Influência pedológica e topográfica na distribuição de espécies arbóreas em diferentes estágios de regeneração

Dullius, Michelle; Dalmolin, Ricardo Simão Diniz; Pedron, Fabrício de Araújo; Longhi, Solon Jonas; Horst, Taciara Zborowski; Moura-Bueno, Jean Michel

doi:10.5039/agraria.v13i4a5589

Cited by 2 publications

(4 citation statements)

References 16 publications

(23 reference statements)

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“…Similarly to VDCN, the NDVI has the potential to discriminate the RR and RL classes from the others due to differences in the range of this index. This fact is related to the association of soil classes with incipient pedogenesis and the land use of native forest (Dullius et al, 2018), which predominate in areas of relief varying from strong wavy to mountainous (Samuel-Rosa et al, 2011).…”

Section: Resultsmentioning

confidence: 99%

“…The land use/land cover that predominates in the area is made up of shrubland and native grassland occupying more than half of the area, followed by native semi-deciduous forests, eucalyptus forestry, and annual crop agriculture. Dullius et al (2018) observed that there is a relationship between vegetation variability and pedology. Native forests are predominant in strong wavy and mountainous relief.…”

Section: Methodsmentioning

confidence: 99%

“…In addition, the normalized difference vegetation index (NDVI), obtained from Landsat 8 satellite images from November 2016, was used. This covariate was included to represent the organism factor in soil formation, considering the relationship between land use/land cover and soil class (Samuel-Rosa et al, 2011;Dullius et al, 2018), which was observed during the soil sampling stage in the study area. The covariates were derived in the SAGA-GIS, version 2.1.2, software (Conrad et al, 2015).…”

Section: Methodsmentioning

confidence: 99%

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Prediction of soil classes in a complex landscape in Southern Brazil

Moura-Bueno

Dalmolin

Horst-Heinen

et al. 2019

Pesq. agropec. bras.

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The objective of this work was to evaluate the use of covariate selection by expert knowledge on the performance of soil class predictive models in a complex landscape, in order to identify the best predictive model for digital soil mapping in the Southern region of Brazil. A total of 164 points were sampled in the field using the conditioned Latin hypercube, considering the covariates elevation, slope, and aspect. From the digital elevation model, environmental covariates were extracted, composing three sets, made up of: 21 covariates, covariates after the exclusion of the multicollinear ones, and covariates chosen by expert knowledge. Prediction was performed with the following models: decision tree, random forest, multiple logistic regression, and support vector machine. The accuracy of the models was evaluated by the kappa index (K), general accuracy (GA), and class accuracy. The prediction models were sensitive to the disproportionate sampling of soil classes. The best predicted map achieved a GA of 71% and K of 0.59. The use of the covariate set chosen by expert knowledge improves model performance in predicting soil classes in a complex landscape, and random forest is the best model for the spatial prediction of soil classes.

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Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Prediction of soil classes in a complex landscape in Southern Brazil

Moura-Bueno

Dalmolin

Horst-Heinen

et al. 2019

Pesq. agropec. bras.

View full text Add to dashboard Cite

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“…Environmental characteristics can also be used in the study and management of biodiversity. Altitude, for example, comprises vertical distance measurements that show differences in relief (Tuler 2014), which have a direct influence on various abiotic factors and plant communities (Fritzsons et al 2008;Dullius et al 2018). The characteristics of phytoecological regions and biomes allow them to be used as units for the management and conservation of biodiversity, since they make up the landscape and are determined by geology, climate and species distribution (Coutinho 2006;IBGE 2019b;IBGE 2022a).…”

Section: Introductionmentioning

confidence: 99%

Distribution of Pleroma asperius (Melastomataceae) in Rio Grande do Sul, Brazil: spatial analysis for conservation strategie

2022

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Pleroma asperius is a shrubby Melastomataceae endemic to southern Brazil and threatened with extinction in the Rio Grande do Sul (RS) state. Aiming to better understand the distribution of the species in different environments in RS and contribute to its conservation, a literature review, searches on online platforms and field expeditions were carried out. From 270 records obtained, ninety-one presented location data and were treated as distinct occurrence points, six of them located in Conservation Units (CUs). Maps for P. asperius occurrence (82 non-overlapping points) were constructed, with layers referring to relief, biomes with phytogeographic regions, and hydrographic basins. Most points are below 50 m.a.s.l. in altitude (86.8%). The Pampa biome and Pioneer Formations have the highest percentages of occurrence points (74.4 and 71.9%, respectively). The interpolation of the occurrence points with environmental data characterized the preferential distribution of P. asperius in low-altitude areas, especially in wetlands and in the Pioneer Formations. These environments undergo intense changes by anthropic interventions. We draw attention to the low number of occurrence points in UCs. We still recommend to review the conservation status of P. asperius, include it for recomposing vegetation in degraded areas and create more CUs in the Pampa biome.

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Influência pedológica e topográfica na distribuição de espécies arbóreas em diferentes estágios de regeneração

Cited by 2 publications

References 16 publications

Prediction of soil classes in a complex landscape in Southern Brazil

Prediction of soil classes in a complex landscape in Southern Brazil

Distribution of Pleroma asperius (Melastomataceae) in Rio Grande do Sul, Brazil: spatial analysis for conservation strategie

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