Remote sensing is widely recognized as a key technology across a wide range of technical and scientific domains, especially in agriculture. Although satellite data have long supported crop monitoring, their limitations in spatial resolution, revisit frequency and cloud coverage have often constrained their applications. High-resolution satellites, available from the beginning of the 2000s, have improved performance, particularly in the field of precision agriculture, but they remain expensive and inflexible. Unmanned Aerial Vehicles perform better in precision agriculture, offering flexibility and high levels of detail; however, their limited operational areas and short endurance flight times constrain their effectiveness. In this evolving landscape, High Altitude Pseudo Satellites (HAPSs), particularly high-altitude balloons, are emerging as a promising new technology that could fill the gaps between satellite and drone remote sensing. These platforms provide large area coverage with high-resolution imagery and long endurance flights at low operational expenses and ease of deployment. This study investigates the operational characteristics, strengths, and geometric limitations of data acquired by the CubeHAPS® platform, a high-altitude pseudo-satellite system, as a prerequisite for its application in precision agriculture. Focusing on experimental campaigns conducted in northern Italy in summer 2024 and 2025, the research characterizes platform stability, image block consistency, and photogrammetric quality through internal metrics. The results demonstrate measurable improvements between the two campaigns, attributed to the introduction of a stabilization system in 2025 and establishing the conditions under which the platform can support reliable photogrammetric reconstruction.
Abstract. UAS photogrammetry has become an efficient solution for acquiring high-resolution geospatial data for urban mapping, environmental monitoring, and 3D modelling. However, mission planning still involves a trade-off between data quality and operational efficiency, particularly regarding flight altitude, which directly affects ground sample distance (GSD), point cloud density, and positional accuracy. This study evaluates the influence of flight altitude through a controlled comparison of two urban photogrammetric surveys: a low-altitude flight at 61.2 m (GSD = 1.56 cm/pix, 420 images) and a higher-altitude flight at 121 m (GSD = 3.11 cm/pix, 116 images). Both surveys used RGB cameras with equivalent image resolution mounted on different platforms, which constitutes an experimental limitation, while overlap and processing parameters were kept constant. The results show that the lower-altitude flight produced denser data and better geometric performance, with lower reprojection error and lower check point RMSE. In contrast, the higher-altitude flight provided greater operational efficiency, covering a larger area with fewer images and lower computational demand. These findings indicate that both strategies are technically viable but suited to different objectives: lower altitudes favour geometric detail and positional accuracy, whereas higher altitudes improve productivity and area coverage. Therefore, flight altitude should be selected according to project requirements, balancing geometric quality and operational efficiency.
Emanuel Amorim, Diogo Inojosa, Jailson Rodrigues Júnior et al.· The International Archives o...· 0 citations
High-resolution spatial data is crucial for riverine modeling and flood mitigation. Traditional data often lacks necessary resolution or flexibility, making Unmanned Aerial Vehicles (UAVs) a transformative solution for generating precise Digital Elevation Models (DEMs). This systematic review analyzes 65 peer-reviewed studies published between 2014 and 2025. Following PRISMA guidelines, studies were selected based on specific inclusion and exclusion criteria focusing on riverine hydraulic applications to evaluate data acquisition methods, spatial accuracy, and operational challenges. The synthesis reveals a standard workflow using multirotor platforms, Structure from Motion (SfM) photogrammetry, and Ground Control Points (GCPs) to feed hydrodynamic models like HEC-RAS. While the literature consistently reports centimeter-level vertical accuracy—ideal for mapping flood inundation—critical challenges persist regarding the optical penetration of dense vegetation and submerged bathymetry. Ultimately, while UAV photogrammetry is a robust spatial analysis tool, advancing high-fidelity riverine analytics requires hybrid approaches integrating technologies like UAV-borne LiDAR and sonar. Creating these seamless topobathymetric models is essential for improving reliable flood risk management and informing effective environmental policy.
Nabil Muhamad, Yassir Arafat, A. Yunar· Journal of Geospatial Scienc...· 1 citation
Abstract. This paper examines how to align PlanetScope and Sentinel-2 vegetation indices, focusing on the Normalized Difference Red Edge (NDRE) index, which is commonly used in precision agriculture for prescription maps. While Sentinel-2 is popular for crop monitoring, its low spatial resolution limits use in small or irregular fields. PlanetScope provides higher-resolution, more frequent imagery, but its sensor differs from the Sentinel-2, limiting compatibility with current research and tools. By testing three adjustment methods, the study shows that it is possible to align PlanetScope NDRE values with Sentinel-2: M1 (Linear Regression + Histogram Shifting + Histogram Matching), M2 (Histogram Matching), and M3 (per-band linear regression before index calculation). Two dates from 2022 were selected as representative seasonal extremes from the broader 2021–2023 dataset of 56 image pairs (Baldin, 2025), which was further analyzed through time-series methods. Resampling direction (PS→10 m, S2→3 m) minimally affects RMSE/MAE but significantly alters spatial structure and Moran’s I values; downscaling PS to 10 m decreases Moran’s I. M2 is suitable for standard applications, whereas M3 is preferable when preservation of spatial structure is important. Across the four examined scenarios, all methods reduce RMSE below the 0.07 agronomic threshold, with calibrated RMSE ranging from 0.02 to 0.05 (up to 0.06 across the full 56-pair dataset). M3’s advantage lies in how effectively it reduces spatial autocorrelation mismatch: a 43.4% reduction in Moran’s I (versus ~18.2% with M1 and M2) in the four example scenarios, and 39.5% versus 28.4% (M1) and 28.2% (M2) reduction over the full dataset.
