PFG-Journal of Photogrammetry Remote Sensing and Geoinformation Science最新文献

Since the end of 2022, two ground motion services that cover the complete area of Germany are available as web services: the German Ground Motion Service (Bodenbewegungsdienst Deutschland, BBD) provided by the Federal Institute for Geosciences and Natural Resources (BGR), and the first release of the European Ground Motion Service (EGMS) as part of the Copernicus Land Monitoring Service. Both services are based on InSAR displacement estimations generated from Sentinel‑1 data. It would seem relevant to compare the products of the two services against one another, assess the data coverage they provide, and investigate how well they perform compared to other geodetic techniques. For a study commissioned by the surveying authority of the state of Baden-Württemberg (Landesamt für Geoinformation und Landentwicklung Baden-Württemberg, LGL), BBD and EGMS data from different locations in Baden-Württemberg, Saarland, and North Rhine-Westphalia (NRW) were investigated and validated against levelling and GNSS data. We found that both services provide good data quality. BBD shows slightly better calibration precision than EGMS. The coverage provided by EGMS is better than that of BBD on motorways, federal roads, and train tracks of the Deutsche Bahn. As an example, where both services have difficulties in determining the correct displacements, as they cannot be described well by the displacement models used for processing, we present the test case of the cavern field at Epe (NRW). Finally, we discuss the implications of our findings for the use of the products of BBD and EGMS for monitoring tasks.

Terrestrische spektrale Verfahren für verschiedene thematische Anwendungen erleben mit der Verbreitung der digitalen Aufnahmetechnik in vielen Fachbereichen eine Renaissance oder es erschließen sich neue Anwendungsmöglichkeiten. Bei den daraus resultierenden Veröffentlichungen, zumeist in den Fachzeitschriften der Anwender, entsteht teilweise der Eindruck, dass ein neues Anwendungsfeld erschlossen worden sei. Häufig wurden bereits vor Jahrzehnten diese Themen als relevant eingestuft und mit den zu dieser Zeit aktuellen Sensoren und Methoden bearbeitet. Vermutlich ist eine der Ursachen dieser Auffassung, dass diese alten analogen Literaturquellen noch nicht im Internet verfügbar sind. Mit dem vorliegenden Artikel soll versucht werden, eine Übersicht über verschiedene Anwendungen terrestrischer analoger spektraler fotografischer Aufnahmemethoden in Deutschland zu geben. Thematisch orientieren die Beispiele vor allem auf die Bereiche Geologie, Bergbau, Böden und Vegetation.

Vielleicht gibt der Artikel auch die Anregung, das gesamte Inhaltsverzeichnis unserer Fachzeitschriften und anderer Veröffentlichungen digital aufzubereiten und damit einen Beitrag zur Wissenschaftsgeschichte zu leisten.

Short-term monitoring of alpine glaciers is crucial to understand their response to climate change. This paper presents a low-cost multi-camera system tailored for 4D glacier monitoring using deep learning stereo-photogrammetry. Our approach integrates multi-temporal 3D reconstruction from stereo cameras and surface velocity estimation from a monoscopic camera through digital image correlation. To address the challenges posed by wide camera baselines in complex environments, we have integrated state-of-the-art deep learning feature matching algorithms into ICEpy4D, a Python toolkit designed for 4D monitoring (https://github.com/franioli/icepy4d). In a pilot study conducted on the debris-covered Belvedere Glacier (Italian Alps), our stereoscopic setup, with a camera base–height ratio close to one, captured daily images from May to November 2022. Our approach utilized SuperPoint and SuperGlue for feature matching, resulting in a daily 3D reconstruction of the glacier terminus, as traditional SIFT-like feature matching fails in this scenario. Using dense point clouds with decimetric accuracy, we estimated daily ice volume loss and glacier retreat at the terminus. The total ice volume loss was (63,000,text{m})({}^{3}) and the retreat was (17.8,text{m}). Surface kinematics revealed three times higher surface velocity during the warm season (May–September) than in the fall (September–November). Daily analyses revealed a significant short-term correlation between air temperature, glacier surface velocity and ice ablation, providing insight into the glacier’s response to external forces. The low cost and ease of deployment of the proposed system facilitates replication at other sites for short-term monitoring of glacier dynamics.

This study aimed to assess the temporal changes in glacier surface temperature (GST) for the debris-covered Alamkouh glacier (over 88% of the total glacier area is debris covered), located in Iran, over the period from 1985 to 2020. The analysis employed the Landsat surface temperature product at a spatial resolution of 30 m. The research pursued three primary objectives: (1) a spatiotemporal analysis of GST changes, (2) an evaluation of correlations between GST and glacier variables such as ice-thickness change and albedo, and (3) the identification of factors influencing GST, including air temperature, cloud cover, precipitation, and snowfall, utilizing the Global Land Data Assimilation System dataset. Spatial changes were analyzed using the Mann–Kendall trend test and Sen’s slope estimator, revealing statistically significant positive or negative trends in all multitemporal parameters. The spatial change analysis showed that GST increased between 0 and +0.2 °C/a from 1985 to 2020. The mean annual GST increase for the entire glacier is 0.086 °C/a, signifying a 3 °C rise over 36 years. High-altitude regions exhibit more substantial GST increases than lower-altitude areas do, although a discernible pattern across the glacier’s surface remains elusive. To complement the spatial GST analysis, we divided the study period into four periods, 1985–1990, 1990–2000, 2000–2010, and 2010–2020, and mean GST was calculated separately for ablation months. Results indicate stability in mean GST from 1985–1990 to 1990–2000, followed by a significant increase of 2.3 °C/decade from 1990–2000 to 2000–2010, representing the largest increase observed. Temporal change analysis over 36 years reveals that the most significant warming occurs in debris-covered areas (0.139 °C/a), with less warming observed in debris-free regions (0.097 °C/a) during both accumulation and ablation months. The study employed the normalized difference snow index to identify debris-free areas and assess their potential impact on GST. First, the results establish a robust inverse relationship between GST and the extent of debris-free terrain. Second, the analysis demonstrates a significant reduction in debris-free terrain at a rate of −0.035% per month since 1985, culminating in a 15.12% decline over 36 years, encompassing both accumulation and ablation periods. Additionally, outcomes from the albedo analysis reveal a robust negative correlation between albedo and mean GST, with an R² of 0.57. The examination of albedo alterations shows a substantial annual decrease of approximately −0.08/a across the entirety of the glacier terrain, while albedo remains stable in low-elevation areas over the 36-year period, with significant changes occurring in high-elevation debris-free regions. In contrast, a comprehensive examination reveals that a robust association between the glacier ice-thinning rate and GST change cannot be ascertained. Among climate variables, air temperature exhibits s

