Number Of Ground Control Points Research Articles

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.

Assessment of photogrammetric mapping accuracy based on variation ground control points number using unmanned aerial vehicle

Abstract Digital Surface Models and orthoimages at high spatial and temporal resolution and accuracy is of increasing importance for many applications. From several years ago photogrammetry-UAV is being used to produce these topographic products. The aim of this study is to analyse the influence of the number of Ground Control Points used for georeferencing on Digital Surface Model and orthoimage accuracies obtained with UAV-photogrammetry. In this purpose, 160 images were taken on a 17.64 ha surface at 120 m altitude above ground level, and five replications of photogrammetric projects taking into account 4, 5, 6, 7, 8, 9, 10, 15, and 20 GCPs were made. Root Mean Square Error (RMSE) was used as accuracy measurement. Optimal results for RMSE X , RMSE Y and RMSE XY mean ± standard deviation values were reached for 15 GCPs: 3.3 ± 0.346, 3.2 ± 0.441, 4.6 ± 0.340 and 4.5 ± 0.169 cm respectively. Similar conclusions was derived for vertical accuracy: lower RMSE Z mean ± standard deviation values were reached for 15 and 20 GCPs: 5.8 ± 1.21 cm and 4.7 ± 0.860 cm respectively. In view of these results maps at 1:150 scale and contour interval of 15 cm can be obtained from UAV-photogrammetry.

Computer vision–based orthorectification and georeferencing of aerial image sets

Generating a georeferenced mosaic map from unmanned aerial vehicle (UAV) imagery is a challenging task. Direct and indirect georeferencing methods may fail to generate an accurate mosaic map due to the erroneous exterior orientation parameters stored in the inertial measurement unit (IMU), erroneous global positioning system (GPS) data, and difficulty in locating ground control points (GCPs) or having a sufficient number of GCPs. This paper presents a practical framework to orthorectify and georeference aerial images using the robust features-based matching method. The proposed georeferencing process is fully automatic and does not require any GCPs. It is also a near real-time process which can be used to determine whether aerial images taken by UAV cover the entire target area. We also extend this framework to use the inverse georeferencing process to update the IMU/GPS data which can be further used to calibrate the camera of the UAV, reduce IMU/GPS errors, and thus produce more accurate mosaic maps by employing any georeferencing method. Our experiments demonstrate the effectiveness of the proposed framework in producing comparable mosaic maps as commercial software Agisoft and the effectiveness of the extended framework in significantly reducing the errors in the IMU/GPS data.

Ground-Control Networks for Image Based Surface Reconstruction: An Investigation of Optimum Survey Designs Using UAV Derived Imagery and Structure-from-Motion Photogrammetry

The use of small UAV (Unmanned Aerial Vehicle) and Structure-from-Motion (SfM) with Multi-View Stereopsis (MVS) for acquiring survey datasets is now commonplace, however, aspects of the SfM-MVS workflow require further validation. This work aims to provide guidance for scientists seeking to adopt this aerial survey method by investigating aerial survey data quality in relation to the application of ground control points (GCPs) at a site of undulating topography (Ennerdale, Lake District, UK). Sixteen digital surface models (DSMs) were produced from a UAV survey using a varying number of GCPs (3-101). These DSMs were compared to 530 dGPS spot heights to calculate vertical error. All DSMs produced reasonable surface reconstructions (vertical root-mean-square-error (RMSE) of <0.2 m), however, an improvement in DSM quality was found where four or more GCPs (up to 101 GCPs) were applied, with errors falling to within the suggested point quality range of the survey equipment used for GCP acquisition (e.g., vertical RMSE of <0.09 m). The influence of a poor GCP distribution was also investigated by producing a DSM using an evenly distributed network of GCPs, and comparing it to a DSM produced using a clustered network of GCPs. The results accord with existing findings, where vertical error was found to increase with distance from the GCP cluster. Specifically vertical error and distance to the nearest GCP followed a strong polynomial trend (R2 = 0.792). These findings contribute to our understanding of the sources of error when conducting a UAV-SfM survey and provide guidance on the collection of GCPs. Evidence-driven UAV-SfM survey designs are essential for practitioners seeking reproducible, high quality topographic datasets for detecting surface change.

