Applied Geomatics最新文献

Satellite radar interferometry (InSAR) has become an invaluable tool for monitoring dams and large ponds, providing significant advantages when complemented with geotechnical and geodetic monitoring. InSAR uses radar signals from satellites to detect ground movements with millimeter precision by comparing phase differences between images taken at different times. This technique enables large-scale, continuous monitoring, which is critical for identifying potential structural problems and preventing catastrophic failures. Unlike traditional geotechnical and geodetic monitoring, which require extensive equipment and manual data collection, InSAR provides a non-intrusive, efficient solution that covers vast areas with high temporal frequency. The European Ground Motion Service (EGMS) exemplifies these advantages by providing standardized ground motion data across Europe, derived from Sentinel-1 satellite radar data. EGMS enables routine and comprehensive monitoring of ground stability and infrastructure integrity, assisting in the early detection of deformation patterns and supporting proactive maintenance and risk management. For dam managers, the integration of InSAR with traditional methods enhances the reliability of structural health assessments. Geotechnical sensors offer localized information on soil and material properties, while geodetic methods provide precise positional data; InSAR complements these by delivering comprehensive, continuous deformation maps. This synergy ensures robust monitoring and enhances the ability to predict and mitigate potential problems, significantly improving the effectiveness and efficiency of monitoring dams and large ponds, and contributing to safer and more resilient infrastructure management. This work presents several case studies from the SIAGUA project as examples, highlighting the practical applications and benefits of combining InSAR with traditional monitoring techniques.

The monitoring of dams is essential to ensure their safe operation for the production of renewable energy. Common tools to monitor dams are permanently installed plumblines and surveying by means of total station and leveling within a geodetic network. The main drawback of these methods is their limited spatial and temporal resolution. Recent studies have shown promising results using Ground-Based InSAR for geodetic dam monitoring. The fast acquisition speed combined with the surface monitoring capabilities enable to monitor several hundreds to thousands of points on the dam every day or several times a day. However, GB-SAR is a relative phase-measurement technique, and any interruption in the data acquisition leads to difficulties to unwrap differential phase observations and join the disjunct time series. The combination with other absolute measurement tools is promising to create an absolute deformation map of the dam. GNSS is a very efficient and reliable method providing point-wise absolute displacement time series and mm-accuracy. This paper proposes a combination of GNSS and GB-SAR observations to enhance the consistency of the surface-based dam displacement maps obtained by solely GB-SAR measurements. A method to detect unwrapping errors over long time series is proposed. The corrected GB-SAR time displacement maps are compared to a numerical model and confirm the correctness of the applied corrections.

Accurately detecting significant changes in the Earth’s surface is essential for timely intervention. As a key techniques in Interferometric Synthetic Aperture Radar (InSAR), Persistent Scatterer Interferometry (PSI) generates time series data of Persistent Scatterers (PS), which are stable points on the Earth’s surface that enable precise displacement measurements over time. While many studies have focused on statistical methods for identifying anomalies in PS time series, few have explored the potential of deep learning for change point (CP) detection. A major challenge with supervised deep learning is the need for large labeled datasets. To overcome this, we implemented a simulation algorithm to generate an extensive set of PS points with corresponding target CPs, reflecting the statistical characteristics of PS time series. To identify changes in slope and intercept, We used two deep learning models: Bidirectional Long Short-Term Memory (BiLSTM), designed for time series data, and U-Net, developed for image data. A spectral analysis technique is applied to remove seasonal components from the time series data before feeding into the networks. The models were evaluated using metrics such as F1-score, precision, and recall, and were compared to a Bayesian-based approach. Additionally, the methodology was applied to real PS time series from a study area in Germany. We analyzed the detected CPs along with the neighboring PS time series within a 15-meter radius. The results indicated that the deep learning models outperformed the Bayesian approach in terms of precision, recall, and F1-score with simulated PS time series, highlighting their potential for precise CP detection. Furthermore, the models demonstrated their effectiveness when applied to the real PS time series.

This paper is dedicated to an automated detection of geomorphological changes in photogrammetric 4D point clouds, which are acquired using low-cost wildlife cameras at a subarctic riverbank. In these regions, a better understanding of complex erosion processes is required for modelling sediment dynamics and to understand climate change effects. Therefore, a spatiotemporally detailed dataset was collected with two-hourly images from four cameras over six months (approx. 900 epochs). Changes are extracted as 4D objects-by-change (4D-OBCs), a method of spatiotemporal segmentation that considers time series information which was originally developed for permanent terrestrial laser scanning data. This contribution investigates the transferability of the 4D-OBC method to noisy photogrammetric point clouds in terms of detection reliability and quantification accuracy. Focus is on the detection methods for linear changes in time series. An extension of the method is developed for fusing 4D-OBCs in a second step, as the fully automatic extraction often leads to oversegmentation. This object fusion is based on spatial and temporal overlap of individual objects. For quantitative evaluation, reference objects are extracted manually. Further validation is performed visually using the original time-lapse photos. The analysis results in a total of 946 4D-OBCs extracted as erosion or accumulation events. The object fusion results in a significantly higher agreement with the reference objects (volume ratio between 4D-OBCs and references of 0.26 before and 0.85 after fusion). By this, our research increases the applicability of an automatic time series-based change analysis method to low-cost photogrammetric data and to new change types of riverbank erosion. The use case further contributes to the interpretation of riverbank processes in subarctic regions enabled by time-lapse photogrammetry.

