Applied Geomatics最新文献

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.

The Precise Point Positioning (PPP) approach to GNSS observables is widely used for processing data from permanent stations, providing highly precise coordinates. However, the performance of PPP for observation sessions shorter than 24 h has not yet been thoroughly investigated in the case of multi-constellation acquisitions. In recent years, the PRIDE PPP-AR software package has been made freely available. Since it includes a graphical user interface (GUI) version that runs under Windows, it can also be easily used by technical surveyors aiming to process data acquired from a single GNSS receiver. This is particularly valuable for surveys conducted in areas lacking dense geodetic infrastructures or reliable augmentation services. In this paper, based on a wide and consistent dataset, the coordinate precision obtained from observation sessions ranging from 30 min to 24 h processed with PRIDE PPP-AR is analyzed. In addition to multi-constellation GNSS data (GPS + Galileo + GLONASS + BeiDou), independent GPS-only and Galileo-only processing was also evaluated. Furthermore, the reliability of the formal errors provided by the software was examined, as these represent the only available information for assessing coordinate quality in surveys that lack geometric redundancy. While several online PPP services already exist, PRIDE PPP-AR overcomes common limitations related to the number of processed files and the choice of GNSS constellations. The results show that two-hour observation sessions can reliably achieve horizontal coordinate accuracy within 2 cm and vertical accuracy within 5 cm, whereas 30-minute sessions are suitable for applications requiring 5–10 cm accuracy.

Since the 1980s, Global Navigation Satellite Systems (GNSS) have revolutionized positional surveying. The absolute GNSS method, which uses a single receiver, and the relative method, which employs multiple receivers, are widely used. A notable variant of the absolute method is Precise Point Positioning (PPP), which utilizes precise satellite orbits and clock data to achieve high accuracy with a single receiver. This study evaluates the accuracy of PPP using multiple GNSS constellations and frequencies, comparing it with short-baseline Relative Positioning. A permanent GNSS station was installed on the roof of a building to monitor structural deformation. PPP results were compared with Relative Positioning over an eight-month period, with data processed in two 24-hour sessions each month. Ionospheric scintillation was assessed using the S4 index, which quantifies rapid fluctuations in GNSS signal amplitude caused by ionospheric irregularities. While the S4 index remained low on average, a specific date with high S4 values was analyzed to evaluate PPP performance under challenging ionospheric conditions. The results showed that PPP, although less accurate than Relative Positioning, achieved sub-centimeter precision in some cases with modernized triple-frequency observables. Relative Positioning yielded superior average Root Mean Square (RMS) values: 3.6 mm East, 0.6 mm North, and 4.2 mm Up in the Local Geodetic System (LGS). PPP showed average RMS errors of 14.8 mm East, 9.5 mm North, and 12.7 mm Up in the LGS, with greater variability observed in the East and Up components. A paired T-Student test confirmed that PPP and Relative Positioning are statistically equivalent at a 95% confidence level for centimeter-level measurements.

Snowfall and snow cover area (SCA) are critical for maintaining glaciers' health and regulating river discharge in the Himalayas. This study analyzed seasonal SCA dynamics in the Pindari and Kafni glacier valleys (Kumaon Himalaya), combining field and remote sensing observations acquired from Landsat satellite imageries during the accumulation period (November – December and January-April) for the last two decades, 2008–2009, 2015–2016, and 2021–2022. We employed the Normalized Difference Snow Index (NDSI) and the recently developed Snow Water Index (SWI) to delineate and compare SCA across these adjacent basins. Results were validated and incorporated with field-based observations and high-resolution Google Earth imagery. The overall accuracy of NDSI and SWI was 70% and 72%, respectively. SWI may offer a superior approach to NDSI in effectively handling cloudy images for water and snow cover analysis. Specifically, year-wise SCA exhibited an increasing trend: 1.3 times higher was quantified in the year 2021–22 and 1.2 times in 2015–16 as compared to the SCA estimation in 2008–09. Both NDSI and SWI analyses revealed minimum SCA in December and maximum SCA in April, with a remarkable exception of 2021–22, which showed minimum SCA (14.49%) in April and maximum SCA (20.52%) in January. These findings underscore an increasing sensitivity of SCA to climate warming in recent years, leading to rapid snow melting. In comparison to our results with other neighboring regions of the Indian and Nepal Himalaya, our results indicate an overall increase in SCA and snow mass trend, while the snowpack is melting rapidly, due to substantial heterogeneity, atmospheric dynamics, and Rain-on-snow (ROS). Our results also suggest a subtle increasing trend in SCA; a highly possible shift in water phenology may not compensate for the increasing water demand, particularly during the lean melt season, which deserves further investigation to continue long-term in-situ monitoring of SCA estimation for the accurate and more reliable results. The outcome of the present research work provides an extensive understanding of the present state of SCA estimation in the central Himalayan region. The study grasps the significant value for the fields of glaciology, hydrology, climatology, and cryospheric science.

