Planimetric Errors Research Articles

The Influence of Refractive Index Changes in Water on Airborne LiDAR Bathymetric Errors

Due to the limitations of measurement equipment and the influence of factors such as the environment and target, measurement errors may occur during the data acquisition process of airborne LiDAR bathymetry (ALB). The refractive index of water is defined as the propagation ratio of the speed of light waves in a vacuum to that in water; this ratio influences not only the propagation speed of the laser pulse in water but also the propagation direction of the laser pulse entering water. Therefore, the influence of refractive index changes in water on the ALB errors needs to be analyzed. To this end, the principle of ALB is first briefly introduced. Then, the calculation method for the refractive index of water is described with Snell’s law and an empirical formula. Finally, the influence of refractive index changes on ALB errors is analyzed using the derived formula at the water–air interface and in the water column. The experimental results showed that in a constant elevation of 50 m for a bathymetric floor, the refractive index changes in water caused by temperature, salinity, and depth are less than 0.001. The maximum bathymetric error and maximum planimetric error caused by the refractive index changes at the water–air interface are 0.036 m and 0.015 m, respectively. The ALB errors caused by refractive index changes in the water column are relatively low, and the water column does not need to be layered to calculate the ALB errors. The influence of refractive index changes in water on the ALB error is minimal, accounting for only a small proportion of all bathymetric errors. Thus, it is necessary to determine whether the effect of the ALB error due to refractive index changes in water needs to be corrected based on the accuracy requirements of the data acquisition. This study and analysis can provide a reference basis for correcting ALB errors.

Meta-Universe Enabled Digital Modeling for Urban 3D Reality Visualization

Abstract The rapid acquisition of 3D data with high accuracy and efficiency, along with the reduction of production cycles and costs, are pressing challenges in the field of 3D reality modeling. This paper introduces a novel approach for urban 3D real-view modeling from a metacosmic perspective. Utilizing tilt-shift photography technology, this method captures three-dimensional data of urban scenes under the meta-universe framework. The data are then processed through an integration principle, combining DOM (Digital Orthophoto Map), DEM (Digital Elevation Model), and vector data to filter out interference. Subsequently, a high-density digital point cloud is generated using tilted imagery combined with aerial triangulation. This point cloud, along with a TIN (Triangulated Irregular Network) model, facilitates the construction of a comprehensive three-dimensional visualization model of urban environments, enabling detailed digital analysis. Data presentation demonstrates that with an increase in the number of image control points, the planimetric error decreases from 0.0536 m to 0.0388 m, reflecting a 27.61% improvement. Similarly, elevation accuracy improves from 0.0927 m to 0.0539 m, marking a 41.86% enhancement. This methodology supports the creation of highly precise and cost-effective three-dimensional realistic models of urban built-up areas, providing robust data support for the development and management of smart cities.

Horizontal accuracy assessment of a novel algorithm for approximate a surface to a DEM

Abstract. This study evaluates the horizontal positional accuracy of a new algorithm that defines a surface that approximates DEM data by means of a spline function. This algorithm allows evaluating the surface at any point in its definition domain and allows analytically estimating other parameters of interest, such as slopes, orientations, etc. To evaluate the accuracy achieved with the algorithm, we use a reference DEM 2 m × 2 m (DEMref) from which the derived DEMs are obtained at 4 m × 4 m, 8 m × 8 m and 16 m × 16 m (DEMder). For each DEMder its spline approximant is calculated, which is evaluated at the same points occupied by the DEMref cells, getting a resampled DEM 2 × 2 m (DEMrem). The horizontal accuracy is obtained by computing the area amongs the homologous contour lines derived from DEMref and DEMrem, respectively. It has been observed that the planimetric errors of the proposed algorithm are very small, even in flat areas, where you could expect major differences. Therefore, this algorithm could be used when an evaluation of the horizontal positional accuracy of a DEM product at lower resolution (DEMpro) and a different producing source than the higher resolution DEMref is wanted.

