A high-resolution (1-meter) tree canopy assessment was completed for the Twin Cities Metropolitan Area. Mapping of existing and potential tree canopy is critical for urban tree management at the landscape level. This classification was created from combined 2015 aerial imagery, LIDAR data, and ancillary thematic layers. These data sets were integrated using an Object-Based Image Analysis (OBIA) approach through multi-resolution image segmentation and an iterative set of classification commands in the form of customized rulesets. eCognitionÂ® Developer was used to develop the rulesets and produce raster classification products for TCMA. The results were evaluated using randomly placed and independent verified assessment points. The classification product was analyzed at regional scales to compare distributions of tree canopy spatially and at different resolutions. The combination of spectral data and LiDAR through an OBIA method helped to improve the overall accuracy results providing more aesthetically pleasing maps of tree canopy with highly accurate results.
|Date made available||2017|
|Publisher||Data Repository for the University of Minnesota|