Object-based delineation of urban tree canopy: assessing change in Oklahoma City, 2006–2013. (January 2019) - Ellis, Emily A.; Mathews, Adam J.
Abstract: With a burgeoning global population, the pressures of urbanization are increasingly prevalent. The need to quantify urban greenness remains significant due to environmental impact and its relationship with human well-being. Utilizing 1 m discrete-return airborne lidar-derived digital terrain models (DTMs) and digital surface models (DSMs), aerial imagery, and lidar-imagery fusion, this study assesses vegetation, specifically tree canopy, change within Oklahoma City between 2006 and 2013. Specifically, we (1) identify an accurate object-based image analysis (OBIA) method for the detection of urban vegetation outlines, and (2) apply that method to locate and quantify vegetation change and assess spatial patterns in Oklahoma City between 2006 and 2013. The proposed OBIA approach extracts urban vegetation coverage from aerial imagery and lidar-based models with around 89% accuracy. Regarding vegetation change, Oklahoma City lost 9.69 km 2 (3.74 mi 2 ) of tree canopy coverage, which accounted for a 2% loss in total greenness. Highlights: An open source, adoptable object-based method was developed to extract tree canopy in the urban environment. The method was tested using three input datasets: lidar, aerial imagery, and lidar-imagery fusion. The lidar-imagery fusion data yielded the most accurate segmentation results. The object-based method was 89% accurate at extracting tree canopy extents. Between 2006 and 2013, Oklahoma City lost 2% of its urban tree canopy extent.