Green spaces improve societal well-being, foster connectivity to nature, and attenuate climate change. Despite Rwanda and other developing countries increasingly pursuing green economies, urban greening efforts still need multi-conceptual models that comprehensively address socio-economic and environmental requirements. This study employs a GIS-based Multi-Criteria Analysis (MCA) constructed on an Analytical Hierarchy Process (AHP) to predict green space intervention suitability across Kigali City, Rwanda. The study was based on nine factors namely: population density, slope, land cover types, proximity to roads, Normalised Difference Vegetation Index (NDVI), proximity to existing green spaces, proximity to water bodies, nitrogen dioxide concentrations, and elevation, to be used as criteria for the MCA. The findings indicate that 2.49% (1,816.19 ha) of Kigali City is highly suitable while 12% (8,744.68 ha) is unsuitable for green space interventions. Population density emerged as the most influential factor, with the city’s densely populated west-central areas exhibiting high suitability for green space initiatives. Strategically placing green spaces near population centres enhances their contribution to societal well-being, reduces transport costs, and encourages frequent use. By integrating GIS-based MCA with AHP, this study offers a robust framework for addressing green space accessibility challenges in Kigali, while simultaneously advancing climate-resilient urban development. We recommend planners prioritise Kigali City’s west-central areas for green space interventions, researchers leverage the GIS-MCA-AHP methodology for climate-resilient urban studies, and practitioners replicate this framework to advance socio-economically inclusive greening strategies.

Riverbank erosion is one of the most frequent natural hazards worldwide. Bangladesh is highly affected by this natural hazard every year. The lower segment of the Meghna River is highly vulnerable to this phenomenon. While previous studies have primarily focused on socio-economic impacts in study area or erosion-accretion detection in other major rivers, this study aimed to investigate the spatiotemporal dynamics of riverbank erosion, bank line shifting, and morphological changes in the Meghna River at Haimchar Upazila, Chandpur. Additionally, the study explored the factors driving erosion and potential mitigation strategies. A combination of primary and secondary data was used, including field surveys and satellite image analysis. Normalized Difference Water Index (NDWI) and unsupervised classification techniques were employed to analyze Landsat images from 1980, 1988, 2000, 2010, and 2021. Morphometric parameters such as river width, sinuosity index, and braided index were quantified to assess morphological changes using cross-sections and equations. Results indicate that the highest erosion (4219 ha) occurred between 1988 and 2000, while the lowest (2218 ha) was recorded from 2010 to 2021. Accretion peaked (4215 ha) between 2000 and 2010 and declined thereafter. Over the 42-year study period, the average annual rates of erosion and accretion were 85 ha/yr and 87.8 ha/yr, respectively. Variations in morphological parameters reflect dynamic channel changes, including the formation of bars and islands. Field surveys identified key erosion drivers and highlighted mitigation strategies relevant to the region. The findings underscore the need for integrated river management and adaptive planning to mitigate the adverse effects of riverbank erosion on local livelihoods. Incorporating social factors into future erosion management frameworks could enhance the effectiveness of mitigation measures. This study provides a foundation for developing targeted interventions and sustainable river management practices.

This research presents a multimodal and meta-learning approach that integrates multi-source satellite sensor and field plot-level data for enhanced retrieval of 3D vegetation structure. Specifically, the combined effect of integrating multispectral data from Landsat 8 OLI and Sentinel-2 MSI with radar data from Sentinel-1 CSAR was examined. For the utilization of multi-source inputs, the synergistic integration was implemented using efficient machine learning regressors—Random Forest Regressor (RFR) and Extreme Gradient Boosting Regressor (GBR)—ensembled within a meta-learning framework. Three meta-model layers—Multiple Linear Regressor (MLR), K-Nearest Neighbors Regressor (KNR), and RFR—were employed and evaluated. As a subroutine of this integration, a model-specific and data-type-specific feature selection method was employed, which involved training each model on a unique subset of features identified through permutation importance. The idea of the multimodal and meta-learning approach was implemented using extensive plot-wise data from diverse forest types in the New England region utilizing a rich dataset comprising spectral, spectral indices, and backscattering characteristics to capture the variability of forest biomass. The efficacy of multiple ensembling strategies was evaluated, specifically ensembling across data types or regressors, as well as meta-learning across both data types and regressors. Ensembling across data types, which leverages the strengths of both spectral and backscattering information, demonstrated a higher predictive ability, achieving an R2 of 0.68 and an RMSE of 54.21 Mg/ha. This was higher than the ensembling strategy across regressors using the same data type, which yielded an R2 of 0.59 and an RMSE of 61.4 Mg/ha. Nevertheless, the multimodal and meta-learning approach, which collectively leverages both data types and machine learning regressors, achieved superior performance, with an R2 of 0.82 and an RMSE of 40.5 Mg/ha. This was significantly greater than a conventional ensemble method, which lacked the meta-layer integration. Additionally, the meta-model layer using RFR yielded better results compared to the KNR or MLR layers, demonstrating the capability of RFR in handling complex interactions across variables. These results highlight the superior accuracy and reliability of the multimodal and meta-learning approach, indicating its substantial potential to enhance precision in ecological monitoring and carbon management.

