Digital Photogrammetric Camera Evaluation Generation of Digital Elevation Models

Haala, Norbert; Hastedt, H.; Wolf, Kirsten; Ressl, Camillo; Baltrusch, S.

doi:10.1127/1432-8364/2010/0043

Cited by 81 publications

(66 citation statements)

References 3 publications

(1 reference statement)

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Haala et al [42] compared the accuracies of an ALS and image-based point cloud for a well-defined surface (a level sports field) and found that the standard deviation for the ALS measures (with a point density 10 points/m 2 ) was less than 2 cm, whilst the standard deviation for the image-based point cloud (with a point density of 20 points/m 2 )…”

Section: Acquisition Processing and Productsmentioning

confidence: 99%

“…Finally, metrics may be calculated from the image-based point cloud that resemble-but are not necessarily the same-as those generated from the ALS point cloud. [42]). …”

Section: Image-based Point Cloudsmentioning

confidence: 99%

“…Flying heights between 550 and 4800 m above ground level have been used with 60%-90% along-track and 30%-60% across-track overlap for forestry applications, providing GSDs ranging from 0.05 to 0.48 m [8,9]. For forestry applications, the need for highly redundant (i.e., many overlapping images), multi-image information is especially acute to overcome occlusions that are common in the canopy [41,42]. There are currently several airborne optical photogrammetric systems capable of acquiring the stereo imagery required for image-based point cloud generation, including the Zeiss Intergraph Digital mapping camera, the Leica ADS, the Microsoft UltraCam, and the Rapid three-dimensional Mapper developed by SAAB [8].…”

Section: Image-based Point Cloudsmentioning

confidence: 99%

See 2 more Smart Citations

The Utility of Image-Based Point Clouds for Forest Inventory: A Comparison with Airborne Laser Scanning

White

Wulder

Vastaranta

et al. 2013

Forests

270

217

View full text Add to dashboard Cite

Airborne Laser Scanning (ALS), also known as Light Detection and Ranging (LiDAR) enables an accurate three-dimensional characterization of vertical forest structure. ALS has proven to be an information-rich asset for forest managers, enabling the generation of highly detailed bare earth digital elevation models (DEMs) as well as estimation of a range of forest inventory attributes (including height, basal area, and volume). Recently, there has been increasing interest in the advanced processing of high spatial resolution digital airborne imagery to generate image-based point clouds, from which vertical information with similarities to ALS can be produced. Digital airborne imagery is typically less costly to acquire than ALS, is well understood by inventory practitioners, and in addition to enabling the derivation of height information, allows for visual interpretation of attributes that are currently problematic to estimate from ALS (such as species, health status, and maturity). At present, there are two limiting factors associated OPEN ACCESSForests 2013, 4 519 with the use of image-based point clouds. First, a DEM is required to normalize the image-based point cloud heights to aboveground heights; however DEMs with sufficient spatial resolution and vertical accuracy, particularly in forested areas, are usually only available from ALS data. The use of image-based point clouds may therefore be limited to those forest areas that already have an ALS-derived DEM. Second, image-based point clouds primarily characterize the outer envelope of the forest canopy, whereas ALS pulses penetrate the canopy and provide information on sub-canopy forest structure. The impact of these limiting factors on the estimation of forest inventory attributes has not been extensively researched and is not yet well understood. In this paper, we review the key similarities and differences between ALS data and image-based point clouds, summarize the results of current research related to the comparative use of these data for forest inventory attribute estimation, and highlight some outstanding research questions that should be addressed before any definitive recommendation can be made regarding the use of image-based point clouds for this application.

show abstract

Section: Acquisition Processing and Productsmentioning

confidence: 99%

“…Finally, metrics may be calculated from the image-based point cloud that resemble-but are not necessarily the same-as those generated from the ALS point cloud. [42]). …”

Section: Image-based Point Cloudsmentioning

confidence: 99%

Section: Image-based Point Cloudsmentioning

confidence: 99%

See 1 more Smart Citation

The Utility of Image-Based Point Clouds for Forest Inventory: A Comparison with Airborne Laser Scanning

White

Wulder

Vastaranta

et al. 2013

Forests

270

217

View full text Add to dashboard Cite

show abstract

“…The acquisition of aerial stereo imagery has a long tradition in many countries and such images are used to update topographic maps and for orthophoto production. There are a variety of studies based on digital aerial images for canopy height generation which have been mainly carried out in Canada [12,13], Germany [14][15][16], Sweden [17] and Finland [18][19][20][21].…”

Section: Introductionmentioning

confidence: 99%

Countrywide Stereo-Image Matching for Updating Digital Surface Models in the Framework of the Swiss National Forest Inventory

