Mauricio Galleguillos

Using a Multistructural Object-Based LiDAR Approach to Estimate Vascular Plant Richness in Mediterranean Forests With Complex Structure

A multistructural object-based LiDAR approach to predict plant richness in complex structure forests is presented. A normalized LiDAR point cloud was split into four height ranges: 1) high canopies (points above 16 m); 2) middle-high canopies (8-16 m); 3) middle-low canopies (2-8 m); and 4) low canopies (0-2 m). A digital canopy model (DCM) was obtained from the full normalized LiDAR point cloud, and four pseudo-DCMs (pDCMs) were obtained from the split point clouds. We applied a multiresolution segmentation algorithm to the DCM and the four pDCMs to obtain crown objects. A partial least squares path model (PLS-PM) algorithm was applied to predict total vascular plant richness using object-based image analysis (OBIA) variables, derived from the delineated crown objects, and topographic variables, derived from a digital terrain model. Results showed that the object-based model was able to predict the total richness with an r2 of 0.64 and a root-mean-square error of four species. Topographic variables showed to be more important than the OBIA variables to predict richness. Furthermore, high-medium canopies (8-16 m) showed the biggest correlation with the total plant richness within the structural segments of the forest.

Comparison of Airborne LiDAR and Satellite Hyperspectral Remote Sensing to Estimate Vascular Plant Richness in Deciduous Mediterranean Forests of Central Chile

The Andes foothills of central Chile are characterized by high levels of floristic diversity in a scenario, which offers little protection by public protected areas. Knowledge of the spatial distribution of this diversity must be gained in order to aid in conservation management. Heterogeneous environmental conditions involve an important number of niches closely related to species richness. Remote sensing information derived from satellite hyperspectral and airborne Light Detection and Ranging (LiDAR) data can be used as proxies to generate a spatial prediction of vascular plant richness. This study aimed to estimate the spatial distribution of plant species richness using remote sensing in the Andes foothills of the Maule Region, Chile. This region has a secondary deciduous forest dominated by Nothofagus obliqua mixed with sclerophyll species. Floristic measurements were performed using a nested plot design with 60 plots of 225 m2 each. Multiple predictors were evaluated: 30 topographical and vegetation structure indexes from LiDAR data, and 32 spectral indexes and band transformations from the EO1-Hyperion sensor. A random forest algorithm was used to identify relevant variables in richness prediction, and these variables were used in turn to obtain a final multiple linear regression predictive model (Adjusted R2 = 0.651; RSE = 3.69). An independent validation survey was performed with significant results (Adjusted R2 = 0.571, RMSE = 5.05). Selected variables were statistically significant: catchment slope, altitude, standard deviation of slope, average slope, Multiresolution Ridge Top Flatness index (MrRTF) and Digital Crown Height Model (DCM). The information provided by LiDAR delivered the best predictors, whereas hyperspectral data were discarded due to their low predictive power.

Using Ridge Regression Models to Estimate Grain Yield from Field Spectral Data in Bread Wheat (Triticum Aestivum L.) Grown under Three Water Regimes

Plant breeding based on grain yield (GY) is an expensive and time-consuming method, so new indirect estimation techniques to evaluate the performance of crops represent an alternative method to improve grain yield. The present study evaluated the ability of canopy reflectance spectroscopy at the range from 350 to 2500 nm to predict GY in a large panel (368 genotypes) of wheat (Triticum aestivum L.) through multivariate ridge regression models. Plants were treated under three water regimes in the Mediterranean conditions of central Chile: severe water stress (SWS, rain fed), mild water stress (MWS; one irrigation event around booting) and full irrigation (FI) with mean GYs of 1655, 4739, and 7967 kg∙ha−1, respectively. Models developed from reflectance data during anthesis and grain filling under all water regimes explained between 77% and 91% of the GY variability, with the highest values in SWS condition. When individual models were used to predict yield in the rest of the trials assessed, models fitted during anthesis under MWS performed best. Combined models using data from different water regimes and each phenological stage were used to predict grain yield, and the coefficients of determination (R2) increased to 89.9% and 92.0% for anthesis and grain filling, respectively. The model generated during anthesis in MWS was the best at predicting yields when it was applied to other conditions. Comparisons against conventional reflectance indices were made, showing lower predictive abilities. It was concluded that a Ridge Regression Model using a data set based on spectral reflectance at anthesis or grain filling represents an effective method to predict grain yield in genotypes under different water regimes.

Predicting Vascular Plant Richness in a Heterogeneous Wetland Using Spectral and Textural Features and a Random Forest Algorithm

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 (R2 = 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.

Evaluation of impacts of management in an anthropogenic peatland using field and remote sensing data

Peatlands 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.

Tree carbon stock in evergreen forests of Chiloé, Chile

The carbon stock associated with tree biomass was estimated in evergreen forests near the town of Inio in Chiloe Island, Chile (43o21’ S, 74o07’ W), analyzin...

Predicting Vascular Plant Diversity in Anthropogenic Peatlands: Comparison of Modeling Methods with Free Satellite Data

Peatlands are ecosystems of great relevance, because they have an important number of ecological functions that provide many services to mankind. However, studies focusing on plant diversity, addressed from the remote sensing perspective, are still scarce in these environments. In the present study, predictions of vascular plant richness and diversity were performed in three anthropogenic peatlands on Chiloe Island, Chile, using free satellite data from the sensors OLI, ASTER, and MSI. Also, we compared the suitability of these sensors using two modeling methods: random forest (RF) and the generalized linear model (GLM). As predictors for the empirical models, we used the spectral bands, vegetation indices and textural metrics. Variable importance was estimated using recursive feature elimination (RFE). Fourteen out of the 17 predictors chosen by RFE were textural metrics, demonstrating the importance of the spatial context to predict species richness and diversity. Non-significant differences were found between the algorithms; however, the GLM models often showed slightly better results than the RF. Predictions obtained by the different satellite sensors did not show significant differences; nevertheless, the best models were obtained with ASTER (richness: R2 = 0.62 and %RMSE = 17.2, diversity: R2 = 0.71 and %RMSE = 20.2, obtained with RF and GLM respectively), followed by OLI and MSI. Diversity obtained higher accuracies than richness; nonetheless, accurate predictions were achieved for both, demonstrating the potential of free satellite data for the prediction of relevant community characteristics in anthropogenic peatland ecosystems.

Actual evapotranspiration and its relation with floristic composition and topographical features in an arid watershed

Actual evapotranspiration (ET) represents an ecosystem key functional variable, directly related with vegetation properties, particularly in water limited environments. The present study aims to map ET using a surface energy fluxes modelling approach, and explore its relation with topographical and floristic composition features, in an arid watershed of Central Chile. Several topographical and vegetation variables were significantly related to ET, however the most important were altitude, northing, easting, the height of herbaceous plants and tree species richness. The patterns of ET rate of the selected variables were explained by the singular eco-hydrological condition of the watershed. These variables were used in a multivariate linear model which allowed an accurate prediction of daily ET for a spring day corresponding to a critical growing vegetation condition.