Lists layers available from the WFS geoserver. This is similar to sending the
WFS request of getFeatureTypes. listLayers() returns a data.frame with the 'Name' and title of the
layers available. The 'Name' is what is used within vicmap_query() while the title provides somewhat of a
description/clarification about the layer.
listLayers(..., abstract = TRUE)Additional arguments passed to grep. The pattern argument can be used to search for specific layers with matching names or titles.
Whether to return a column of abstract (and metadata ID), the default is true. Switching to FALSE will provide a data.frame with only 2 columns and may be slightly faster.
data.frame of 2 (abstract = FALSE) or 4 (abstract = TRUE) columns
# \donttest{
try(
listLayers(pattern = "trees", ignore.case = TRUE)
)
#> Name Title
#> 1 open-data-platform:isc2010_large_trees isc2010_large_trees
#> 2 open-data-platform:tree_density tree_density
#> 3 open-data-platform:tree_urban tree_urban
#> Abstract
#> 1 The ISC2010_LARGE_TREES polygon features represent trees that exceed a predefined height and crown area criteria. These criteria can vary depending on the Ecological Vegetation Class (EVC). Large Trees are mapped within a 300m buffer of the ISC2010_STREAMBED_WIDTH feature but are assessed for Metrics within the 40m riparian zone (defined as 40m from Streambed Width) . This data set is derived from source Fractional Cover and Canopy Height raster data. The data set also contains additional polygon divisions created by the introduction of Side and 40m buffer boundaries. Small polygons (<10m2) that were created by the introduction of these boundaries have been removed.\n\nRiver condition in Victoria is assessed every 5 years using the Index of Stream Condition (ISC). The Department of Environment and Primary Industries (DEPI) developed a methodology to assess the Physical Form and Riparian Vegetation components of the ISC using remote sensing data, specifically LIDAR and aerial photography. \n\nA State Wide mapping project was undertaken in 2010-13 to accurately map the Physical Form and Riparian Vegetation metrics of the ISC . Other ISC metrics were not assessed in the project and were derived from other sources.\n\nThe Physical Form and Riparian Vegetation Metric products are a combination of mapped Vector and Raster data as well as Tabular Summary Statistics about the mapped features. In the context of the project, the term Metrics is used to refer to both the mapped features and the summary statistics. \n\nRemote sensing data used includes 15cm true colour and infra-red aerial photography and four return multi-pulse LiDAR data. This source data was used to derive a variety of Raster data sets including Digital Terrain Models, Slope, Vegetation Height and Vegetation Cover. The Digital Terrain and Slope rasters were used to map Physical Form metrics including Stream Bed, Top of Bank and River Centre Lines while the Vegetation Height and Cover rasters were used to map the Riparian Vegetation metrics.\nThe Project Report "Aerial Remote Sensing for Physical Channel Form and Riparian Vegetation Mapping" describes the remote sensing and mapping approach used to create this data set.
#> 2 The Vicmap Vegetation Tree Extent dataset was generalised to 2m pixels and then clustering rules were applied to group the data into three density classes (Dense, Medium, Sparse). This classification was a pixel by pixel assessment where a pixel was allocated a density classification based on neighbouring pixels. The raster dataset was then converted to vector. \n\nThe process of grouping tree cover into density classes simplifies the representation of trees and reduces the complexity of the vector dataset. It is a effective way of representing tree cover. The original raw 20cm raster dataset is maintained as a separate dataset, Vicmap Vegetation Tree Extent.
#> 3 The Vicmap Vegetation Tree Urban represents trees as points across Metropolitan Melbourne and the urban environment within four regional councils: Wangaratta, Sale, Shepparton and Ballarat. \n\nThis product is derived from machine learning of high resolution aerial photography with no post processing human intervention.
#> metadataID
#> 1 665e0d7c-897c-542a-9762-d659e865611c
#> 2 1af4942a-fff4-58c2-80f7-10c6054c31c4
#> 3 51eba84d-7cb2-55bc-bf96-f10cb44bc923
# }