Year-end Sale % OFF 
Abstract
The objective of this study is to evaluate operational methods for creating a particular type of urban vegetation map—one focused on vegetation over rooftops (VOR), specifically trees that extend over urban residential buildings. A key constraint was the use of passive remote sensing data only. To achieve this, we (1) conduct a review of the urban remote sensing vegetation classification literature, and we then (2) discuss methods to derive a detailed map of VOR for a study area in Calgary, Alberta, Canada from a late season, high-resolution airborne orthomosaic based on an integration of Geographic Object-Based Image Analysis (GEOBIA), pre-classification filtering of image-objects using Volunteered Geographic Information (VGI), and a machine learning classifier. Pre-classification filtering lowered the computational burden of classification by reducing the number of input objects by 14%. Accuracy assessment results show that, despite the presence of senescing vegetation with low vegetation index values and deep shadows, classification using a small number of image-object spectral attributes as classification features (n = 9) had similar overall accuracy (88.5%) to a much more complex classification (91.8%) comprising a comprehensive set of spectral, texture, and spatial attributes as classification features (n = 86). This research provides an example of the very specific questions answerable about precise urban locations using a combination of high-resolution passive imagery and freely available VGI data. It highlights the benefits of pre-classification filtering and the judicious selection of features from image-object attributes to reduce processing load without sacrificing classification accuracy.
Highlights
In support of that program, the goal of this study is to evaluate operational methods to create detailed maps of vegetation over rooftops (VOR)— trees that extend over urban residential buildings—from high-resolution multispectral imagery, which can be used to further refine rooftop emissivity corrections for high-resolution thermal infrared (TIR) imagery
To create VOR maps, we describe the use of Geographic Object-Based Image Analysis (GEOBIA), pre-classification filtering of image-objects using Volunteered Geographic Information (VGI)— the OpenStreetMap (OSM) database—and a machine learning classifier
Little research has been published with a passive-only focus on high-resolution urban vegetation mapping, and in our review, we found no works in which the classification or mapping of vegetation over rooftops was the explicit aim of the work
Summary
Research efforts into the effects of urban vegetation come from diverse fields beyond arboriculture and forestry, such as energy, ecology, and economics [1]. Many unobtrusive beneficial effects of urban trees have been demonstrated, such as reducing sulphur dioxide [2], lowering urban surface temperatures [3], and increasing rainfall interception [4] (see [1,5] for comprehensive reviews). Vegetation maps are important for urban forest management, such as in setting benchmarks for tree planting initiatives [7], monitoring vegetation health over time, and preparing climate change vulnerability assessments and adaptation strategies [8,9]. While urban trees are increasingly impacted by climate change, they are potential agents for its mitigation; where urban trees may prove most important is in their use helping cities adapt for climate change [10]
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
