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The use of global navigation satellite systems (GNSS) technology within the fields of ecology and biology has increased over recent years. With Global Positioning System, GLObalnaya NAvigatsionnaya Sputnikovaya Sistema, Galileo, and BeiDou systems fully deployed, >140 navigation satellites are currently available for navigation and high precision positioning applications. The technological improvements in GNSS devices, mainly due to the multi‐frequency capability, reduce signal acquisition time and increase the reliability, the continuity, and the accuracy of the estimated position, especially in complex environments like forests or areas with a steep topography. This study aims to test experimentally the influence of multi‐constellation multi‐frequency modules on the performance of the GNSS collars used to monitor wildlife. We applied static and kinematic tests designed to assess and compare the performance of GNSS collars equipped with a single‐frequency versus a multi‐frequency chipset. We evaluated the availability and continuity of solutions, number of satellites used, dilution of precision, precision, accuracy, and repeatability of these quality parameters for GNSS devices in southeastern Spain from February 2021 to June 2022. The results confirmed that the multi‐constellation multi‐frequency GNSS collar showed a stable and good performance in terms of the number of satellites used (>10), horizontal dilution of precision (<1.3), fix success rate (100%), mean location error (<1.5 m), and circular error probability (CEP), which was better by approximately one order of magnitude than the single‐frequency collar (1–10 m). The use of reliable and accurate GNSS devices will expand our knowledge of animal behavior and the interactions between species. Multi‐frequency GNSS collars allow collection of accurate locations, providing fine‐scale information about animal behavior (e.g., feeding strategies, competition for resources), whereas the single‐frequency GNSS collars can used for broad‐scale studies (e.g., home ranges, habitat use). [ABSTRACT FROM AUTHOR]

We propose an optimized Structure-from-Motion (SfM) Multi-View Stereopsis (MVS) workflow, based on minimizing different errors and inaccuracies of historical aerial photograph series (1945, 1979, 1984, and 2008 surveys), prior to generation of elevation-calibrated historical Digital Surface Models (hDSM) at 1 m resolution. We applied LiDAR techniques on Airborne Laser Scanning (ALS) point clouds (Spanish PNOA LiDAR flights of 2014 and 2019) for comparison and validation purposes. Implementation of these products in multi-temporal analysis requires quality control due to the diversity of sources and technologies involved. To accomplish this, (i) we used the Mean Absolute Error (MAE) between GNSS-Validation Points and the elevations observed by DSM-ALS to evaluate the elevation accuracy of DSM-ALS generated with the LAScatalog processing engine; (ii) optimization of the SfM sparse clouds in the georeferencing step was evaluated by calculating the Root Mean Square Error (RMSE) between the Check Points extracted from DSM-ALS and the predicted elevations per sparse cloud; (iii) the MVS clouds were evaluated by calculating the MAE between ALS-Validation Points and the predicted elevations per MVS cloud; iv) the accuracy of the resulting historical SfM-MVS DSMs were assessed using the MAE between ALS-Validation Points and the observed elevations per historical DSM; and (v) we implemented a calibration method based on a linear correction to reduce the elevation discrepancies between historical DSMs and the DSM-ALS 2019 reference elevations. This optimized workflow can generate high-resolution (1 m pixel size) hDSMs with reasonable accuracy: MAE in z ranges from 0.41 m (2008 DSM) to 5.21 m (1945 DSM). Overall, hDSMs generated using historical images have great potential for geo-environmental processes monitoring in different ecosystems and, in some cases (i.e., sufficient image overlapping and quality), being an acceptable replacement for LiDAR data when it is not available. [ABSTRACT FROM AUTHOR]

Water vapor is a fundamental component of the Earth's atmosphere with a high spatial and temporal variability. This work studies to what extent low-cost infrared thermometers can infer precipitable water (variable commonly used to characterize atmospheric water vapor). In a calibration process, infrared thermometer readings recorded at Badajoz (Spain) during the 2015–2018 period are compared against precipitable water data measured with reference ground-based Global Navigation Satellite Systems (GNSS) in order to obtain conversion factors through regression analyses considering two exponential fits. After this calibration, using the equation of the best fit, thermometer readings for the year 2019 are transformed into precipitable water estimates. A validation analysis in which these estimates are compared with GNSS measurements yields rms differences of 19% and 17% when normal and seasonal calibration had been employed, respectively. These results are similar (or even better) to those obtained with satellite data. In addition, we explore if certain factors, such as solar elevation, precipitable water content, precipitable water measurements used as reference and equations to convert temperature readings into precipitable water estimates, can significantly affect the quality of the estimates. In view of our results, low-cost infrared thermometers could be used to create an extensive and dense network for a better characterization of the spatial and temporal variability of water vapor. • We made daily infrared low-cost thermometer readings during five years. • These data are compared to precipitable water data measured with GNSS. • Low-cost infrared thermometers could be used to characterize the water vapor. [ABSTRACT FROM AUTHOR]