Christian Massimiliano Baldin, V. Casella· The International Archives o...· 0 citations
Multi-angle measurements provide essential data on the anisotropic reflectance properties of vegetation, enabling more robust retrievals of leaf area index (LAI), clumping index (CI), and canopy gap fraction compared to conventional single-view remote sensing. While Unmanned Aerial Vehicles (UAVs) offer unprecedented centimeter-level spatial resolution and flexible deployment, their application exposes a fundamental scale mismatch between ultra-high-resolution imagery and traditional bidirectional reflectance distribution function (BRDF) models. This review explicitly identifies that conventional 1D radiative transfer models (RTMs), which rely on the assumption of a statistically homogeneous canopy, suffer from severe scale-dependent biases, such as systematically underestimating hotspot reflectance (e.g., observed biases of 25% to 40% in 5-cm resolution UAV studies over specific vegetation canopies), and structural-optical confounding when directly applied to UAV data. At centimeter scales, macroscopic structural heterogeneity disrupts this homogeneity, necessitating the use of 3D RTMs that can explicitly simulate geometric occlusion and complex multiple scattering processes in highly heterogeneous environments. To bridge these theoretical and operational gaps, this review uniquely synthesizes UAV-specific multi-angle methodologies, systematically correlating canopy architectural types with optimal sensor configurations, flight strategies, and BRDF modeling frameworks. By evaluating recent advancements in multimodal data fusion, physics-informed machine learning, and physiological parameter retrieval, this review provides a comprehensive roadmap for decoupling structural and biochemical traits, highlighting how multi-angle directional signatures can substantially elevate classification accuracy, with specific experiments on spectrally similar crops and mixed tree species demonstrating improvements from roughly 40% to over 89%. Ultimately, it establishes practical, decision-oriented guidelines for overcoming transient illumination and co-registration errors, advancing high-fidelity quantitative monitoring and stress detection in complex forest ecosystems.
Rui Wang, Zheng-Jun Wang, Leizhen Liu et al.· Forests· 0 citations
Spaceborne Synthetic Aperture Radar (SAR) is a non-contact remote sensing technology that detects surface deformation by analyzing the phase differences between radar images acquired over the same area at different times. Due to its extensive coverage, high spatial resolution, and all-weather operational capability, spaceborne SAR has become an established technique for large-scale, continuous monitoring of civil infrastructure. Transportation networks constitute a fundamental component of urban infrastructure, playing a pivotal role in enabling efficient mobility and fostering regional economic development. Extreme weather events severely threaten the durability and operational safety of transportation networks. However, limited funding restricts the deployment of traditional sensors for detailed and comprehensive monitoring of the entire transportation network system. In this research, a stack of Sentinel SAR images acquired over a two-years period is collected from the Copernicus Data Space Ecosystem, and subsequently processed with Persistent Scatterer Interferometric Synthetic Aperture Radar (PS-InSAR) technology. A dedicated post-processing procedure, consisting of PS points refinement and clustering analysis, is applied to the displacement time series derived from the PS-InSAR processing. Then, statistical control limits method is employed to evaluate the risk levels across transportation network. Finally, the reliability and effectiveness of the proposed risk assessment framework are validated through a specific bridge case study. These findings demonstrate the potential of the proposed framework for large-scale, risk-informed assessment of transportation networks, thereby contributing to more proactive and data-driven transportation network management strategies.
Yi Xu, You Dong, Yi-qing Ni· e-Journal of Nondestructive...· 0 citations
Abstract. Airborne mapping projects are vital for modern infrastructure and urban planning. The primary goal is to produce and update base maps that serve as essential tools in both public and private sectors to enable a variety of applications. As a result, large airborne surveying and mapping projects are conducted regularly around the world to acquire high-resolution aerial imagery and LiDAR point clouds that can encompass entire countries, states, provinces, counties, or cities. By leveraging advanced technology and navigating the complexities of LiDAR, photogrammetric, and geodetic systems, these projects can significantly contribute to the development and maintenance of accurate base maps, ultimately enhancing decision-making processes across sectors. This paper presents tests of a new fully integrated multi-sensor airborne system that comprises LiDAR, multiple cameras, inertial measuring unit (IMU), GNSS, and their associated software for data acquisition, processing, integration, calibration, and map production. The technical analysis presented in this paper focuses on multi-sensor system integration that statistically addresses a multi-stream of LiDAR ranges, pixels from multiple cameras, position and orientation of each LiDAR range and each photo center derived from the GNSS/IMU trajectory. The study evaluates two trajectory processing approaches: Post-Processed Kinematic (PPK) using a single base station and Trimble Post-Processed RTX (PP-RTX). Real-world datasets collected with the Vexcel Imaging UltraCam Dragon in Austria and the United States were used to assess system performance under operational conditions. Results demonstrate that both Single Base and PP-RTX approaches provide reliable trajectory processing and consistent georeferencing accuracy for both imagery and LiDAR data.
B. Schachinger, M. Mostafa, N. Jaeger et al.· The International Archives o...· 0 citations
