Accuracy Assessment of Digital Surface Models Based on WorldView-2 and ADS80 Stereo Remote Sensing Data

Digital surface models (DSMs) are widely used in forest science to model the forest canopy. Stereo pairs of very high resolution satellite and digital aerial images are relatively new and their absolute accuracy for DSM generation is largely unknown. For an assessment of these input data two DSMs based on a WorldView-2 stereo pair and a ADS80 DSM were generated with photogrammetric instruments. Rational polynomial coefficients (RPCs) are defining the orientation of the WorldView-2 satellite images, which can be enhanced with ground control points (GCPs). Thus two WorldView-2 DSMs were distinguished: a WorldView-2 RPCs-only DSM and a WorldView-2 GCP-enhanced RPCs DSM. The accuracy of the three DSMs was estimated with GPS measurements, manual stereo-measurements, and airborne laser scanning data (ALS). With GCP-enhanced RPCs the WorldView-2 image orientation could be optimised to a root mean square error (RMSE) of 0.56 m in planimetry and 0.32 m in height. This improvement in orientation allowed for a vertical median error of −0.24 m for the WorldView-2 GCP-enhanced RPCs DSM in flat terrain. Overall, the DSM based on ADS80 images showed the highest accuracy of the three models with a median error of 0.08 m over bare ground. As the accuracy of a DSM varies with land cover three classes were distinguished: herb and grass, forests, and artificial areas. The study suggested the ADS80 DSM to best model actual surface height in all three land cover classes, with median errors <1.1 m. The WorldView-2 GCP-enhanced RPCs model achieved good accuracy, too, with median errors of −0.43 m for the herb and grass vegetation and −0.26 m for artificial areas. Forested areas emerged as the most difficult land cover type for height modelling; still, with median errors of −1.85 m for the WorldView-2 GCP-enhanced RPCs model and −1.12 m for the ADS80 model, the input data sets evaluated here are quite promising for forest canopy modelling.

[1]  Camillo Ressl,et al.  Digital Photogrammetric Camera Evaluation - Generation of Di- gital Elevation Models , 2010 .

[2]  Michael Höhle,et al.  Accuracy assessment of digital elevation models by means of robust statistical methods , 2009 .

[3]  ABRAHAM THOMAS,et al.  Aerial Photography , 1919, Nature.

[4]  X. Tong,et al.  Bias-corrected rational polynomial coefficients for high accuracy geo-positioning of QuickBird stereo imagery , 2010 .

[5]  B. St-Onge,et al.  MEASURING FOREST CANOPY HEIGHT USING A COMBINATION OF LIDAR AND AERIAL PHOTOGRAPHY DATA , 2001 .

[6]  David A. Holland,et al.  High Resolution Earth Imaging for Geospatial Information , 2005 .

[7]  Niklaus E. Zimmermann,et al.  Sensitivity assessment on continuous landscape variables to classify a discrete forest area , 2006 .

[8]  C. Fraser,et al.  Sensor orientation via RPCs , 2006 .

[9]  Poli Daniela,et al.  Evaluation of WORLDVIEW-1 Stereo Scenes , 2009 .

[10]  Marco Gianinetto,et al.  Evaluation of Cartosat-1 Multi-Scale Digital Surface Modelling Over France , 2009, Sensors.

[11]  Heike Lischke,et al.  Swiss National Forest Inventory: Methods and Models of the Second Assessment , 2001 .

[12]  San Diego,et al.  New Approaches to Generating and Processing High Resolution Elevation Data with Imagery , 2007 .

[13]  Markus Hollaus,et al.  Airborne Laser Scanning of Forest Stem Volume in a Mountainous Environment , 2007, Sensors (Basel, Switzerland).

[14]  Naser El-Sheimy,et al.  Digital terrain modeling - acquistion, manipulation, and applications , 2005 .

[15]  E. Baltsavias,et al.  Assessing changes of forest area and shrub encroachment in a mire ecosystem using digital surface models and CIR aerial images , 2008 .

[16]  Mathias Schardt,et al.  Single tree detection in very high resolution remote sensing data , 2007 .

[17]  Emmanuel P. Baltsavias,et al.  A comparison between photogrammetry and laser scanning , 1999 .

[18]  Akemi Itaya,et al.  Canopy height changes of an old-growth evergreen broad-leaved forest analyzed with digital elevation models , 2004 .

[19]  C. Fraser,et al.  Quality Assessment Of Digital Surface Models Generated From IKONOS Imagery , 2005 .

