International Journal of Applied Earth Observation and Geoinformation

Journal: International Journal of Applied Earth Observation and Geoinformation

Publisher

Elsevier

ISSN

0303-2434
1872-826X
1569-8432

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Publications 1 - 10 of 19

Evaluating state-of-the-art 3D scanning methods for stem-level biodiversity inventories in forests
Fol, Cyprien R.; Kükenbrink, Daniel; Rehush, Nataliia; et al. (2023)
International Journal of Applied Earth Observation and Geoinformation
Monitoring biodiversity in forests is crucial for their management and preservation, especially in light of increasing climatic disturbances. However, traditional methods of surveying forest biodiversity, such as the inventory of tree-related microhabitats (TreMs), are costly and time-consuming. For many years, terrestrial laser scanning (TLS) was the main method for producing highly accurate 3D models of forests. However, with recent advancements in 3D scanning technologies, there are now numerous alternatives available on the market. The aim of this study was to evaluate the performance of four different 3D data acquisition methods, i.e. close-range photogrammetry (CRP), fish-eye photogrammetry (FEP), mobile laser scanning (MLS), and mixed reality depth camera (MRDC), in terms of accuracy and ability to measure biodiversity (TreMs) at tree-stem level, in comparison to TLS. Analysis was performed based on geometric accuracy and point neighbourhood relevance. CRP was the most accurate alternative to TLS for TreM measurement with a median error of 1.5 cm, while FEP provided a good balance between accuracy (median error 1.4 cm) and speed of data collection. Although MLS showed promising results (median error 1.6 cm), noise in the point cloud limited its ability to identify TreMs. MRDC, on the other hand, had lower quality (median error 3.6 cm) and lower point density, making it unsuitable for TreM segmentation. Nevertheless, the study demonstrated the feasibility of augmenting the real world with virtual content at single-tree-stem level using mixed reality technology. Overall, the 3D scanning technologies presented hold great promise for recording the evolution of biodiversity at stem level.
Assimilating Sentinel-2 data in a modified vegetation photosynthesis and respiration model (VPRM) to improve the simulation of croplands CO2 fluxes in Europe
Bazzi, Hassan; Ciais, Philippe; Abbessi, Ezzeddine; et al. (2024)
International Journal of Applied Earth Observation and Geoinformation
In Europe, the heterogeneous features of crop systems with majority of small to medium sized agricultural holdings, and diversity of crop rotations, require high-resolution information to estimate cropland Net Ecosystem Exchange (NEE) and its two main components of Gross Ecosystem Exchange (GEE) and the Ecosystem Respiration (RECO). In this context, this paper presents an assimilation of high-resolution Sentinel-2 indices with eddy covariance measurements at selected European cropland flux sites in a new modified version of Vegetation Photosynthesis Respiration Model (VPRM). VRPM is a data-driven model simulating CO2 fluxes previously applied using satellite derived vegetation indices from the Moderate Resolution Imaging Spectroradiometer (MODIS). This study proposes a modification of the VPRM by including an explicit soil moisture stress function to the GEE and changing the equation of RECO. It also compares the model results driven by S2 indices instead of MODIS. The parameters of the VPRM model are calibrated using eddy-covariance data. All possible parameters optimization scenarios include the use of the initial version vs. the proposed modified VPRM, S2, or MODIS vegetation indices, and finally the choice of calibrating a single set of parameters against observations from all crop types, a set of parameters per crop type, or one set of parameters per site. Then, we focus the analysis on the improvement of the model with distinct parameters for different crop types vs. parameters optimized without distinction of crop types. Our main findings are: (1) the superiority of S2 vegetation indices over MODIS for cropland CO2 fluxes simulations, leading to a root mean squared error (RMSE) for NEE of less than 3.5 μmolm− 2s− 1 with S2 compared to 5 μmolm− 2s− 1 with MODIS (2) better performances of the modified VPRM version leading to a significant improvement of RECO, and (3) better performances when the parameters are optimized per crop-type instead of for all crop types lumped together, with lower RMSE and Akaike information criterion (AIC), despite a larger number of parameters. Associated with the availability of crop-type land cover maps, the use of S2 data and crop-type modified VPRM parameterization presented in this study, provide a step forward for upscaling cropland carbon fluxes at European scale.
