Browsing by Author "Olea, Matias"

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    A Probabilistic Multi-Source Remote Sensing Approach to Evaluate Extreme Precursory Drought Conditions of a Wildfire Event in Central Chile
    (2022) Chavez, Roberto O.; Castillo-Soto, Miguel E.; Traipe, Katherine; Olea, Matias; Lastra, Jose A.; Quinones, Tomas
    Forest fires are a major issue worldwide, and especially in Mediterranean ecosystems where the frequency, extension and severity of wildfire events have increased related to longer and more intense droughts. Open access remote sensing and climate datasets make it possible to describe in detail the precursory environmental conditions triggering major fire events under drought conditions. In this study, a probabilistic methodological approach is proposed and tested to evaluate extreme drought conditions prior to the occurrence of a wildfire in Central Chile, an area suffering an unprecedented prolonged drought. Using 21 years of monthly records of gridded climate and remotely sensed vegetation water status data, we detected that vegetation at the ground level, by means of fine and dead fuel moisture (FDFM), and canopy level, by means of the enhanced vegetation index (EVI) were extremely dry for a period of about 8 months prior to the fire event, showing records that fall into the 2.5% of the lowest values recorded in 21 years. These extremely dry conditions of the vegetation, consequence of low air humidity and precipitation, favored the ignition and horizontal and vertical propagation of this major wildfire. Post fire, we found high severity values for the native vegetation affected by the fire, with dNBR values >0.44 3 days after the fire and significant damage to the Mediterranean sclerophyllous and deciduous forest present in the burned area. The proposed probabilistic model is presented as an innovation and an alternative to evaluate not only anomalies of the meteorological and vegetation indices that promote the generation of extreme events, but also how unusual or extreme these conditions are. This is achieved by placing the abnormal values in the context of the reference historical frequency distribution of all available records, in this case, more than 20 years of remote sensing and climate data. This methodology can be widely applied by fire researchers to identify critical precursory fire conditions in different ecosystems and define environmental indicators of fire risk.
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    Andean peatlands at risk? Spatiotemporal patterns of extreme NDVI anomalies, water extraction and drought severity in a large-scale mining area of Atacama, northern Chile
    (2023) Chavez, Roberto O.; Meseguer-Ruiz, Oliver; Olea, Matias; Calderon-Seguel, Matias; Yager, Karina; Isela Meneses, Rosa; Lastra, Jose A.; Nunez-Hidalgo, Ignacio; Sarricolea, Pablo; Serrano-Notivoli, Roberto; Prieto, Manuel
    In the Andes, multiple human and climatic factors threaten the conservation of bofedales, a type of high altitude peat forming wetland widely distributed in the tropical and subtropical Andes. In northern Chile, climate change and water extraction for industrial activities are among the most significant threats to these relevant socio-hydrological systems hosting indigenous pastoral communities. In this study, we present an integrated anal-ysis of Normalized Difference Vegetation Index (NDVI) anomalies, drought severity and water rights granted to industry to provide insight on the conservation status of bofedales, historical drivers of their transformation, and current threats. Using Landsat satellite imagery from 1986 to 2018, we identify spatio-temporal NDVI changes of 442 bofedales in one of the leading copper producing regions of the world. The NDVI time series analysis over 32 growing seasons was used to detect extreme anomalies, i.e. values outside the 95 % of the reference frequency distribution, indicating periods of extreme changes in the productivity of these high Andes wetlands. To evaluate the relationship between bofedales NDVI extreme periods to drought and continued water extraction activities, we combine a climate-based multi-temporal-scale drought index (SPEI) with the geospatial latitudinal distri-bution of water rights granted for extractive industries in the study area. Over the time period of analysis, the total amount of granted water rights increased 465 % from 1,201 l/s recorded before 1985 to 5,584 l/s in 2018. In the areas where the highest amount of water rights are concentrated, i.e. between 21.3 degrees S and 22.1 degrees S, "green" bofedales (NDVI>=0.23) are practically absent. NDVI of the austral summer (JFM) was highly correlated with the severity of drought occurring during the three months of the growing season peak. While our findings show bofedal productivity is mostly influenced by precipitation and temperature of the wet season (JFM) during the study period, results also raise questions regarding possible bofedal loss occurring over the previous 80 years prior to the satellite record, wherein water extraction activities have significantly increased according to official records.
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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.