Mobile Terrestrial Photogrammetry for Street Tree Mapping and Measurements

Roberts, John W.; Koeser, Andrew K.; Abd-Elrahman, Amr; Wilkinson, Benjamin; Hansen, Gail; Landry, Shawn M.; Perez, A. Gonzalez

doi:10.3390/f10080701

Cited by 28 publications

(16 citation statements)

References 61 publications

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“…TP as a passive remote sensing, is easy to be influenced by light and surrounding environment [54,55]. TP has a weak ability to obtain information about branches or tree canopy [56]. As an active remote sensing technology, TLS can be sampled point by point.…”

Section: Limitations and Application Potentialmentioning

confidence: 99%

Low Cost Automatic Reconstruction of Tree Structure by AdQSM with Terrestrial Close-Range Photogrammetry

Dong

Fan

Zhou

et al. 2021

Forests

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The quantitative structure model (QSM) contains the branch geometry and attributes of the tree. AdQSM is a new, accurate, and detailed tree QSM. In this paper, an automatic modeling method based on AdQSM is developed, and a low-cost technical scheme of tree structure modeling is provided, so that AdQSM can be freely used by more people. First, we used two digital cameras to collect two-dimensional (2D) photos of trees and generated three-dimensional (3D) point clouds of plot and segmented individual tree from the plot point clouds. Then a new QSM-AdQSM was used to construct tree model from point clouds of 44 trees. Finally, to verify the effectiveness of our method, the diameter at breast height (DBH), tree height, and trunk volume were derived from the reconstructed tree model. These parameters extracted from AdQSM were compared with the reference values from forest inventory. For the DBH, the relative bias (rBias), root mean square error (RMSE), and coefficient of variation of root mean square error (rRMSE) were 4.26%, 1.93 cm, and 6.60%. For the tree height, the rBias, RMSE, and rRMSE were—10.86%, 1.67 m, and 12.34%. The determination coefficient (R2) of DBH and tree height estimated by AdQSM and the reference value were 0.94 and 0.86. We used the trunk volume calculated by the allometric equation as a reference value to test the accuracy of AdQSM. The trunk volume was estimated based on AdQSM, and its bias was 0.07066 m3, rBias was 18.73%, RMSE was 0.12369 m3, rRMSE was 32.78%. To better evaluate the accuracy of QSM’s reconstruction of the trunk volume, we compared AdQSM and TreeQSM in the same dataset. The bias of the trunk volume estimated based on TreeQSM was −0.05071 m3, and the rBias was −13.44%, RMSE was 0.13267 m3, rRMSE was 35.16%. At 95% confidence interval level, the concordance correlation coefficient (CCC = 0.77) of the agreement between the estimated tree trunk volume of AdQSM and the reference value was greater than that of TreeQSM (CCC = 0.60). The significance of this research is as follows: (1) The automatic modeling method based on AdQSM is developed, which expands the application scope of AdQSM; (2) provide low-cost photogrammetric point cloud as the input data of AdQSM; (3) explore the potential of AdQSM to reconstruct forest terrestrial photogrammetric point clouds.

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Section: Limitations and Application Potentialmentioning

confidence: 99%

Low Cost Automatic Reconstruction of Tree Structure by AdQSM with Terrestrial Close-Range Photogrammetry

Dong

Fan

Zhou

et al. 2021

Forests

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“…Thus, aerial imagery (hyperspectral, RGB or PPC) may play an important role in tree mapping, both in the characterization and delineation of tree crowns and in species identification, since these data have enough spectral and spatial resolution [29,81,102]. However, Katz et al [11] reported that one of the main limitations of aircraft aerial imagery is the reduced availability of these images over time, given that images are taken at specific times, making it difficult to analyze vegetation at different phenological stages.…”

Section: Aerial Imagerymentioning

confidence: 99%

“…Meanwhile, although hyperspectral information from aerial images represent a valuable resource for species identification through spectral signatures and shape study, several studies showed that two individuals of the same species may have different spectral and structural characteristics due to the growth form and phenology [93,102]. Another limitation regarding the use of aerial images in the identification of trees in the city is the heterogeneity of the landscape where elements, such as facades, roofs, and others can be mistaken for trees or partly hide them [5], in addition to objects adjacent to the trees that can lead to misclassifications when using PPCs [100,103].…”