Design and experiments of X-type artificial control targets for a UAV-LiDAR system

ABSTRACT Unmanned Aerial Vehicle-Light Detecting and Ranging (UAV-LiDAR) systems can potentially generate high precision Digital Terrain Models (DTM), especially for railway surveys. However, traditional feature points do not exist in LiDAR systems due to discrete sampling. Artificial control targets (ACTs) can be used as ground control points (GCPs) in a LiDAR system. In this paper, we present a new type of ACT suitable for a UAV-LiDAR system. The design of X-type ACTs and railway survey experiments with a multi-rotor UAV-LiDAR system are illustrated. Six ACTs are settled in the study area. The root mean square errors (RMSE) in the x, y direction are 0.15 m and 0.13 m, respectively, from collected multi-rotor UAV-LiDAR data. Planimetric errors in the LiDAR data were corrected with the six ACTs. With 22 horizontal and 32 elevation checkpoints, the accuracy estimation results demonstrate that RMSE values in the x, y, z directions are 0.06 m, 0.07 m and 0.030 m, respectively. The design of X-type ACTs, accuracy estimations and improvements for railway surveys with the multi-rotor UAV-LiDAR system presented in this paper will provide a reference for 3D measurements along other railways.

Admissible relative errors of the parcel aerea of a land parcel map base created as a synthesis of indiviual survey maps

Today the land parcel map of Uruguay is available as an updated, integrated legacy digital cadastre which planimetric accuracy is not enough to assure geometric interoperability. The creation of a brand new land parcel map is being considered with a procedure yet to be defined but in any case subject to reasonable goals in terms of geometric accuracy. In this paper we have tested a direct procedure in which each parcel is joined to its immediate neighbors until a block is formed. In Uruguay such block will grow until a road, river or similar object is found. The block is manually inserted within the road network, which has absolute coordinates.The rural survey maps (approx. 250 000) are already scanned but they need to be vectorized and edited through scaling, rotation and manual matching to its neighbors. GNSS tracks for roads and highways are available for the whole country, so we could fit the block within them in order to properly register it. The resulting geometric accuracy was evaluated in terms of the relative error in area, which has a normal distribution with a mean 0.10% and a standard deviation 3.51%, leaving 82.0% of the parcels with relative area error less than 5%. The planimetric error, crucial to assure interoperability, was 53.79 m at the 95% confidence level.

Admissible relative errors of the parcel area of a land parcel map base created as a synthesis of individual survey maps

Actualmente en Uruguay la cartografía base catastral está dada por un parcelario digital integrado y actualizado pero cuyas características geométricas no son adecuadas para que sea interoperable con otras capas de información de alta exactitud.Se está considerando generar una nueva cartografía, para lo que hay que especificar metodología y requerimientos, determinando la exactitud geométrica que es posible y razonable alcanzar para posteriormente exigirla a la hora de la contratación de dicho trabajo. En este trabajo se ensayó el procedimiento de agregar cada parcela con la(s) parcela(s) lindera(s) formando una unidad. Como en Uruguay las áreas públicas no disponen de mensura, el proceso de unión se interrumpe al llegar a una ruta, un curso de agua, etc. Los planos de mensura rurales (del orden de 250 000) están digitalizados pero no vectorizados, por lo cual tras hacerlo se realizó un trabajo de escalado, rotación y eventualmente deformación para luego integrarlo con sus vecinos. Para las rutas y caminos públicos se dispone de trazas GNSS, lo que permitió darle a cada bloque coordenadas absolutas. La exactitud de la cartografía resultante se valoró en términos del error relativo del área de cada parcela, resultando que el error cometido es de distribución normal, con media 0.10% y desviación estándar 3.51%, dejando así al 82.0% de las parcelas con menos del 5% de error. En términos de error planimétrico, crucial para asegurar la interoperabilidad, el mismo fue de 53.79 m al 95% de nivel de confianza.

