The causal drivers of short-term changes (days to months) in human-, wind-, and wave-driven sand transport on a sandy beach are not often considered in an integral and data-driven approach. However, improving current knowledge on (urban) sandy beach topographical change requires the incorporation of multi-scale, cross-sectional and human factors. In this research we process a time series of 21,194 hourly point clouds, obtained in a Permanent Terrestrial Laser Scanning setup. From this 3D time series we extract 5,102 short-term temporary surface dynamics, through a method called 4D objects-by-change (4D-OBCs). The causal drivers of two of these 4D-OBCs are investigated in detail. One is interpreted as an aeolian depositional surface dynamic (1), and one as a bulldozer deposit, that consecutively eroded under high wave energy conditions (2). The dynamics show clear correlation to a particular combination of wind direction and intensity (1), and wave height and wave period (2), indicating that point cloud time series derived 4D-OBCs are useful data to study causality of short-term surface dynamics of different origins. However, to study these surface dynamics systematically and derive statistical proof of causal relations we must consider multivariate correlations, as well as spatiotemporal dependence between sediment dynamics and larger scale morphological changes on the beach.

Point cloud fusion is crucial in geospatial analysis, combining data from multiple sources (e.g, LiDAR and photogrammetry) to provide a more complete and accurate environmental representation. However, integrating airborne hybrid sensors or cross-source point clouds remains challenging due to variations in geometric accuracy, data precision, gaps, and sensor attributes. Despite recent advancements, these challenges remain and are among the most demanding aspects in geospatial data processing for remote sensing applications. We propose a new point cloud fusion algorithm that leverages local plane constraints to achieve advanced semantic consistency. The proposed method dynamically fits local planes to the target point clouds, enabling robust alignment of source points to these planes. Evaluation on two real-world datasets demonstrates significant gains in accuracy and preservation of geometric details. Our algorithm also improves the accuracy of downstream tasks such as semantic segmentation. In our experiment, the overall accuracy for the Dudelange dataset increases from 48.5% to 80.1%, and that for the Dublin dataset increases from 72.9% to 88.0%. While challenges persist with sparse and noisy datasets, experimental results highlight the effectiveness of the proposed method, offering valuable insights for maximizing the potential of cross-source point cloud data.

Botanical trees demonstrate complicated motion patterns under wind forcing. Studying these motion patterns is relevant as tree parts may become hazardous during storms, and the movement of these trees affects the surrounding wind field, providing, for example, wind shadows. Eventually, wind enforced tree motion may even be incorporated in wind simulation models used to design liveable neighbourhoods. These motion patterns are, however, difficult to assess as in situ sensors lack coverage while video imagery lacks full 3D capacity. Recently, fast 3D laser scanners became available. For one such scanner, the Livox Avia, a workflow is presented that allows scanning tree movement at frequencies of up to 2 Hz. The scanner, possibly triggered to scan by a wind-meter, is building up a space-time array, consisting of range measurements at a give time, in a given direction. In a consecutive step, this space-time array is decomposed into single point clouds of a certain duration. The decomposition can be done over different time intervals; point clouds of shorter duration show lower coverage of measurements, but are also less affected by moving tree parts during scanning. After decomposition, motion vector fields between consecutive point clouds representing different time frames are estimated using the existing PlantMove algorithm. Applications of the workflow on different moving tree scenarios demonstrate how this novel measurement strategy is able to reveal complex and detailed spatial and temporal patterns of tree movement, with observed displacements on the order of decimeters, under wind stress. Extending the setup to e.g. three scanners will enable to capture wind induced complex tree movement at branch level, which is essential input for running reliable wind flow simulations at neighbourhood level.