Automated updating of road databases from aerial images
Baltsavias, Emmanuel; Chunsun, Zhang (2005)
International Journal of Applied Earth Observation and Geoinformation
Glacial lake mapping using remote sensing Geo-Foundation Model
Jiang, Di; Li, Shiyi; Hajnsek, Irena; et al. (2025)
International Journal of Applied Earth Observation and Geoinformation
Glacial lakes are vital indicators of climate change, offering insights into glacier dynamics, mass balance, and sea-level rise. However, accurate mapping remains challenging due to the detection of small lakes, shadow interference, and complex terrain conditions. This study introduces the U-ViT model, a novel deep learning framework leveraging the IBM-NASA Prithvi Geo-Foundation Model (GFM) to address these issues. U-ViT employs a U-shaped encoder–decoder architecture featuring enhanced multi-channel data fusion and global-local feature extraction. It integrates an Enhanced Squeeze-Excitation block for flexible fine-tuning across various input dimensions and combines Inverted Bottleneck Blocks to improve local feature representation. The model was trained on two datasets: a Sentinel-1&2 fusion dataset from North Pakistan (NPK) and a Gaofen-3 SAR dataset from West Greenland (WGL). Experimental results highlight the U-ViT model's effectiveness, achieving an F1 score of 0.894 on the NPK dataset, significantly outperforming traditional CNN-based models with scores below 0.8. It excelled in detecting small lakes, segmenting boundaries precisely, and handling cloud-shadowed features compared to public datasets. Notably, the U-ViT demonstrated robust performance with a 50% reduction in training data, underscoring its potential for efficient learning in data-scarce tasks. However, its performance on the WGL dataset did not surpass that of DeepLabV3+, revealing limitations stemming from differences between pre-training and input data modalities. The code supporting this study is available online. This research sets the stage for advancing large-scale glacial lake mapping through the application of GFMs.
Dense 3D displacement estimation for landslide monitoring via fusion of TLS point clouds and embedded RGB images
Wang, Zhaoyi; Butt, Jemil Avers; Huang, Shengyu; et al. (2026)
International Journal of Applied Earth Observation and Geoinformation
Landslide monitoring is essential for understanding geohazards and mitigating associated risks. Existing point cloud-based methods, however, typically rely on either geometric or radiometric information and often yield sparse or non-3D displacement estimates. In this paper, we propose a hierarchical partitioning-based coarse-to-fine approach that fuses 3D point clouds and co-registered RGB images to estimate dense 3D displacement vector fields. Patch-level matches are constructed using both 3D geometry and 2D image features, refined via geometric consistency checks, and followed by rigid transformation estimation per match. Experimental results on two real-world landslide datasets demonstrate that the proposed method produces 3D displacement estimates with high spatial coverage (79% and 97%) and accuracy. Deviations in displacement magnitude with respect to external measurements (total station or GNSS observations) are 0.15 m and 0.25 m on the two datasets, respectively, and only 0.07 m and 0.20 m compared to manually derived references, all below the mean scan resolutions (0.08 m and 0.30 m). Compared with the state-of-the-art method F2S3, the proposed approach improves spatial coverage while maintaining comparable accuracy. The proposed approach offers a practical and adaptable solution for TLS-based landslide monitoring and is extensible to other types of point clouds and monitoring tasks.
Remotely sensed MODIS wetland components for assessing the variability of methane emissions in Indian tropical/subtropical wetlands
Bansal, Sangeeta; Katyal, Deeksha; Saluja, Ridhi; et al. (2018)
International Journal of Applied Earth Observation and Geoinformation
In-season forecasting of within-field grain yield from Sentinel-2 time series data
