A method to predict vascular plant richness using spectral and textural variables in a heterogeneous wetland is presented. Plant richness was measured at 44 sampling plots in a 16-ha anthropogenic peatland. Several spectral indices, first-order statistics (median and standard deviation), and second-order statistics [metrics of a gray-level co-occurrence matrix (GLCM)] were extracted from a Landsat 8 Operational Land Imager image and a Pleiades 1B image. We selected the most important variables for predicting richness using recursive feature elimination and then built a model using random forest regression. The final model was based on only two textural variables obtained from the GLCM and derived from the Landsat 8 image. An accurate predictive capability was reported (R-2 = 0.6; RMSE = 1.99 species), highlighting the possibility of obtaining parsimonious models using textural variables. In addition, the results showed that the mid-resolution Landsat 8 image provided better predictors of richness than the high-resolution Pleiades image. This is the first study to generate a model for plant richness in a wetland ecosystem.CONICYT through the FONDECYT 113093

Artículo de publicación ISIPeatlands are a type of wetland characterized by the accumulation of organic matter, called peat, and are important carbon reservoirs. In areas with poor drainage, human-induced forest fires and logging can produce flooded conditions and organic matter accumulation, which generates an ecosystem called anthropogenic peatland. Productive management activities such as Sphagnum moss harvesting and livestock grazing take place there. Our hypothesis was that productive management has a strong impact on the aboveground C reservoir and increases the presence of exotic species. We established 44 sampling points in a 16-ha anthropogenic peatland on Chiloe Island, Chile, comparing productive and conservation types of managements. Carbon stocks, vegetation structure and composition variables were quantified. These variables were used to classify the ecosystem into microsites to analyze the different locations in the peatland. In addition, predictive models of aboveground carbon were created using Landsat 8 OLI and Pleiades images. The results revealed a carbon stock of 11.99 +/- 0.77 kg C m(-2), which is smaller than in natural peatlands, and showed a wide variability of conditions within the peatland itself. This variability, mainly expressed in aboveground carbon, produces microsites dominated by either shrubs, species of the genus Juncus or grasses. Productive management reduced accumulated carbon in the aboveground stock and in the woody debris. However, the strongest impact was found on the vegetation variables, with a decrease in total cover, cover of shrubs and herbaceous plants, and in vegetation height. There was also an increase in the richness and presence of exotic species. The spatial prediction of aboveground carbon yielded significant results using only spectral indices, showing also that the impact of productive management is not homogenous, being less intense in waterlogged areas. This study is the first to quantify carbon reservoirs in this type of ecosystem and to propose variables that can be used as indicators of the impact of human activities.FONDECYT 113093

Abstract. Making accurate estimations of daily and annual Rs fluxes is key for understanding the carbon cycle process and projecting effects of climate change. In this study we used high-frequency sampling (24 measurements per day) of Rs in a temperate rainforest during 1 year, with the objective of answering the questions of when and how often measurements should be made to obtain accurate estimations of daily and annual Rs. We randomly selected data to simulate samplings of 1, 2, 4 or 6 measurements per day (distributed either during the whole day or only during daytime), combined with 4, 6, 12, 26 or 52 measurements per year. Based on the comparison of partial-data series with the full-data series, we estimated the performance of different partial sampling strategies based on bias, precision and accuracy. In the case of annual Rs estimation, we compared the performance of interpolation vs. using non-linear modelling based on soil temperature. The results show that, under our study conditions, sampling twice a day was enough to accurately estimate daily Rs (RMSE  <  10 % of average daily flux), even if both measurements were done during daytime. The highest reduction in RMSE for the estimation of annual Rs was achieved when increasing from four to six measurements per year, but reductions were still relevant when further increasing the frequency of sampling. We found that increasing the number of field campaigns was more effective than increasing the number of measurements per day, provided a minimum of two measurements per day was used. Including night-time measurements significantly reduced the bias and was relevant in reducing the number of field campaigns when a lower level of acceptable error (RMSE  <  5 %) was established. Using non-linear modelling instead of linear interpolation did improve the estimation of annual Rs, but not as expected. In conclusion, given that most of the studies of Rs use manual sampling techniques and apply only one measurement per day, we suggest performing an intensive sampling at the beginning of the study to determine minimum daily and annual frequencies of sampling.

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Urban heat islands (UHIs) can present significant risks to human health. Santiago, Chile has around 7 million residents, concentrated in an average density of 480 people/km2. During the last few summer seasons, the highest extreme maximum temperatures in over 100 years have been recorded. Given the projections in temperature increase for this metropolitan region over the next 50 years, the Santiago UHI could have an important impact on the health and stress of the general population. We studied the presence and spatial variability of UHIs in Santiago during the summer seasons from 2005 to 2017 using Moderate Resolution Imaging Spectroradiometer (MODIS) satellite imagery and data from nine meteorological stations. Simple regression models, geographic weighted regression (GWR) models and geostatistical interpolations were used to find nocturnal thermal differences in UHIs of up to 9 °C, as well as increases in the magnitude and extension of the daytime heat island from summer 2014 to 2017. Understanding the behavior of the UHI of Santiago, Chile, is important for urban planners and local decision makers. Additionally, understanding the spatial pattern of the UHI could improve knowledge about how urban areas experience and could mitigate climate change.