Abstract
Accurate biomass estimation is important for forest management and climate change mitigation. This study evaluates the potential of using LiDAR (Light Detection and Ranging) data, acquired through Unmanned Aerial Vehicles (UAVs), for estimating above-ground and total biomass in Eucalyptus globulus and Pinus pinaster stands in central and northern Portugal. The acquired LiDAR point clouds were processed to extract structural metrics such as canopy height, crown area, canopy density, and volume. A multistep variable selection procedure was applied to reduce collinearity and select the most informative predictors. Multiple linear regression (MLR) models were developed and validated using field inventory data. Random Forest (RF) models were also tested for E. globulus, enabling a comparative evaluation between parametric and machine learning regression models. The results show that the 25th height percentile, canopy cover density at two meters, and height variance demonstrated an accurate biomass estimation for E. globulus, with coefficients of determination (R2) varying between 0.86 for MLR and 0.90 for RF. Although RF demonstrated a similar predictive performance, MLR presented advantages in terms of interpretability and computational efficiency. For P. pinaster, only MLR was applied due to the limited number of field data, yet R2 exceeded 0.80. Although absolute errors were higher for Pinus pinaster due to greater biomass variability, relative performance remained consistent across species. The results demonstrate the feasibility and efficiency of UAV LiDAR point cloud data for stand-level biomass estimation, providing simple and effective models for biomass estimation in these two species.

Abstract
Soil degradation is a critical challenge to global agricultural sustainability, driven by intensive land use, unsustainable farming practices, and climate change. Conventional soil monitoring techniques often rely on invasive sampling methods, which can be labor-intensive, disruptive, and limited in spatial coverage. In contrast, non-invasive geophysical techniques, particularly ground-penetrating radar, have gained attention as tools for assessing soil properties. However, an assessment of ground-penetrating radar's applications in agricultural soil research-particularly for detecting soil structural changes related to degradation-remains undetermined. To address this issue, a systematic literature review was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses 2020 guidelines. A search was conducted across Scopus and Web of Science databases, as well as relevant review articles and study reference lists, up to 31 December 2024. This process resulted in 86 potentially relevant studies, of which 24 met the eligibility criteria and were included in the final review. The analysis revealed that the ground-penetrating radar allows the detection of structural changes associated with tillage practices and heavy machinery traffic in agricultural lands, namely topsoil disintegration and soil compaction, both of which are important indicators of soil degradation. These variations are reflected in changes in electrical permittivity and reflectivity, particularly above the tillage horizon. These shifts are associated with lower soil water content, increased soil homogeneity, and heightened wave reflectivity at the upper boundary of compacted soil. The latter is linked to density contrasts and waterlogging above this layer. Additionally, ground-penetrating radar has demonstrated its potential in mapping alterations in electrical permittivity related to preferential water flow pathways, detecting shifts in soil organic carbon distribution, identifying disruptions in root systems due to tillage, and assessing soil conditions potentially affected by excessive fertilization in iron oxide-rich soils. Future research should focus on refining methodologies to improve the ground-penetrating radar's ability to quantify soil degradation processes with greater accuracy. In particular, there is a need for standardized experimental protocols to evaluate the effects of monocultures on soil fertility, assess the impact of excessive fertilization effects on soil acidity, and integrate ground-penetrating radar with complementary geophysical and remote sensing techniques for a holistic approach to soil health monitoring.

Abstract
Monitoring vineyard diseases such as downy mildew (Plasmopara viticola) is important for viticulture, enabling an early intervention and optimized disease management. This is crucial for disease monitoring, and the use of high-spatial-resolution multispectral data from unmanned aerial vehicles (UAVs) can allow to for a better understanding of disease progression. This study explores the application of UAV-based multispectral data for monitoring downy mildew infection in vineyards through multi-temporal analysis. This study was conducted in a vineyard plot in the Vinho Verde region (Portugal), where 84 grapevines were monitored, half of which received phytosanitary treatments while the other half were left untreated in this way during the growing season. Seven UAV flights were performed across different phenological stages to assess the effects of infection using spectral bands, vegetation indices, and morphometric parameters. The results indicate that downy mildew affects canopy area, height, and volume, restricting the vegetative growth. Spectral analysis reveals that infected grapevines show increased reflectance in the visible and red-edge bands and a progressive decline in near-infrared (NIR) reflectance. Several vegetation indices demonstrated a suitable response to the infection, with some of them being capable of detecting early-stage symptoms, while vegetation indices using red edge and NIR allowed us to track disease progression. These results highlight the potential of UAV-based multi-temporal remote sensing as a tool for vineyard disease monitoring, supporting precision viticulture and the assessment of phytosanitary treatment effectiveness.