Spectral indices emphasize pixels by combining spectral reflectance values from two or more spectral bands in multispectral imagery. These indices highlight features of interest by undergoing specific mathematical processing, often referred to as band math or map algebra. Many Digital Image Processing (DIP) software has tools to execute these indices automatically. ArcGIS Desktop, a widely used geospatial software, supports the reading and processing of satellite imagery. While it includes some tools for pre-processing and spectral index estimation, these are limited in scope comparted to dedicated DIP software and require extensive manual setup for repetitive tasks. As of today, these limitations remain a gap in ArcGIS Desktop’s capabilities. To address this gap, the present study develops a semi-automated approach for generating multiple spectral indices from multiband satellite imageries with minimal user intervention. The ArcIndices toolbox organizes different spectral indices into categories as script tools under a toolset. Each interactive script tool is programmed using ArcPy (Python site package for ArcGIS) to execute a sequential procedure of steps within the ArcGIS environment. Users can invoke these tools as functions in other Python scripts, Model Builder workflows, or even as geoprocessing services on ArcGIS Server for cloud-based operations. The tools are validated and tested using Landsat 8 Operational Land Imager (OLI) image bands across multiple study areas, with the accuracy of outputs confirmed by comparing the results with expected index ranges reported in scientific literature. Performance evaluation of the developed tools highlights their advantages over manual workflows using the ArcGIS Raster Calculator. Comparative measurements presented in this study show that the automated script tools significantly improve performance, achieving a 100% reduction in human errors and reducing processing times by up to 100% for most indices. These improvements make the tools not only faster and more accurate but also easier to use for both novice and expert users. The ArcIndices Toolbox bridges the gap between manual and automated workflows, addressing current limitations in ArcGIS Desktop for spectral index estimation and extending its functionality for modern geospatial analysis.

One of the biggest risks to the environment and food security is soil erosion. Also, it results in the loss of its ability to produce and the soil fertility is greatly reduced by this phenomenon which also has a significant impact on agricultural activities. Moreover, in arid and semi-arid regions soil loss due to water erosion is regarded as a major factor in land degradation. Also, in many Mediterranean areas, soil erosion by water is a serious environmental issue that is caused by a variety of geomorphological, geological, hydro-climatic, and human-related variables. So, the prediction of soil erosion is crucial for conservation practices, erosion prevention measures, and pertinent recommendations for soil conservation. Hence, modeling processes are required for the accurate estimation of soil erosion in such areas in the absence of measured data. As a result, predictive erosion models integrated into geographic information systems have been demonstrated to be useful tools for assessing soil erosion and creating plans to mitigate soil erosion. In addition, the Revised Universal Soil Loss Equation (RUSLE) model is a helpful instrument for determining, assessing, and controlling soil erosion. Also, RUSLE has been widely used to estimate annual average soil loss rates. The purpose of this study was to predict the risk of soil erosion in the study area utilizing the RUSLE model in a framework of a geographic information system (GIS). In addition, to apply the RUSLE model is calculated the erosion susceptibility for each pixel is based on the following parameters namely; topographic, conservation practice, crop management, rainfall erosivity, soil erodibility. These layers are all created in a GIS environment utilizing a variety of data sources and data processing methods. In the current study, the results indicate that the total soil erosion quantity ranged values from 0 to > 2500 ton ha^­1 year ^­1, with an average spatial distribution of 53.64 ton ha^­1 year ^­1. Additionally, depending on estimations of soil loss in every grid cell, a soil erosion risk map in the study area was created through five risk classes; Low (36.83%),Moderate (7.21%),High (13.24%),Very high (19.83%), and Extreme high (22.89%). Indeed, the current assessment provided a trustworthy evaluation of the rates of soil loss and classification of erosion-prone locations within the study area. The results can undoubtedly help with the application of soil management and conservation techniques to decrease soil loss and may offer managers and developers useful data for land management. Finally, this model shown here is suitable for adjusting to similar environments in arid and semi-arid regions.

We propose a GIS–Fuzzy SWARA framework to identify suitable sites for vertical farms in Menemen (İzmir, Türkiye, 694 km²). Using seven spatial criteria at 30 m resolution, we compared equal weighting with expert-derived fuzzy weights. Water proximity dominated expert judgments (w = 0.30), increasing the share of “high suitability” areas from 2.44% under equal weights to 8.21% with fuzzy weighting. Natural sunlight was incorporated not as a crop growth factor but as a proxy for rooftop photovoltaic (PV) potential, while accessibility criteria captured links to markets and transport networks. Expert judgments showed variability, and hydrological proximity was represented through rivers and canals as the most reliable local sources. These findings demonstrate that fuzzy MCDM methods better reflect expert reasoning than simple averages, leading to more realistic suitability outcomes. The proposed GIS–Fuzzy SWARA framework provides a transparent and reproducible decision-support tool that can guide planners and policymakers in integrating vertical farming into urban strategies. While outputs represent suitability potentials rather than validated outcomes, the framework highlights the central role of water availability and energy potential in supporting resilient urban food production.

Susceptibility of urban woodlands to plant invasions is often associated with their structural characteristics making timely detection and monitoring of invasive alien plant species (IAPs) important. The three-dimensional structure (3D) of forests can now be captured using unmanned aerial vehicles (UAV) based Structure-from-Motion (SfM) Digital Aerial Photogrammetry (DAP), a low-cost alternative to Light Detection and Ranging (LiDAR). However, there is still need to test whether ecologically meaningful linkages can be observed between IAPs occurrence and UAV-DAP derived forest structural and textural metrics as proxies of forest structure. In this study, we test whether UAV-DAP derived structural and textural metrics can explain the presence of Psidium guajava, an invasive alien plant using an ensemble of small models (ESMs). We assessed nine Grey-Level Co-occurrence Matrix textural features and 17 forest structural diversity metrics. Using a data set of 24 occurrences, we obtained relatively high Area under the curve (AUC) with the final ensemble of the ESMs achieving an AUC of 0.83 and True skills statistics (TSS) of 0.65. Rumple, maximum tree height (zmax), variability in tree height (zMADmedian), horizontal canopy texture (glcm mean and glcm variance) and leaf area (LAI) significantly influenced P.guajava habitat suitability. Results of this study, suggest that P.guajava invades woodland areas with less complex and heterogeneous structure. Our results highlight the usefulness of ESMs in combination with UAV-DAP derived forest structural and textural metrics in capturing forest structural characteristics linked to IAP occurrence. The approach can allow a low-cost efficient approach to monitoring IAPs in urban woodlands.