Persistent Scatterers (PS) are points selected by Persistent Scatterer for Synthetic Aperture Radar Interferometry (PS-InSAR) technology. PS density and quality determine the accuracy of deformation monitoring results. A comprehension of PS and its influencing factors could provide suggestions for data selection and parameter setting in the time series of InSAR, and it can also provide the decision basis for radar satellite engineers to select imaging modes for different application requirements. PS characteristics are mainly affected by SAR image resolution, wavelength and land cover type, etc. However, these influencing factors are coupled together, so it is difficult to study the relationship between the single factor and PS characteristics. Therefore, this paper adopted the Split-Spectrum to TerraSAR datasets to construct a series of simulated SAR datasets with different resolutions while keeping the other imaging parameters the same. We found that the PS density presents a declining linear trend as the bandwidth (resolution) decreases, while the deformation patterns of PS obtained from different bandwidth datasets are consistent. In addition, we proposed a simplified model to estimate the PS density obtained from 1/k bandwidth datasets. Then, we compared the PS results obtained from X-band TerraSAR datasets and C-band Sentinel-1A datasets and analyzed the reason for the difference from the perspective of spatiotemporal decorrelation. Finally, combined with the land cover map and Bayesian estimation, we obtained the distribution probability of PS on land cover types.

Remote sensing approaches using Unmanned Aerial Vehicles (UAVs) have become an established method to monitor agricultural systems. They enable data acquisition with multi- or hyperspectral, RGB, or LiDAR sensors. For non-destructive estimation of crop or sward traits, photogrammetric analysis using Structure from Motion and Multiview Stereopsis (SfM/MVS) has opened a new research field. SfM/MVS analysis enables the monitoring of plant height and plant growth to determine, e.g., biomass. A drawback in the SfM/MVS analysis workflow is that it requires ground control points (GCPs), making it unsuitable for monitoring managed fields which are typically larger than 1 ha. Consequently, accurately georeferenced image data acquisition would be beneficial as it would enable data analysis without GCPs. In the last decade, substantial progress has been achieved in integrating real-time kinematic (RTK) positioning in UAVs, which can potentially provide the desired accuracy in cm range. Therefore, to evaluate the accuracy of crop and sward height analysis, we investigated two SfM/MVS workflows for RTK-tagged UAV data, (I) without and (II) with GCPs. The results clearly indicate that direct RTK-georeferenced UAV data perform well in workflow (I) without using any GCPs (RMSE for Z is 2.8 cm) compared to the effectiveness in workflow (II), which included the GCPs in the SfM/MVS analysis (RMSE for Z is 1.7 cm). Both data sets have the same Ground Sampling Distance (GSD) of 2.46 cm. We conclude that RTK-equipped UAVs enable the monitoring of crop and sward growth greater than 3 cm. At greater plant height differences, the monitoring is significantly more accurate.

Tropical montane forests (TMFs) are highly valuable for their above-ground biomass (AGB) and their potential to sequester carbon, but they remain understudied. Sentinel-1, -2, biophysical data and Machine Learning were used to estimate and map the AGB and above-ground carbon (AGC) stocks in Benguet, Philippines. Non-destructive field AGB measurements were collected from 184 plots, revealing that pine forests had 33.57% less AGB than mossy forests (380.33 Mgha⁻¹), whilst the grassland summit had 39.93 Mgha⁻¹. In contrast to the majority of literature, AGB did not decrease linearly with elevation. NDVI, LAI, fAPAR, fCover and elevation were the most effective predictors of field-derived AGB as determined by Random Forest (RF) feature selection in R. WEKA was used to evaluate and validate 26 Machine Learning algorithms. The results show that the Machine Learning K star (K*) (r = 0.213–0.832; RMSE = 106.682 Mgha⁻¹–224.713 Mgha⁻¹) and RF (r = 0.391–0.822; RMSE = 108.226 Mgha⁻¹–175.642 Mgha⁻¹) exhibited high modelling capabilities to estimate AGB across all predictor categories. Consequently, spatially explicit models were carried out in Whitebox Runner software to map the study site’s AGB, demonstrating RF with the highest predictive performance (r = 0.982; RMSE = 53.980 Mgha⁻¹). The study area’s carbon stock map ranged from 0 to 434.94 Mgha⁻¹, highlighting the significance of forests at higher elevations for forest conservation and carbon sequestration. Carbon-rich mountainous regions of the county can be encouraged for carbon sequestration through REDD + interventions. Longer wavelength radar imagery, species-specific allometric equations and soil fertility should be tested in future carbon studies. The produced carbon maps can help policy makers in decision-planning, and thus contribute to conserve the natural resources of the Benguet Mountains.