Modelling the orthoimage accuracy using DEM accuracy and off-nadir angle

Orthoimages are differentially rectified images that are corrected for the distortions caused especially by image tilt and topographic relief. The orientation, digital elevation model (DEM) and off-nadir angle plays an important role in orthoimage accuracy. The orientation error mostly occurs due to the quality and distribution of the ground control points. In this study, an attempt has been made to model the remaining errors by keeping the orientation error constant. To model the accuracy, orthoimages are produced with eight DEMs having different accuracies and are assessed using 50 check points. As the theoretical model cannot reflect the real world exactly, an empirical model is used for estimating the orthoimage accuracy. This proposed model was validated by another dataset. It is concluded that statistically there is no significant difference between the calculated model and real planimetric errors. The proposed model can be used in predicting orthoimage accuracy provided that the DEM accuracy and off-nadir angles of the points are known.

High accuracy mapping with cartographic assessment for a fixed-wing remotely piloted aircraft system

The lack of updated maps on large scale representations has encouraged the use of remotely piloted aircraft systems (RPAS) to generate maps for a wide range of professionals. However, some questions arise: do the orthomosaics generated by these systems have the cartographic precision required to use them? Which problems can be identified in stitching orthophotos to generate orthomosaics? To answer these questions, an aerophotogrammetric survey was conducted in an environmental conservation unit in the city of Goiânia. The flight plan was set up using the E-motion software, provided by Sensefly—a Swiss manufacturer of the RPAS Swinglet CAM used in this work. The camera installed in the RPAS was the Canon IXUS 220 HS, with the number of pixels in the sensor array of 12.1 megapixel, complementary metal oxide semiconductor 1 ∶ 2.3 ? (4000 × 3000 pixel), horizontal and vertical pixel sizes of 1.54 μm. Using the orthophotos, four orthomosaics were generated in the Pix4D mapper software. The first orthomosaic was generated without using the control points. The other three mosaics were generated using 4, 8, and 16 premarked ground control points. To check the precision and accuracy of the orthomosaics, 46 premarked targets were uniformly distributed in the block. The three-dimensional (3-D) coordinates of the premarked targets were read on the orthomosaic and compared with the coordinates obtained by the geodetic survey real-time kinematic positioning method using the global navigation satellite system receiver signals. The cartographic accuracy standard was evaluated by discrepancies between these coordinates. The bias was analyzed by the Student’s t test and the accuracy by the chi-square probability considering the orthomosaic on a scale of 1 ∶ 250, in which 90% of the points tested must have a planimetric error of <0.13 m with a standard deviation of 0.08 m and altimetric errors of <0.30 m with a standard deviation of 0.20 m. It was observed that some buildings in the orthomosaics were not properly orthorectified. The orthomosaics generated with 8 or more points reached the scale of 1 ∶ 250, whereas without control points the scale was 10-fold smaller (1 ∶ 3000).

USE OF GEOSTATISTICS ON ABSOLUTE POSITIONAL ACCURACY ASSESMENT OF GEOSPATIAL DATA

Abstract: In the area of Geosciences it is intuitive to think of spatial correlation as a phenomenon under study, and Geostatistics has tools to identify and represent the behavior of such dependency. The spatial analysis of the results of an inspection of the quality of a cartographic product is generally not addressed in the standards, which are restricted to descriptive and tabular findings, based on the assumption of the Classical Statistics of independence of observed data. At the Brazilian National Infrastructure of Spatial Data (INDE), various cartographic products should be made available to society, along with their metadata. This paper proposes a methodology for quality inspection based on international standards and on the Cartographic Accuracy Standard (PEC), using geostatistical methods and spatial representation of this benchmarking, through positional quality maps. The method of evaluating the quality of data was applied to Brazil's Continual Cartographic Base, at the scale of 1:250000 - BC250, with a focus on absolute positional accuracy. The quality map generated presented regionalizations of the planimetric error confirmed by the producer team of the referred cartographic base of IBGE. Such information can help users and producers to understand the spatial behavior of cartographic product quality in study

Optimising UAV topographic surveys processed with structure-from-motion: Ground control quality, quantity and bundle adjustment