2015

View full text Add to dashboard Cite

Surface models provide key knowledge of the 3-d structure of forests. Aerial stereo imagery acquired during routine mapping campaigns covering the whole of Switzerland (41,285 km 2 ), offers a potential data source to calculate digital surface models (DSMs). We present an automated workflow to generate a nationwide DSM with a resolution of 1 × 1 m based on photogrammetric image matching. A canopy height model (CHM) is derived in combination with an existing digital terrain model (DTM). ADS40/ADS80 summer images from 2007 to 2012 were used for stereo matching, with ground sample distances (GSD) of 0.25 m in lowlands and 0.5 m in high mountain areas. Two different image matching strategies for DSM calculation were applied: one optimized for single features such as trees and for abrupt changes in elevation such as steep rocks, and another optimized for homogeneous areas such as meadows or glaciers. The country was divided into 165,500 blocks, which were matched independently using an automated workflow. The completeness of successfully matched points was high, 97.9%. To test the accuracy of the derived DSM, two reference data sets were used: (1) topographic survey points (n = 198) and (2) stereo measurements (n = 195,784) within the framework of the Swiss National Forest Inventory (NFI), in order to distinguish various land cover types. An overall median accuracy of 0.04 m with a normalized median absolute deviation (NMAD) of 0.32 m was found using the topographic survey points. The agreement between the stereo measurements and the values of the DSM revealed acceptable NMAD values between 1.76 and 3.94 m for forested areas. A good correlation (Pearson's r = 0.83) was found between terrestrially OPEN ACCESSRemote Sens. 2015, 7 4344 measured tree height (n = 3109) and the height derived from the CHM. Optimized image matching strategies, an automatic workflow and acceptable computation time mean that the presented approach is suitable for operational usage at the nationwide extent. The CHM will be used to reduce estimation errors of different forest characteristics in the Swiss NFI and has high potential for change detection assessments, since an aerial stereo imagery update is available every six years.

show abstract

“…While several researchers have reported on the accuracy and practical advantages of PPCs when exploited through the ABA [37][38][39][40][41][42][43], only recently has the possibility of applying ITC approaches to PPCs started to emerge [44,45]; see also [46] for a precursor hybrid approach. Although some non-forest centric studies have documented the accuracy of PPCs with the most advanced image matching algorithms on different surfaces [47][48][49], it is still unclear if the current generation of PPCs provide 3D data sufficient to ensure proper tree delineation, if they are precise enough to reflect the height distribution of single trees, and if they contain reliable 3D and reflectance features for identifying tree species. In these regards, PPC-based ITC results have not been compared to corresponding lidar-based results.…”

Section: Introductionmentioning

confidence: 99%

Characterizing the Height Structure and Composition of a Boreal Forest Using an Individual Tree Crown Approach Applied to Photogrammetric Point Clouds

St-Onge

Audet

Bégin

2015

Forests

View full text Add to dashboard Cite

Photogrammetric point clouds (PPC) obtained by stereomatching of aerial photographs now have a resolution sufficient to discern individual trees. We have produced such PPCs of a boreal forest and delineated individual tree crowns using a segmentation algorithm applied to the canopy height model derived from the PPC and a lidar terrain model. The crowns were characterized in terms of height and species (spruce, fir, and deciduous). Species classification used the 3D shape of the single crowns and their reflectance properties. The same was performed on a lidar dataset. Results show that the quality of PPC data generally approaches that of airborne lidar. For pixel-based canopy height models, viewing geometry in aerial images, forest structure (dense vs. open canopies), and composition (deciduous vs. conifers) influenced the quality of the 3D reconstruction of PPCs relative to lidar. Nevertheless, when individual tree height distributions were analyzed, PPC-based results were very similar to those extracted from lidar. The random forest classification (RF) of individual trees performed better in the lidar case when only 3D metrics were used (83% accuracy for lidar, 79% for PPC). However, when 3D and intensity or multispectral data were used together, the accuracy of PPCs (89%) surpassed that of lidar (86%).

show abstract

Digital Photogrammetric Camera Evaluation Generation of Digital Elevation Models

Cited by 81 publications

References 3 publications

The Utility of Image-Based Point Clouds for Forest Inventory: A Comparison with Airborne Laser Scanning

The Utility of Image-Based Point Clouds for Forest Inventory: A Comparison with Airborne Laser Scanning

Countrywide Stereo-Image Matching for Updating Digital Surface Models in the Framework of the Swiss National Forest Inventory

Characterizing the Height Structure and Composition of a Boreal Forest Using an Individual Tree Crown Approach Applied to Photogrammetric Point Clouds

Contact Info

Product

Resources

About