sensed vegetation indices, but also to quantify the position of the anomalous observations within the historical frequency distribution of the phenological annual records. This version of the R package includes two new key functions to detect and map extreme vegetation anomalies: ExtremeAnom and ExtremeAnoMap. The npphen package allows remote sensing users to detect vegetation changes for a wide range of ecosystems, taking advantage of the flexibility of kernel density estimations to account for any shape of annual phenology and its variability through time. It provides a uniform statistical framework to study all types of vegetation dynamics, contributing to global monitoring efforts such as the GEO-BON Essential Biodiversity Variables.
ISSN:2072-4292
2072-4292
DOI:10.3390/rs15010073