Geomatics, Volume 3, Issue 2 (June 2023) – 4 articles

Sediment Barriers (SBs) are crucial for effective erosion control, and understanding their capacities and limitations is essential for environmental protection. This study compares the accuracy and effectiveness of Terrestrial Laser Scanning (TLS) and Robotic Total Station (RTS) techniques for quantifying sediment retention in SBs. To achieve this, erosion tests were conducted in a full-scale testing apparatus with TLS and RTS methods to collect morphological data of sediment retention surfaces before and after each experiment. The acquired datasets were processed and integrated into a Building Information Modeling (BIM) platform to create Digital Elevation Models (DEMs). These were then used to calculate the volume of accumulated sediment upstream of the SB system. The results indicated that TLS and RTS techniques could effectively measure sediment retention in a full-scale testing environment. However, TLS proved to be more accurate, exhibiting a standard deviation of 0.41 ft³ in contrast to 1.94 ft³ for RTS and more efficient, requiring approximately 15% to 50% less time per test than RTS. The main conclusions of this study highlight the benefits of using TLS over RTS for sediment retention measurement and provide valuable insights for improving erosion control strategies and sediment barrier design. Full article

Invasive alien plants (IAPs) pose a serious threat to biodiversity, agriculture, health, and economies globally. Accurate mapping of IAPs is crucial for their management, to mitigate their impacts and prevent further spread where possible. Remote sensing has become a valuable tool in detecting IAPs, especially with freely available data such as Sentinel-2 satellite imagery. Yet, remote sensing methods to map herbaceous IAPs, which tend to be more difficult to detect, particularly in shrubland Mediterranean-type ecosystems, are still limited. There is a growing need to detect herbaceous IAPs at a large scale for monitoring and management; however, for countries or organizations with limited budgets, this is often not feasible. To address this, we aimed to develop a classification methodology based on optical satellite data to map herbaceous IAP’s using Echium plantagineum as a case study in the Fynbos Biome of South Africa. We investigate the use of freely available Sentinel-2 data, use the robust non-parametric classifier Random Forest, and identify the most important variables in the classification, all within the cloud-based platform, Google Earth Engine. Findings reveal the importance of the shortwave infrared and red-edge parts of the spectrum and the importance of including vegetation indices in the classification for discriminating E. plantagineum. Here, we demonstrate the potential of Sentinel-2 data, the Random Forest classifier, and Google Earth Engine for mapping herbaceous IAPs in Mediterranean ecosystems. Full article

Sustainable agricultural management requires knowledge of where and when crops are grown, what they are, and for how long. However, such information is not yet available in Nepal. Remote sensing coupled with farmers’ knowledge offers a solution to fill this gap. In this study, we created a high-resolution (10 m) seasonal crop map and cropping pattern in a mountainous area of Nepal through a semi-automatic workflow using Sentinel-2 A/B time-series images coupled with farmer knowledge. We identified agricultural areas through iterative self-organizing data clustering of Sentinel imagery and topographic information using a digital elevation model automatically. This agricultural area was analyzed to develop crop calendars and to track seasonal crop dynamics using rule-based methods. Finally, we computed a pixel-level crop-intensity map. In the end our results were compared to ground-truth data collected in the field and published crop calendars, with an overall accuracy of 88% and kappa coefficient of 0.83. We found variations in crop intensity and seasonal crop extension across the study area, with higher intensity in plain areas with irrigation facilities and longer fallow cycles in dry and hilly regions. The semi-automatic workflow was successfully implemented in the heterogeneous topography and is applicable to the diverse topography of the entire country, providing crucial information for mapping and monitoring crops that is very useful for the formulation of strategic agricultural plans and food security in Nepal. Full article