Abstract
Reduced-impact logging (RIL) has been recognized as a promising strategy for biodiversity conservation and carbon sequestration within sustainable forest management (SFM) areas. However, monitoring the forest understory-a critical area for assessing logging impacts-remains challenging due to limitations in conventional methods such as field inventories and global navigation satellite system (GNSS) surveys, which are time-consuming, costly, and often lack accuracy in complex environments. Additionally, aerial and satellite imagery frequently underestimate the full extent of disturbances as the forest canopy obscures understory impacts. This study examines the effectiveness of the relative density model (RDM), derived from airborne LiDAR data, for mapping and monitoring understory disturbances. A field-based validation of LiDAR-derived RDM was conducted across 25 sites, totaling 5504.5 hectares within the Jamari National Forest, Rond & ocirc;nia, Brazil. The results indicate that the RDM accurately delineates disturbances caused by logging infrastructure, with over 90% agreement with GNSS field data. However, the model showed the greatest discrepancy for skid trails, which, despite their lower accuracy in modeling, accounted for the largest proportion of the total impacted area among infrastructure. The findings include the mapping of 35.1 km of primary roads, 117.4 km of secondary roads, 595.6 km of skid trails, and 323 log landings, with skid trails comprising the largest proportion of area occupied by logging infrastructure. It is recommended that airborne LiDAR assessments be conducted up to two years post-logging, as impacts become less detectable over time. This study highlights LiDAR data as a reliable alternative to traditional monitoring approaches, with the ability to detect understory impacts more comprehensively for monitoring selective logging in SFM areas of the Amazon, providing a valuable tool for both conservation and climate mitigation efforts.

Abstract
Highlights What are the main findings? UAV multispectral data combined with machine learning enabled the estimation of grapevine biophysical parameters, including LAI, pruning wood biomass, and yield. Geometric features, such as canopy area and volume, improved model accuracy and reduced the number of predictors, while the integration of spectral and geometric data improved prediction robustness across different phenological stages. What is the implication of the main finding? UAV-based monitoring can be applied in different grapevine varieties without cultivar-specific calibration, providing a non-invasive tool for vineyard assessment. Identifying the most informative features and suitable acquisition periods supports more accurate decisions in vineyard management, including pruning, canopy control, and yield estimation.Highlights What are the main findings? UAV multispectral data combined with machine learning enabled the estimation of grapevine biophysical parameters, including LAI, pruning wood biomass, and yield. Geometric features, such as canopy area and volume, improved model accuracy and reduced the number of predictors, while the integration of spectral and geometric data improved prediction robustness across different phenological stages. What is the implication of the main finding? UAV-based monitoring can be applied in different grapevine varieties without cultivar-specific calibration, providing a non-invasive tool for vineyard assessment. Identifying the most informative features and suitable acquisition periods supports more accurate decisions in vineyard management, including pruning, canopy control, and yield estimation.Abstract The accurate estimation of grapevine biophysical parameters is important for decision support in precision viticulture. This study addresses the use of unmanned aerial vehicle (UAV) multispectral data and machine learning (ML) techniques to estimate leaf area index (LAI), pruning wood biomass, and yield, across mixed-variety vineyards in the Douro Region of Portugal. Data were collected at three phenological stages, from veraison to maturation and two modeling approaches were tested: one using only spectral features, and another combining spectral and geometric features derived from photogrammetric elevation data. Multiple linear regression (MLR) and five ML algorithms were applied, with feature selection performed using both forward and backward selection procedures. Logarithmic transformations were used to mitigate data skewness. Overall, ML algorithms provided better predictive performance than MLR, particularly when geometric features were included. At harvest-ready, Random Forest achieved the highest accuracy for LAI (R2 = 0.83) and yield (R2 = 0.75), while MLR produced the most accurate estimates for pruning wood biomass (R2 = 0.83). Among geometric variables, canopy area was the most informative. For spectral data, the Modified Soil-Adjusted Vegetation Index (MSAVI) and the Soil-Adjusted Vegetation Index (SAVI) were the most relevant. The models performed well across grapevine varieties, indicating that UAV-based monitoring can serve as a practical, non-invasive, and scalable approach for vineyard management in heterogeneous vineyards.

Abstract
Continuous Integration and Continuous Deployment are known methodologies for software development that increase the overall quality of the development process. Several robotic software repositories make use of CI/CD tools as an aid to development. However, very few CI pipelines take advantage of using cloud computing to run simulations. Here, a CI pipeline is proposed that takes advantage of such features, applied to the development of ATOM, a ROS-based application capable of carrying out the calibration of generalized robotic systems. The proposed pipeline uses GitHub Actions as a CI/CD engine, AWS RoboMaker as a service for running simulations on the cloud and Rigel as a tool to both containerize ATOM and execute the tests. In addition, a static analysis and unit testing component is implemented with the use of Codacy. The creation of the pipeline was successful, and it was concluded that it constitutes a valuable tool for the development of ATOM and a blueprint for the creation of similar pipelines for other robotic systems.