Marine geoinformatics in the Baltic Sea

Research examples

Tolvanen, H. & T. Suominen (2005). Quantification of openness and wave activity in archipelago environments. Estuarine, Coastal and Shelf Science, 64(2–3), pp. 436–446. DOI.

Abstract:
Ecological and geomorphological processes in the littoral zone are greatly influenced by waves. Despite its importance wave exposure is not often quantified as a spatial parameter in coastal research. We present three GIS-based approaches for openness and wave exposure assessment in archipelago environments. First, we studied openness by fetch measurements: a theme map of sea area openness showing average fetch lengths for 1 km2 study cells over the SW-Finnish archipelago area of 16 900 km2 was produced. Second, we calculated wave exposure based on average wave power values for shoreline points to create a shoreline exposure classification within the same study areas. Finally, we made temporal graphs to study the variation in shoreline wave power and work at selected sites during a four-year period. The fetch and wave power calculations using GIS provide interesting possibilities for spatial studies concerning shoreline exposure in fragmented archipelago environments. Different kinds of numerical information of the shoreline exposure are required for diverse purposes in coastal regions: the average fetch maps assess effectively the sea area openness, and shoreline exposure and the temporal graphs quantify parameters relevant to littoral processes.

Rönkä, M., H. Tolvanen, E. Lehikoinen, M. von Numers & M. Rautkari (2008). Breeding habitat preferences of 15 bird species on south-western Finnish archipelago coast: The applicability of existing digital spatial data archives to habitat assessment. Biological Conservation 141(2), pp. 403–417. DOI.

Abstract:
Knowledge about the importance of physical habitat characteristics to the breeding site selection of birds is a prerequisite for understanding their breeding habitat ecology and distribution, as well as managing their habitats. Geographical information systems (GIS) and existing digital data archives provide new possibilities for the quantitative and cost-effective assessment of coastal breeding habitats of birds. We tested the applicability of GIS and digital data archives for the analysis of coastal bird habitats by conducting a multivariate analysis on the relationship between physical island characteristics and the breeding site selection of 15 species of ducks, waders, larids and alcids in 2001–2005 in the fragmented archipelago coast of south-western Finland. We used GIS and environmental databases containing shoreline, bathymetry and elevation data to calculate five physical parameters for 71 small islands and their vicinity. Island area was generally the most important factor determining the presence of the species, but also water depth, shore openness, and island elevation were important for some species. The differences and similarities in habitat preferences within and between species groups seem to reflect the breeding habitat ecology of the species. GIS and spatial data archives are becoming increasingly valuable for research and development, as well as administrative tasks. Our results indicate that physical island characteristics affect the breeding site selection and the distribution of our target species, and that GIS and digital data archives provide applicable information on the breeding habitats of coastal birds and thus function as a tool for conservation and management.

Murtojärvi, M., T. Suominen, H. Tolvanen, V. Leppänen & O. Nevalainen (2007). Quantifying distances from points to polygons—applications in determining fetch in coastal environments. Computers & Geosciences 33(7), pp. 843–852. DOI.

Abstract:
Distance from a point to adjacent borderlines is a variable that has many applications in environmental research. Geographical information systems (GIS) include tools for measuring such distances, but these tools are inefficient if there are multiple, i.e. millions of distances to be calculated. In this paper we propose an efficient algorithm which calculates the distances in multiple predetermined directions from a large number of points to polygon borders.
The problem is significantly simplified by the fact that the distances are calculated in some directions, only. An interval tree is utilized for efficiently retrieving those line segments describing the coastal lines and the borders of the islands that are relevant in determining these distances. The algorithm is also robust so that it gives meaningful results in the presence of rounding errors regardless of the positions of the study points with respect to the polygon borders. In coastal environments the straight-line distance from a point to the nearest shoreline over an open water surface is referred to as fetch length. The fetch lengths in multiple directions indicate general openness around a studied point and it may also be used as a variable in wave power calculations. An implementation of the algorithm was used for calculating fetch data for the archipelago of SW-Finnish coast in the Baltic Sea. The map data contained 3 million vertices and fetch lengths were calculated for 2.5 million points in 48 directions. The algorithm enabled determining fetch lengths in the complex archipelago environment quickly in high spatial accuracy and it may have applications also in other geographical research and image processing.

