PhD Senion lecturer (assistant professor) in ecological geograpbhy
director of PhD education in Physical Geography
Department för naturgeografi
106 91 Stockholm
Phone: (+46) 8 164773 eller (+46) 70 5614434
See also research unit in biogeography and geomatics
Commisions of trust within Stockholm University:
Represenging the ection for geoscience and environmenti Naturvetenskapliga fakultetens forskarubildningsberedning
Medlem i Bolincentrets Climate Research School advisory board
A selection from Stockholm University publication database
An innovative use of orthophotos - possibilities to assess plant productivity from colour infrared aerial orthophotos
2019. Rasmus Erlandsson (et al.). Remote Sensing in Ecology and ConservationArticle
Studies of ecological processes should focus on a relevant spatial scale, as crude spatial resolution will fail to detect small scale variation which is of potentially critical importance. Remote sensing methods based on multispectral satellite images are used to assess primary productivity and aerial photos to map vegetation structure. Both methods are based on the principle that photosynthetically active vegetation has a characteristic spectral signature. Yet they are applied differently due to technical differences. Satellite images are suitable for calculations of vegetation indices, for example Normalized Difference Vegetation Index (NDVI). Colour infrared aerial photography was developed for visual interpretation and never regarded for calculation of indices since the spectrum recorded and post processing differ from satellite images. With digital cameras and improved techniques for generating colour infrared orthophotos, the implications of these differences are uncertain and should be explored. We tested if plant productivity can be assessed using colour infrared aerial orthophotos (0.5 m resolution) by applying the standard NDVI equation. With 112 vegetation samples as ground truth, we evaluated an index that we denote rel‐NDVIortho in two areas of the Fennoscandian mountain tundra. We compared the results with conventional SPOT5 satellite‐based NDVI (10 m resolution). rel‐NDVIortho was related to plant productivity (Northern area: P = <0.001, R2 = 0.73; Southern area: P = <0.001, R2 = 0.39), performed similar to SPOT5 satellite NDVI (Northern area: P = <0.001, R2 = 0.76; Southern area: P = <0.001, R2 = 0.40) and the two methods were highly correlated (cor = 0.95 and cor = 0.84). Despite different plant composition, the results were consistent between areas. Our results suggest that vegetation indices based on colour infrared aerial orthophotos can be a valuable tool in the remote sensing toolbox, offering a high‐spatial resolution proxy for plant productivity with less signal degradation due to atmospheric interference and clouds, compared to satellite images. Further research should aim to investigate if the method is applicable to other ecosystems.
2017. Alistair G. Auffret (et al.). Methods in Ecology and Evolution 8 (11), 1453-1457Article
Habitat destruction and degradation represent serious threats to biodiversity, and quantification of land-use change over time is important for understanding the consequences of these changes to organisms and ecosystem service provision. Comparing land use between maps from different time periods allows estimation of the magnitude of habitat change in an area. However, digitizing historical maps manually is time-consuming and analyses of change are usually carried out at small spatial extents or at low resolutions. HistMapR contains a number of functions that can be used to semi-automatically digitize historical land use according to a map's colours, as defined by the RGB bands of the raster image. We test the method on different historical land-use map series and compare results to manual digitizations. Digitization is fast, and agreement with manually digitized maps of around 80-90% meets common targets for image classification. We hope that the ability to quickly classify large areas of historical land use will promote the inclusion of land-use change into analyses of biodiversity, species distributions and ecosystem services.
The Need for Awareness of Semantic Plasticity in International Harmonization of Geographical Information
2015. Alexandra Björk, Helle Skånes. Land Use and Land Cover Semantics, 41-58Chapter
The aim of this chapter is to address and clarify the important issues and challenges of semantic plasticity when it comes to forest classification and geographical information. Necessary improvements for international data harmonization and implementation are highlighted along with the need for increased awareness of the consequences for ecological modeling. We envisage a combination of thoroughly described metadata and controlled vocabularies as a means to ensure the future use of a wide range of regional and national classification systems in an ontological framework that enables crosswalks between classification systems and spatial comparisons between existing data sets. This would allow for a wide range of old, contemporary, and future data sets to be used together in landscape-related analyses.
