Jaime Hernández

Vegetation diversity in the Santiago de Chile urban ecosystem

Summary This study took in urban and periurban areas of Santiago, Chile. The 967km2 metropolitan area is composed of approximately six million inhabitants from various social and economic backgrounds. The purpose of this paper was to assess alpha (α) and beta (ß) vegetation diversity in the 36 metropolitan boroughs, and analyze the relationship of the assessed biodiversity to social and economic indices. Preliminary results showed a tendency to increased vegetation diversity as the social and economic status of boroughs increased.

Stratified aboveground forest biomass estimation by remote sensing data

Abstract Remote sensing-assisted estimates of aboveground forest biomass are essential for modeling carbon budgets. It has been suggested that estimates can be improved by building species- or strata-specific biomass models. However, few studies have attempted a systematic analysis of the benefits of such stratification, especially in combination with other factors such as sensor type, statistical prediction method and sampling design of the reference inventory data. We addressed this topic by analyzing the impact of stratifying forest data into three classes (broadleaved, coniferous and mixed forest). We compare predictive accuracy (a) between the strata (b) to a case without stratification for a set of pre-selected predictors from airborne LiDAR and hyperspectral data obtained in a managed mixed forest site in southwestern Germany. We used 5 commonly applied algorithms for biomass predictions on bootstrapped subsamples of the data to obtain cross validated RMSE and r2 diagnostics. Those values were analyzed in a factorial design by an analysis of variance (ANOVA) to rank the relative importance of each factor. Selected models were used for wall-to-wall mapping of biomass estimates and their associated uncertainty. The results revealed marginal advantages for the strata-specific prediction models over the unstratified ones, which were more obvious on the wall-to-wall mapped area-based predictions. Yet further tests are necessary to establish the generality of these results. Input data type and statistical prediction method are concluded to remain the two most crucial factors for the quality of remote sensing-assisted biomass models.

Modeling forest biomass using very-high-resolution data - combining textural, spectral and photogrammetric predictors derived from spaceborne stereo images

Abstract We used spectral, textural and photogrammetric information from very-high resolution (VHR) stereo satellite data (Pléiades and WorldView-2) to estimate forest biomass across two test sites located in Chile and Germany. We compared Random Forest model performances of different predictor sets (spectral, textural, and photogrammetric), forest inventory designs and filter sizes (texture information). Best model performances were obtained with photogrammetric combined with either textural or spectral information and smaller, but more field plots. Stereo-VHR images showed a great potential for canopy height model (CHM) generation and could be an adequate alternative to LiDAR and InSAR techniques.

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.

Do corridors promote connectivity for bird-dispersed trees? The case of Persea lingue in Chilean fragmented landscapes

Disentangling the contribution of corridors to landscape connectivity is crucial for adopting efficient measures in conservation, but their actual role in heterogeneous landscapes is not yet fully understood. We assessed the hypothesis that corridors, consisting of hedgerows and riparian vegetation strips, are important landscape elements promoting functional connectivity for the lingue (Persea lingue), a tree endemic to southern Chile and Argentina whose seeds are mainly dispersed by the habitat generalist austral thrush (Turdus falcklandii). For this purpose, we used empirical estimates of seed production, fruit consumption and bird movement patterns, in combination with a seed dispersal model and a graph-theoretical approach for network connectivity analysis. We found that for this plant-animal interaction, the hypothesis mentioned above is not supported. Functional connectivity decreased as the structural connectivity provided by corridors increased, and stepping stones were much more effective connectivity providers than corridors. Our findings are not generalizable to other situations because thrushes contribute to the dispersal of seeds along narrow and sinuous corridors, which provide unsuitable conditions for the establishment of lingues. We conclude that (a) the effectiveness of corridors for promoting connectivity and successful dispersal is landscape- and species-specific; and that (b) effective conservation of Chilean forest biodiversity involves a tradeoff between enhancing the availability of stepping stones and providing corridors of sufficient width and appropriate shape to meet the needs and dispersal modalities of different species.

