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
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Abstract
This paper presents a novel approach to improve the conversion of interferometric synthetic aperture radar (InSAR) ascending and descending orbit measurements into horizontal and vertical deformation components, explicitly considering SAR product characteristics (acquisition geometry, resolution, and positional accuracy). Conventional decomposition methods use square grids, inadequately addressing directional biases associated with satellite images characteristics, reducing measurement accuracy. It is proposed optimized alternative geometries - rectangle, hexagon, and double inverted isosceles trapezoid (diIT) - derived from theoretical analysis of scatterer influence areas for Sentinel-1 imagery and calibrated data from the European ground motion service (EGMS). Validation was conducted comparing results against global navigation satellite system (GNSS) ground-truth data. Accuracy was quantitatively evaluated using deformation velocity (DV) and average Euclidean distance (ED) metrics. Results demonstrated an average 25% improvement in DV detection over traditional square grids, with only minor trade-offs, such as lower scatterer density and sub-millimetric increases in error for hexagon and diIT geometries.

Abstract
Monitoring the impacts of selective logging in tropical forests remains challenging due to the reliance on labor intensive field surveys. This study relies on the use of pre- and post-logging airborne LiDAR data to provide a precise and scalable method for quantifying canopy disturbances, carried out within the Sustainable Management Plan for the Jamari National Forest in Rond & ocirc;nia. The analysis of the airborne LiDAR data revealed a significant increase in canopy gaps after logging (F= 63.5,p <0.001 ), with canopy gaps corresponding to an average increase of 3.9 +/- 0.4% relative to the total plot area due to logging activities. The mean canopy gap area per felled tree was 158.29 m(2) ( +/- 35.7). A strong positive correlation was found between canopy gaps that emerged after logging and the logged AGB (18.4 +/- 1.7Mg ha(-1) ). A significant reduction in mean canopy height was also observed, decreasing from 26.26 +/- 0.40 m before logging to 24.62 +/- 0.33 m after logging (F= 9.86,p= 0.005) . The mean canopy gap area shifted from 40.68 +/- 2.30 m(2) to 77.07 +/- 2.82 m(2). Furthermore, there was an increase of 14.6% in the total number of gaps. The average Gini coefficient was 0.50 +/- 0.02 before logging and 0.64 +/- 0.01 in the post-logging areas and the average total impact on the canopy was 16.6 +/- 1.5% of the selectively logged area. The results obtained using the proposed methodology were consistent with field observations, demonstrating high accuracy of LiDAR-detected impacts when compared with inventory and GNSS data. This high detection rate highlights the sensitivity of LiDAR point cloud data in capturing small structural changes. Compared to pre-logging conditions, the observed alterations demonstrate that LiDAR provides a more precise and scalable approach for quantifying the impact of selective logging on forest structure.

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.