Evaluation and prediction of shrub cover in coastal Oregon forests (USA)

Kerns, Becky K.; Ohmann, Janet L.

doi:10.1016/j.ecolind.2003.12.002

Cited by 51 publications

(32 citation statements)

References 53 publications

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“…In addition, according to Kernes et al [88] tree models had a better understanding of the relationship and the boundaries, than logistic regression models for predicting the shrub cover spatial shifting. Also, according to [89], RFs are often used in very large geographical areas and when the number of samples in classes is unbalanced, the RF algorithm can be used with an acceptable level of accuracy for classification in such instances and it can be one of the factors for superiority over the SVM and k-NN.…”

Section: Discussionmentioning

confidence: 99%

Prediction of Dominant Forest Tree Species Using QuickBird and Environmental Data

Abdollahnejad

Panagiotidis

Shataee

et al. 2017

Forests

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Abstract:Modelling the spatial distribution of plants is one of the indirect methods for predicting the properties of plants and can be defined based on the relationship between the spatial distribution of vegetation and environmental variables. In this article, we introduce a new method for the spatial prediction of the dominant trees and species, through a combination of environmental and satellite data. Based on the basal area factor (BAF) frequency for each tree species in a total of 518 sample plots, the dominant tree species were determined for each plot. Also, topographical maps of primary and secondary properties were prepared using the digital elevation model (DEM). Categories of soil and the climate maps database of the Doctor Bahramnia Forestry Plan were extracted as well. After pre-processing and processing of spectral data, the pixel values at the sample locations in all the independent factors such as spectral and non-spectral data, were extracted. The modelling rates of tree and shrub species diversity using data mining algorithms of 80% of the sampling plots were taken. Assessment of model accuracy was conducted using 20% of samples and evaluation criteria. Random forest (RF), support vector machine (SVM) and k-nearest neighbor (k-NN) algorithms were used for spatial distribution modelling of dominant species groups using environmental and spectral variables from 80% of the sample plots. Results showed physiographic factors, especially altitude in combination with soil and climate factors as the most important variables in the distribution of species, while the best model was created by the integration of physiographic factors (in combination with soil and climate) with an overall accuracy of 63.85%. In addition, the results of the comparison between the algorithms, showed that the RF algorithm was the most accurate in modelling the diversity.

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Section: Discussionmentioning

confidence: 99%

Prediction of Dominant Forest Tree Species Using QuickBird and Environmental Data

Abdollahnejad

Panagiotidis

Shataee

et al. 2017

Forests

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“…Managers are often concerned about potential deleterious effects owing to spring burning because it is outside the natural range of variability for these systems. We concentrate on the forest understory (shrubs, grasses, forbs, regenerating conifers) because the understory contributes virtually all plant biodiversity in western conifer forests; and helps regulate many processes such as conifer regeneration, soil retention, nutrient cycling, and watershed function; and underpins faunal diversity (Harrod 2001, Allen et al 2002, Kerns and Ohmann 2004, Moore et al 2006). …”

Section: Introductionmentioning

confidence: 99%

Reintroducing fire into a ponderosa pine forest with and without cattle grazing: understory vegetation response

et al. 2011

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Abstract. Reestablishing historical fire regimes is a high priority for North American coniferous forests, particularly ponderosa pine (Pinus ponderosa) ecosystems. These forests are also used extensively for cattle (Bos spp.) grazing. Prescribed fires are being applied on or planned for millions of hectares of these forests to reduce fuel loads, alter forest structure, and maintain and enhance the productivity of native plant communities. However, cattle grazing is ubiquitous in ponderosa pine forests and the consequences of post-fire cattle grazing on plant communities are not well understood. We evaluated cattle grazing effects (grazing, no grazing) on upland bunchgrass and Carex geyeri dominated ponderosa pine plant communities over five growing seasons after prescribed fires (spring reburn, fall reburn, no burn). Vegetation was measured prior to a 5-year interval reburn and the subsequent exclusion of cattle, and in the second and fifth growing seasons thereafter. We found no interactions between reburning and grazing for the understory response variables. For all reburn treatments, including unburned areas, five growing seasons of cattle grazing exclusion significantly increased: (1) total vegetative cover, (2) native perennial forb cover, (3) grass stature, (4) grass flowering stem density, and (5) the cover of some shrub species and functional groups. Grazing exclusion did not strongly affect plant compositional patterns, although differences were detected. Compared to unburned areas, neither spring nor fall reburning increased perennial native species cover or richness, and reburning reduced sedge cover. Fall reburning increased cover of native colonizers, and exotic species cover and richness (largely Bromus tectorum and Cirsium vulgare), although overall exotic cover remains low (,1%). We document several potentially chronic impacts of cattle grazing in both burned and unburned areas, and show that the understory release from a long history of cattle grazing caused a greater degree of change than the initial reintroduction of fire. If a goal of ecological restoration in these forests is increased cover of native perennial plants, and the potential for increased native perennial grass reproduction, then cattle grazing exclusion, or a change in cattle management, could provide critically important options in restoration plans.

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“…The ability of RF in determining important coefficients, weighting the independent variables and its non-necessity of decision tree structure pruning are all factors that enhance the functionality and effectiveness of this algorithm. On the contrary, k-NN and SVM algorithms use the same proportions of weighting for all independent variables (Kernes, Ohmann 2004). Comparing this with other algorithms, data mining algorithms are easier to comprehend; they need little data preparation (no need to normalize data) and can handle numerical and categorical data, in addition to that, a large amount of data can be analysed by data mining algorithms in a reasonable time.…”

Section: Discussionmentioning

confidence: 99%

Investigation of a possibility of spatial modelling of tree diversity using environmental and data mining algorithms

Abdollahnejad¹,

Panagiotidis²,

Surový³

2016

J. For. Sci.

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Biological diversity is the basis for a wide array of goods and services provided by forests. The variety of forest trees and shrubs plays a vital role in the daily life of forest communities. The purpose of this study is to investigate the possibility of modelling the diversity of tree species by characteristics of topography, soil and climate, using data mining algorithms k-NN, RF and SVM in Dr. Bahramnia forestry plan in the north of Iran. Based on the basal area factor for each species in a total of 518 sample plots, diversity indices such as species richness, evenness and heterogeneity were calculated for each plot. Topographic maps of primary and secondary properties were prepared using the digital elevation model. Categories of the soil and climate maps database of Dr. Bahramnia forestry plan were extracted. Modelling rates of tree and shrub species diversity using data mining algorithms, with 80% of the sampling plots were taken. Assessment of the model accuracy, using 20% of samples and evaluation criteria, was conducted. Results showed that topographic features, especially elevation, had the highest impact on the species diversity index. The modelling results also showed that Camargo evenness index had lowest root mean square error (RMSE) (0.14) and RMSE% (24.35), compared to other indicators of diversity. In addition, the results of the comparison between the algorithms showed that the random forest algorithms were more accurate in modelling the diversity.

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Evaluation and prediction of shrub cover in coastal Oregon forests (USA)

Cited by 51 publications

References 53 publications

Prediction of Dominant Forest Tree Species Using QuickBird and Environmental Data

Prediction of Dominant Forest Tree Species Using QuickBird and Environmental Data

Reintroducing fire into a ponderosa pine forest with and without cattle grazing: understory vegetation response

Investigation of a possibility of spatial modelling of tree diversity using environmental and data mining algorithms

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