Point Clouds

Leberl, Franz; Irschara, Arnold; Pock, Thomas; Meixner, Philipp; Gruber, Michael; Scholz, Steffen; Wiechert, Alexander

doi:10.14358/pers.76.10.1123

Cited by 217 publications

(55 citation statements)

References 3 publications

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“…The calculation of 3D point clouds from overlapping UAS-images is usually integrated in the workflow of these programmes and is typically part of the production of a true UAS-orthoimage with high spatial accuracy. The height information from image-based dense point clouds generated with Structure-from-Motion software based on algorithms such as Semi-Global Matching [27] can be comparable to that from airborne LiDAR [28] and even low-cost systems such as a UAS equipped with a consumer grade camera allow for surface reconstructions of high quality when the image overlap is high [29]. Once a dense point cloud is constructed, the production of a UAS-DSM is only a minor effort.…”

Section: Discussionmentioning

confidence: 99%

Combining Spectral Data and a DSM from UAS-Images for Improved Classification of Non-Submerged Aquatic Vegetation

2017

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Abstract:Monitoring of aquatic vegetation is an important component in the assessment of freshwater ecosystems. Remote sensing with unmanned aircraft systems (UASs) can provide sub-decimetre-resolution aerial images and is a useful tool for detailed vegetation mapping. In a previous study, non-submerged aquatic vegetation was successfully mapped using automated classification of spectral and textural features from a true-colour UAS-orthoimage with 5-cm pixels. In the present study, height data from a digital surface model (DSM) created from overlapping UAS-images has been incorporated together with the spectral and textural features from the UAS-orthoimage to test if classification accuracy can be improved further. We studied two levels of thematic detail: (a) Growth forms including the classes of water, nymphaeid, and helophyte; and (b) dominant taxa including seven vegetation classes. We hypothesized that the incorporation of height data together with spectral and textural features would increase classification accuracy as compared to using spectral and textural features alone, at both levels of thematic detail. We tested our hypothesis at five test sites (100 m × 100 m each) with varying vegetation complexity and image quality using automated object-based image analysis in combination with Random Forest classification. Overall accuracy at each of the five test sites ranged from 78% to 87% at the growth-form level and from 66% to 85% at the dominant-taxon level. In comparison to using spectral and textural features alone, the inclusion of height data increased the overall accuracy significantly by 4%-21% for growth-forms and 3%-30% for dominant taxa. The biggest improvement gained by adding height data was observed at the test site with the most complex vegetation. Height data derived from UAS-images has a large potential to efficiently increase the accuracy of automated classification of non-submerged aquatic vegetation, indicating good possibilities for operative mapping.

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Section: Discussionmentioning

confidence: 99%

Combining Spectral Data and a DSM from UAS-Images for Improved Classification of Non-Submerged Aquatic Vegetation

2017

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“…Alternative methods based on LiDAR (light detection and ranging) [9,10], ultrasonic sensors [11], or high resolution RGB imagery [12] have been developed recently. While LiDAR sensors provide highly accurate and dense 3D point measurements of crop surfaces, they are still very expensive, and require specific expertise for handling of sensors and the subsequent analysis of the data [13]. Although less cost intensive alternatives exist (Ehlert 2010), they cannot be used to cover large areas due to their limited mobility.…”

Section: Introductionmentioning

confidence: 99%

Estimation of Vegetable Crop Parameter by Multi-temporal UAV-Borne Images

et al. 2018

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3D point cloud analysis of imagery collected by unmanned aerial vehicles (UAV) has been shown to be a valuable tool for estimation of crop phenotypic traits, such as plant height, in several species. Spatial information about these phenotypic traits can be used to derive information about other important crop characteristics, like fresh biomass yield, which could not be derived directly from the point clouds. Previous approaches have often only considered single date measurements using a single point cloud derived metric for the respective trait. Furthermore, most of the studies focused on plant species with a homogenous canopy surface. The aim of this study was to assess the applicability of UAV imagery for capturing crop height information of three vegetables (crops eggplant, tomato, and cabbage) with a complex vegetation canopy surface during a complete crop growth cycle to infer biomass. Additionally, the effect of crop development stage on the relationship between estimated crop height and field measured crop height was examined. Our study was conducted in an experimental layout at the University of Agricultural Science in Bengaluru, India. For all the crops, the crop height and the biomass was measured at five dates during one crop growth cycle between February and May 2017 (average crop height was 42.5, 35.5, and 16.0 cm for eggplant, tomato, and cabbage). Using a structure from motion approach, a 3D point cloud was created for each crop and sampling date. In total, 14 crop height metrics were extracted from the point clouds. Machine learning methods were used to create prediction models for vegetable crop height. The study demonstrates that the monitoring of crop height using an UAV during an entire growing period results in detailed and precise estimates of crop height and biomass for all three crops (R 2 ranging from 0.87 to 0.97, bias ranging from −0.66 to 0.45 cm). The effect of crop development stage on the predicted crop height was found to be substantial (e.g., median deviation increased from 1% to 20% for eggplant) influencing the strength and consistency of the relationship between point cloud metrics and crop height estimates and, thus, should be further investigated. Altogether the results of the study demonstrate that point cloud generated from UAV-based RGB imagery can be used to effectively measure vegetable crop biomass in larger areas (relative error = 17.6%, 19.7%, and 15.2% for eggplant, tomato, and cabbage, respectively) with a similar accuracy as biomass prediction models based on measured crop height (relative error = 21.6, 18.8, and 15.2 for eggplant, tomato, and cabbage).

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“…Aerial images have emerged as one possible source of data that can be used to derive threedimensional (3D) data similar to those generated by ALS (Baltsavias 1999;Leberl et al 2010). 3D data from aerial images describes only the upper part of the canopy surface, whereas LiDAR penetrates the canopy and also measures the vertical canopy structure and the ground.…”

Section: Introductionmentioning

confidence: 99%

Mapping forest attributes using data from stereophotogrammetry of aerial images and field data from the national forest inventory

Bohlin¹,

Bohlin²,

Jonzén³

et al. 2017

Silva Fenn.

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Highlights• Image based forest attribute map generated using NFI plots show similar accuracy as currently used LiDAR based forest attribute map.• Also similar accuracies were found for different forest types.• Aerial images from leaf-off season is not recommended. AbstractExploring the possibility to produce nation-wide forest attribute maps using stereophotogrammetry of aerial images, the national terrain model and data from the National Forest Inventory (NFI). The study areas are four image acquisition blocks in mid-and south Sweden. Regression models were developed and applied to 12.5 m × 12.5 m raster cells for each block and validation was done with an independent dataset of forest stands. Model performance was compared for eight different forest types separately and the accuracies between forest types clearly differs for both image-and LiDAR methods, but between methods the difference in accuracy is small at plot level. At stand level, the root mean square error in percent of the mean (RMSE%) were ranging: from 7.7% to 10.5% for mean height; from 12.0% to 17.8% for mean diameter; from 21.8% to 22.8% for stem volume; and from 17.7% to 21.1% for basal area. This study clearly shows that aerial images from the national image program together with field sample plots from the NFI can be used for large area forest attribute mapping.

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Point Clouds

Cited by 217 publications

References 3 publications

Combining Spectral Data and a DSM from UAS-Images for Improved Classification of Non-Submerged Aquatic Vegetation

Combining Spectral Data and a DSM from UAS-Images for Improved Classification of Non-Submerged Aquatic Vegetation

Estimation of Vegetable Crop Parameter by Multi-temporal UAV-Borne Images

Mapping forest attributes using data from stereophotogrammetry of aerial images and field data from the national forest inventory

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