| 1. |
- Allard, Christina, et al.
(författare)
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Rasbiologiskt språkbruk i statens rättsprocess mot sameby : DN Debatt 2015-06-11
- 2015
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Annan publikation (populärvet., debatt m.m.)abstract
- Statens hantering av forskningsresultat i rättsprocessen med Girjas sameby utgör ett hot mot Sverige som rättsstat och kunskapsnation. Åratal av svensk och internationell forskning underkänns och man använder ett språkbruk som skulle kunna vara hämtat från rasbiologins tid. Nu måste staten ta sitt ansvar och börja agera som en demokratisk rättsstat, skriver 59 forskare.
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| 2. |
- Gilichinsky, Michael, et al.
(författare)
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Application of national forest inventory for remote sensing classification of ground lichen in nothern Sweden
- 2010
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Ingår i: ISPRS Archives. - : International Society for Photogrammetry and Remote Sensing. ; 38-4-8, s. 146-152
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Konferensbidrag (refereegranskat)abstract
- Lichen is a major forage resource for reindeer and may constitute up to 80% of a reindeer's winter diet. The reindeer grazing area in Sweden covers almost half of the country, with reindeer using mountainous areas in the summer and forested areas in the winter. Knowledge about the spatial distribution of ground lichens is important for both practical and sustainable decisionmaking purposes. Since the early 1980s, remote sensing research of lichen cover in northern environments has focused on reindeer grazing issues. The objective of the present study was to use lichen information from the Swedish Forest Inventory (NFI) for classification of satellite data into ground lichen classes. The classification procedure was focused on using of NFI plots as training sets for supervised classification of the ground lichen cover in purpose to classify areas with different lichen coverage. The present research has shown the advantage of use forest inventory plot data by assessment of three methods: mahalanobis distance (MD) classification, maximum likelihood (ML) classification and spectral mixture analysis (SMA). The results of this study demonstrate high classification accuracy of SPOT imagery in distinction between lichenabundant and lichen-poor areas by mahalanobis distance classifier (overall accuracy 84.3%, kappa=0.68). The highest classification accuracy for Landsat scene was achieved by maximumlikelihood classification (overall accuracy 76.8%, kappa=0.53). The continuation research on more detailed fragmentation of lichen cover into fractions is proposed.
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| 3. |
- Gilichinsky, Michael, et al.
(författare)
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Mapping ground lichens using forest inventory and optical satellite data
- 2011
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Ingår i: International Journal of Remote Sensing. - : Informa UK Limited. - 0143-1161 .- 1366-5901. ; 32:2, s. 455-472
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Tidskriftsartikel (refereegranskat)abstract
- Lichen is a major forage resource for reindeer and may constitute up to 80% of areindeer’s winter diet. The reindeer grazing area in Sweden covers almost half of thecountry, with reindeer using mountainous areas in the summer and forested areas inthe winter. Knowledge about the spatial distribution of ground lichens is importantfor both practical and decision-making purposes. Since the early 1980s, remotesensing research of lichen cover in northern environments has focused on reindeergrazing issues. The objective of this study was to use lichen information collected inthe Swedish National Forest Inventory (NFI) as training data to classify opticalsatellite images into ground lichen cover classes. The study site was located within thereindeer husbandry area in northern Sweden and consisted of the common areabetween two contiguous Satellite Pour l’Observation de la Terre (SPOT)-5 scenesand one Landsat-7 Enhanced Thematic Mapper Plus (ETMþ) scene. Three classificationmethods were tested: Mahalanobis distance, maximum likelihood andspectral mixture analysis. Post-classification calibration was applied using a membershipprobability threshold in order to match the NFI-measured proportions oflichen coverage classes. The classification results were assessed using an independentlycollected field dataset (229 validation areas). The results demonstrated highclassification accuracy of SPOT imagery for the classification of lichen-abundantand lichen-poor areas when using theMahalanobis distance classifier (overall accuracy84.3%, kappa ¼ 0.68). The highest classification accuracy for Landsat wasachieved using a maximum likelihood classification (overall accuracy 76.8%, kappa¼ 0.53). These results provided an initial indication of the utility of NFI data astraining data in the process of mapping lichen classes over large areas.
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| 4. |
- Horstkotte, Tim, 1981-, et al.
(författare)
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Pastures under pressure : Effects of other land users and the environment
- 2022
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Ingår i: Reindeer Husbandry and Global Environmental Change. - London : Routledge. - 9781000593402 - 9780367632670 ; , s. 76-98
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Bokkapitel (refereegranskat)abstract
- Reindeer husbandry has a long history of sharing landscapes with a multitude of other forms of land use. By competing for space, industrial resource developments from the early 20th century onwards have affected where, when and how the landscape can be used for reindeer grazing. Extending from the local to the landscape level, these impacts can reduce pastures either directly or indirectly as a result of increasing landscape fragmentation or changing reindeer behaviour. Furthermore, environmental drivers influence the dynamics of forage availability or accessibility for reindeer. The observed trend of shrinking pastures in the three countries is caused by these cumulative impacts. As a consequence, grazing pressure on the remaining pastures increases, and it curtails reindeer herders’ options to respond to the challenges of climate change. Reversing the continuing decrease in pastures requires the restoration of grazing resources and increasing landscape connectivity to facilitate movement and grazing rotation. However, socio-political incentives are also required to sustain reindeer pastures in the future. This includes an increase in the influence of reindeer herders on land use decisions and the inclusion of their traditional ecological knowledge of pasture management to identify alternative approaches to natural resource management.
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