Rapid urbanization challenges urban micro-climates, strains resources and affects public health. Understanding micro-climate dynamics is key to effective mitigation and sustainable development. Local Climate Zone (LCZ) classification supports climate-resilient planning but is complicated by the diversity and complexity of diverse urban landscapes and the coexistence of varying land uses and materials within small areas. While LCZ classification typically uses multispectral imagery, LiDAR, and land-use data, these sources often miss temporal thermal dynamic patterns. Thermal satellite imagery improves LCZ classification by distinguishing zones with similar structures but differing thermal behavior. This research proposes using deep learning-based multitemporal semantic segmentation to classify urban LCZs based solely on temporal thermal patterns from ECOSTRESS satellite imagery. The methodology is applied in a in a case study around the near coastal cities of Rotterdam and The Hague in The Netherlands and demonstrates how spatial and temporal factors (both diurnal and seasonal) influence the performance of the semantic segmentation model on different LCZ classes. The study shows that a U-Net architecture applied on spatio-temporal thermal imagery effectively classifies urban LCZs, achieving a test accuracy of 0.75. Temporal factors significantly impact model performance, with higher accuracies observed for daytime (0.8) and Spring/Summer imagery (0.78), as these conditions provide clearer thermal separability for distinguishing LCZs. The model achieved its highest test accuracy (0.83) when trained and tested on thermal images with the highest LST values. This suggests that focusing on high-value LST images with sufficient variability enhances classification performance compared to a generalized approach using the full dataset.

Dynamic beaches world-wide are monitored by so-called Argus cameras. Their automatic capturing results in large databases of ∼ 30' interval coastal images acquired during different illumination conditions. We present a lightweight method to automatically extract sand and supporting classes from ∼ 1 million Argus images, spanning ∼ 10 years, of the Sand Engine, The Netherlands, a nature-based solution for beach erosion. The workflow consists a series of neural networks. First, a ResNet18 model selects images of sufficient quality. Second, 24 pixel-wise shallow multi-layered perceptrons (MLPs) classify pixels into 5 classes, Water, Foam and Eolian, Wet and Armored Sand. The 24 MLPs correspond to 12 cameras with two different lighting conditions. The results from the 24 MLPs are used as pseudo labels for 24 CNNs that improve the initial classification by including spatial awareness. These 24 CNNs are fused in one ensemble CNN, robust to camera choice and lighting condition. At this stage, still a separate CNN is used to additionally detect vegetation pixels. Results show in most cases good agreement with human interpretation, with an overall accuracy of ∼ 88%. Most promising is that ∼ 150.000 images per day can be processed on a high-end consumer PC at a quality difficult to obtain by a human operator. Future work should focus on exploiting the large database of results for improving our understanding of dynamic processes at this challenging environment. As the workflow is generic in nature, it should be easily applicable for other image based monitoring databases.

Dutch beaches are increasingly urbanized with both permanent beach pavilions and seasonal sheds and holiday houses. The effect of these buildings on long term dune development between 1999 and 2024 is studied in this paper along ~ 100 km of coast on the outer delta in the south western part of the Netherlands. A total of ~ 7000 beach buildings have been manually identified in this period based on satellite images and the time line function of Google earth desktop. The effect of the buildings is determined and analyzed at 477 cross-shore profiles with dune volumes and properties like dune toe, top and heel based on airborne lidar datasets of 1999 and 2024. On natural beaches the dune toe position is derived from profile information, whereas on urbanized beaches near buildings the dune toe is based on the location of the buildings. Yearly volume changes at the profile locations vary between -10 m³/m/y and up to 40 m³/m/y. The results indicate that smaller and standalone buildings allow for larger variations in dune volume changes and suggest that larger buildings and connected buildings impede natural dune dynamics which could impact coastal resilience in the long run.

