Applied Geomatics最新文献

Spatial analyses of geographic data and information provide valuable insights for decision-making and enable understanding of the distribution of geographically localized phenomena. This research aims to explore the spatiotemporal dynamics of burned areas in the Caatinga Biome and their relationship with land use and land cover from 1985 to 2023. The analysis is based on Geographic Information Systems (GIS), integrating burn scar data with land use and land cover classifications. The main results revealed that fires occurred annually in natural vegetation formations, Savanna Formation, Forest Formation, and Grassland Formation (in this order), followed by land use classes such as Mosaic of Uses and Pasture Areas. However, it is not possible to conclude that the fires originated in natural vegetation. Burned areas within natural regions may have originated from anthropized areas, such as pastures in their immediate surroundings. The recurrent occurrence of fires in natural vegetation areas, potentially triggered by adjacent anthropized zones, highlights the need for preventive actions in transition areas between human-modified landscapes and native ecosystems. The annual variation patterns of burned areas remained consistent and persistent, although fluctuations were observed in pasture areas across all analyzed periods.

This study semi-automatically detects road lines using object-based classification methods from orthophoto produced from UAV images. Three studies were carried out on images with visible and infrared wavelengths. The study's major aim is to examine the effect of spectral bands and different areas on classification accuracy. First, orthophotos were produced from UAV images of different areas and an object-based classification algorithm was used to detect roads. Then, statistical comparisons were made and, user accuracy in the three study regions ranged from 85 to 91%. Overall accuracies were calculated between 0.819 and 0.889, and the results were within the confidence interval.

Vertical Control Networks are critical in establishing Geodetic Reference Frames at both local and regional levels. The fundamental inputs for propagating geopotential differences, which help determine physical heights essential for the vertical components of these frames, are geometric leveling and gravity observations. In this study, we apply orthometric and normal corrections to the Vertical Control Network of Ecuador to obtain height differences that account for the effects of gravity. Since not all level references within the Vertical Control Network have corresponding gravity observations, we derive gravity values for calculating orthometric corrections through interpolation and Global Geopotential Models. Finally, we analyze the results by calculating the misclosure errors of the network loops. This analysis considers the impact of orthometric and normal corrections and variations in the gravity sources.

Urban expansion in the aftermath of natural disasters presents critical challenges to sustainable land use planning and environmental resilience. This study models post-disaster urban growth trajectories by analysing land use and land cover (LULC) changes from 2017 to 2025 and simulating future development scenarios up to 2040 using an Artificial Neural Network (ANN) integrated within the QGIS MOLUSCE module. Sentinel-2 satellite imagery and digital elevation models were employed to classify six LULC categories: built-up areas, croplands, rangelands, bare land, forested areas, and water bodies. The methodology was applied to the Central District of Elazığ, Türkiye a region significantly affected by the 2020 earthquake. Results reveal a substantial 60.89% increase in built-up areas, primarily driven by rapid post-disaster reconstruction. This expansion has coincided with notable reductions in cropland and rangeland coverage, while forest and water body areas have increased due to afforestation projects and water-related infrastructure investments. Scenario-based projections indicate that, if current urbanisation trends persist, pressure on ecologically sensitive and agriculturally valuable lands will likely intensify by 2040. The ANN model demonstrated high predictive accuracy, with an overall correctness of 97.52% and a Kappa coefficient of 0.96. Based on these findings, the study recommends integrated planning strategies that: (i) prevent development on fertile agricultural plains, (ii) incorporate ecological thresholds in urban site selection, (iii) avoid construction near seismic fault lines and water bodies, and (iv) promote green infrastructure solutions including ecological corridors, urban forests, and sustainable stormwater systems within post-disaster urban development frameworks.

Spatial information is critical to supporting the “Precision Village Data” concept, as accurate village-level spatial data enhances governmental decision-making and delivers various benefits. However, acquiring precise spatial data in developing countries such as Indonesia remains challenging due to outdated base maps and inconsistent reference frameworks. In response, Indonesia’s One Map Policy aims to resolve these issues. Unmanned Aerial Vehicles (UAVs) have emerged as a promising alternative for photogrammetric mapping. Modern UAVs can perform direct georeferencing to attain high levels of mapping accuracy. This study evaluates UAV-based georeferencing techniques for precision mapping in village-level spatial planning as part of implementing Indonesia’s One Map Policy. The novelty of this research lies in the comparative assessment of two UAV-based georeferencing systems (Emlid and Pixhawk) across a village-scale area of 140 hectares—larger than most comparable studies (< 100 hectares)—under identical operational conditions. Specifically, this study compares and evaluates the accuracy of two widely used georeferencing systems: Emlid and Pixhawk. The results indicate that while direct georeferencing without ground control points (GCP) achieves acceptable accuracy for medium-scale mapping, only the Emlid + GCP configuration meets Class I precision standards (< 0.1 m Circular Error at 90%). While using all available GCPs remains critical for high-precision applications, Emlid provides a more practical solution in contexts in which installing numerous GCPs is not feasible. This research contributes to optimizing UAV mapping techniques to meet Class I precision standards (1:5,000-scale maps), thereby supporting the achievement of the Sustainable Development Goals (SDGs) in rural areas through the implementation of “Precision Village Data.”