Structure-from-motion (SfM) algorithms greatly facilitate the production of detailed topographic models from photographs collected using unmanned aerial vehicles (UAVs). However, the survey quality achieved in published geomorphological studies is highly variable, and sufficient processing details are never provided to understand fully the causes of variability. To address this, we show how survey quality and consistency can be improved through a deeper consideration of the underlying photogrammetric methods. We demonstrate the sensitivity of digital elevation models (DEMs) to processing settings that have not been discussed in the geomorphological literature, yet are a critical part of survey georeferencing, and are responsible for balancing the contributions of tie and control points. We provide a Monte Carlo approach to enable geomorphologists to (1) carefully consider sources of survey error and hence increase the accuracy of SfM-based DEMs and (2) minimise the associated field effort by robust determination of suitable lower-density deployments of ground control. By identifying appropriate processing settings and highlighting photogrammetric issues such as over-parameterisation during camera self-calibration, processing artefacts are reduced and the spatial variability of error minimised.We demonstrate such DEM improvements with a commonly-used SfM-based software (PhotoScan), which we augment with semi-automated and automated identification of ground control points (GCPs) in images, and apply to two contrasting case studies — an erosion gully survey (Taroudant, Morocco) and an active landslide survey (Super-Sauze, France). In the gully survey, refined processing settings eliminated step-like artefacts of up to ~50mm in amplitude, and overall DEM variability with GCP selection improved from 37 to 16mm. In the much more challenging landslide case study, our processing halved planimetric error to ~0.1m, effectively doubling the frequency at which changes in landslide velocity could be detected. In both case studies, the Monte Carlo approach provided a robust demonstration that field effort could by substantially reduced by only deploying approximately half the number of GCPs, with minimal effect on the survey quality. To reduce processing artefacts and promote confidence in SfM-based geomorphological surveys, published results should include processing details which include the image residuals for both tie points and GCPs, and ensure that these are considered appropriately within the workflow.

GPS YER KONTROL NOKTALARI KULLANILARAK LiDAR VERİSİNİN DOĞRULUK ANALİZİ

Gunumuzde, harita uretiminde kullanilan calismalarda, geleneksel olcum tekniklerinden farkli olarak hem daha hizli, hem de dogrulugu daha yuksek yeryuzu verisi elde etme cabalari gun gectikce artmaktadir. Bu calismada, elektromanyetik spektrumun 1.064nm yakin infrared bolgesinde algilama yapan bir Uzaktan Algilama Teknigi olan Hava LiDAR teknolojisinin, yatay ve dusey dogrulugunun RTK/GPS verileri ile (Real Time Kinematik- Global Positioning System) test edilmesi amaclanmistir. Bu kapsamda, Turkiye’nin Dogu Karadeniz bolgesinde yer alan Artvin Ili Borcka ilcesinde 2x2 km2 buyuklugunde bir ormanlik bolge, calisma alani olarak secilmistir. Calismada, RTK/GPS ile elde edilmis Yer Kontrol Noktalari (YKN) ile bu noktalarin karsilik geldigi LiDAR noktalarinin belirlenmesi icin arama capi yontemi gelistirilmis ve ayrica, degisik arazi kategorileri icin karsilastirmali test sonuclari ortaya konulmustur. Calismanin sonucunda, dusey hata miktari (KOHz) normal dagilimda 0.20 m olarak ve %95 guven araliginda dusey dogruluk (dogrulukz) ise 0.39m olarak bulunmustur. Yatay hata (KOHr) normal dagilimda minimum 0.36m, maksimum 1.01m; yatay dogruluk (dogrulukr) ise %95 guven araliginda minimum 0.62m, maksimum 1.75m duzeyinde gerceklesmistir. Bu calismada kullanilan Hava LiDAR verisi icin dusey dogruluk, yatay dogruluga gore yaklasik iki kat daha prezisyonlu olarak gerceklesmistir.

