Unmanned aerial systems (UAS) are cost-effective, flexible and offer a wide range of applications. If equipped with optical sensors, orthophotos with very high spatial resolution can be retrieved using photogrammetric processing. The use of these images in multi-temporal analysis and the combination with spatial data imposes high demands on their spatial accuracy. This georeferencing accuracy of UAS orthomosaics is generally expressed as the checkpoint error. However, the checkpoint error alone gives no information about the reproducibility of the photogrammetrical compilation of orthomosaics. This study optimizes the geolocation of UAS orthomosaics time series and evaluates their reproducibility. A correlation analysis of repeatedly computed orthomosaics with identical parameters revealed a reproducibility of 99% in a grassland and 75% in a forest area. Between time steps, the corresponding positional errors of digitized objects lie between 0.07 m in the grassland and 0.3 m in the forest canopy. The novel methods were integrated into a processing workflow to enhance the traceability and increase the quality of UAS remote sensing.

Ecosystem functions and services are severely threatened by unprecedented global loss in biodiversity. To counteract these trends, it is essential to develop systems to monitor changes in biodiversity for planning, evaluating, and implementing conservation and mitigation actions. However, the implementation of monitoring systems suffers from a trade‐off between grain (i.e., the level of detail), extent (i.e., the number of study sites), and temporal repetition. Here, we present an applied and realized networked sensor system for integrated biodiversity monitoring in the Nature 4.0 project as a solution to these challenges, which considers plants and animals not only as targets of investigation, but also as parts of the modular sensor network by carrying sensors. Our networked sensor system consists of three main closely interlinked components with a modular structure: sensors, data transmission, and data storage, which are integrated into pipelines for automated biodiversity monitoring. We present our own real‐world examples of applications, share our experiences in operating them, and provide our collected open data. Our flexible, low‐cost, and open‐source solutions can be applied for monitoring individual and multiple terrestrial plants and animals as well as their interactions. Ultimately, our system can also be applied to area‐wide ecosystem mapping tasks, thereby providing an exemplary cost‐efficient and powerful solution for biodiversity monitoring. Building upon our experiences in the Nature 4.0 project, we identified ten key challenges that need to be addressed to better understand and counteract the ongoing loss of biodiversity using networked sensor systems. To tackle these challenges, interdisciplinary collaboration, additional research, and practical solutions are necessary to enhance the capability and applicability of networked sensor systems for researchers and practitioners, ultimately further helping to ensure the sustainable management of ecosystems and the provision of ecosystem services.

Lack of knowledge about freshwater use in ports of tourist destinations hinders accurate assessment of water availability and water planning. In this study, freshwater use in the Port of Palma (Spain) is analyzed by sector (commercial, mixed, navy and recreational) for the period 2007–2018. This study shows the dynamics of consumption and evaluates the effects of increased cruise tourism from 2007 to 2018 in the port. Water data supplied by the Port Authority of the Balearic Islands for each sector, together with water volumes recharged by ships, allow a detailed analysis of the water used by merchants and cruise lines. Results reveal a significant increase in freshwater withdrawals by cruise ships in the Port of Palma in the last ten years, closely related to the boom of cruise activity. Water use and recharge by cruise ships increased in both the high and low tourist seasons. Homeport cruises have a significant effect on the increase of freshwater withdrawals, as each homeport cruise ship recharged a mean volume of 628 m³ per mooring. This paper proposes a water withdrawal indicator of liters loaded per passenger at the port. Given the current lack of restriction on the number of cruise ships per day docking in the Port of Palma, cruise activity may well become a threat to water availability during drought episodes and another environmental cost to add to the already questioned cruise tourism activity of the island.

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.

Prescribed burning and pyric herbivory play pivotal roles in mitigating wildfire risks, underscoring the imperative of consistent biomass monitoring for assessing fuel load reductions. Drone-derived surface models promise uninterrupted biomass surveillance but require complex photogrammetric processing. In a Mediterranean mountain shrubland burning experiment, we refined a Structure from Motion (SfM) and Multi-View Stereopsis (MVS) workflow to diminish biases in 3D modeling and RGB drone imagery-based surface reconstructions. Given the multitude of SfM-MVS processing alternatives, stringent quality oversight becomes paramount. We executed the following steps: (i) calculated Root Mean Square Error (RMSE) between Global Navigation Satellite System (GNSS) checkpoints to assess SfM sparse cloud optimization during georeferencing; (ii) evaluated elevation accuracy by comparing the Mean Absolute Error (MAE) of six surface and thirty terrain clouds against GNSS readings and known box dimensions; and (iii) complemented a dense cloud quality assessment with density metrics. Balancing overall accuracy and density, we selected surface and terrain cloud versions for high-resolution (2 cm pixel size) and accurate (DSM, MAE = 57 mm; DTM, MAE = 48 mm) Digital Elevation Model (DEM) generation. These DEMs, along with exceptional height and volume models (height, MAE = 12 mm; volume, MAE = 909.20 cm³) segmented by reference box true surface area, substantially contribute to burn impact assessment and vegetation monitoring in fire management systems.

The availability of high spatial resolution historical remote sensing products and advances in Structure from Motion (SfM), Multi-View Stereopsis (MVS) and LiDAR (Light Detection And Ranging) techniques offer a wide range of applications to understand landscape evolution and to monitor geomorphological changes. In this work, we apply an optimised SfM-MVS workflow based on minimising georeferencing error on black and white and colour historical photographs acquired in 1945 (American flight series A), 1979 (Spanish Interministerial Order), 1991 (Spanish Coastal Directorate General) and 2006 (PNOA flights) to generate 3D point clouds, Digital Elevation Models (DEM) and orthomosaics at 1 m resolution for the beach-dune system and coastal area of Es Trenc (southern Mallorca). In addition, we applied LiDAR techniques on the Airborne Laser Scanning (ALS) point clouds collected by the PNOA LiDAR flights in 2014 and 2019 to generate DEMs. The use of these products in multi-temporal analysis requires quality control of their spatial accuracy due to the diversity of sources and technologies used. The first quality control was based on evaluating the SfM sparse cloud optimisation process in the orthomosaic georeferencing step by calculating the RMSE between the Ground Validation Points (GVP) surveyed with Global Navigation Satellite System (GNSS) readings and the predicted height values at the closest point of each SfM sparse cloud. The second quality control was based on systematically assessing the vertical accuracy of the dense MVS and ALS clouds as a step prior to point interpolation to generate DEMs at 1 m resolution. The height errors of these clouds were estimated by calculating the RMSE between the Ground Test Points (GTP) read by GNSS on the ground and the predicted values at the respective nearest point for each of the MVS and ALS cloud series. Preliminary results show that the optimised SfM-MVS method applied on historical imagery can generate high-resolution orthomosaics and DEMs with acceptable accuracy: RMSE in z ranges from 0.2 to 10 m, with the lower accuracy obtained for the 1945 DEM, due to the lower resolution and coarse grain size (texture) of the photographs used. Overall, these products in combination with current LiDAR-derived DEMs have great potential for monitoring historical landscape evolution in coastal ecosystems.