Accuracy of Digital Surface Models and Orthophotos Derived from Unmanned Aerial Vehicle Photogrammetry

AbstractThis paper explores the influence of flight altitude, terrain morphology, and the number of ground control points (GCPs) on digital surface model (DSM) and orthoimage accuracies obtained with unmanned aerial vehicle (UAV) photogrammetry. For this study, 60 photogrammetric projects were carried out considering five terrain morphologies, four flight altitudes (i.e., 50, 80, 100, and 120 m), and three different numbers of GCPs (i.e., 3, 5, and 10). The UAV was a rotatory wing platform with eight motors, and the sensor was a nonmetric mirrorless reflex camera. The root-mean-square error (RMSE) was used to assess the accuracy of the DSM (Z component) and orthophotos (X, Y, and XY components RMSEX, RMSEY, and RMSEXY, respectively). The results show that RMSEX, RMSEY, and RMSEXY were not influenced by flight altitude or terrain morphology. For horizontal accuracy, differences between terrain morphologies were observed only when 5 or 10 GCPs were used, which were the best accuracies for the flattest morph...

THE SYSTEM OVERVIEW AND GEOMETRIC IMAGE QUALITY OF THE TH-1 SATELLITE

Abstract. The Tian-Hui 1 (TH-1) is the first stereo mapping transmission satellite in China, and the primary mission goal of the satellite is for topographic mapping at 1:50,000 scale without Ground Control Points (GCPs). 1st, 2nd and 3rd satellite of TH-1 was launched on August 24, 2010, May 6, 2012 and October 26, 2015. In TH-1 satellite, many payloads are put on a small satellite platform, which has a low cost. The optical camera of TH-1 includes Line-Matrix CCD (LMCCD) camera, high resolution camera and multispectral camera with 60 km ground swath width. To get high geometric accuracy without GCPs, the on-orbit calibration camera parameters and the Equivalent Frame Photo (EFP) Multi-functional bundle adjustment are proposed and realized in ground image processing of TH-1. In order to evaluate the location accuracy of TH-1, some testing fields are established. All GCPs of testing fields are measured by GPS. The GCPs are not participated the EFP Multi-functional bundle adjustment, and are only as Check Points (CPs) to evaluate the location accuracy. The evaluation of 1st satellite is shown: the horizontal accuracy is 10.3 m (RMSE) and the vertical accuracy is 5.7 m (RMSE) without GCPs, which can satisfy for topographic mapping at 1:50,000 scale. The overviews of TH-1 satellite are described in this paper: First, the system overview is introduced, including mission and optical camera of TH-1. Then, the on-orbit calibration camera parameters using LMCCD image and the EFP Multi-functional bundle adjustment are presented. Finally, the location performance is analysed without GCPs and with different number of GCPs. In addition, the products of TH-1 are introduced.

THE SYSTEM OVERVIEW AND GEOMETRIC IMAGE QUALITY OF THE TH-1 SATELLITE

The Tian-Hui 1 (TH-1) is the first stereo mapping transmission satellite in China, and the primary mission goal of the satellite is for topographic mapping at 1:50,000 scale without Ground Control Points (GCPs). 1st, 2nd and 3rd satellite of TH-1 was launched on August 24, 2010, May 6, 2012 and October 26, 2015. In TH-1 satellite, many payloads are put on a small satellite platform, which has a low cost. The optical camera of TH-1 includes Line-Matrix CCD (LMCCD) camera, high resolution camera and multispectral camera with 60 km ground swath width. To get high geometric accuracy without GCPs, the on-orbit calibration camera parameters and the Equivalent Frame Photo (EFP) Multi-functional bundle adjustment are proposed and realized in ground image processing of TH-1. In order to evaluate the location accuracy of TH-1, some testing fields are established. All GCPs of testing fields are measured by GPS. The GCPs are not participated the EFP Multi-functional bundle adjustment, and are only as Check Points (CPs) to evaluate the location accuracy. The evaluation of 1st satellite is shown: the horizontal accuracy is 10.3 m (RMSE) and the vertical accuracy is 5.7 m (RMSE) without GCPs, which can satisfy for topographic mapping at 1:50,000 scale. The overviews of TH-1 satellite are described in this paper: First, the system overview is introduced, including mission and optical camera of TH-1. Then, the on-orbit calibration camera parameters using LMCCD image and the EFP Multi-functional bundle adjustment are presented. Finally, the location performance is analysed without GCPs and with different number of GCPs. In addition, the products of TH-1 are introduced.