Многокритериальная оптимизационная модель планиметрической погрешности при ректификации изображений дистанционного зондирования

Многокритериальная оптимизационная модель планиметрической погрешности при ректификации изображений дистанционного зондирования

Simulations of direct and reflected wave trajectories for ground-based GNSS-R experiments

Abstract. The detection of Global Navigation Satellite System (GNSS) signals that are reflected off the surface, along with the reception of direct GNSS signals, offers a unique opportunity to monitor water level variations over land and ocean. The time delay between the reception of the direct and reflected signals gives access to the altitude of the receiver over the reflecting surface. The field of view of the receiver is highly dependent on both the orbits of the GNSS satellites and the configuration of the study site geometries. A simulator has been developed to determine the location of the reflection points on the surface accurately by modeling the trajectories of GNSS electromagnetic waves that are reflected by the surface of the Earth. Only the geometric problem was considered using a specular reflection assumption. The orbit of the GNSS constellation satellites (mainly GPS, GLONASS and Galileo), and the position of a fixed receiver, are used as inputs. Four different simulation modes are proposed, depending on the choice of the Earth surface model (local plane, osculating sphere or ellipsoid) and the consideration of topography likely to cause masking effects. Angular refraction effects derived from adaptive mapping functions are also taken into account. This simulator was developed to determine where the GNSS-R receivers should be located to monitor a given study area efficiently. In this study, two test sites were considered: the first one at the top of the 65 m Cordouan lighthouse in the Gironde estuary, France, and the second one on the shore of Lake Geneva (50 m above the reflecting surface), at the border between France and Switzerland. This site is hidden by mountains in the south (orthometric altitude up to 2000 m), and overlooking the lake in the north (orthometric altitude of 370 m). For this second test site configuration, reflections occur until 560 m from the receiver. The planimetric (arc length) differences (or altimetric difference as WGS84 ellipsoid height) between the positions of the specular reflection points obtained considering the Earth's surface as an osculating sphere or as an ellipsoid were found to be on average 9 cm (or less than 1 mm) for satellite elevation angles greater than 10°, and 13.9 cm (or less than 1 mm) for satellite elevation angles between 5 and 10°. The altimetric and planimetric differences between the plane and sphere approximations are on average below 1.4 cm (or less than 1 mm) for satellite elevation angles greater than 10° and below 6.2 cm (or 2.4 mm) for satellite elevation angles between 5 and 10°. These results are the means of the differences obtained during a 24 h simulation with a complete GPS and GLONASS constellation, and thus depend on how the satellite elevation angle is sampled over the day of simulation. The simulations highlight the importance of the digital elevation model (DEM) integration: average planimetric differences (or altimetric) with and without integrating the DEM (with respect to the ellipsoid approximation) were found to be about 6.3 m (or 1.74 m), with the minimum elevation angle equal to 5°. The correction of the angular refraction due to troposphere on the signal leads to planimetric (or altimetric) differences of an approximately 18 m (or 6 cm) maximum for a 50 m receiver height above the reflecting surface, whereas the maximum is 2.9 m (or 7 mm) for a 5 m receiver height above the reflecting surface. These errors increase deeply with the receiver height above the reflecting surface. By setting it to 300 m, the planimetric errors reach 116 m, and the altimetric errors reach 32 cm for satellite elevation angles lower than 10°. The tests performed with the simulator presented in this paper highlight the importance of the choice of the Earth's representation and also the non-negligible effect of angular refraction due to the troposphere on the specular reflection point positions. Various outputs (time-varying reflection point coordinates, satellite positions and ground paths, wave trajectories, first Fresnel zones, etc.) are provided either as text or KML files for visualization with Google Earth.

Photogrammetric techniques and surveying applied to historical map analysis

Since historical maps are kept in digital format archives, they have not only the planimetric errors attributable to the cartographer but also those transferred during digitalising. This paper describes a methodology based on photogrammetric techniques and surveying applied to evaluate the accuracy of historical maps. The aim of the study is to take reference points on tracing paper from the original map and ground control points in order to use photogrammetric techniques to recover the metrics of the digitalised map. This ensures that the historical map in digital form has the same metric quality as the original map, since the errors arising from scanning are removed. This method is applied to 1775 map of the Real Sitio de Aranjuez (Spain), demonstrating that the application of photogrammetric techniques are useful tools for correction and analysis of historical maps. Consequently, photogrammetric techniques applied to the analysis of historical maps open up new perspectives for the study of the historical Cartography.