Rinne, H., A. Kaskela, A.-L. Downie, H. Tolvanen, M. von Numers & J. Mattila (2014). Predicting the occurrence of rocky reefs in a heterogeneous archipelago area with limited data. Estuarine, Coastal and Shelf Science, 138, pp. 90–100. DOI.

Abstract:
The lack of spatial distribution data on marine habitats often presents an obstacle to their protection. The Annex I of the Habitats Directive (European Council Directive 92/43/EEC) lists habitats that are important in biodiversity protection and should be maintained (or restored) to a favourable conservation status. The habitats listed should be protected within an ecological network of protected areas, the Natura 2000 network. However, in the past the establishment of the marine Natura 2000 network has been largely based on insufficient knowledge on the distribution of the habitats. Annex I habitat type reefs are defined as formations of hard compact biogenic or geogenic substrata, which arise from the seafloor in the sublittoral and littoral zone. As obtaining marine data is time-consuming and costly, the bathymetric and substratum data needed for their identification on a larger scale are often scarce. Furthermore, the use of data may be limited due to e.g. national security reasons. This study identifies reefs in a complex archipelago area in the northern Baltic Sea using the best, although limited, data currently available. In the area reefs are elevated rocky outcrops and the associated algal communities and blue mussel beds are vital in maintaining biodiversity in the relatively species poor Baltic Sea. In addition to identifying the physical reef structures, an estimate of their ecological value is obtained by modelling the distribution of four key species occurring on reefs. The results are encouraging, as 55 out of 68 of the potential reefs ground-truthed were confirmed to be reefs. Furthermore the number of predicted species occurring on the reefs, correlated significantly with the number of species observed. The presented maps serve as a valuable background for more detailed mapping of the species diversity occurring on reefs as well as for monitoring their ecological status. Map-based information on important habitats is essential in conservation and marine spatial planning to minimize human impact on marine ecosystems.

Stock, A., H. Tolvanen & R. Kalliola (2010). Crossing natural and dataset boundaries: Coastal terrain modelling in the Southwest Finnish Archipelago. International Journal of Geographical Information Science, 24(9), pp. 1435–1452. DOI.

Abstract:
Geographical information systems (GIS) are important tools in coastal research and management. Coastal GIS applications involve special challenges, because the coastal environment is a complex transitional system between the terrestrial and marine realms. Also acquisition methods and responsibilities for spatial data (and thus their properties) change at the shoreline. This article explores the consequences of this land-sea divide for coastal terrain modelling. We study how methods designed for terrestrial environments can be used to create integrated raster coastal terrain models (CTMs) from coarse elevation and depth data. We focus on shore slopes, because many particularities of coastal terrain and the data which describe it as well as the resulting problems are concentrated in the shore zone. Based on shorelines, terrestrial contours, depth contours and depth points, we used the ANUDEM algorithm to interpolate CTMs at different spatial resolutions, with and without drainage enforcement, for two test areas in a highly complex archipelago coast. Slope aspect and gradient rasters were derived from the CTMs using Horn’s algorithm. Values were assigned from the slope rasters to thousands of points along the test areas’ shorelines in different ways. Shore slope gradients and aspects were also calculated directly from the shorelines and contours. These modelled data were compared to each other and to field-measured shore profiles using a combination of qualitative and quantitative methods. As far as the coarse source data permitted, the interpolation and slope calculations delivered good results at fine spatial resolutions. Vector-based slope calculations were very sensitive to quality problems of the source data. Fine-resolution raster data were consequently found most suitable for describing shore slopes from coarse coastal terrain data. Terrestrial and marine parts of the CTMs were subject to different errors, and modelling methods and parameters had different consequences there. Thus, methods designed for terrestrial applications can be successfully used for coastal terrain modelling, but the choice of methods and parameters and the interpretation of modelling results require special attention to the differences of terrestrial and marine topography and data.