Mapping Tree Canopy in Urban Environments Using Point Clouds from Airborne Laser Scanning and Street Level Imagery

Abstract

Resilient cities incorporate a social, ecological, and technological systems perspective through their trees, both in urban and peri-urban forests and linear street trees, and help promote and understand the concept of ecosystem resilience. Urban tree inventories usually involve the collection of field data on the location, genus, species, crown shape and volume, diameter, height, and health status of these trees. In this work, we have developed a multi-stage methodology to update urban tree inventories in a fully automatic way, and we have applied it in the city of Pamplona (Spain). We have compared and combined two of the most common data sources for updating urban tree inventories: Airborne Laser Scanning (ALS) point clouds combined with aerial orthophotographs, and street-level imagery from Google Street View (GSV). Depending on the data source, different methodologies were used to identify the trees. In the first stage, the use of individual tree detection techniques in ALS point clouds was compared with the detection of objects (trees) on street level images using computer vision (CV) techniques. In both cases, a high success rate or recall (number of true positive with respect to all detectable trees) was obtained, where between 85.07% and 86.42% of the trees were well-identified, although many false positives (FPs) or trees that did not exist or that had been confused with other objects were always identified. In order to reduce these errors or FPs, a second stage was designed, where FP debugging was performed through two methodologies: (a) based on the automatic checking of all possible trees with street level images, and (b) through a machine learning binary classification model trained with spectral data from orthophotographs. After this second stage, the recall decreased to about 75% (between 71.43 and 78.18 depending on the procedure used) but most of the false positives were eliminated. The results obtained with both data sources were robust and accurate. We can conclude that the results obtained with the different methodologies are very similar, where the main difference resides in the access to the starting information. While the use of street-level images only allows for the detection of trees growing in trafficable streets and is a source of information that is usually paid for, the use of ALS and aerial orthophotographs allows for the location of trees anywhere in the city, including public and private parks and gardens, and in many countries, these data are freely available.

Towards an Automated Approach for Monitoring Tree Phenology Using Vehicle Dashcams in Urban Environments

“…Of particular note, is that local variability in the green-up start date was captured, which is extremely important considering the heterogeneity of the urban fabric and morphology. The use of dashcams in this way offers a particular advantage over other proximal remote sensing approaches, for example Google Street View data [ 20 , 21 ], in the high temporal frequency afforded (and determined by the user). Further research is required however, and the aspects of this are discussed below.…”

Tree Species Identification in Urban Environments Using TensorFlow Lite and a Transfer Learning Approach

“…In recent years, identifying plant species [12][13][14][15][16], flowers [17], medicinal plants [18], and trees [19] has been achieved using photographs. Examples of this are some mobile applications available in the android market, such as LeafSnap, PictureThis, PlantNet, Blossom, Seek, and others [7].…”

“…Plant identification approaches are usually based on analyzing a single leaf followed by a flower [7]. For these image analysis systems that apply Artificial Vision and Deep Learning methods, it is possible to customize models of local flora [20], to assess the health status of trees and plants [16,21] and aid in leaf recognition and classification [22,23] using artificial neural networks.…”