Amin, Eatidal; Pipia, Luca; Belda, Santiago; et al. (2024)
International Journal of Applied Earth Observation and Geoinformation
Precise knowledge of cropland productivity is relevant for farmers to enable optimizing managing practices; particularly with the perspective of anticipating crop yield ahead of harvest. The current availability of high spatiotemporal resolution Sentinel-2 satellite data offers a unique opportunity to monitor croplands over time. In this context, the recently introduced kernel NDVI (kNDVI) statistically optimizes the conventional NDVI formulation by applying a nonlinear function to the involved bands, and so maximizes the spectral information extraction. This study proposes a workflow for within-field yield forecasting from Sentinel-2 kNDVI time series analysis focusing on winter cereal croplands in Switzerland over three years, comparing with NDVI as baseline. For a temporally continuous modelling of crop yields, Gaussian Process Regression (GPR) was applied to reconstruct cloud-free time series of the complete crop growing seasons. Following, distinct machine learning regression models (GPR, Kernel Ridge Regression and Random Forest) were developed to forecast yield at any point in time throughout the cropland growing season. The integration of Growing Degree Days (GDD) information as temporal spacing reference of the time series considerably improved the accuracy and consistency of in-season yield forecasting. Training and testing within the same year demonstrated that yield can be accurately forecast approximately 2-2.5 months ahead of harvest, at crops' anthesis (flowering) phase, with an RMSE up to 0.71 t/ha and a relative RMSE of 7.60%. Although the forecasting accuracy of the models decreased when predicting yield for the unseen years, still satisfactory results were obtained: RMSE = 0.97 t/ha, relative RMSE = 11.47%.
Recent advances in (soil moisture) triple collocation analysis
Gruber, A.; Su, C. H.; Zwieback, Simon; et al. (2016)
International Journal of Applied Earth Observation and Geoinformation
Automatic extrinsic calibration of terrestrial laser scanner and digital camera by MoG image correlation
Qiao, Jing; Wu, Hangbin; Baumann-Ouyang, Andreas; et al. (2023)
International Journal of Applied Earth Observation and Geoinformation
The current terrestrial laser scanners (TLS) are generally equipped with digital cameras which can capture the scene along with the scanner. These two types of sensors offer complementary properties in modeling and visualization of real-world scenes. TLSs can provide geometric information of the real scene with accurate 3D coordinates of the point clouds; cameras are used to acquire high-resolution images and provide good texture information of the environment. Fusing the extracted information from these two sensors helps to create a better virtual representation of the real-world. For a TLS with several external cameras, their acquisition centers are not identical and the axis of their coordinate systems are not aligned either. This paper proposes an automatic camera and TLS extrinsic calibration approach using correspondences extracted from both measurements. To overcome the intrinsic difference between back-projected images of point clouds colored by intensities and the RGB camera images, we innovatively generate both magnitude of gradient images, enabling effective image correlation and accurate correspondence extraction. The 3 external cameras mounted on top, side and bottom of Leica RTC360 3D laser scanner are calibrated. Dependent on the distribution of observations, we achieve different calibration accuracy for each camera. With scans from multiple stations, the cameras obtain an offset accuracy of 0.12 – 0.36 mm and angular accuracy of 3.7 – 8.3″. After calibration, the excellent overlap of images from the two sensors further verifies the proposed method's success. The idea of correspondence identification demonstrated in this study can also be applied to the extrinsic calibration/registration of other types of scanner and digital cameras.
Synergy of in situ and space borne observation for snow depth mapping in the Swiss Alps
Foppa, Nando; Stoffel, Andreas; Meister, Roland (2007)
International Journal of Applied Earth Observation and Geoinformation

Publications 1 - 10 of 19

Journal: International Journal of Applied Earth Observation and Geoinformation

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