Geomatics, Volume 5, Issue 2 (June 2025) – 5 articles

Higher education institutions have played a central role in building sustainability awareness. However, current models only show an effect on students’ knowledge about sustainable development, with a large gap in transformative solutions that shift from understanding problems towards solutions. This case study explores a new model that integrates sustainability reflections in a Geographic Information Science (GIS) Capstone Project course. Through collaborations with external partners and reflections on sustainability modules, students analyzed complex problems and developed sustainability competencies. The assessment tool adopted in this study combines reflective writing, scenario testing, performance observation, and self-assessment. Based on the set of key competencies in sustainability, half of the students developed systems-thinking and strategies-thinking, while a quarter of the students developed futures-thinking and values-thinking. Their development of sustainability competencies went beyond simply acquiring knowledge, also critically evaluating different perspectives and implementing or integrating the concepts when addressing the problems. Geospatial information tackles three key aspects of sustainability, which are relational, distributional, and directional, making it ideal in analyzing sustainability issues and providing insights for informed decisions. This study fills another important gap of integrating sustainability competency development in GIS education. Full article

Spectral indices such as NDRE (Normalized Difference Red Edge Index), CCCI (Canopy Chlorophyll Content Index), and IRECI (Inverted Red Edge Chlorophyll Index), derived from the Red Edge band of MSI/Sentinel-2A (B05, B06, B07), are critical for coffee monitoring. To align the Red Edge band (20 m resolution) with the NIR band (10 m resolution), the nearest neighbor, bilinear, cubic and Lanczos resampling methods were used, available in the Terra package in the R software(4.4.0). This study evaluates these methods using two original B05 images from 24 November 2023, and 21 September 2023, covering the “Ouro Verde” (15 ha) and “Canto do Rio” (45 ha) farms in Bahia, Brazil. A total of 500 random points were analyzed using PSF, linear models, and cross-validation with R², MAE, and RMSE. PSF analysis confirmed data integrity, and the cubic method demonstrated the best performance (R² = 0.996, MAE = 0.008 and RMSE = 0.012 in the “Ouro Verde” Farm and R² = 0.995, MAE = 0.007 and RMSE = 0.011 in the “Canto do Rio” Farm). The results highlight the importance of selecting appropriate resampling methods for precise remote sensing in coffee cultivation, ensuring accurate digital processing aligned with study objectives. Full article

The cultivation of Moringa oleifera Lam. (M. oleifera) has steadily increased over the past few decades, and interest in the crop continues to rise due to its unique multi-purpose properties. However, knowledge pertaining to its water use to guide decision-making in relation to the growth and management of this crop remains fairly limited. Since acquiring such information can be challenging using traditional in situ or remote sensing-based methods, particularly in resource-poor regions, this study aims to explore the potential of using the cloud-based Earth Engine Evapotranspiration Flux (EEFlux) model to quantify the water use of M. oleifera in a semi-arid region of South Africa. For this purpose, EEFlux estimates were acquired and compared with eddy covariance measurements between November 2022 and May 2023. The results of these comparisons demonstrated that EEFlux unsatisfactorily estimated ET, producing root mean square error, mean absolute error, and R² values of 2.03 mm d⁻¹, 1.63 mm d⁻¹, and 0.24, respectively. The poor performance of this model can be attributed to several factors such as the quantity and quality of the in situ data as well as inherent model limitations. While these results are less than satisfactory, EEFlux affords users a quick and convenient approach to extracting crucial ET and ancillary data. Subsequently, with further refinement and testing, EEFlux can potentially serve to provide a wide variety of users with an invaluable tool to guide and inform decision-making with regards to agricultural water use management, particularly those in resource-constrained environments. Full article

The Cape Floristic Region (CFR) boasts rich biodiversity but faces threats from invasive species and land-use changes. Fynbos wetland vegetation within the CFR is under-mapped despite its crucial role in supporting biodiversity and maintaining hydrological cycles. This study assessed the potential of UAV VIS-NIR data, gathered during Spring and Summer, to identify the spectral characteristics of eleven Fynbos wetland species in a seep wetland. Spectral distances derived from reflectance data revealed distinct spectral clustering of plant species, highlighting which species could be distinguished from each other. UAV data also captured differences in reflectance across spectral bands for both dates. Spectral statistics indicated that certain species could be more accurately classified in Spring than in Summer, and vice versa. These findings underscore the efficacy of UAV multispectral data in analyzing the reflectance patterns of fynbos wetland species. Additionally, the sensitivity of UAV multispectral data to foliar pigment composition across different seasonal stages was confirmed. Lastly, species classification results demonstrated that a random forest classifier is well suited, with relative producer and user accuracies aligning with the derived spectral distances. The results highlight the potential of UAV imagery for monitoring these endemic species and creating opportunities for scalable mapping of Fynbos seep wetlands. Full article

Positional accuracy in cadastral data is fundamental for secure land tenure and efficient land administration. However, many land administration systems (LASs) experience difficulties to meet accuracy standards, particularly when data come from various sources or historical maps, leading to disruptions in land transactions. This study investigates the use of unsupervised clustering algorithms to identify and characterize systematic spatial error patterns in cadastral maps. We compare Fuzzy c-means (FCM), Density-Based Spatial Clustering of Applications with Noise (DBSCAN), and Gaussian Mixture Models (GMMs) in clustering error vectors using two different case studies from Greece, each with different error origins. The analysis revealed distinctly different error structures: a systematic rotational pattern surrounding a central random-error zone in the first, versus localized gross errors alongside regions of different discrepancies in the second. Algorithm performance was context-dependent: GMMs excelled, providing the most interpretable partitioning of multiple error levels, including gross errors; DBSCAN succeeded at isolating the dominant systematic error from noise. However, FCM struggled to capture the complex spatial nature of errors in both cases. Through the automated identification of problematic regions with different error characteristics, the proposed approach provides actionable insights for targeted, cost-effective cadastral renewal. This aligns with fit-for-purpose land administration principles, supporting progressive improvements towards more reliable cadastral data and offering a novel methodology applicable to other LASs facing similar challenges. Full article