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Abstract:In this paper, results from the analysis of the gimbal impact on the determination of the camera exterior orientation parameters of an Unmanned Aerial Vehicle (UAV) are presented and interpreted. Additionally, a new approach and methodology for testing the influence of gimbals on the exterior orientation parameters of UAV acquired images is presented. The main motive of this study is to examine the possibility of obtaining better geometry and favorable spatial bundles of rays of images in UAV photogrammetric surveying. The subject is a 3-axis brushless gimbal based on a controller board (Storm32). Only two gimbal axes are taken into consideration: roll and pitch axes. Testing was done in a flight simulation, and in indoor and outdoor flight mode, to analyze the Inertial Measurement Unit (IMU) and photogrammetric data. Within these tests the change of the exterior orientation parameters without the use of a gimbal is determined, as well as the potential accuracy of the stabilization with the use of a gimbal. The results show that using a gimbal has huge potential. Significantly, smaller discrepancies between data are noticed when a gimbal is used in flight simulation mode, even four times smaller than in other test modes. In this test the potential accuracy of a low budget gimbal for application in real conditions is determined.

The emergence of hand-held Personal Laser Scanning (H-PLS) systems in recent years resulted in initial research on the possibility of its application in forest inventory, primarily for the estimation of the main tree attributes (e.g. tree detection, stem position, DBH, tree height, etc.). Research knowledge acquired so far can help to direct further research and eventually include H-PLS into operational forest inventory in the future. The main aims of this review are: - to present the current state of the art for H-PLS systems - briefly describe the fundamental concept and methods for H-PLS application in forest inventory - provide an overview of the results of previous studiesÞ emphasize pros and cons for H-PLS application in forest inventory in relation to conventional field measurements and other similar laser scanning systems - highlight the main issues that should be covered by further H-PLS-based forest inventory studies.

This article has been accepted for inclusion in a future issue of this journal. Content is final as presented, with the exception of pagination.

<p><strong>Abstract.</strong> Different spatial resolutions satellite imagery with global almost daily revisit time provide valuable information about the earth surface in a short time. Based on the remote sensing methods satellite imagery can have different applications like environmental development, urban monitoring, etc. For accurate vegetation detection and monitoring, especially in urban areas, spectral characteristics, as well as the spatial resolution of satellite imagery is important. In this research, 10-m and 20-m Sentinel-2 and 3.7-m PlanetScope satellite imagery were used. Although in nowadays research Sentinel-2 satellite imagery is often used for land-cover classification or vegetation detection and monitoring, we decided to test a fusion of Sentinel-2 imagery with PlanetScope because of its higher spatial resolution. The main goal of this research is a new method for Sentinel-2 and PlanetScope imagery fusion. The fusion method validation was provided based on the land-cover classification accuracy. Three land-cover classifications were made based on the Sentinel-2, PlanetScope and fused imagery. As expected, results show better accuracy for PS and fused imagery than the Sentinel-2 imagery. PlanetScope and fused imagery have almost the same accuracy. For the vegetation monitoring testing, the Normalized Difference Vegetation Index (NDVI) from Sentinel-2 and fused imagery was calculated and mutually compared. In this research, all methods and tests, image fusion and satellite imagery classification were made in the free and open source programs. The method developed and presented in this paper can easily be applied to other sciences, such as urbanism, forestry, agronomy, ecology and geology.</p>

ABSTRACT:Development of the technology in the area of the cameras, computers and algorithms for 3D the reconstruction of the objects from the images resulted in the increased popularity of the photogrammetry. Algorithms for the 3D model reconstruction are so advanced that almost anyone can make a 3D model of photographed object. The main goal of this paper is to examine the possibility of obtaining 3D data for the purposes of the close-range photogrammetry applications, based on the open source technologies. All steps of obtaining 3D point cloud are covered in this paper. Special attention is given to the camera calibration, for which two-step process of calibration is used. Both, presented algorithm and accuracy of the point cloud are tested by calculating the spatial difference between referent and produced point clouds. During algorithm testing, robustness and swiftness of obtaining 3D data is noted, and certainly usage of this and similar algorithms has a lot of potential in the real-time application. That is the reason why this research can find its application in the architecture, spatial planning, protection of cultural heritage, forensic, mechanical engineering, traffic management, medicine and other sciences.

The quality and accuracy of Unmanned Aerial System (UAS) products greatly depend on the methods used to define image orientations before they are used to create 3D point clouds. While most studies were conducted in non- or partially-forested areas, a limited number of studies have evaluated the spatial accuracy of UAS products derived by using different image block orientation methods in forested areas. In this study, three image orientation methods were used and compared: (a) the Indirect Sensor Orientation (InSO) method with five irregularly distributed Ground Control Points (GCPs); (b) the Global Navigation Satellite System supported Sensor Orientation (GNSS-SO) method using non-Post-Processed Kinematic (PPK) single-frequency carrier-phase GNSS data (GNSS-SO1); and (c) using PPK dual-frequency carrier-phase GNSS data (GNSS-SO2). The effect of the three methods on the accuracy of plot-level estimates of Lorey’s mean height (HL) was tested over the mixed, even-aged pedunculate oak forests of Pokupsko basin located in Central Croatia, and validated using field validation across independent sample plots (HV), and leave-one-out cross-validation (LOOCV). The GNSS-SO2 method produced the HL estimates of the highest accuracy (RMSE%: HV = 5.18%, LOOCV = 4.06%), followed by the GNSS-SO1 method (RMSE%: HV = 5.34%, LOOCV = 4.37%), while the lowest accuracy was achieved by the InSO method (RMSE%: HV = 5.55%, LOOCV = 4.84%). The negligible differences in the performances of the regression models suggested that the selected image orientation methods had no considerable effect on the estimation of HL. The GCPs, as well as the high image overlaps, contributed considerably to the block stability and accuracy of image orientation in the InSO method. Additional slight improvements were achieved by replacing single-frequency GNSS measurements with dual-frequency GNSS measurements and by incorporating PPK into the GNSS-SO2 method.

The height to crown base (hcb) is a critical measure used in many investigations as an input variable to investigate the vigour of the stands, the social position of the trees, and to evaluate the behaviour of forest fires, among other uses. Though measuring height-related variables in the field is always time-consuming, the foremost benefits offered by modelling hcb are that it permits to generalize and average a very uneven attribute and, furthermore, provides insights about which tree and stand variables have a significant impact on hcb. However, there are many species in which models of the crown base height have not been developed in Croatia. The objective of this research was to develop a height to base crown model for each of the main species present in the two-layered mixed stands of this study. According to previous investigations, logistic models provide the highest precision and require the lowest inventory cost owing to less frequent measurements. Tree- and plot-level variables with distance-independent competition indexes were studied in the fitting model. In this research, we obtained models for the main stand species: Acer campestre (root mean squared error (RMSE) = 2.28 m, R2 = 82.80%); Alnus glutinosa (RMSE = 1.78 m, R2 = 85.36%); Carpinus betulus (RMSE = 2.47 m, R2 = 67.55%); Fraxinus angustifolia (RMSE = 2.46 m, R2 = 82.45%); Quercus robur (RMSE = 2.60 m, R2 = 80.57%); Tilia sp. (RMSE = 2.01 m, R 2 = 89.07%); and Ulmus laevis (RMSE = 1.71 m, R2 = 92.42%). The combination of the total height, tree, and plot-level variables with distance-independent competition indexes contributed to the prediction accuracy of proposed model significantly.