A SYSTEMATIC COMPARISON OF DIRECT AND IMAGE-BASED GEOREFERENCING IN CHALLENGING URBAN AREAS

Abstract. Image-based mobile mapping systems enable an efficient acquisition of georeferenced image sequences, which can be used for geo-data capture in subsequent steps. In order to provide accurate measurements in a given reference frame while e.g. aiming at high fidelity 3D urban models, high quality georeferencing of the captured multi-view image sequences is required. Moreover, sub-pixel accurate orientations of these highly redundant image sequences are needed in order to optimally perform steps like dense multi-image matching as a prerequisite for 3D point cloud and mesh generation. While direct georeferencing of image-based mobile mapping data performs well in open areas, poor GNSS coverage in urban canyons aggravates fulfilling these high accuracy requirements, even with high-grade inertial navigation equipment. Hence, we conducted comprehensive investigations aiming at assessing the quality of directly georeferenced sensor orientations as well as the expected improvement by image-based georeferencing in a challenging urban environment. Our study repeatedly delivered mean trajectory deviations of up to 80 cm. By performing image-based georeferencing using bundle adjustment for a limited set of cameras and a limited number of ground control points, mean check point residuals could be lowered from approx. 40 cm to 4 cm. Furthermore, we showed that largely automated image-based georeferencing is capable of detecting and compensating discontinuities in directly georeferenced trajectories.

A SYSTEMATIC COMPARISON OF DIRECT AND IMAGE-BASED GEOREFERENCING IN CHALLENGING URBAN AREAS

Image-based mobile mapping systems enable an efficient acquisition of georeferenced image sequences, which can be used for geo-data capture in subsequent steps. In order to provide accurate measurements in a given reference frame while e.g. aiming at high fidelity 3D urban models, high quality georeferencing of the captured multi-view image sequences is required. Moreover, sub-pixel accurate orientations of these highly redundant image sequences are needed in order to optimally perform steps like dense multi-image matching as a prerequisite for 3D point cloud and mesh generation. While direct georeferencing of image-based mobile mapping data performs well in open areas, poor GNSS coverage in urban canyons aggravates fulfilling these high accuracy requirements, even with high-grade inertial navigation equipment. Hence, we conducted comprehensive investigations aiming at assessing the quality of directly georeferenced sensor orientations as well as the expected improvement by image-based georeferencing in a challenging urban environment. Our study repeatedly delivered mean trajectory deviations of up to 80 cm. By performing image-based georeferencing using bundle adjustment for a limited set of cameras and a limited number of ground control points, mean check point residuals could be lowered from approx. 40 cm to 4 cm. Furthermore, we showed that largely automated image-based georeferencing is capable of detecting and compensating discontinuities in directly georeferenced trajectories.

Assessing the Accuracy of High Resolution Digital Surface Models Computed by PhotoScan® and MicMac® in Sub-Optimal Survey Conditions

For monitoring purposes and in the context of geomorphological research, Unmanned Aerial Vehicles (UAV) appear to be a promising solution to provide multi-temporal Digital Surface Models (DSMs) and orthophotographs. There are a variety of photogrammetric software tools available for UAV-based data. The objective of this study is to investigate the level of accuracy that can be achieved using two of these software tools: Agisoft PhotoScan® Pro and an open-source alternative, IGN© MicMac®, in sub-optimal survey conditions (rugged terrain, with a large variety of morphological features covering a range of roughness sizes, poor GPS reception). A set of UAV images has been taken by a hexacopter drone above the Rivière des Remparts, a river on Reunion Island. This site was chosen for its challenging survey conditions: the topography of the study area (i) involved constraints on the flight plan; (ii) implied errors on some GPS measurements; (iii) prevented an optimal distribution of the Ground Control Points (GCPs) and; (iv) was very complex to reconstruct. Several image processing tests are performed with different scenarios in order to analyze the sensitivity of each software package to different parameters (image quality, numbers of GCPs, etc.). When computing the horizontal and vertical errors within a control region on a set of ground reference targets, both methods provide rather similar results. A precision up to 3–4 cm is achievable with these software packages. The DSM quality is also assessed over the entire study area comparing PhotoScan DSM and MicMac DSM with a Terrestrial Laser Scanner (TLS) point cloud. PhotoScan and MicMac DSM are also compared at the scale of particular features. Both software packages provide satisfying results: PhotoScan is more straightforward to use but its source code is not open; MicMac is recommended for experimented users as it is more flexible.

