The geometrical accuracy of georeferenced digital surface models (DTM) obtained from images captured by micro-UAVs and processed by using structure from motion (SfM) photogrammetry depends on several factors, including flight design, camera quality, camera calibration, SfM algorithms and georeferencing strategy. This paper focusses on the critical role of the number and location of ground control points (GCP) used during the georeferencing stage. A challenging case study involving an area of 1200+ ha, 100+ GCP and 2500+ photos was used. Three thousand, four hundred and sixty-five different combinations of control points were introduced in the bundle adjustment, whilst the accuracy of the model was evaluated using both control points and independent check points. The analysis demonstrates how much the accuracy improves as the number of GCP points increases, as well as the importance of an even distribution, how much the accuracy is overestimated when it is quantified only using control points rather than independent check points, and how the ground sample distance (GSD) of a project relates to the maximum accuracy that can be achieved.

A fuel-type map of a predominantly shrub-land area in central Portugal was generated for a fire research experimental site, by combining airborne light detection and ranging (LiDAR), and simultaneous color infrared ortho imaging. Since the vegetation canopy and the ground are too close together to be easily discerned by LiDAR pulses, standard methods of processing LiDAR data did not provide an accurate estimate of shrub height. It was demonstrated that the standard process to generate the digital ground model (DGM) sometimes contained height values for the top of the shrub canopy rather than from the ground. Improvement of the DGM was based on separating canopy from ground hits using color infrared ortho imaging to detect shrub cover, which was measured simultaneously with the LiDAR data. Potentially erroneous data in the DGM was identified using two criteria: low vegetation height and high Normalized Difference Vegetation Index (NDVI), a commonly used spectral index to identify vegetated areas. Based on the height of surrounding pixels, a second interpolation of the DGM was performed to extract those erroneously identified as ground in the standard method. The estimation of the shrub height improved significantly after this correction, and increased determination coefficients from R 2 = 0.48 to 0.65. However, the estimated shrub heights were still less than those observed in the field.Additional keywords: color infrared ortho image, fuel types, LiDAR, shrub height.

It is well established that digital elevation models (DEMs) derived from unmanned aerial vehicle (UAV) images and processed by structure from motion may contain important systematic vertical errors arising from limitations in camera geometry modelling. Even when significant, such ‘dome’‐shaped errors can often remain unnoticed unless specific checks are conducted. Previous methods used to reduce these errors have involved: the addition of convergent images to supplement traditional vertical datasets, the usage of a higher number of ground control points, precise direct georeferencing techniques (RTK/PPK) or more refined camera pre‐calibration. This study confirms that specific UAV flight designs can significantly reduce dome errors, particularly those that have a higher number of tie points connecting distant images, and hence contribute to a strengthened photogrammetric network. A total of 22 flight designs were tested, including vertical, convergent, point of interest (POI), multiscale and mixed imagery. Flights were carried out over a 300 × 70 m2 flat test field area, where 143 ground points were accurately established. Three different UAVs and two commercial software packages were trialled, totalling 396 different tests. POI flight designs generated the smallest systematic errors. In contrast, vertical flight designs suffered from larger dome errors; unfortunately, a configuration that is ubiquitous and most often used. By using the POI flight design, the accuracy of DEMs will improve without the need to use more ground control or expensive RTK/PPK systems. Over flat terrain, the improvement is especially important in self‐calibration projects without (or with just a few) ground control points. Some improvement will also be observed on those projects using camera pre‐calibration or with stronger ground control. © 2020 John Wiley & Sons, Ltd.

Selecting the appropriate receiver is an issue when a major portion of global positioning system ͑GPS͒ data collection is below forest canopies. This study compares four low-cost GPS receivers, in order to determine the most suitable receiver for position assessment under different forest canopy covers, in terms of ease of use, accuracy, and reliability. A total of 33 positional assessments were gathered per receiver, plot, and method, in 18 forest locations. Data were described and analyzed through a sample comparison analysis at 95% confidence level ͑Mann-Whitney nonparametric test͒, in order to determine the existence of differences in accuracy and precision in positioning between receivers. Results showed that there were significant differences between the receivers regarding accuracy and precision measuring coordinates; moreover, accuracies were different depending on the canopy cover and forest characteristics. Therefore, practical recommendations for each case were settled in order to help foresters to select the most suitable receiver. Moreover, key forest variables regarding GPS performance were identified, so that forest environments could be effectively clustered by them.

The adoption of precision viticulture requires a detailed knowledge of variation in soil chemical, physical and profile properties. This study evaluates the usefulness of apparent electrical conductivity (EC a ) data within a GIS framework to identify variations in soil chemical and physical properties and moisture content. The work was conducted in a vineyard located in the Carneros Region (Napa Valley, California). The soil was sampled using 44 boreholes to quantify chemical and physical characteristics and 9 open pits to verify the borehole observations. Moisture content was determined using time domain reflectometry (TDR). To characterize soil EC a , three campaigns were undertaken using a soil electrical conductivity meter (EM38). Linear regressions between soil EC a and soil properties were determined. Boreholes and TDR data were interpolated by kriging to characterize the spatial distribution of soil variables. The resulting maps were compared to the results obtained using the best EC a linear regressions. Using EC a measurements, soil properties like extractable Na ? and Mg 2? , clay and sand content were well estimated, while best estimates were obtained for extractable Na ? (r 2 = 0.770) and clay content (r 2 = 0.621). The best estimates for soil moisture content corresponded to moisture in the deeper soil horizons (r 2 = 0.449). The methods described above provided maps of soil properties estimated by EC a in a GIS framework, and could save time and resources during vineyard establishment and management.