Browsing by Author "Decuyper, Mathieu"

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    MODIS Time Series Reveal New Maximum Records of Defoliated Area by Ormiscodes amphimone in Deciduous Nothofagus Forests, Southern Chile
    (2023) Estay, Sergio A.; Chavez, Roberto O.; Lastra, Jose A.; Rocco, Ronald; Gutierrez, Alvaro G.; Decuyper, Mathieu
    Outbreaks of the Ormiscodes amphimone moth are among the largest biotic disturbances in South America, defoliating vast areas of native Nothofagus pumilio forests in the Chilean and Argentinian Patagonia in the last decade. Using MODIS 16-day composites of the enhanced vegetation index and the new functions of the latest release of the "npphen" R-package, we identified new maximum records of continuously defoliated area in the Aysen region (Chilean Patagonia). This approach allowed us to detect 55,193 ha and 62,344 ha of extremely defoliated N. pumilio forest in 2019 and 2022, respectively, in an area locally known as "Mallin Grande". Extreme defoliation was accounted for by means of negative EVI anomalies with values falling among 5% of the lowest EVI records of the reference period (2000-2010). These new 2019 and 2022 outbreaks in Mallin Grande were the largest reported insect outbreaks in South American Patagonia in this century.
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    npphen: An R-Package for Detecting and Mapping Extreme Vegetation Anomalies Based on Remotely Sensed Phenological Variability
    (2023) Chavez, Roberto O.; Estay, Sergio A.; Lastra, Jose A.; Riquelme, Carlos G.; Olea, Matias; Aguayo, Javiera; Decuyper, Mathieu
    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.