Existing Tree Canopy: The amount of urban tree canopy present when viewed from above using aerial or satellite imagery.
ETC % = tree canopy / land area
Possible Tree Canopy - Vegetated: Grass or shrub area that is theoretically available for the establishment of tree canopy.
e.g. residential areas
Possible Tree Canopy - Impervious: Asphalt, concrete or bare soil surfaces, excluding roads and buildings, that are theoretically available for the establishment of tree canopy without having to remove paved surfaces
e.g. any areas with no trees, buildings, roads, or bodies of water
Possible-Vegetation category should serve as a guide for further analysis, not a prescription of where to plant trees since other factors, such as land use, social, and financial (e.g. golf courses, agricultural and recreational fields), are involved.
Not Suitable: Areas where it is highly unlikely that new tree canopy could be established (primarily buildings and roads).
Relative tree canopy change - change of tree canopy over a period of time
e.g (for 1 hexagon) relative tree canopy change % = (tree_canopy_area_2019 - tree_canopy_area_2012) / tree_canopy_area_2012 - Acre gain per
Canopy height - proxy for tree age
Steps
Segment tree canopy into polygons approximating individual trees
Attribute each polygon with a height from both the starting date to end date (e.g. 2012 and 2019 ) LiDAR data
Interpretation example
Trees in the 0-60 foot height class experienced gain, while there was minimal gain in the other taller height classes.
Therefore, many new trees planted and canopy expanding on existing trees.
Diverse height structure corresponds to a healthy and diverse tree age distribution
Very mature trees in the 130 height class points to the height potential for certain tree species provided the right conditions
Canopy Assessment
Packages
{greenR} - An R Package for Quantifying Urban Greenness
Provides a new and scalable method to assign green indices to individual street segments.
Offers a comprehensive solution by facilitating green index quantification, analysis, and visualization.
Supports Mapbox visualizations for interactive exploration of different types of geospatial data, enhancing the analysis with dynamic and engaging visual tools.
Tree benefit: reducing stormwater runoff near streets and decreasing the urban heat island effect
Above surface factors such as sidewalks to utilities can affect the suitability of a site for tree planting.
Important to preserve trees in the 10-50 foot height range, so they can grow into the 60+ foot range while planting a variety of new trees to continue the lifecycle
Losses are generally easier to detect than gains as losses tend to be due to a large event, such as tree removal, whereas gains are incremental growth or new tree plantings, both of which are smallerin size
Factors that can affect change in tree canopy
Natural
Invasive species
Natural disasters such as storms
Climate change may cause trees to grow more quickly but could also result in inhospitable conditions for native species
Anthropogenic
Preservation and conservation efforts, the strength of tree ordinances, and the impacts of new development
Tree removal due to homeowner preferences and not being replaced by new trees
Proximity to roads: Regular salting, compaction, limited space, clearance pruning, and plow collisions
Data sources
LiDAR
Features distinguished by their spectral (color) properties
Trees and shrubs can appear spectrally similar or obscured by shadow, LiDAR, which consists of 3D height information enhances the accuracy of the mapping
Resolution of 30-meters
“LiDAR datasets were acquired under leaf-off conditions and thus tend to underestimate tree canopy slightly” (i.e. Fall or Winter?)
LiDAR and imagery datasets are not directly comparable due to differences in the sensor, time of acquisition, and processing techniques employed.
Resources:
Paper summarizing their tree canapy mapping approach
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