vegetation classification and mapping - mtnhp.orgmtnhp.org/ecology/nvcs/veg_mapping_ppt.pdf ·...
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Vegetation Classification and Mapping
Developed by Montana Natural Heritage Program1515 East 6th Avenue
Helena, MT 59620-1800http://mtnhp.org/
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Vegetation classification
� Vegetation classification is the process of grouping similar stands together based on shared characteristics.
� For ecologists classifying vegetation, the relevant shared characteristics are physiognomy and floristics
� For mappers classifying vegetation, the relevant shared characteristics are spectral signatures and detectable structural characteristics.
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The National Vegetation ClassificiationStandard and Mapping
� The NVC is not a mapping standard
� However, the NVC can be used as a map legend
� The level of the NVC that can be used as a map legend will depend on the size of the area being mapped
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Map-based classification
� Across large areas (e.g., a state, a region), formations, divisions, or macrogroupscan be used as mapping units
� Across smaller areas (e.g., a wetland complex, a forest) it is possible to use groups, associations or alliances as mapping units
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Refresher: the NVC Hierarchy
� Formation Class: Forest and Woodland
� Formation:Cool Temperate Forest� Division: Western North America
Cool Temperate Forest� Macrogroup: Northern Rocky
Mountain Subboreal-MontaneForest
� Group: Northwestern Great Plains Aspen Forest and Woodland
� Alliance: Populus tremuloidesWoodland Forest
� Association: Populustremuloides/Osmorhizaoccidentalis Association
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Steps in classification and mapping
� Plots are used to gather field data
� In small areas, all vegetation types are sampled
� In large areas, representative types are sampled
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Sampling
� Once sample sites are located (by either community-based or site-based methods), plots are placed in areas of homogenous vegetation which are determined to be representative of the vegetation type.
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Stand selection and plot design
� A plot is established in a relatively homogenous unit of vegetation that is of sufficient size to represent the total species composition and abundance of the stand.
� Species composition and abundance, structure and canopy cover are measured to describe vegetation within the plot.
� The NVC association and/or alliance is determined for mapping purposes
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Turning field data into maps
� Field data is used to develop “signatures” for different vegetation types
� The signatures are used to photointerpret aerial images with the human eye or to “train” image classification software
� Images are transformed into maps with appropriate legends
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Image segmentation and classification
� One approach to vegetation classification involves segmenting the image
� The software parses out the image into segments with distinct signatures
� The segments are then grouped and classified based on the field data.
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Classifying segmented images
� A spatial database is used to house attributes (e.g. elevation, slope, soils) for each region in the segmented image.
� The data is analyzed with a classification algorithm to classify segments by vegetation type. When field data is used as training data, it is referred to as “supervised classification.”
� The segments are then labelledusing a map legend based on a classification, e.g., the NVC
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Post-processing
� Classified images are post processed using expert knowledge and ancillary datasets
� Expert knowledge helps adjust spatial accuracy (e.g., eliminates mapped associations beyond their range)
� Ancillary datasets (e.g. soils data overlaid on right) help refine maps by aligning vegetation types with appropriate habitats
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Scale Matters!
Landsat TMresampled to 15 m
NAIP1 m
SPOT2.5 m
QuickBird0.7 m
•The scale of the image being classified will determine the detail that can be captured, and the level of the NVC that can be used as a map legend
•The scale of the image will always affect accuracy
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Field verification (ground truthing)
� The larger and more variable a landscape, the less accurate the classification will be
� Field verification helps determine the accuracy of maps.
� Verification data is added to training data sets and the classification can be rerun
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Maps are representations, not reality
� Classification-based vegetation maps are useful for general characterization of broad landscapes
� Classification-based maps are least useful for identifying specific boundaries of individual vegetation types, especially at the association or alliance level