Augmented Reality (AR) offers new opportunities for engaging audiences with cultural and scientific heritage. This study investigates the use of AR in astronomy education through the Copernicus Garden mobile application, developed for a weeklong event at the Olsztyn Planetarium. The research aimed to assess user engagement, perceived usefulness, and interface usability when interacting with AR-based representations of historical astronomical instruments. Data were collected from 767 participants using a mixed-methods approach: telemetry logs from the application, post-visit surveys, and in-depth interviews. Telemetry revealed that 43% of total usage time was dedicated to AR scenes, indicating high levels of interaction with 3D models such as the aerial telescope and solar quadrant. Survey responses confirmed positive perceptions of AR’s usefulness for learning, while interviews highlighted usability challenges related to device performance, scanning instructions, and connectivity. The findings suggest that AR can enhance visitor engagement and support informal science education, although evidence of direct learning outcomes remains limited. We also identify key design considerations for scalability, including simplified modes for low-spec devices, offline caching, and improved onboarding tutorials. This study contributes to research on immersive heritage interpretation by providing empirical evidence from a real-world deployment. Limitations include the short-term scope of the event and the reliance on engagement metrics rather than controlled learning assessments. Future work should extend to longitudinal studies and integration of AR into permanent cultural programming.

The expansion of the raft and aquaculture has substantially impacted worldwide seafood production in the 21st century. Aquaculture monitoring serves to identify both the progress of aquaculture development and water quality status while preventing environmental issues related to pollution or habitat destruction. Nowadays, deep learning models using optical and synthetic-aperture-radar remote sensing images makes monitoring and identifying raft aquaculture possible. This study aims to test DL models based on U-shaped and DeepLab architectures for detecting different types of raft aquaculture using optical and SAR data obtained from Sentinel sensors. Various options to optimize both models related to input fusion data, sub-input sizes, and model structure were applied. As a result, the U- shaped models using the Sentinel-2 images have higher performance in detecting rafts and floating rafts than the models using DeepLab and Sentinel-1 data. Optimal models can detect rafts with accuracy higher than 94% and F1 higher than 80%. The models using Sentinel-1 can only recognize integrated rafts, whereas Sentinel-2 models can distinguish both integrated and bamboo ones. The Sentinel-2 has a high level of detail in spatial resolution, allowing for highly accurate identification. On the other hand, Sentinel-1’s SAR technology is very effective in any weather conditions, making it highly important for uninterrupted surveillance and suitable for real-time monitoring. Integrating these data types requires careful consideration of operational and environmental factors to identify drifting fish rafts. The best model was used to track Vietnamese aquacultural zones throughout seasons and years and may be valuable for coastal managers.

The proposed research problem involves devising a complete numerical procedure for adjusting a distance-distance intersection. The objective of the problem is to find the optimum point of intersection for several circles, where the radii are the results of survey measurements. Two alternative methods are proposed: adjusting the radius lengths as indirect observations and adjusting the individual intersection point coordinates as direct pseudo-observations. Each method involves assessing the location accuracy of the intersected point. The derived equations were tested numerically on practical examples. The devised procedures will be integrated into an exhaustive numerical algorithm for diverse surveying problems that can be easily reduced to multiple distance-distance intersections.

The recent literature on land surface temperature (LST) and urban heat island (UHI) highlights the imperative for more studies on the relationship between LST/UHI and biophysical/socioeconomic factors, especially in arid environments. This study examines the spatio-temporal variations of LST or surface UHI (SUHI) induced by biophysical and socioeconomic factors in Riyadh, Saudi Arabia. Additionally, the normalization methods for comparing LSTs of different periods were examined. The LSTs of the study area for four years between 1985 and 2015 in June/July were derived from multi-date Landsat images. The SUHI index of the different land-use/land-cover types (high-density residential, medium-density residential, low-density residential, industrial, vegetation, and desert) was computed from the LST data to analyse their relationships. Thereafter, geographical random forest (GRF) analysis was used to determine the influence of biophysical and socioeconomic factors on LST/SUHI. The findings show differences in the minimum temperatures from 1995 in all the land-use types. The industrial area has the highest temperatures while the temperatures of the vegetation area are the lowest. However, the means of the normalised LST values depict decreasing values. The use of the normalized ratio scale (NRS) was not successful. The GRF analysis indicates that land use/land cover (65%) has the highest indirect influence on LST/SUHI index, followed by nighttime light (20%), traffic (16%), tweet density (15%), population (14%) and built-up area (4%). In conclusion, land use types and socioeconomic factors influence variations in LST/SUHI. The article contributes to the knowledge of planning for urban heat island mitigation by highlighting the influencing factors.

Global Digital Elevation Models (DEMs), such as the Shuttle Radar Topography Mission (SRTM), play critical roles in different geoscientific works across the world. Nevertheless, such models, geo-referenced to the earth, can still lack accuracy in particular areas, warranting validation on the ground. The validation of the vertical accuracy of the SRTM DEM in the Berinj area, Iraq, was carried out using a ground Global Navigation Satellite System (GNSS) based high precision DEM as a benchmark. A ground campaign using Real Time Kinematic (RTK) and Precise Point Positioning (PPP) accumulated 783 elevation points. Kriging interpolation to a DEM in a Geographic Information System (GIS) environment provided a continuous, highly accurate reference DEM. There is a visible disparity in the results. As an example, 19.49 and 27.27 m represent the elevation range for the GNSS-based DEM. On the other hand, 21.00 and 34.00 m represent the SRTM DEM range. The results indicate that there is a disparity in range and maximum elevation between SRTM DEM and the GNSS-based DEM in favor of the SRTM DEM. The SRTM DEM also had a Root Mean Square Error (RMSE) of 3.65 m discrepancy, which is considered rather significant. The results of the study have demonstrated the local accuracy of the GNSS-based model, and highlighted the necessity of ground truthing for precise elevation data required for detailed hydrological and geomorphological models. A more accurate topographic map of the study area is the ultimate final product of the study.