Presencia, abundancia y asociatividad de Citronella mucronata en bosques secundarios de Nothofagus obliqua en la precordillera de Curicó, región del Maule, Chile

Corvalán, Patricio; Galleguillos, Mauricio; Hernández, Jaime

doi:10.4067/s0717-92002014000300002

Cited by 3 publications

(4 citation statements)

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“…Summaries of the biomass reference data are given in Table 1 and Table 2. Further details regarding the test sites, sampling and biomass calculations are given in Fassnacht et al [2014] and Corvalán et al [2014]. Fassnacht et al [2014].…”

Section: Field Sampling and Reference Data Calculationmentioning

confidence: 99%

Modeling forest biomass using Very-High-Resolution data—Combining textural, spectral and photogrammetric predictors derived from spaceborne stereo images

Maack

Kattenborn

Fassnacht

et al. 2015

European Journal of Remote Sensing

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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.

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Section: Field Sampling and Reference Data Calculationmentioning

confidence: 99%

Modeling forest biomass using Very-High-Resolution data—Combining textural, spectral and photogrammetric predictors derived from spaceborne stereo images

Maack

Kattenborn

Fassnacht

et al. 2015

European Journal of Remote Sensing

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“…The 11 LiDAR selected predictors were nine topographical variables: mean altitude (DTM10), standard deviation of altitude (SDAl30), mean slope (MSl30), median catchment slope (MCS60m), mean catchment slope (MCS60), MrRTF (multiresolution index of the ridge top flatness) (MRT10), standard deviation of slope (SDS15), and two structural predictors: mean digital crown model (DCM15), median digital crown model (DCM90). The selected predictors were strongly associated with the results of Corvalan et al [40], where altitude and the landform indexes were used as variables to explain the presence, abundance, and associativity of several tree species in the same area. The 12 predictors defined by the exploratory approach were used in a best subset regression approach to define the optimal number of predictors with the highest adjustment.…”

Section: Final Predictive Modelmentioning

confidence: 73%

“…The range of elevation in the study was about 700 m, which generates an important gradient of temperature conditions that is a key factor in mountain ecosystems determining different vegetation zones [8]. The same variable proved to be heavily discriminatory in characterizing several tree species in the same area [40]. The importance of this variable is consistent with the results of several studies [19,41,47], where altitude (at different resolutions) is always among the selected variables in predictive models, particularly on the regional scale [46].…”

Section: Ecological Implicationsmentioning

confidence: 99%

“…We established six nested subplots on each plot, the largest of which was 225 m plots, thereby obtaining the total richness of vascular species for each group (conglomerate) of plots [40]. The establishment of the field sampling area was made based on a visual inspection of a larger area, making sure that the selected area was representative of the variability present within the Hyperion pixel.…”

Section: Field Datamentioning

confidence: 99%

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

et al. 2015

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Abstract: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 m 2 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 R 2 = 0.651; RSE = 3.69). An independent validation survey was performed with significant results (Adjusted R 2 = 0.571, RMSE = 5.05). Selected variables were statistically significant: OPEN ACCESSRemote Sens. 2015, 7 2693 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.

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Comparing Generalized Linear Models and random forest to model vascular plant species richness using LiDAR data in a natural forest in central Chile

Lopatin

Dološ

Hernández

et al. 2016

Remote Sensing of Environment

142

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Presencia, abundancia y asociatividad de Citronella mucronata en bosques secundarios de Nothofagus obliqua en la precordillera de Curicó, región del Maule, Chile

Cited by 3 publications

References 4 publications

Modeling forest biomass using Very-High-Resolution data—Combining textural, spectral and photogrammetric predictors derived from spaceborne stereo images

Modeling forest biomass using Very-High-Resolution data—Combining textural, spectral and photogrammetric predictors derived from spaceborne stereo images

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

Comparing Generalized Linear Models and random forest to model vascular plant species richness using LiDAR data in a natural forest in central Chile

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