Combined Calibration Method Based on Rational Function Model for the Chinese GF-1 Wide-Field-of-View Imagery

The internal geometric distortion of charge-coupled-device (ccd) chips degrades the internal geometric accuracy of satellite images. Even if a large number of ground control points (GCPs) are used for geometric correction, the satellite images cannot always be used for direct mapping. Because of the current lack of core parameters for building a rigorous geometric model, this paper proposes a combined interior-orientation calibration method based on the rational function model (rfm) for multiple GF-1 wide-field-of-view (wfv) images of the same general scene. A polynomial model of look angles in the image space is chosen to compensate for the systemic error of CCD chips. After calibration for sensors 1 to 4, the orientation accuracy is improved to 0.4 pixels. Moreover, the residual error figures show no obvious trend. Other GF-1 WFV images are calibrated using the obtained interior-orientation parameters, and the improvement in their orientation accuracy is demonstrated to validate the effectiveness of the proposed method.

UAVを利用した空中写真の河川地形計測への適用性

Unmanned Aerial Vehicles (UAVs) are a new type of platform for high-resolution aerial photogrammetry. Despite of their growing applications there has been little assessment of their performance. This study reports the results from two field experiments designed to evaluate the accuracy of digital surface model (DSM) derived from the imagery acquired with a low-cost digital camera onboard an UAV. Concurrently the survey was carried out using RTK-GPS and Total Station. The first experiment was performed to evaluate the highest measurement precision at a parking lot at Hiroshima University. Another experiment was conducted in the reaches of the Jyoge River. Comparisons between the DSM and the survey data have pointed out that the number of Ground Control Point and the quality of photographic lens are important factors for high precision measurement and that the r.m.s. errors at altitude of 100 m are less than 2 cm, suggesting that UAV photogrammetry has high applicability to river morphological mapping.

BUILDING DAMAGE ASSESSMENT AFTER EARTHQUAKE USING POST-EVENT LiDAR DATA

Abstract. After an earthquake, damage assessment plays an important role in leading rescue team to help people and decrease the number of mortality. Damage map is a map that demonstrates collapsed buildings with their degree of damage. With this map, finding destructive buildings can be quickly possible. In this paper, we propose an algorithm for automatic damage map generation after an earthquake using post-event LiDAR Data and pre-event vector map. The framework of the proposed approach has four main steps. To find the location of all buildings on LiDAR data, in the first step, LiDAR data and vector map are registered by using a few number of ground control points. Then, building layer, selected from vector map, are mapped on the LiDAR data and all pixels which belong to the buildings are extracted. After that, through a powerful classifier all the extracted pixels are classified into three classes of “debris”, “intact building” and “unclassified”. Since textural information make better difference between “debris” and “intact building” classes, different textural features are applied during the classification. After that, damage degree for each candidate building is estimated based on the relation between the numbers of pixels labelled as “debris” class to the whole building area. Calculating the damage degree for each candidate building, finally, building damage map is generated. To evaluate the ability proposed method in generating damage map, a data set from Port-au-Prince, Haiti’s capital after the 2010 Haiti earthquake was used. In this case, after calculating of all buildings in the test area using the proposed method, the results were compared to the damage degree which estimated through visual interpretation of post-event satellite image. Obtained results were proved the reliability of the proposed method in damage map generation using LiDAR data.

A Comparison of the Performance of Bias-Corrected RSMs and RFMs for the Geo-Positioning of High-Resolution Satellite Stereo Imagery

High-resolution stereo satellite imagery is widely used in environmental monitoring, topographic mapping, and urban three-dimensional (3D) reconstruction. However, a critical issue in these applications using high-resolution stereo satellite imagery is to improve the accuracy of point geo-positioning. This paper presents a framework for comparison of the performance of the three-dimensional (3D) geo-positioning of the bias-corrected Rigorous Sensor Models (RSMs) and rational function models (RFMs) with respect to the high-resolution QuickBird stereo images in three spaces (i.e., orbital space, image space and object space). The compared models include a bias-corrected RSM in the orbital space, a bias-corrected RSM and RFM in the image space, and a bias-corrected RSM and RFM in the object space. In the comparison, the RSMs and RFMs use the vendor-provided orbit data and Rational Polynomial Coefficients (RPCs), respectively. The experimental results indicated that, (1) these five bias-corrected models can provide a sub-pixel geo-positioning accuracy. With the zero-order polynomial correction model in the orbital space and a minimum of three Ground Control Points (GCPs), the accuracy based on RPCs better than 0.8 m in horizontal direction and 1.3 m in vertical direction. With an increase in the number of GCPs, or in the order of correction models, the regenerated orbital parameters achieve a slight improved positioning accuracy of 0.5 m in horizontal direction and 0.8 m in vertical direction with 25 GCPs, which indicates that the low-order correction model in the orbital space can accurately model the effects of ephemeris and attitude errors; (2) the performances of bias-corrected RSM and RFM in image space are rather similar. However, the bias-corrected RSM and RFM in image space achieve a better accuracy than the bias-corrected RSM and RFM in object space, with the same configuration of GCPs.