[20]  Bingcai Zhang,et al.  NEXT GENERATION AUTOMATIC TERRAIN EXTRACTION USING MICROSOFT ULTRACAM IMAGERY , 2007 .

[21]  C. Fraser,et al.  Generation of Digital Surface Model from High Resolution Satellite Imagery , 2008 .

[22]  Erfassung von Waldlücken mittels Laserscanning | Mapping of Woodland Gaps through Laserscanning (reviewed paper) , 2005 .

[23]  Clive S. Fraser,et al.  High-Resolution satellite imaging for digital surface models , 2006 .

[24]  André Streilein,et al.  FOR THE VERIFICATION OF COUNTRYWIDE TERRAIN AND SURFACE MODELS , 2003 .

[25]  Thierry Toutin,et al.  Comparison of stereo-extracted DTM from different high-resolution sensors: SPOT-5, EROS-a, IKONOS-II, and QuickBird , 2004, IEEE Transactions on Geoscience and Remote Sensing.

[26]  Clive S. Fraser,et al.  Accuracy assessment of QuickBird stereo imagery , 2004 .

[27]  Y. Hu,et al.  Mapping the height and above‐ground biomass of a mixed forest using lidar and stereo Ikonos images , 2008 .

[28]  K. Jacobsen,et al.  Orthoimages and DEMs by QuickBird and IKONOS , 2003 .

[29]  Angela Altmaier,et al.  Digital surface model generation from CORONA satellite images , 2002 .

[30]  Lars T. Waser,et al.  High‐quality image matching and automated generation of 3D tree models , 2008 .

[31]  Zeng-yuan Li,et al.  Estimation of stand mean crown diameter from high-spatial-resolution imagery based on a geostatistical method , 2010 .

[32]  R. Stengele,et al.  3D MAPPING OF SWITZERLAND -CHALLENGES AND EXPERIENCES , 2005 .

[33]  Joachim Höhle,et al.  The EuroSDR Test "Checking and Improving of Digital Terrain Models" , 2006 .

[34]  Next-Generation Automatic Terrain Extraction ( NGATE ) Innovation in the cost-effective derivation of elevation data from imagery , .

[35]  Myriam Kamphues,et al.  Luftbildinterpretation LFI3. Interpretationsanleitung zum dritten Landesforstinventar , 2005 .

[36]  George Alan Blackburn,et al.  Quantifying the spatial properties of forest canopy gaps using LiDAR imagery and GIS , 2004 .

[37]  J. Grodecki,et al.  Block Adjustment of High-Resolution Satellite Images Described by Rational Polynomials , 2003 .

[38]  E. Baltsavias,et al.  POTENTIAL OF IKONOS AND QUICKBIRD IMAGERY FOR ACCURATE 3D POINT POSITIONING, ORTHOIMAGE AND DSM GENERATION , 2004 .

[39]  Shoichi Oki,et al.  Evaluation of Accuracy of 3 D model from QuickBird Stereo Imagery , 2003 .

[40]  E. Baltsavias,et al.  Radiometric and geometric evaluation of IKONOS Geo images and their use for 3D building modelling , 2001 .

[41]  Cédric Véga,et al.  Height growth reconstruction of a boreal forest canopy over a period of 58 years using a combination of photogrammetric and lidar models , 2008 .

[43]  P. Zandbergen.  Characterizing the error distribution of lidar elevation data for North Carolina , 2011 .

[44]  Christopher M. Gold,et al.  Accuracy of Digital Terrain Models , 2004 .

[45]  Benoît St-Onge,et al.  Spatially explicit characterization of boreal forest gap dynamics using multi-temporal lidar data , 2008 .

[46]  H. S. Wolff,et al.  iRun: Horizontal and Vertical Shape of a Region-Based Graph Compression , 2022, Sensors.

[47]  R Core Team,et al.  R: A language and environment for statistical computing. , 2014 .

[48]  I. Florinsky.  Digital Terrain Modeling , 2012 .

[49]  Li Zhang,et al.  Multi-image matching for DSM generation from IKONOS imagery , 2006 .

[50]  Christian Ginzler,et al.  High‐resolution digital surface models (DSMs) for modelling fractional shrub/tree cover in a mire environment , 2008 .

[51]  Remondino Fabio,et al.  Radiomeric and Geometric Analysis of Worldview-2 Stereo Scenes , 2010 .

[52]  Christian Heipke,et al.  High Resolution Earth Imaging for Geospatial Information , 2012 .

[53]  Thierry Toutin,et al.  Three-dimensional topographic mapping with ASTER stereo data in rugged topography , 2002, IEEE Trans. Geosci. Remote. Sens..