Section: Aerial Imagerymentioning

confidence: 99%

Remotely Sensed Tree Characterization in Urban Areas: A Review

et al. 2021

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Urban trees and forests provide multiple ecosystem services (ES), including temperature regulation, carbon sequestration, and biodiversity. Interest in ES has increased amongst policymakers, scientists, and citizens given the extent and growth of urbanized areas globally. However, the methods and techniques used to properly assess biodiversity and ES provided by vegetation in urban environments, at large scales, are insufficient. Individual tree identification and characterization are some of the most critical issues used to evaluate urban biodiversity and ES, given the complex spatial distribution of vegetation in urban areas and the scarcity or complete lack of systematized urban tree inventories at large scales, e.g., at the regional or national levels. This often limits our knowledge on their contributions toward shaping biodiversity and ES in urban areas worldwide. This paper provides an analysis of the state-of-the-art studies and was carried out based on a systematic review of 48 scientific papers published during the last five years (2016–2020), related to urban tree and greenery characterization, remote sensing techniques for tree identification, processing methods, and data analysis to classify and segment trees. In particular, we focused on urban tree and forest characterization using remotely sensed data and identified frontiers in scientific knowledge that may be expanded with new developments in the near future. We found advantages and limitations associated with both data sources and processing methods, from which we drew recommendations for further development of tree inventory and characterization in urban forestry science. Finally, a critical discussion on the current state of the methods, as well as on the challenges and directions for future research, is presented.

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“…The images were acquired approximately 3.5 m above the ground with the platform moving at 0.5 m/sec providing 70% image overlap and 60% sidelap. This configuration allows for photogrammetric analysis of the images to produce point clouds, orthoimages, and Digital Surface Model (DSM) [35,36]. The four beds adjacent to those containing the plots (two from each side) were also surveyed to provide overlapping images for the photogrammetric analysis as shown in Figure 1.…”

Section: Image Data Acquisition and Preprocessingmentioning

confidence: 99%

Automated Canopy Delineation and Size Metrics Extraction for Strawberry Dry Weight Modeling Using Raster Analysis of High-Resolution Imagery

et al. 2020

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Capturing high spatial resolution imagery is becoming a standard operation in many agricultural applications. The increased capacity for image capture necessitates corresponding advances in analysis algorithms. This study introduces automated raster geoprocessing methods to automatically extract strawberry (Fragaria × ananassa) canopy size metrics using raster image analysis and utilize the extracted metrics in statistical modeling of strawberry dry weight. Automated canopy delineation and canopy size metrics extraction models were developed and implemented using ArcMap software v 10.7 and made available by the authors. The workflows were demonstrated using high spatial resolution (1 mm resolution) orthoimages and digital surface models (2 mm) of 34 strawberry plots (each containing 17 different plant genotypes) planted on raised beds. The images were captured on a weekly basis throughout the strawberry growing season (16 weeks) between early November and late February. The results of extracting four canopy size metrics (area, volume, average height, and height standard deviation) using automatically delineated and visually interpreted canopies were compared. The trends observed in the differences between canopy metrics extracted using the automatically delineated and visually interpreted canopies showed no significant differences. The R2 values of the models were 0.77 and 0.76 for the two datasets and the leave-one-out (LOO) cross validation root mean square error (RMSE) of the two models were 9.2 g and 9.4 g, respectively. The results show the feasibility of using automated methods for canopy delineation and canopy metric extraction to support plant phenotyping applications.

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Mobile Terrestrial Photogrammetry for Street Tree Mapping and Measurements

Cited by 28 publications

References 61 publications

Low Cost Automatic Reconstruction of Tree Structure by AdQSM with Terrestrial Close-Range Photogrammetry

Low Cost Automatic Reconstruction of Tree Structure by AdQSM with Terrestrial Close-Range Photogrammetry

Remotely Sensed Tree Characterization in Urban Areas: A Review

Automated Canopy Delineation and Size Metrics Extraction for Strawberry Dry Weight Modeling Using Raster Analysis of High-Resolution Imagery

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