Area-based quality control of airborne laser scanning 3D models for different land classes using terrestrial laser scanning: sample survey in Houston, USA

Airborne laser scanning (ALS) is a remote-sensing technique that provides scale-accurate 3D models consisting of dense point clouds with x, y planimetric coordinates and altitude z. Using ALS, very high-resolution (VHR) digital surface models (DSMs) have been widely used for commercial and scientific applications since the early 1990s. Although there is widespread usage, there has been little comprehensive investigation of quality control for ALS DSMs in the literature, as most studies have been limited to assessing point-based vertical accuracy. This article is dedicated to investigating the quality of ALS DSMs for different land classes using statistical and visual approaches based on absolute and relative vertical accuracy metrics. Rather than a limited number of ground control points (GCP), the model-to-model-based approach is applied and DSMs derived from terrestrial laser scanning (TLS) point clouds that have around 5 mm absolute and 3 mm relative geolocation accuracy were used as the reference data for comparison. The results demonstrate that in open, grass, and building land classes, the ALS DSMs reached both standard deviation (σ) and normalized median absolute deviation (NMAD) of 3–5 cm after the elimination of any systematic biases. This result sufficiently satisfies the vertical accuracy requirements for 1/1000-scale topographic maps determined by National Digital Elevation Program (NDEP) specifications. In tall vegetation, a higher number of discrepancies larger than 0.5 m exist, reversing the relation between σ and NMAD. These vegetation errors also do not appear to be normally distributed. As an additional investigation, the performance of ALS DEMs under dense high-vegetation areas was assessed. These under-canopy ALS DEMs, created using only classified ground returns, offer both σ and NMAD of 12–14 cm, a performance level that is difficult to achieve under-canopy using photogrammetric techniques.

Implementation of Rational Function Model for Rad-Orthokit Generation on Cartosat-1 Data

Sensor models are required to establish the relationship between 3D object space and 2D image space. Traditionally this is done using the physical sensor model where the complete parameters of physical imaging system are known. The replacement sensor models are required to establish this relation without the knowledge of the physical sensor model. The rational function model (RFM) is one of the replacement model used in remote sensing with 78 rational polynomial coefficients (RPCs). RFM is a complete mathematical model, which approximately describes the physical imaging process in photogrammetry and remote sensing. In the absence of interior and exterior orientation such as camera model, position and orientation information of specific sensor, large number of ground control points (GCPs) are needed to solve all the unknown coefficients of the RFM and to achieve higher accuracies in the photogrammetric processing. The rational function model(RFM) can be used either as a replacement for physical sensor model ( terrain dependent) or to express the physical model in the form of RPCs ( terrain independent) for further processing. In this paper the implementation aspects of terrain dependent RFM model for Cartosat-1 data for the Chitrapur, Simla, Himachal Pradesh state, India and the accuracies achieved and the stability of the model are discussed. The direct least square solutions to the RFM are implemented using row reduction. The validation of RFM is done at check points and achieved planimetric accuracy 1.5m,3.38m with respect to CE90 in X and Y directions respectively. General Terms Check Points ,Ground Control Points, RMSE.

COMPARATIVE ANALYSIS OF ASTER DEM, ASTER GDEM, AND SRTM DEM BASED ON GROUND-TRUTH GPS DATA

This study aims to compare the accuracies of ASTER DEM, ASTER GDEM, and SRTM DEM for the area of Universiti Teknologi Malaysia (UTM) and surrounding. In doing so, a number of Ground Control Points (GCPs) were collected using GPS technology and used to generate an absolute DEM using the ASTER stereo imagery. Moreover, two well-known DEMs including ASTER GDEM and SRTM DEM were obtained for the same area with ASTER image. Subsequently, several high accuracy ground-truth points were established around UTM using dual frequency GPS and used to assess the accuracies of the obtained DEMs. The results indicate that an elevation Root Mean Square Error (RMSE) of ±14.86m is achieved for the generated ASTER DEM, which is less than the 15m pixel size of ASTER image. The results further show that the elevation RMSEs of the ASTER GDEM and SRTM DEM are respectively ±4.52m and ±4.14m for the study area. The results illustrate although the resolution of SRTM DEM is much lower than ASTER GDEM, it could provide higher elevation accuracy. Finally, although the accuracy of the ASTER DEM in this study is not high in comparison with the accuracies of ASTER GDEM and SRTM DEM, based on the selected number of check points and resolution of ASTER image, it could be useful for various geoinformation applications.