Above ground biomass and leaf area models based on a non destructive method for urban trees of two communes in Central Chile

La biomasa es considerada un importante indicador de los procesos ecologicos y de manejo que ocurren en la vegetacion urbana. Es dificil de medir pero facil de interpretar, pues refleja las condiciones del sitio y de los recursos edaficos, hidricos y de radiacion solar disponibles en el. En las ciudades, las practicas de manejo sobre los arboles afectan la distribucion de la biomasa en su interior y es necesario usar ecuaciones calibradas localmente para poder evaluar cada componente. Sin embargo, los metodos destructivos tradicionales, que se usan para recopilar los datos necesarios en la construccion de tales modelos, son poco aplicables en ambientes urbanos. En este estudio se utilizo el muestreo aleatorio de ramas (RBS), un metodo probabilistico no destructivo, y variables dendrometricas de facil medicion como DAP y altura total para estimar la biomasa aerea y el area foliar de arboles urbanos mas comunes en Santiago, Chile. Los resultados del estudio arrojaron estimaciones de biomasa aerea comparables, y dentro del rango de valores, a los reportados en la literatura internacional, para bosques y arboles urbanos. Las estimaciones para area foliar arrojaron valores mas razonables en comparacion con los datos de campo y referencias internacionales. Estas ecuaciones pueden ser incorporadas en los modelos forestales urbanos incluyendo estimaciones mas precisas y ajustadas a la realidad de America Latina. Aumentando la intensidad de muestreo de metodo RBS se podria usar como un metodo no-destructivo, replicable, para estimar diferentes tipos de caracteristicas en arboles urbanos.

The heterogeneity and composition of agricultural landscapes influence native and exotic coccinellids in alfalfa fields

The current trend toward simplification of agricultural landscapes, as well as the associated loss of perennial cover types, can decrease landscape heterogeneity and also natural enemy abundance and diversity, favouring exotic species. We evaluated the effects of agricultural landscape composition and heterogeneity at two different spatial scales (radii of 250 and 1000 m), on the diversity and abundance of native and exotic coccinellids, associated with alfalfa fields located in two regions of Central Chile. Data were analyzed using partial least square regressions, considering the whole coccinellid assemblage and the three most abundant species. In both regions, coccinellid diversity and the abundance and proportion of native coccinellids in alfalfa responded differentially from total abundance and the abundance of exotic coccinellids. The diversity and abundance of coccinellids in alfalfa increased with the compositional and configurational heterogeneity of the landscape. The abundance of both native and exotic coccinellids in alfalfa fields decreased as the area covered by cultivated lands, such as annual crops and orchards, increased. Nevertheless, the responses of native and exotic coccinellids were not consistent among regions, which may be explained by responses of the dominant species in each region. The results of the present study suggest that variables related to a higher intensification of agricultural landscapes (lower compositional and configurational heterogeneity, as well as more annual crops) reduce coccinellid diversity and abundance in alfalfa fields. To maintain a higher abundance and diversity of these natural enemies in alfalfa, more heterogeneous landscapes with less annual crops should be promoted.

A GEOSTATISTICAL APPROACH TO OPTIMIZE SAMPLING DESIGNS FOR LOCAL FOREST INVENTORIES

In forest management, it is of interest to obtain detailed inventories such that the local prediction errors on forest attributes are less than a prespecified threshold, while keeping the number of ground samples as low as possible. Given an initial sampling design, we propose an algorithm to determine the additional sample locations. The algorithm relies on two tools: geostatistical simulation, which allows measuring the uncertainty in the values of the attribute of interest, and simulated annealing, which allows finding an infill design that minimizes a given objective function. The proposed approach is applied to a data set from a Prosopis spp. plantation located in the Atacama Desert, in which the measured attribute is the rate of tree survival.

Estimation of Above-Ground Forest Biomass Using Landsat ETM+, Aster GDEM and Lidar

The measurement of above-ground biomass is important to understand carbon flow between trees and the atmosphere; remote sensing plays an important role in making this possible for extensive and hard-to-reach areas. This study compared above-ground forest biomass estimation models using data from different sources, including Landsat ETM+, Aster GDEM, ALS (LiDAR) and forest inventories. Two sets of predictors were established: the first included variables extracted from Landsat ETM+ and Aster GDEM, while the second included variables from Landsat in combination with LiDAR products (Digital Terrain Model, Digital Surface Model and Canopy Height Model). The Random Forest algorithm was used to build all models; this method explicitly returns the importance of each predictor and therefore allows the selection of the best set of variables. Estimations were made separately by forest cover for Pinus radiata, Eucalyptus globulus and second-growth Nothofagus glauca. Better results were obtained using the combination Landsat-LiDAR than those using Landsat-Aster GDEM data. Also, the results were better when applying the model to pine cover (pseudo R2 77.22%).