Temporal Variability of MODIS Phenological Indices in the Temperate Rainforest of Northern Patagonia

Abstract

Western Patagonia harbors unique and sparsely studied terrestrial ecosystems that are threatened by land use changes and exposure to basin-scale climatic variability. We assessed the performance of two satellite vegetation indices derived from MODIS-Terra, EVI (Enhanced Vegetation Index) and NDVI (Normalized Difference Vegetation Index), over the northern and southern sectors of the Chiloé Island System (CIS) to advance our understanding of vegetation dynamics in the region. Then we examined their time-varying relationships with two climatic indices indicative of tropical and extratropical influence, the ENSO (El Niño-Southern Oscillation) and the Antarctic Oscillation (AAO) index, respectively. The 17-year time series showed that only EVI captured the seasonal pattern characteristic of temperate regions, with low (high) phenological activity during Autumn-Winter (Spring-Summer). NDVI saturated during the season of high productivity and failed to capture the seasonal cycle. Temporal patterns in productivity showed a weakened seasonal cycle during the past decade, particularly over the northern sector. We observed a non-stationary association between EVI and both climatic indices. Significant co-variation between EVI and the Niño-Southern Oscillation index in the annual band persisted from 2001 until 2008-2009; annual coherence with AAO prevailed from 2013 onwards and the 2009-2012 period was characterized by coherence between EVI and both climate indices over longer temporal scales. Our results suggest that the influence of large-scale climatic variability on local weather patterns drives phenological responses in the northern and southern regions of the CIS. The imprint of climatic variability on patterns of primary production across the CIS may be underpinned by spatial differences in the anthropogenic modification of this ecosystem, as the northern sector is strongly modified by forestry and agriculture. We highlight the need for field validation of satellite indices around areas of high biomass and high endemism, located in the southern sector of the island, in order to enhance the utility of satellite vegetation indices in the conservation and management of austral ecosystems.

Coupled Biospheric Synchrony of the Coastal Temperate Ecosystem in Northern Patagonia: A Remote Sensing Analysis

“…This breakdown of the seasonal cycle and its relation to the antiphase between SAM (negative phase) and ENSO has been reported for the region before [16,24]. Briefly, after a prolonged near-neutral period during the early 2000's, the SAM experienced positive anomalies, firstly during the observed multidecadal antiphase with ENSO, and culminating with an in-phase period during the 2015-2016 warm ENSO event [39,64]. The latter climate pattern has been related to several phenological anomalies (e.g., anomalous timing of algal blooms), and local environmental disruption in the Inner Sea of Chiloé observed during the summer of 2016 [29,64].…”

“…The properties of vegetation indices derived from MODIS provide a substantially improved dataset for vegetation monitoring [34,41]. Recently, Lara et al [39] showed that EVI efficiently captures inter and intra-annual phenology pattern in Chiloé Island ecosystem. In this study, the authors suggested that the weakening in the spectral power of the vegetation activity in both northern and southern sections could be related to the change in the ocean-atmosphere conditions in the Pacific Ocean.…”

“…River discharge in this region follows a nival regime and comes primarily from the Petrohué, Cochamó, Puelo, Riñihue and Yelcho Rivers, located in our study region. The combined freshwater discharge from these rivers influences local ocean circulation and the spring-autumn phytoplankton bloom dynamics [25,39]. Due to the topographic and bathymetric complexity and the distinct environmental regimes in the northern Patagonia ecosystem [16], we separated the Chiloé Island and Inner Sea of Chiloé regions into two areas: north (41-42.5 • S) and south (42.5-44 • S).…”

“…The EVI index was developed to optimize the detection of vegetation signals, improving sensitivity in high biomass regions, and reducing atmospheric influence [41]. The EVI index is based on 469, 645 and 858 MODIS wavelength bands and has shown better performance than the Normalized Difference Vegetation Index in regions with high vegetation density such as our study region [39,41].…”

VCI-Based Analysis on Spatiotemporal Variations of Spring Drought in China

“…where the Morlet wavelet ϕ(t) is the result of sine complex exp(−iw 0 t) and Gaussian wave package exp −t 2 f on /2 , t is the dimensionless time, w 0 is the dimensionless frequency, and π −1/4 is a standardized factor to ensure unit variance [37].…”