QUANTITATIVE VEGETATION MAPPING OF URBAN AREA USING HIGH-RESOLUTION MULTISPECTRAL SATELLITE IMAGERY

Maintenance of green spaces within the city is very important under current ecological conditions. In the present context, assessment of green spaces held by visually determining indicators plantations clarifying its instrumental measurements. The accuracy of this method strongly depends on the qualifications and professional experience of the employee conducting an assessment. High spatial and temporal resolution satellite systems provide information for operational monitoring of environmental conditions in the city and promote management decision making. Normalized Difference Vegetation Index (NDVI) – index of plant “greenness” or photosynthetic activity – is widely used for vegetation studying. However, a more important phytometric parameter of vegetation is Leaf Area Index (LAI). Actually LAI is defined as the one sided green leaves area per unit of ground area in broadleaf canopies, or as the projected needle leaves area per unit of ground area in needle canopies. Multispectral imagery processing to evaluate the NDVI and LAI provides informative and reliable indicators of vegetation quantity. High-resolution image from Pleiades-1A satellite was used in the study. Complete geoinformation technology for integrated quantitative assessment of vegetation in urbanized territory provides a distribution of LAI within the study area. The first group of operations was used for preprocessing of multispectral satellite image. Output of these operations is a scene surface reflectance. The second group of operations comprises the steps of LAI calculations based on vegetation mask and LAI versus NDVI regression dependence. Final group of operations provides visualization and interpretation of the results. The methodology for calculating the vegetation area within the land registry boundaries gives biased values of green mass and its status. At the beginning of the growing season the regulatory area of green zone is much greater than the actual amount of vegetation. It is possible to determine more accurately the boundaries and the total area of vegetation cover using the NDVI. Also the described LAI-based geoinformation technology provides tuning out the current phase of the phenological cycle. As a result, more accurate estimation of vegetation amount at moment and its changes over time are available too. Further studies aimed at assessing not only the quantity but also the quality of vegetation. The increase in number of ground control points and number of in-situ measurements are planned to clarify the regression confidence. There will also be studying the state of plant communities at different phases of the phenological cycle. High-resolution image from Pleiades-1A satellite was kindly provided by TVIS-INFO LLC (TVIS) Company, which is one of the leading geospatial and GIS solutions provider in Ukraine.

Merge of sub-images from two PAN CCD lines of Kompsat-3 AEISS

The Kompsat-3 Advanced Earth Imaging Sensor System (AEISS) camera contains two PAN CCD lines for a larger swath width. Merge of sub-images from individual CCD lines requires precise camera calibration before launch as well as in-flight calibration with a number of ground control points to determine the geometric relations between the CCD lines. Even with accurately determined sensor alignment parameters, there can still be exterior effects such as those associated with the ephemeris and terrain elevation. We generated a grid of tie points over the sub-images based on the physical sensor modelling and then evaluated the impacts of sensor alignment, ephemeris effects, and terrain elevation on the coordinate differences between the tie points. This enabled us to develop a procedure to precisely merge the sub-images into a single image. An experiment with three Kompsat-3 images produced successful results and displayed negligible image discrepancy in the overlap area.

The surface modelling based on UAV Photogrammetry and qualitative estimation

Recently, the interest in Unmanned Aerial Vehicle (UAV) application in photogrammetric environment has increased in many countries. The fixed-wing UAV, the model of EPP-FPV with mounted digital camera Canon S100 was used as a platform for images acquisition. Implemented means are low-cost, mobile and simple. Digital photogrammetry technology with Pix4D software application has been applied for UAV images processing and area mapping. High quality of images is a significant factor for the efficiency and accuracy generating standard mapping products. The correctness of digital surface models and orthophotos mainly depend on camera resolution, flight height and ground control point (GCP) accuracy. The paper reports on investigations how number of GCPs used for UAV image transformation influences the mapping results. The demand of such investigations arises because the flight paths with a fixed wing UAV have general form, in contrast to classical paths which are pricewise straight lines, as well as the flight significantly depends on weather conditions (especially on wind) and the platform shows considerable tilt because of its light weight. The results of DSM accuracy investigation demonstrate the quality of UAV Photogrammetry product with the use of appropriate number of GCPs.