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opencc-zeroDec 2017View details →

Data from: Impact of ecological redundancy on the performance of machine learning classifiers in vegetation mapping

Vegetation maps are models of the real vegetation patterns and are considered important tools in conservation and management planning. Maps created through traditional methods can be expensive and time‐consuming, thus, new more efficient approaches are needed. The prediction of vegetation patterns using machine learning shows promise, but many factors may impact on its performance. One important factor is the nature of the vegetation–environment relationship assessed and ecological redundancy. We used two datasets with known ecological redundancy levels (strength of the vegetation–environment relationship) to evaluate the performance of four machine learning (ML) classifiers (classification trees, random forests, support vector machines, and nearest neighbor). These models used climatic and soil variables as environmental predictors with pretreatment of the datasets (principal component analysis and feature selection) and involved three spatial scales. We show that the ML classifiers produced more reliable results in regions where the vegetation–environment relationship is stronger as opposed to regions characterized by redundant vegetation patterns. The pretreatment of datasets and reduction in prediction scale had a substantial influence on the predictive performance of the classifiers. The use of ML classifiers to create potential vegetation maps shows promise as a more efficient way of vegetation modeling. The difference in performance between areas with poorly versus well‐structured vegetation–environment relationships shows that some level of understanding of the ecology of the target region is required prior to their application. Even in areas with poorly structured vegetation–environment relationships, it is possible to improve classifier performance by either pretreating the dataset or reducing the spatial scale of the predictions.

opencc-by-4.0Dec 2021View details →

Maps of relevant snow and frost parameters for the analysis of future vegetation changes in Swiss forests

The latest climate scenarios for Switzerland (CH2018) predict significant changes in temperature and precipitation in the future. This dataset contains maps snow and frost parameters calculated from climate variables that might have significant influence on future forest development. The following factors were calculated for the reference period 1981 - 2010 and the future period 2070 - 2099: Mean first and mean last frost day of the year, date of freezing and thawing of the ground, start and end of constant snow layer,  risk of drought caused by frozen ground, wet snow intensity, probability of tree damages because of wet snow. Three different results were calculated for the period 2070 - 2099, based on three different representative concentration pathways (RCP2.6, RCP4.5, RCP8.5, CH2018). Explanations concerning model structure, usability and uncertainties in the data sets can be found in the project report.

opencc-zeroMar 2022View details →

Mapping scrub vegetation cover from photogrammetric point-clouds

This dataset is derived from photogrammetric point cloud models of UAV imagery. It includes the Above ground models of vegetation as well as the isolated scrub vegetation. We illustrate the method with two case studies from the UK. The scrub cover at Daneway Banks, a calcareous grassland site in Gloucestershire was calculated at 21.8% of the site. The scrub cover at Flat Holm Island, a maritime grassland in the Severn Estuary was calculated at 7%. This approach enabled the scrub layer to be readily measured and if required, modelled to provide a visual guide of what a projected management objective would look like. This approach provides a new tool in reserve management, enabling habitat management strategies to be informed, and progress towards objectives monitored.

FIGURE 6 in Hidden in the dry woods: Mapping the collection history and distribution of Gymnanthes boticario, a well-collected but very recently described species restricted to the dry vegetation of South America

FIGURE 6. Distributional map of Gymnanthes boticario in the semiarid Caatinga of Northeastern Brazil, showing that when the species was described in 2010 collections were already available for seven of the ten Brazilian states where Caatinga occurs, including five of its eight Ecorregions (sensu Velloso et al.2002). States where we recorded the species: PI- Piauí; CE: Ceará; RN: Rio Grande do Norte; PB: Paraíba; PE: Pernambuco; BA: Bahia; MG: Minas Gerais (Map elaboration: M.F. Moro).

FIGURE 1 in Hidden in the dry woods: Mapping the collection history and distribution of Gymnanthes boticario, a well-collected but very recently described species restricted to the dry vegetation of South America

FIGURE 1. Number of collections per year of Gymnanthes boticario up to 2010, when the species was described.

FIGURE 7 in Hidden in the dry woods: Mapping the collection history and distribution of Gymnanthes boticario, a well-collected but very recently described species restricted to the dry vegetation of South America

FIGURE 7. Mapped distribution of Gymnanthes boticario in South America, a species typical of dry forests, including the first recorded occurrences to Bolivia, in the Chiquitano dry forests, to Paraguay, in the Cerro Léon, and to Mato Grosso do Sul, in the Pantanal (Map elaboration: M.F. Moro).

FIGURE 3 in Hidden in the dry woods: Mapping the collection history and distribution of Gymnanthes boticario, a well-collected but very recently described species restricted to the dry vegetation of South America

FIGURE 3. Number of duplicates of Gymnanthes boticario available in the consulted herbaria up to 2010, when the species was described

opennotspecifiedFeb 2021View details →

FIGURE. Myrcia cf. obversa. A: Habit; B–C: Fruits; D. Calyx indumentum in fruit: E: Infrutescence; F. Vegetative branch; G: Leaf abaxial surface; H: Distribution map. (A, B: Hatschbach 16698; C–G: Brotto 2561). in Myrcia (Myrtaceae) in the state of Paraná, Brazil

FIGURE. Myrcia cf. obversa. A: Habit; B–C: Fruits; D. Calyx indumentum in fruit: E: Infrutescence; F. Vegetative branch; G: Leaf abaxial surface; H: Distribution map. (A, B: Hatschbach 16698; C–G: Brotto 2561).

publicMar 2022View details →

Mapping scrub vegetation cover from photogrammetric point-clouds

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publicMay 2019View details →

Data from: Impact of ecological redundancy on the performance of machine learning classifiers in vegetation mapping

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