Benthic substrates are an important component of fish habitat and preferred substrates vary with species and life history traits. Understanding the location and areal extent of these substrates helps inform protection and management of fish and other aquatic species. Traditional methods of substrate mapping can require substantial effort and necessitate specialized equipment and personnel to work at and travel to sites. Satellite mapping of bottom types has been conducted in the past, though most of this work has been done in ocean systems and relatively little in freshwater. Using several permutations of input data and processing methods, we accurately map benthic substrates in the clear freshwater ecosystem of Fathom Five National Marine Park, Lake Huron, Canada. Using a novel approach, we were able to map substrate with relatively limited inputs to the model, making the method easily transferable among systems. An object-based approach to classification proved beneficial for accuracy, as was using higher resolution (< 2 m) satellite data to achieve our target accuracies. We also grouped accuracies by depth bins within the site to show that accuracy does not decrease linearly out to the maximum observable depth. Using a more limited depth range for classification results in higher overall and depth-specific accuracies, which may be beneficial when only a shallower portion of the site is necessary to map. With this model and information, accurate substrate maps for an area of interest could be developed to assist with the identification and management of aquatic habitat.

Traditionally, the expansion of nature-based tourism has involved a meticulous process, often incorporating spatial analyses. In this context, a multi-criteria decision-making (MCDM) model has been introduced and implemented to assess the suitability of natural-based tourism sites in the Kashmir Valley, India. The data used in this study comprised both primary and secondary sources, with primary data collection involving field surveys, interviews, and questionnaires administered to professionals in relevant fields of study. Leveraging Geographic Information System (GIS) technology and the Analytical Hierarchical Process (AHP) thirteen criteria were identified, encompassing assessments of natural beauty, infrastructure, and various physical and socio-economic parameters within the study area. The analysis reveals that while the lower reaches or valley floor exhibit minimal tourism potential, the upper reaches demonstrate considerable potential, with a significant proportion of this high-potential area being the most extensive. The information, data, and methodology presented in this study serve as a valuable resource for decision-makers and stakeholders in the research area, offering insights applicable to sustainable tourism development in similar environments and regions.

The twenty-first century marks a significant shift in soil fertility evaluation, driven by advancements in pedometrics and Digital Soil Mapping (DSM). Pedometrics introduces quantitative methods to assess soil variability using statistical and geostatistical techniques, enhancing understanding of soil properties. DSM builds on this by creating high-resolution predictive maps, offering valuable data for researchers and practitioners. An in-depth bibliometric analysis on the Scopus platform (2000–2023) revealed 133 articles on pedometrics and an impressive 1,172 on DSM, underscoring growing interest in these technologies.The integration of Geographic Information Systems (GIS) and Remote Sensing (RS) has further advanced these fields, enabling extensive geospatial data collection and real-time monitoring. Machine Learning (ML) has also been transformative, facilitating complex pattern recognition and predictive analysis to improve soil fertility mapping and management. A review of 364 studies from 2000 to 2023 highlights the development and impact of these technologies, detailing their advantages and limitations. The surge in related publications and citations since 2000 reflects a rising interest in sustainable agriculture and environmental management. Significant milestones occurred in 2019 and 2022 with the introduction of new soil management technologies, while RS and GIS technologies surged in popularity in 2016 and 2020, driven by satellite advancements like Sentinel and Landsat. The capabilities of ML techniques were notably effective in 2019 and 2022. Countries like India, China, and Iran have been key adopters, transforming soil fertility mapping into a non-invasive, large-scale process that enhances agricultural decision-making.This transition emphasizes the value of specialized publications that advocate for GIS, RS, pedometrics, and DSM, which are crucial for addressing environmental challenges. In conclusion, integrating traditional and advanced methodologies provides a holistic, adaptable approach to sustainable land management, supporting data-driven decisions to enhance agricultural and environmental sustainability.