Ground control points (GCPs) are used in the process of indirectly georeferencing Unmanned Aerial Systems (UAS) images. A minimum of three ground control points (GCPs) is required but increasing the number of GCPs will lead to higher accuracy of the final results. The aim of this study is to provide the answer to the question of how many ground control points are necessary in order to derive high precision results. To obtain the results, an area of about 1 ha was photographed with a low-cost UAS, namely, the DJI Phantom 3 Standard at two different heights, 28 m and 35 m above ground, the camera being oriented in a nadiral position, and 50 ground control points were measured using a total station. In the first and the second scenario, the UAS images were processed using the Pix4D Mapper Pro software and 3DF Zephyr, respectively, by performing a full bundle adjustment process with the number being gradually increased from three GCPs to 40. The third test was made with 3DF Zephyr Pro software using a free-network approach in the bundle adjustment. Also, the point clouds and the mesh surfaces derived automatically after using the minimum and the optimum number of GCPs, respectively, were compared with a terrestrial laser scanner (TLS) point cloud. The results expressed a clear overview of the number of GCPs needed for the indirect georeferencing process with minimum influence on the final results.

Currently, products that are obtained by Unmanned Aerial Systems (UAS) image processing based on structure-from-motion photogrammetry (SfM) are being investigated for use in high precision projects. Independent of the georeferencing process being done directly or indirectly, Ground Control Points (GCPs) are needed to increase the accuracy of the obtained products. A minimum of three GCPs is required to bring the results into a desired coordinate system through the indirect georeferencing process, but it is well known that increasing the number of GCPs will lead to a higher accuracy of the final results. The aim of this study is to find the suitable number of GCPs to derive high precision results and what is the effect of GCPs systematic or stratified random distribution on the accuracy of the georeferencing process and the final products, respectively. The case study involves an urban area of about 1 ha that was photographed with a low-cost UAS, namely, the DJI Phantom 3 Standard, at 28 m above ground. The camera was oriented in a nadiral position and 300 points were measured using a total station in a local coordinate system. The UAS images were processed using the 3DF Zephyr software performing a full BBA with a variable number of GCPs i.e., from four up to 150, while the number and the spatial location of check points (ChPs) was kept constant i.e., 150 for each independent distribution. In addition, the systematic and stratified random distribution of GCPs and ChPs spatial positions was analysed. Furthermore, the point clouds and the mesh surfaces that were automatically derived were compared with a terrestrial laser scanner (TLS) point cloud while also considering three test areas: two inside the area defined by GCPs and one outside the area. The results expressed a clear overview of the number of GCPs needed for the indirect georeferencing process with minimum influence on the final results. The RMSE can be reduced down to 50% when switching from four to 20 GCPs, whereas a higher number of GCPs only slightly improves the results.

Forest fire is one of the most common natural hazards occurring in the Western Ghats region of Kerala and is one of the reasons for forest degradation. This natural disaster causes considerable damage to the biodiversity of this region during the dry fire season. The area selected for the present study, Eravikulam National Park, which is predominantly of grassland vegetation, is also prone to forest fires. This study aims to delineate the forest fire risk zones in Eravikulam National Park using remote sensing (RS) data and geographic information system (GIS) techniques. In the present study, methods such as Analytic Hierarchy Process (AHP) and Frequency Ratio (FR) were used to derive the weights, and the results were compared. We have used seven factors, i.e. land cover types, normalized difference vegetation index, normalized difference water index, slope angle, slope aspect, distance from the settlement, and distance from the road to prepare the fire risk zone map. The area of the prepared risk zone maps is divided into three zones, namely low, moderate, and high. From the study, it was found that the fire occurring in this area is due to natural as well as anthropogenic factors. The prepared forest fire risk zone maps are validated using the fire incidence data for the period from January 2003 to June 2019 collected from the records of the Forest Survey of India. The investigation revealed that 72% and 24% of the fire incidences occurred in the high risk zone of the maps prepared using the AHP and FR methods, respectively, which ascertained the superiority of the AHP method over the FR method for forest fire risk zone mapping. The receiver operating characteristic (ROC) curve analysis gives an area under the ROC curve (AUC) value of 0.767 and 0.567 for the AHP and FR methods, respectively. The risk zone maps will be useful for staff of the forest department, planners, and officials of the disaster management department to take effective preventive and mitigation measures.

Due to the large number of technological developments in recent years, UAS systems are now used for monitoring purposes and in projects with high precision demand, such as 3D model-based creation of dams, reservoirs, historical monuments etc. These unmanned systems are usually equipped with an automatic pilot device and a digital camera (photo/video, multispectral, Near Infrared etc.), of which the lens has distortions; but this can be determined in a calibration process. Currently, a method of “self-calibration” is used for the calibration of the digital cameras mounted on UASs, but, by using the method of calibration based on a 3D calibration object, the accuracy is improved in comparison with other methods. Thus, this paper has the objective of establishing a 3D calibration field for the digital cameras mounted on UASs in terms of accuracy and robustness, being the largest reported publication to date. In order to test the proposed calibration field, a digital camera mounted on a low-cost UAS was calibrated at three different heights: 23 m, 28 m, and 35 m, using two configurations for image acquisition. Then, a comparison was made between the residuals obtained for a number of 100 Check Points (CPs) using self-calibration and test-field calibration, while the number of Ground Control Points (GCPs) variedand the heights were interchanged. Additionally, the parameters where tested on an oblique flight done 2 years before calibration, in manual mode at a medium altitude of 28 m height. For all tests done in the case of the double grid nadiral flight, the parameters calculated with the proposed 3D field improved the results by more than 50% when using the optimum and a large number of GCPs, and in all analyzed cases with 75% to 95% when using a minimum of 3 GCP. In this context, it is necessary to conduct accurate calibration in order to increase the accuracy of the UAS projects, and also to reduce field measurements.

Airborne Laser Scanning (ALS) point cloud classification in ground and non-ground points can be accurately performed using various algorithms, which rely on a range of information, including signal analysis, intensity, amplitude, echo width, and return number, often focusing on the last return. With its high point density and the vast majority of points (approximately 99%) measured with the first return, filtering LiDAR-UAS data proves to be a more challenging task when compared to ALS point clouds. Various algorithms have been proposed in the scientific literature to differentiate ground points from non-ground points. Each of these algorithms has advantages and disadvantages, depending on the specific terrain characteristics. The aim of this research is to obtain an enhanced Digital Terrain Model (DTM) based on LiDAR-UAS data and to qualitatively and quantitatively compare three filtering approaches, i.e., hierarchical robust, volume-based, and cloth simulation, on a complex terrain study area. For this purpose, two flights over a residential area of about 7.2 ha were taken at 60 m and 100 m, with a DJI Matrice 300 RTK UAS, equipped with a Geosun GS-130X LiDAR sensor. The vertical and horizontal accuracy of the LiDAR-UAS point cloud, obtained via PPK trajectory processing, was tested using Check Points (ChPs) and manually extracted features. A combined approach for ground point classification is proposed, using the results from a hierarchic robust filter and applying an 80% slope condition for the volume-based filtering result. The proposed method has the advantage of representing with accuracy man-made structures and sudden slope changes, improving the overall accuracy of the DTMs by 40% with respect to the hierarchical robust filtering algorithm in the case of a 60 m flight height and by 28% in the case of a 100 m flight height when validated against 985 ChPs.