New York Hazard Tree Mapping
Project TypeNon-Bank Project
LocationNew York | New York State
SolutionClimate Adaptation & Flood Resilience, Natural Resource Planning/Restoration
Ecological SettingGrasslands, Prairies, & Upland Forests
To identify ash trees and other hazard trees, the RES Geospatial team provided LiDAR mapping, high-resolution multi-spectral imagery, and vegetation mapping and analysis for 245 linear miles in a corridor of mixed voltage transmission lines in the state of New York.
Aerial imagery and reflectance values associated with forest canopy were used for the distinction of species and conditions. Ash tree and hazard tree identification were completed using a combination of LiDAR and imagery data.
The project team utilized an object-based supervised classification method for delineating and characterizing tree canopy extents using the following steps for this cutting-edge classification system:
- Training Data: Geo-located points associated with known tree species, used to inform the “training” of a supervised classification strategy
- Data Segmentation: Rule-based automated process which generates polygons that represent key features of interest, based on input data (LiDAR and orthophotography)
- Classification: Utilizes segment attributes (spectral characteristics: spectral reflection, the size of an object, and its texture, shape, and context) associated with delineated objects for classifying feature type or tree canopy