Detecting subpixel deciduous components to complement traditional land cover classifications in Southwest Finland
2015. Timo P. Pitkänen, Helle Skånes, Niina Käyhkö. International Journal of Applied Earth Observation and Geoinformation 42, 97-105Article
To ensure successful conservation of ecological and cultural landscape values, detailed and up-to-datespatial information of existing habitat patterns is essential. However, traditional satellite-based and rasterclassifications rely on pixels that are assigned to a single category and often generalized. For many frag-mented key habitats, such a strategy is too coarse and complementary data is needed. In this paper,we aim at detecting pixel-wise fractional coverage of broadleaved woodland and grassland componentsin a hemiboreal landscape. This approach targets ecologically relevant deciduous fractions and com-plements traditional crisp land cover classifications. We modeled fractional components using a k-NNapproach, which was based on multispectral satellite data, assisted by a digital elevation model and acontemporary map database. The modeled components were then analyzed based on landscape struc-ture indicators, and evaluated in conjunction with CORINE classification. The results indicate that bothbroadleaved forest and grassland components are widely distributed in the study area, principally orga-nized as transition zones and small patches. Landscape structure indicators show a substantial variationbased on the fractional threshold, pinpointing their dependency on the classification scheme and grain.The modeled components, on the other hand, suggest high internal variation for most CORINE classes,indicating their heterogeneous appearance and showing that the presence of deciduous components inthe landscape are not properly captured in a coarse land cover classification. To gain a realistic perceptionof the landscape, and use this information for the needs of spatial planning, both fractional results andexisting land cover classifications are needed. This is because they mutually contribute to an improvedunderstanding of habitat patterns and structures, and should be used to complement each other.
Change trajectories and key biotopes
2006. Niina Käyhkö, Helle Skånes. Landscape and Urban Planning 75 (3-4), 300-321Article
This paper presents a methodological synthesis of two congruent approaches into a common landscape change trajectory analysis and the assessment of landscape dynamics and sustainability. The emphasis of the analysis is on the retrospective relationship between the past and the present-day landscape patterns and associated key biotopes. The example key biotopes, oak woodlands and grasslands, represent valuable habitats in the hemiboreal landscapes of Finland and Sweden. The paper presents a conceptual stepwise approach for change trajectory analysis utilising multiple spatio-temporal data and techniques available in image processing and geographical information systems (GIS) including the following steps: (I) specification of spatio-temporal data and their representation of target objects, (II) the choice of direct or indirect change trajectory analysis, (III) hierarchical structuring of landscape information, (IV) compilation of landscape information into a GIS database, and (V) identification of paths for landscape change trajectory analysis. In this case study, we have focused on three interlinked trajectory analysis approaches, and their role in the assessment of landscape sustainability from a potential biodiversity perspective. We conclude that proposed landscape change trajectory analysis can improve the assessment of the key biotopes as well as present day landscape characteristics, in maintaining biodiversity and related ecological values by providing information on landscape stability, continuity, change processes and boundary dynamics. This approach can be useful in the assessment of natural capital, but requires data-specific and context sensitive data processing and analysis solutions. The results should be interpreted as an approximation and generalisation of the spatio-temporal complexity of landscape reality and therefore be used in conjunction with additional habitat function measures.
Visual interpretation of key properties in vegetation structur from Lidar data
2010. Helle Skånes, Anders Glimskär, Anna Allard.Conference
This paper discusses early findings from one of several projects within a recently launched research program devoted to environmental mapping and monitoring with airborne laser and digital images (EMMA) financed by the Swedish EPA. Policy makers and land managers along with the global community increasingly demand hard figures regarding the state and trends of biodiversity and habitat qualities of importance to nature conservation and international environmental quality goals. Although remote sensing and GIS based methods have greatly improved, there is still a lack of spatially detailed and consistent habitat data to meet these requirements. Key vegetation qualities are often hidden from visual and automatic classification in high resolution remote sensing imagery since they are typically covered by trees. Laser beams can partly penetrate through the canopy and the data derived from the reflected pulses will add crucial detail and consistency in vegetation mapping. The aim of the project is to visually explore LiDAR data focusing on habitats within agricultural and alpine environments for enhanced vegetation classification and registration of habitat qualities and structures. Initially a number of key variables (vertical and horizontal structure, influence of land use, and site conditions) have been explored through visual interpretation of two time sets of high resolution 3D laser point data (density>5 points/m²) and derivates processed to enhance objects of interest. The initial results from a wooded pasture indicate that key properties, such as ditches, field and shrub layer characteristics and distribution, fallen trees and various man made remnants are in fact detectable. The use of laser-generated high-quality bare earth models is crucial to distinguish the field layer and low shrubs from boulders and uneven ground surface. These bare earth models will as they become widely available enhance all types of habitat modeling and landscape analysis.