Special Issue “Earth Observation by GNSS and GIS Techniques”

Over the past few decades, Geographic Information Systems (GIS) and Global Navigation Satellite Systems (GNSS) have undergone transformative developments that have profoundly influenced positioning/navigation, data science, and geospatial technologies. The origins of GIS can be traced to the 1960s, when advances in computer technology and the emergence of computational and quantitative geography provided the foundation for spatial data analysis. In 1963, geographer Roger Tomlinson initiated a national land-use management program for the Canadian government aimed at cataloging natural resources. During this project, he introduced the term Geographic Information System, establishing the conceptual and methodological basis for digital spatial analysis. The United States Department of Defense initiated the Global Positioning System (GPS) project in 1973 to develop a satellite-based navigation system capable of overcoming the limitations of earlier ground-based positioning technologies. The first commercial GPS receivers became available in 1989, though their large size and high cost initially restricted widespread adoption. Nowadays, four fully operational GNSS constellations, i.e., GPS, GLONASS, Galileo, and BeiDou, collectively provide precise global positioning and navigation capabilities, forming the backbone of modern geospatial science and applications.

In this framework, billions of people worldwide rely on GIS and GNSS every day to enhance convenience and improve their quality of life. These technologies are embedded in a wide range of commercial devices, including smartphones, tablets, automobiles, buses, airplanes, unmanned aerial vehicles (UAVs), and many other systems. The integration of GIS and GNSS supports a variety of services, such as car navigation, public transport optimization, logistics and goods delivery, supply chain management, daily fitness tracking, as well as travel and tourism. In fact, the global GIS market was valued at approximately $77 billion in 2021 and is expected to grow to $174 billion by 2027. Similarly, the market for Location-Based Services (LBS), which combines GIS and GNSS technologies, was valued at $37.22 billion in 2025, with projections indicating that it will increase to $125.92 billion by 2032 [1].

At the same time, GIS and GNSS technologies are increasingly utilized by scientists and researchers across a wide range of environmental and scientific fields to deepen our understanding of Earth processes. These applications include environmental management [2,3], precision agriculture [4,5], landslide mapping [6,7], crustal motion monitoring [8], archeological site reconstruction and conservation [9,10], water resources management [11], coastal evolution studies [12], and a broad array of Earth observation research. In light of this, the current Special Issue has gathered eight high-quality original research articles, focused on the application of GNSS and GIS methodologies for Earth observation purposes. It incorporates articles focusing on different scientific topics such as vegetation monitoring [13], rockfalls [14], coastal monitoring [15], geoid computations [16], landform erosion [17], wetlands mapping [18], open ocean and inland water conditions mapping [19], and deformation monitoring [20].

Specifically, Badioui et al. [13] integrated medium-resolution multispectral data with various numerical models within a GIS environment to analyze the evolution of vegetation in the challenging and hard-to-access environment of an oasis. They processed PROBA-V and Sentinel-3 satellite data, using the Tool for Raster Data Exploration to generate two vegetation indices: the Leaf Area Index (LAI) and the Normalized Difference Vegetation Index (NDVI). To ensure the robustness of their methodology, the authors validated the results through in situ measurements, aiming to develop a reliable approach for future applications.

Another study examined the influence of the spatial resolution of various Digital Surface Models (DSMs) on the simulation of rockfall trajectories within a GIS environment [14]. The HY-STONE software, designed for 3D numerical modeling of rockfall processes, was utilized to simulate rock movements, including free fall, impact, and rolling dynamics. The study investigated two well-documented rockfall events in Western Greece as test sites. In total, seven freely available DSMs, with spatial resolutions ranging from centimeters to 90 m, were evaluated to determine their impact on the accuracy of the rockfall trajectory simulations.

Additionally, four diachronic datasets, spanning the period from 2017 to 2021, consisting of very high-resolution satellite and aerial imagery, were visually analyzed to map the morphometric features and monitor the annual mobility of boulders along the eastern coast of the Gulf of Taranto. QGIS, an open-source software, was used to detect and map the boulder movements, while GNSS measurements were conducted to verify the accuracy of the results [15].

The objective of the next study [16] was to investigate the influence of interpolation methods on the gridding of terrestrial gravity anomalies and their implications for geoid model determination. Four distinct interpolation methods were employed: geostatistical Kriging, nearest neighbor, inverse distance to a power, and artificial neural networks. The accuracy of the interpolation results was validated against independent benchmark points, measured using GNSS. Their finding highlights the critical role of interpolation strategy in accurate geoid determination, with important implications for GNSS-based height systems and geodetic reference frames.

Moreover, Unmanned Aerial Vehicle (UAV) data, GNSS measurements, and 3D spatial analysis techniques within a GIS environment were integrated to evaluate how UAV flight geometry influences landform erosion [17]. Three photogrammetric flights with varying geometries were conducted, and the acquired imagery was processed to produce orthomosaics, DSMs, and 3D point clouds. These datasets were subsequently analyzed to map small-scale erosional landforms, such as rills and gullies, using root mean square error assessment, length measurements, and the Multiscale Model-to-Model Cloud Comparison algorithm.

In terms of wetlands mapping, eleven land-use/land cover maps derived from various remote sensing data with spatial resolutions ranging from 10 m to 1000 m (i.e 10 m, 30 m, 100 m, 500 m and 1000 m) were compared and evaluated for their consistency and accuracy in capturing the spatial and temporal dynamics of wetlands [18] within the Irtysh River Basin, which covers an vast area of 17,000 km². The datasets were processed within a GIS environment, reprojected to a common coordinate system, and analyzed using similarity metrics, such as the Jaccard Similarity Coefficient, as well as classification accuracy measures, including the Kappa Coefficient.

A total of 173 Sentinel-1 SAR images were processed, comprising 143 images for open-ocean conditions and 40 for inland-water conditions [19]. The primary objective of the study was to evaluate the applicability of algorithms, widely used in current practice and primarily developed for scatterometers, under SAR conditions. Additionally, the study examined the influence of various factors on the accuracy of wind speed and direction calculations derived from these algorithms. To validate the wind speed magnitudes derived from SAR image processing, wind direction data were obtained from NOAA NDBC oceanographic buoys. This integration of SAR remote sensing and geophysical modeling enhances the precision of GNSS- and GIS-informed environmental monitoring.

Finally, SAR data with varying spatial resolutions from the Sentinel-1 and Cosmo-SkyMed satellite missions were validated using GNSS measurements to map subsidence phenomena across the Emilia-Romagna Region in Italy [20]. The Small Baseline Subset (SBAS) interferometric technique was applied to both datasets over a ten-year period, from 2012 to 2022. The resulting displacement maps revealed several critical sites exhibiting significant vertical displacements, which may pose risks for future infrastructure stability.

In conclusion, the value of GIS and GNSS in Earth observation has been clearly demonstrated across a wide range of studies and research topics. GNSS remains indispensable for the precise monitoring of Earth’s dynamic systems, while GIS provides the analytical framework that gives these observations context and meaning. The widespread integration of GNSS sensors into daily life, combined with GIS capabilities in data management, harmonization, and spatial analysis, continues to open new opportunities for research. A unifying theme emerging from these studies is the growing importance of interdisciplinary approaches, which leverage the strengths of both GNSS and GIS to advance our understanding of Earth processes and to support innovative applications in environmental monitoring, natural hazard assessment, and sustainable resource management.