Floods are among the most severe consequences of climate change, causing significant damage across several sectors, including agriculture. Nevertheless, the assessment of agricultural flood damage remains limited, particularly in agriculturally intensive regions where timely support is crucial. This work proposes a data-driven approach for assessing crop flood damage through a machine learning classification framework applied to features derived from Earth Observation (EO) data, trained and tested on field-level damage data collected by agronomists. Specifically, we applied a Random Forest model to classify fields into three damage classes by integrating Sentinel-2–derived indices, topographic information, and flood extent maps. The analysis focused on the flood event that struck the Emilia-Romagna region (Italy) in May 2023, one of the costliest floods globally that year. The model was trained and tested on 412 fields, achieving an overall accuracy of 0.74, with precision, recall, and F1 score of 0.75, 0.74, and 0.74, each with a standard deviation of 0.04, indicating stable model performance. The model accurately identified high-damage fields, which were characterized by greater flood exposure, lower elevations, and pronounced declines in vegetation indices. However, it struggled to distinguish between no-damage and medium-damage fields, particularly for permanent crops, where damage often occurs beneath the canopy and flooded areas may be partially occluded. The main novelty of this work lies in the use of in situ crop damage assessments, enabling a data-driven estimation of flood impacts. These results have direct implications for policymakers: the framework relies on free EO data, providing a tool that can support post-event compensation and decision-making in flood-prone regions.

Accurately representing forest canopies in atmospheric models remains challenging because trees interact with airflow in complex ways and strongly modulate surface-atmosphere exchanges. Aerodynamic roughness is therefore a key control variable in models of air quality, meteorology, and atmospheric transport. In this study, we test a physically based, spatially resolved framework for estimating aerodynamic roughness length from remote sensing observations. Using AHN (Actueel Hoogtebestand Nederland) airborne laser scanning data over a coniferous forest in Loobos, within the Veluwe Natura 2000 region in the central Netherlands, we derive geometric roughness parameters and compare them with eddy-covariance (EC) tower measurements. To further evaluate the approach, the LiDAR-derived roughness field is aggregated within sector-specific tower footprint climatologies and compared with tower-derived roughness estimates across 12 wind-direction sectors. Results show that LiDAR-based roughness captures strong directional and structural variability driven by forest stand height and canopy heterogeneity, closely aligning with the anisotropy observed in EC-derived displacement height and roughness length. The sector-wise comparison reproduces the main directional variability of tower-based aerodynamic roughness, although the LiDAR-derived values generally underestimate its magnitude, consistent with the distinction between structural and effective aerodynamic roughness. Seasonal differences between leaf-on and leaf-off conditions further highlight the role of canopy phenology in aerodynamic behaviour. The spatial patterns resolved by AHN demonstrate the potential of high-resolution laser scanning to capture fine-scale canopy-atmosphere interactions missed by traditional land-use-based roughness representations. This framework offers an observation-driven pathway for improving surface roughness parameterization in wind-flow and chemical transport models such as LOTOS-EUROS.

Fitting a smooth curve to 2D, a surface to 3D, and a manifold to 4D irregular point cloud data is becoming a common practice in many engineering and science applications. Piecewise-polynomial spline functions provide a powerful tool applicable to interpolation and approximation problems. This study presents the least squares B-spline approximation (LSBSA) theory, which is a generalized version of the spline interpolation and can be applied to any irregularly scattered point cloud data at knots specified by the user. The formulation allows to apply the well-established body of knowledge of least squares theory to the B-spline approximation. This for example has the benefit of embedding quality control measures such as hypothesis testing and proper error propagation to assess the quality of the approximation problem. The method is applicable to many 1D curve, 2D surface and 3D manifold fitting problems of which both simulated and real data are used to illustrate the efficacy of the proposed theory. In particular, its real-world applications to multi-beam echo-sounder bathymetric data, digital terrain modeling and Greenland ice sheet deformation monitoring will be highlighted. The performance of the method for linear, quadratic, cubic and quartic spline functions will be investigated. The primary application of LSBSA lies in its ability to perform 3D manifold fitting for deformation monitoring. This capability provides the possibility of monitoring changes in continuous spatial and temporal domains. The Python and Matlab source codes of LSBSA are freely accessible at https://github.com/tudelft4d/lsbsa.

