High spatial resolution vegetation mapping for assessment of wildlife habitat
ABSTRACT Management of large herbivore populations requires assessment of both the quality and quantity of habitats available to the animals. Classifying and quantifying their habitats, particularly the availability of forage, is often challenging because of the time and expense associated with coll...
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| Language: | English |
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Wiley
2013-12-01
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| Series: | Wildlife Society Bulletin |
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| Online Access: | https://doi.org/10.1002/wsb.344 |
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| _version_ | 1846120265825648640 |
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| author | Kelly M. Walton Donald E. Spalinger Norman R. Harris William B. Collins James J. Willacker |
| author_facet | Kelly M. Walton Donald E. Spalinger Norman R. Harris William B. Collins James J. Willacker |
| author_sort | Kelly M. Walton |
| collection | DOAJ |
| description | ABSTRACT Management of large herbivore populations requires assessment of both the quality and quantity of habitats available to the animals. Classifying and quantifying their habitats, particularly the availability of forage, is often challenging because of the time and expense associated with collection of the appropriate data over large landscapes. Land‐cover maps generated from remote‐sensing imagery generally provide only qualitative information regarding species composition of the vegetation, and do not adequately quantify forage species availability. Our objective was to determine the feasibility of mapping important forage species for moose (Alces americanus) in South‐central Alaska, USA, using fine‐resolution aerial photography. We used infrared and color digital photography acquired at an altitude of 150 m across 2 study areas with distinctly different vegetation to provide spectral data for supervised classifications of plant species using the maximum likelihood algorithm. We analyzed spring, autumn, and composite sets of the imagery to determine which imagery provides the most accurate classifications. Willow (Salix spp.) was separable from non‐forage species in autumn images with an average overall accuracy of 76% in the Nelchina study area and 78% in Placer Valley. The classifications of composite images separated important moose forages, but with lower average overall accuracies (68% for Nelchina study area and 75% for Placer Valley) compared with the autumn classifications. Spring classifications were least accurate (57% for Nelchina study area and 63% for Placer Valley), likely because the green‐up of plants in the spring occurred rapidly for all species in both study sites, and this resulted in relatively small differences in spectral response patterns between species. © 2013 The Wildlife Society. © The Wildlife Society, 2013 |
| format | Article |
| id | doaj-art-1ea2a7231dae41fda52830d7ab9e250f |
| institution | Kabale University |
| issn | 2328-5540 |
| language | English |
| publishDate | 2013-12-01 |
| publisher | Wiley |
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| series | Wildlife Society Bulletin |
| spelling | doaj-art-1ea2a7231dae41fda52830d7ab9e250f2024-12-16T12:21:17ZengWileyWildlife Society Bulletin2328-55402013-12-0137490691510.1002/wsb.344High spatial resolution vegetation mapping for assessment of wildlife habitatKelly M. Walton0Donald E. Spalinger1Norman R. Harris2William B. Collins3James J. Willacker4Department of Biological SciencesUniversity of Alaska Anchorage3211 Providence DriveAnchorageAK99508USADepartment of Biological SciencesUniversity of Alaska Anchorage3211 Providence DriveAnchorageAK99508USASchool of Natural Resources and Agricultural SciencesUniversity of Alaska Fairbanks1509 S Georgeson DrivePalmerAK99645USADivision of Wildlife ConservationAlaska Department of Fish and Game1800 Glenn HighwayPalmerAK99645USADepartment of Biological SciencesUniversity of Alaska Anchorage3211 Providence DriveAnchorageAK99508USAABSTRACT Management of large herbivore populations requires assessment of both the quality and quantity of habitats available to the animals. Classifying and quantifying their habitats, particularly the availability of forage, is often challenging because of the time and expense associated with collection of the appropriate data over large landscapes. Land‐cover maps generated from remote‐sensing imagery generally provide only qualitative information regarding species composition of the vegetation, and do not adequately quantify forage species availability. Our objective was to determine the feasibility of mapping important forage species for moose (Alces americanus) in South‐central Alaska, USA, using fine‐resolution aerial photography. We used infrared and color digital photography acquired at an altitude of 150 m across 2 study areas with distinctly different vegetation to provide spectral data for supervised classifications of plant species using the maximum likelihood algorithm. We analyzed spring, autumn, and composite sets of the imagery to determine which imagery provides the most accurate classifications. Willow (Salix spp.) was separable from non‐forage species in autumn images with an average overall accuracy of 76% in the Nelchina study area and 78% in Placer Valley. The classifications of composite images separated important moose forages, but with lower average overall accuracies (68% for Nelchina study area and 75% for Placer Valley) compared with the autumn classifications. Spring classifications were least accurate (57% for Nelchina study area and 63% for Placer Valley), likely because the green‐up of plants in the spring occurred rapidly for all species in both study sites, and this resulted in relatively small differences in spectral response patterns between species. © 2013 The Wildlife Society. © The Wildlife Society, 2013https://doi.org/10.1002/wsb.344AlaskaAlces americanusland coverremote sensingSalixsupervised classification |
| spellingShingle | Kelly M. Walton Donald E. Spalinger Norman R. Harris William B. Collins James J. Willacker High spatial resolution vegetation mapping for assessment of wildlife habitat Wildlife Society Bulletin Alaska Alces americanus land cover remote sensing Salix supervised classification |
| title | High spatial resolution vegetation mapping for assessment of wildlife habitat |
| title_full | High spatial resolution vegetation mapping for assessment of wildlife habitat |
| title_fullStr | High spatial resolution vegetation mapping for assessment of wildlife habitat |
| title_full_unstemmed | High spatial resolution vegetation mapping for assessment of wildlife habitat |
| title_short | High spatial resolution vegetation mapping for assessment of wildlife habitat |
| title_sort | high spatial resolution vegetation mapping for assessment of wildlife habitat |
| topic | Alaska Alces americanus land cover remote sensing Salix supervised classification |
| url | https://doi.org/10.1002/wsb.344 |
| work_keys_str_mv | AT kellymwalton highspatialresolutionvegetationmappingforassessmentofwildlifehabitat AT donaldespalinger highspatialresolutionvegetationmappingforassessmentofwildlifehabitat AT normanrharris highspatialresolutionvegetationmappingforassessmentofwildlifehabitat AT williambcollins highspatialresolutionvegetationmappingforassessmentofwildlifehabitat AT jamesjwillacker highspatialresolutionvegetationmappingforassessmentofwildlifehabitat |