GIS-based support for the complex botanical studies at the Molnieboi Spur, Altai

The Molnieboi Spur is located at the northwestern margin of the Katun Range, the high-mountain part of the Altai Mountains. Unique geological and geophysical characteristics of the Molnieboi Spur made it an attractive target for complex botanical studies including botanical, soil, geological, geochemical, geophysical, radiation, and soil gas surveys and analyses. In this paper, we present the first version of the geographic information system (GIS) application for the Molnieboi Spur developed using the software QGIS. A digital elevation model for the study area was derived from a detailed topographic map. The database was filled with tabular data on about 100 parameters including: eight botanical characteristics of the Lonicera caerulea local population, two cytogenetic indices of Lonicera caerulea seeds, five types of biochemical parameters of Lonicera caerulea leaves and fruits, three types of geochemical characteristics of the local soils, three types of radiation parameters of the local soils and Lonicera caerulea plants, and one soil gas parameter. The results of the magnetometric survey were inserted as a raster image. A visual analysis of the maps produced allows one to better understand the spatial relationships between various natural components of the Molnieboi Spur.

💡 Research Summary

The paper presents the first GIS‑based decision‑support system developed for the Molnieboi Spur, a high‑mountain sector of the Altai range that exhibits a unique combination of geological, geophysical, and ecological characteristics. The authors describe the creation of a spatial database in QGIS that integrates a digital elevation model (DEM) derived from a detailed 1:10 000 topographic map with approximately one hundred attribute variables collected during multidisciplinary field campaigns. These variables encompass eight morphological traits of the local Lonicera caerulea (blue honeysuckle) population, two cytogenetic indices of its seeds, five biochemical parameters measured in leaves and fruits, three geochemical descriptors of the surrounding soils, three radiation metrics for soils and plants, and a single soil‑gas measurement (primarily methane and carbon dioxide concentrations). In addition, the results of a magnetometric survey are incorporated as a raster layer.

Methodologically, the study first digitized the topographic map to generate a 5‑meter resolution DEM, which serves as the terrain backbone for subsequent spatial analyses (slope, aspect, elevation gradients). All field samples were georeferenced with GPS, and their laboratory results were entered into attribute tables linked to point layers representing plant individuals, soil sampling sites, and gas measurement stations. The magnetometric data, collected on a dense grid, were processed into a georeferenced raster image and overlaid on the DEM. QGIS’s styling capabilities (color ramps, transparency, graduated symbols) were employed to produce composite maps that simultaneously display, for example, elevation, leaf area, soil radiation levels, and methane concentrations.

Visual inspection of these composites revealed clear spatial patterns: higher elevations corresponded with reduced leaf area in L. caerulea, elevated soil radiation, and distinct clusters of increased methane that coincided with magnetic anomalies, suggesting a link between subsurface geological structures and gas emissions. Such relationships are difficult to discern from isolated laboratory results but become evident when the data are visualized in a geographic context.

The authors argue that this GIS framework enhances data management, reproducibility, and interdisciplinary communication. It allows rapid integration of new measurements, facilitates spatial queries (e.g., “retrieve all soil samples within 200 m of a magnetic anomaly”), and supports preliminary hypothesis generation before formal statistical modeling. Limitations include the current lack of automated data validation, limited temporal resolution (the database reflects a single sampling period), and the absence of advanced spatial statistics or machine‑learning modules.

Future work is outlined to address these gaps: incorporation of high‑resolution satellite or drone imagery, development of a PostgreSQL/PostGIS backend for robust version control, and implementation of spatial regression or clustering algorithms to quantify the observed patterns. The authors also propose extending the system to monitor long‑term environmental changes, such as climate‑driven shifts in plant phenology or the evolution of radiation hotspots.

In conclusion, the study demonstrates that a relatively low‑cost, open‑source GIS platform can successfully integrate heterogeneous botanical, soil, geochemical, geophysical, radiometric, and gas data for a complex mountain environment. By making spatial relationships explicit, the system provides a valuable tool for researchers, land managers, and conservation planners seeking to understand and protect the unique ecosystems of the Molnieboi Spur.