VERIFICATION AND IMPROVING PLANIMETRIC ACCURACY OF AIRBORNE LASER SCANNING DATA WITH USING PHOTOGRAMMETRIC DATA

Abstract. In this study results of planimetric accuracy of LIDAR data were verified with application of intensity of laser beam reflection and point cloud modelling results. Presented research was the basis for improving the accuracy of the products from the processing of LIDAR data, what is particularly important in issues related to surveying measurements. In the experiment, the true-ortho from the large-format aerial images with known exterior orientation were used to check the planimetric accuracy of LIDAR data in two proposed approaches. First analysis was carried out by comparison the position of the selected points identifiable on true-ortho from aerial images with corresponding points in the raster of reflection intensity reflection. Second method to verify planimetric accuracy used roof ridges from 3D building models automatically created from LIDAR data being intersections of surfaces from point cloud. Both analyses were carried out for 3 fragments of LIDAR strips. Detected systematic planimetric error in size of few centimetres enabled an implementation of appropriate correction for analyzed data locally. The presented problem and proposed solutions provide an opportunity to improve the accuracy of the LiDAR data. Such methods allowed for efficient use by specialists in other fields not directly related to the issues of orientation and accuracy of photogrammetric data during their acquisition and pre-processing

A Random Set Approach for Modeling Integrated Uncertainties of Traffic Islands Derived from Airborne Laser Scanning Points

Traffic islands play a major role in transport studies by affecting traffic behavior safety, air pollution, and transport decision support. Point data obtained by laser scanning enable the determination of their locations. Planimetric errors, vertical errors, and limited point spacing however affect their spatial data quality (SDQ). In this study the authors defined uncertainty as the lack of accuracy and analyzed its importance by modeling each traffic island as a random set. The covering functions of the point data and their immediate locations were determined by point segmentation, followed by interpolation. In this way, traffic islands were delineated from the background with a transition zone. The study showed that point spacing has the largest contribution to the positional accuracy of a traffic island. The area of transition zone has a linear relation with the planimetric errors, whereas the influence of the vertical errors on the accuracy decreases with increasing point spacing. Experiments were conducted to investigate the influences of the parameters in an SDQ analysis. The study demonstrated how different sources of uncertainty can be integrated. Results showed the advantages of using random sets for SQD modeling. The authors concluded that modeling of traffic islands by random sets provides meaningful information to integrated uncertainties.

Benchmarking the Performance of Mobile Laser Scanning Systems Using a Permanent Test Field

The performance of various mobile laser scanning systems was tested on an established urban test field. The test was connected to the European Spatial Data Research (EuroSDR) project “Mobile Mapping—Road Environment Mapping Using Mobile Laser Scanning”. Several commercial and research systems collected laser point cloud data on the same test field. The system comparisons focused on planimetric and elevation errors using a filtered digital elevation model, poles, and building corners as the reference objects. The results revealed the high quality of the point clouds generated by all of the tested systems under good GNSS conditions. With all professional systems properly calibrated, the elevation accuracy was better than 3.5 cm up to a range of 35 m. The best system achieved a planimetric accuracy of 2.5 cm over a range of 45 m. The planimetric errors increased as a function of range, but moderately so if the system was properly calibrated. The main focus on mobile laser scanning development in the near future should be on the improvement of the trajectory solution, especially under non-ideal conditions, using both improvements in hardware and software. Test fields are relatively easy to implement in built environments and they are feasible for verifying and comparing the performance of different systems and also for improving system calibration to achieve optimum quality.

