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

• Main Authors: Roberto O. Chávez, Ronald Rocco, Álvaro G. Gutiérrez, Marcelo Dörner, Sergio A. Estay
• Format: Article
• Language: English
• Published: MDPI AG 2019-01-01
• Series: Remote Sensing
• Subjects: insect outbreak; npphen; kernel density; pest management; forest monitoring
• Online Access: https://www.mdpi.com/2072-4292/11/2/204

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 &#8220;true&#8222; 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 &#8220;true&#8222; 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 <i>Ormiscodes amphimone</i> caterpillars which occurred in 2015&#8315;2016 in Chilean Patagonia. By applying the anomaly probability band, we filtered out all pixels with a probability &lt;0.9 of being &#8220;true&#8222; defoliation. Our method enabled a robust spatiotemporal assessment of the <i>O. amphimone</i> outbreak, showing severe defoliation (60&#8315;80% and &gt;80%) over an area of 15,387 ha of <i>Nothofagus pumilio</i> 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.