Cici Alexander

From geospatial datasets to urban tree inventories

Research output: Contribution to conferencePosterResearch

Urban greenery provides a range of ecosystem services and plays an important role in creating liveable cities. Mapping urban vegetation is a prerequisite for the planning and management of these resources for equitable urban development. Although high-resolution geospatial datasets are becoming freely available in many countries, they are often under-utilised for urban planning. Airborne Laser Scanning (ALS), using the technique of Light Detection and Ranging (LiDAR), is increasingly being used to investigate the three-dimensional (3D) structure of vegetation, though the main emphasis has been on trees in forests. Elevation, an important attribute of ALS data from which heights of objects can be derived, helps in classifying vegetation as grass, shrubs, or trees. Urban areas, with their complex spatial arrangement of features, including buildings, pose challenges for classification and information extraction. In this study, local maxima in elevation models and contours representing tree crowns, generated from ALS data, are used to estimate the locations and structural attributes of urban trees as a first step in creating an urban tree inventory. Building footprints and aerial imagery are used as auxiliary data. Building polygons aid in separating treetops from other local maxima, while Normalised Difference Vegetation Index (NDVI), calculated from the infrared and red bands of the aerial imagery, is very useful in separating trees shorter than 3 m from other features. A workflow, using free national geospatial datasets, is developed in this study, which can be implemented in GIS for applications in urban planning.
Original languageEnglish
Publication year19 Sep 2019
Publication statusPublished - 19 Sep 2019
EventNordic Remote Sensing Conference - Aarhus Institute of Advanced Studies (AIAS), Aarhus, Denmark
Duration: 17 Sep 201919 Sep 2019
http://aias.au.dk/events/aias-conference-nordic-remote-sensing-2019-norsc19/

Conference

ConferenceNordic Remote Sensing Conference
LocationAarhus Institute of Advanced Studies (AIAS)
CountryDenmark
CityAarhus
Period17/09/201919/09/2019
Internet address

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ID: 169330567