Satellite-derived bathymetry (SDB) provides a cost-effective solution for coastal mapping, but challenges remain in model interpretability and uncertainty quantification. This study investigates the applicability of the least-squares-based deep learning (LSBDL) framework for SDB, leveraging its hybrid structure that integrates neural networks with the available least-squares theory to enhance model transparency. ICESat-2 photon-counting LiDAR was used to train depth estimation from Sentinel-2 multispectral imagery over an approximately 30 km × 30 km region of near-coastal bathymetry at Anegada, British Virgin Islands. ICESat-2 provided high-precision depth information, of which 80% were used for training and the remainder for validation. LBSDL depth estimation achieved a root-mean-square error (RMSE) of 2.74 m, representing around 10% of the maximum observed depth, with the best performance in the 2–15 m depth range. These findings demonstrate the potential of LSBDL for interpretable and reliable bathymetric mapping, highlighting ICESat-2 as a globally accessible training and validation source and advancing SDB capabilities for data-sparse coastal regions.

Historical aerial imagery provides valuable data from regions and periods with limited geospatial information. A common method to utilize this data is through the generation of ortho-photos and 3D models using Structure-from-Motion (SfM) techniques. However, many of these images were scanned decades after their acquisition and require geometric calibration, along with internal and external camera parameter estimation, for accurate reconstruction. Manual identification of key features, such as fiducial marks and text annotations, is labour-intensive, while existing automated methods struggle with poor-quality datasets. This paper presents an automated workflow that combines computer vision and machine learning techniques to detect and extract these key features from historical aerial images. To address challenges related to image quality, we also introduce estimation protocols that compensate for missing or unreliable detections by leveraging redundancy across multiple flight paths. The methodology was evaluated on the TMA (Trimetrogon Aerial) archive, a collection of historical images from the Antarctic Peninsula. Our test dataset comprised over 7000 images from 20 different flight paths. The workflow demonstrated high success rates in detecting and extracting fiducial marks, image subsets, and textual annotations. Approximately 70% of the images provided usable focal length data, while fiducial mark detection exhibited high accuracy except in cases of severe scanning artifacts. Altitude data extraction proved to be the most challenging, with successful results in only 15% of images due to degraded altimeter readings. Despite these limitations, the automated workflow effectively estimated missing parameters, ensuring robust image reconstruction across flight paths. The code for this workflow is open-source and publicly available on GitHub at https://github.com/fdahle/hist_meta_extraction.

Operational forest fire danger rating systems uses meteorological variables to estimate vegetation conditions and predict fire occurrence and spread. This study introduces a novel approach to relate live fuel conditions retrieved from MODIS optical and thermal bands with fire behaviour and the probability of extreme events. The analysis focusses on land surface temperature (LST) anomaly and on the perpendicular moisture index (PMI) to evaluate fire characteristics like burned area, duration, and rate of spread. Results show that PMI is a strong covariate of burned area and rate of spread but not fire duration, while LST anomaly is a strong covariate of burned area and fire duration, and a weak covariate of rate of spread. Comparing these findings with the Canadian forest fire weather index (FWI) system components reveals that LST anomaly and PMI are effective predictors of fire characteristics, potentially enhancing fire danger models and preparedness strategies.

Many topographic scenes exhibit complex dynamic behavior that is difficult to map, quantify, predict and understand. A terrestrial laser scanner fixed on a permanent position can be used to monitor such scenes in an automated way with centimeter to decimeter quality at ranges of up to several kilometers. Laser scanners are active sensors, and are therefore able to continue operation during night. Their independence from texture conditions ensures that in principle they provide stable range measurements for varying surface conditions. Recent years have seen a strong increase in the employment of such systems for different scientific applications in geosciences, environmental and ecological sciences, including forestry, glaciology, and geomorphology. At the same time, this employment resulted in a new type of 4D topographic data sets (3D point clouds + time) with a significant temporal dimension, as systems are now able to acquire thousands of consecutive epochs in a row. Extracting information from these 4D data sets turns out to be challenging, first, because of insufficient knowledge on error budget and correlations, and, second, because of lack of algorithms, benchmarks, and best-practice workflows. This paper provides an overview of different 4D systems for near-continuous laser scanning, and discusses systematic challenges including instability of the sensor system, meteorological and atmospheric influences, and data alignment, before discussing recently developed methods and scientific software for extracting and parameterizing changes from 4D topographic data sets, in connection to the different applications.