Mapping ground lichens using forest inventory and optical satellite data. Gilichinsky, M., Sandström, P., Reese, H., Kivinen, S., Moen, J., & Nilsson, M. International Journal of Remote Sensing, 32(2):455-472, 2011. ![Mapping ground lichens using forest inventory and optical satellite data [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex 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 decision-making purposes. Since the early 1980s, remote sensing research of lichen cover in northern environments has focused on reindeer grazing issues. The objective of this study was to use lichen information collected in the Swedish National Forest Inventory (NFI) as training data to classify optical satellite images into ground lichen cover classes. The study site was located within the reindeer husbandry area in northern Sweden and consisted of the common area between two contiguous Satellite Pour l'Observation de la Terre (SPOT)-5 scenes and one Landsat-7 Enhanced Thematic Mapper Plus (ETM+) scene. Three classification methods were tested: Mahalanobis distance, maximum likelihood and spectral mixture analysis. Post-classification calibration was applied using a membership probability threshold in order to match the NFI-measured proportions of lichen coverage classes. The classification results were assessed using an independently collected field dataset (229 validation areas). The results demonstrated high classification accuracy of SPOT imagery for the classification of lichen-abundant and lichen-poor areas when using the Mahalanobis distance classifier (overall accuracy 84.3%, kappa = 0.68). The highest classification accuracy for Landsat was achieved using a maximum likelihood classification (overall accuracy 76.8%, kappa = 0.53). These results provided an initial indication of the utility of NFI data as training data in the process of mapping lichen classes over large areas.
@article{RN466,
author = {Gilichinsky, Michael and Sandström, Per and Reese, Heather and Kivinen, Sonja and Moen, Jon and Nilsson, Mats},
title = {Mapping ground lichens using forest inventory and optical satellite data},
journal = {International Journal of Remote Sensing},
volume = {32},
number = {2},
pages = {455-472},
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 decision-making purposes. Since the early 1980s, remote sensing research of lichen cover in northern environments has focused on reindeer grazing issues. The objective of this study was to use lichen information collected in the Swedish National Forest Inventory (NFI) as training data to classify optical satellite images into ground lichen cover classes. The study site was located within the reindeer husbandry area in northern Sweden and consisted of the common area between two contiguous Satellite Pour l'Observation de la Terre (SPOT)-5 scenes and one Landsat-7 Enhanced Thematic Mapper Plus (ETM+) scene. Three classification methods were tested: Mahalanobis distance, maximum likelihood and spectral mixture analysis. Post-classification calibration was applied using a membership probability threshold in order to match the NFI-measured proportions of lichen coverage classes. The classification results were assessed using an independently collected field dataset (229 validation areas). The results demonstrated high classification accuracy of SPOT imagery for the classification of lichen-abundant and lichen-poor areas when using the Mahalanobis distance classifier (overall accuracy 84.3%, kappa = 0.68). The highest classification accuracy for Landsat was achieved using a maximum likelihood classification (overall accuracy 76.8%, kappa = 0.53). These results provided an initial indication of the utility of NFI data as training data in the process of mapping lichen classes over large areas.},
ISSN = {0143-1161},
DOI = {10.1080/01431160903474962},
url = {https://doi.org/10.1080/01431160903474962},
year = {2011},
type = {Journal Article}
}
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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 decision-making purposes. Since the early 1980s, remote sensing research of lichen cover in northern environments has focused on reindeer grazing issues. The objective of this study was to use lichen information collected in the Swedish National Forest Inventory (NFI) as training data to classify optical satellite images into ground lichen cover classes. The study site was located within the reindeer husbandry area in northern Sweden and consisted of the common area between two contiguous Satellite Pour l'Observation de la Terre (SPOT)-5 scenes and one Landsat-7 Enhanced Thematic Mapper Plus (ETM+) scene. Three classification methods were tested: Mahalanobis distance, maximum likelihood and spectral mixture analysis. Post-classification calibration was applied using a membership probability threshold in order to match the NFI-measured proportions of lichen coverage classes. The classification results were assessed using an independently collected field dataset (229 validation areas). The results demonstrated high classification accuracy of SPOT imagery for the classification of lichen-abundant and lichen-poor areas when using the Mahalanobis distance classifier (overall accuracy 84.3%, kappa = 0.68). The highest classification accuracy for Landsat was achieved using a maximum likelihood classification (overall accuracy 76.8%, kappa = 0.53). 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Knowledge about the spatial distribution of ground lichens is important for both practical and decision-making purposes. Since the early 1980s, remote sensing research of lichen cover in northern environments has focused on reindeer grazing issues. The objective of this study was to use lichen information collected in the Swedish National Forest Inventory (NFI) as training data to classify optical satellite images into ground lichen cover classes. The study site was located within the reindeer husbandry area in northern Sweden and consisted of the common area between two contiguous Satellite Pour l'Observation de la Terre (SPOT)-5 scenes and one Landsat-7 Enhanced Thematic Mapper Plus (ETM+) scene. Three classification methods were tested: Mahalanobis distance, maximum likelihood and spectral mixture analysis. Post-classification calibration was applied using a membership probability threshold in order to match the NFI-measured proportions of lichen coverage classes. 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