A Self-Calibrated Non-Parametric Time Series Analysis Approach for Assessing Insect Defoliation of Broad-Leaved Deciduous Nothofagus pumilio Forests

Abstract

Folivorous insects cause some of the most ecologically and economically important disturbances in forests worldwide. For this reason, several approaches have been developed to exploit the temporal richness of available satellite time series data to detect and quantify insect forest defoliation. Current approaches rely on parametric functions to describe the natural annual phenological cycle of the forest, from which anomalies are calculated and used to assess defoliation. Quantification of the natural variability of the annual phenological baseline is limited in parametric approaches, which is critical to evaluating whether an observed anomaly is “true” defoliation or only part of the natural forest variability. We present here a fully self-calibrated, non-parametric approach to reconstruct the annual phenological baseline along with its confidence intervals using the historical frequency of a vegetation index (VI) density, accounting for the natural forest phenological variability. This baseline is used to calculate per pixel (1) a VI anomaly per date and (2) an anomaly probability flag indicating its probability of being a “true” anomaly. Our method can be self-calibrated when applied to deciduous forests, where the winter VI values are used as the leafless reference to calculate the VI loss (%). We tested our approach with dense time series from the MODIS enhanced vegetation index (EVI) to detect and map a massive outbreak of the native Ormiscodes amphimone caterpillars which occurred in 2015–2016 in Chilean Patagonia. By applying the anomaly probability band, we filtered out all pixels with a probability <0.9 of being “true” defoliation. Our method enabled a robust spatiotemporal assessment of the O. amphimone outbreak, showing severe defoliation (60–80% and >80%) over an area of 15,387 ha of Nothofagus pumilio forests in only 40 days (322 ha/day in average) with a total of 17,850 ha by the end of the summer. Our approach is useful for the further study of the apparent increasing frequency of insect outbreaks due to warming trends in Patagonian forests; its generality means it can be applied in deciduous broad-leaved forests elsewhere.

Herbivore resistance in congeneric and sympatric Nothofagus species is not related to leaf habit

“…Although O. amphimone is polyphagous and feeds upon a variety of native and introduced plant species, it has more frequently been observed feeding in N. pumilio than in other species (Paritsis et al., ). Outbreaks of O. amphimone can cause complete tree defoliation over vast areas (hundreds of hectares) of N. pumilio forests when episodes are severe (Paritsis et al., ; Piper et al., ; Chávez et al., ). The fourth and fifth instars are the most damaging stages, causing ~90% of the total foliage consumption (Paritsis et al., ).…”

“…In southern South America, massive outbreaks of the caterpillar Ormiscodes amphimone (Saturniidae) are recurrent in Nothofagus forests (Veblen et al., ; Paritsis and Veblen, 2010a; Paritsis et al., ; Chávez et al., ). Landscape assessments for two sites in Argentinian Patagonia report higher defoliation severity in forests of the winter deciduous species N. pumilio than in those of N. antarctica (also winter deciduous) and N. betuloides (evergreen) (Paritsis et al., ); these patterns may reflect either site or host‐specific effects (Garibaldi et al., 2011a).…”

A Multiscale Productivity Assessment of High Andean Peatlands across the Chilean Altiplano Using 31 Years of Landsat Imagery

“…For each spatial scale, we reconstructed the annual phenological cycle using a non-parametric approach implemented in the "npphen" R package [33]. This tool has proven to be effective for studying vegetation phenological metrics using large remote sensing datasets [34][35][36][37]. With npphen, all NDVI observations ( Figure 5a) are arranged by the phenological year (from July to the following June for the Southern Hemisphere) (Figure 5b).…”

Quantifying massive outbreaks of the defoliator mothOrmiscodes amphimonein deciduousNothofagus‐dominated southern forests using remote sensing time series analysis

“…In this way, our anomaly is defined as the distance between the observed and the expected EVI value for a given DGS. Positive anomalies indicate a “greening” of the vegetated land surface (EVI increment), while negative anomalies indicate a “browning” (EVI drop). To evaluate the precision of the outbreak detection, the probability that the observed anomaly being a “true” anomaly is calculated from the KDE (Chávez et al, ).…”

“…To evaluate the precision of the outbreak detection, the probability that the observed anomaly being a "true" anomaly is calculated from the KDE (Chávez et al, 2019). This is an iterative leave-one-out process where all GS's are left out one by one and the expected EVI value curve is calculated using the rest of the GS's.…”