Simulated impact of sample plot size and co-registration error on the accuracy and uncertainty of LiDAR-derived estimates of forest stand biomass

Regression has been widely applied in Light Detection And Ranging (LiDAR) remote sensing to spatially extend predictions of total aboveground biomass (TAGB) and other biophysical properties over large forested areas. Sample (field) plot size has long been considered a key sampling design parameter and focal point for optimization in forest surveys, because of its impact on sampling effort and the estimation accuracy of forest inventory attributes. In this study, we demonstrate how plot size and co-registration error interact to influence the estimation of LiDAR canopy height and density metrics, regression model coefficients, and the prediction accuracy of least-squares estimators of TAGB. We made use of simulated forest canopies and synthetic LiDAR point clouds, so that we could maintain strict control over the spatial scale and complexity of forest scenes, as well as the magnitude and type of planimetric error inherent in ground-reference and LiDAR datasets. Our results showed that predictions of TAGB improved markedly as plot size increased from 314 (10 m radius) to 1964 m 2 (25 m radius). The co-registration error (spatial overlap) between ground-reference and LiDAR samples negatively impacted the estimation of LiDAR metrics, regression model fit, and the prediction accuracy of TAGB. We found that larger plots maintained a higher degree of spatial overlap between ground-reference and LiDAR datasets for any given GPS error, and were therefore more resilient to the ill effects of co-registration error compared to small plots. The impact of co-registration error was more pronounced in tall, spatially heterogeneous stands than short, homogeneous stands. We identify and briefly discuss three possible ways that LiDAR data could be used to optimize plot size, sample selection, and the deployment of GPS resources in forest biomass surveys.

Using LIDAR-based DEM to orthorectify Ikonos panchromatic images

Recent development in remote sensing technologies has stimulated a great interest in its application in large-scale mapping. For example, the Ikonos satellite images, available since 2000, have an improved resolution of 1 m that afford mapping of a lot of ground objects from the satellite images. However, the use of these images in high-accuracy mapping applications has been limited since the sensor model parameters are not yet released. On the other hand, Light Detection and Ranging (LIDAR) is a fast method for sampling the earth's surface with a high density and high accuracy point cloud that is used to generate high density and high accuracy Digital Elevation Models (DEMs) and DSMs. Integrating both technologies makes it possible to provide reliable and automatic solutions for large-scale mapping applications, 3-D visualization, GIS and change detection. In this research a 1-m resolution LIDAR-based DEM is used to orthorectify a single Geo-panchromatic Ikonos image. The LIDAR-based DEM is first rectified using 12 Ground Control Points (GCPs) surveyed using the Differential GPS (DGPS) technique. The LIDAR-based DEM is then used as a source for other GCPs that are used to orthorectify the Ikonos image. Different 2-D transformation models are used with different sets and distributions of GCPs. The planimetric Root Mean Square Errors (RMSE) achieved, using these models, is about 5 m. The relationship between the planimetric errors and the elevations of the checkpoints suggested using 3-D to 2-D transformation models. Three different types of these models are examined and their results are reported. The results showed that less than 2 m mapping accuracy could be achieved using the 3-D to 2-D transformation models. These results suggest that using a single panchromatic Ikonos image together with a 1-m resolution LIDAR-based DEM could achieve the required planimetric accuracy for 1:5000 topographic maps.

Extraction of ground control points (GCPs) from synthetic aperture radar images and SRTM DEM

Qualified ground control points (GCPs) are crucial in the geolocation of a remotely sensed image. If a region has no published map, the geographical coordinates of GCPs must be obtained indirectly. Although these can be re‐constructed from a synthetic aperture radar (SAR) image using sensor position and velocity, this conventional method does not provide accurate GCPs owing to the variable elevations of actual topography. We propose an inverse geolocation method for GCPs by SAR simulation. This improves the accuracy of extracted GCPs by accommodating topographic effects, and requires a high‐resolution digital elevation model (DEM) and SAR with precise orbit data. The errors in the northing derive mainly from the imaging pulse time, and were improved by applying an azimuth time correction. In this study ERS SAR data with precise Delft orbit and shuttle radar topography mission (SRTM) DEM were used. Mean values for the planimetric distance error in the GCPs were 4.1 and 5.4 m with standard deviations of 10.6 and 16.9 m in northing and easting, respectively. The obtained GCPs were applied to an IRS image for geo‐rectification, and the result was mean image positional errors of 3.6 and 2.7 m with standard deviations of 8.4 and 15.4 m in northing and easting, respectively. The root mean square errors are 9.0 and 15.3 m in northing and easting, respectively.