Finnish Geospatial Research Institute

The timing of peak snowmelt floods in boreal environments has undergone significant changes, characterized by nonlinear and complex patterns. This timing determines when coastal areas of boreal rivers experience the greatest inundation during the spring season. It is highly sensitive to climate change and directly influences local fauna and flora. Despite its critical role in flood risk management, the prediction of spring flood timing, along with the identification of its key drivers and most influential factors, remains insufficiently studied in boreal regions.In this study, we investigate the potential for predicting the timing of annual maximum snowmelt floods by applying a thermal definition of the spring season, along with various climatological and hydrological indices. The analysis is based on a comprehensive daily dataset available with varying record lengths of at least 50 years, available since the early 1960s and extending to 2023 across multiple unregulated Finnish catchments. Among the most important dynamic features are daily discharge records, high-resolution gridded temperature data, and atmospheric teleconnection indices. Additionally, key static catchment characteristics, such as area, slope, and geographical position, are also incorporated into the modeling process, along with other relevant variables.Machine learning algorithms, including Random Forest and SHAP (SHapley Additive exPlanations) values for feature importance, are applied to identify the most influential factors shaping the timing of annual maximum snowmelt floods and to assess the overall predictability of these events across multiple catchments. The study introduces a novel approach using a thermal definition of spring. The findings provide new indices and actionable thresholds that can help identify areas where adaptation measures should be prioritized.

Abstract Regular forest inventory is essential for conservation and management. Over the past decades, laser scanning has emerged as a remote and non-destructive solution to streamline this laborious process. Advanced multispectral (MS) laser scanning systems simultaneously acquire 3D spatial and spectral information across multiple wavelengths, enabling estimation of forest biophysical and biochemical traits. This study investigates the potential of airborne MS laser scanning for fine-grained forest semantic segmentation into six components (ground, low vegetation, trunk, branches, foliage, and woody debris), thereby supporting forest inventory and analysis. We evaluate three state-of-the-art 3D deep learning models (kernel point convolution (KPConv), superpoint transformer (SPT), and point transformer V3 (PTv3)) and random forest model. Our analysis reveals the superiority of PTv3, outperforming the other models by 21.8 percentage points (pp) with the mean intersection over union (mIoU) of 69.1%. Additionally, our rigorous spectral ablation study demonstrates that MS laser scanning data substantially improves the segmentation results, increasing the IoU of woody debris, branches, and trunks by 12.7 pp, 4.5 pp, and 2.5 pp, respectively. This study highlights the strong potential of MS laser scanning to enable automated and accurate forest inventory through prior fine-grained forest semantic segmentation.

Abstract Cold plasma distribution in the ionosphere‐plasmasphere system governs wave‐particle interactions, plasma energization and loss, and radio wave propagation. A longstanding observational gap at altitudes 800–8,000 km has largely prevented studying the coupled dynamics of the two regions. Here, we show that observations by JAXA's Arase mission can bridge this gap. Electron densities inferred from the upper hybrid resonance frequencies measured by Arase are highly consistent with radio occultation profiles from the Constellation Observing System for Meteorology, Ionosphere and Climate (COSMIC) mission, with a median difference of 5%. Using the combined COSMIC‐Arase data set, we provide a convenient way to reconcile the two regions in empirical models based on the analytical Chapman function inversion for scale height. Our results enable studying fundamental questions about the ionosphere‐plasmasphere coupling, their transition, and life cycle of cold plasma in near‐Earth space.

Phytonematodes are parasites of plant tissues, primarily roots. Although they cause significant damage to agriculture, they are still underexplored. Soybean crops suffer significant yield losses due to phytonematode infestations. Remote sensing techniques offer an effective means of detecting and analyzing these infestations. This study aimed to identify the most suitable stage in the soybean plant’s growth cycle for detecting spectral differences between nematode-infested and healthy plants using MSI/Sentinel-2 imagery. The study utilized three sets of Sentinel-2 images, representing different growth stages (December - vegetative, January - grain development, February - maturation). 38 nematological sample points were classified into three infestation levels (no or low, moderate, high). Spectral responses were collected for each growth stage, followed by an analysis of reflectance values and NDVI to assess class separability. The maturation stage exhibited the most significant spectral differences between infestation levels, especially in the near-infrared and red-edge regions. The sum of squared differences demonstrated the greatest separability in the maturation stage, particularly between moderate and no or low infestation categories. The results suggest that the end of the reproductive stage is optimal for detecting spectral differences in soybean plants affected by nematodes.

Abstract. In recent years, flying autonomously with UAVs has been a widely researched topic. While most autonomous flying systems use the Global Navigation Satellite Systems (GNSS) for localization, using GNSS under the forest canopy is not applicable. Thus other sensors such as lidars are needed. The objective of this study was to evaluate the performance of an autonomous flying system for flying inside boreal forests. Two open-sourced algorithms, IPC (Integrated Planning and Control framework) path planner and control algorithm, and LTA-OM (Long-Term Association Lidar-Inertial Odometry and Mapping) simultaneous location and mapping (SLAM) algorithm, were chosen as the base for the custom-built quadrotor system. Livox Mid-360 lidar was used as the sensor. The system was evaluated with a set of extensive experiments. First, a set of simulation experiments were conducted with multiple flights flown in multiple forests with different tree densities and varying target flight velocities. Second, the localization accuracy of LTA-OM was evaluated by measuring the end-point drift from two manually flown real-world flights. Lastly, 33 autonomous real-world flights were performed in two different forest plots with varying levels of difficulty and tree density as well as with varying target flight velocities. Based on real-world experiments, the performance of the system was somewhat promising inside the medium-difficulty forest, but poor inside the dense difficult forest. The success rate of real-world flights was 10/15 inside the medium-difficulty forest and 6/15 inside the difficult forest with a target flight velocity of 1 m/s. The flight distance on the real-world flights was 60 meters.

Abstract High-accuracy GNSS (Global Navigation Satellite System) positioning requires the receiver to use correction data. This data is typically delivered via 4G mobile internet. In this paper, we present a novel method to deliver the data via C-ITS (Cooperative Intelligent Transport Systems and Service). We compare its performance against using 4G and analyze the impact on the accuracy during data gaps, all using data collected in test drives from a real deployment in a small segment of a motorway. The results show that with C-ITS, a comparable performance can be achieved. The observed 2D position errors in our tests were below 3.1 cm for 95% and below 10 cm for more than 99% of the time.