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The National Vegetation Classification Standard applied to the remote sensing classification of two semiarid environments

Environmental Management

By:
, , ,
DOI: 10.1007/s00267-001-0048-5

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Abstract

The National Vegetation Classification Standard (NVCS) was implemented at two US National Park Service (NPS) sites in Texas, the Padre Island National Seashore (PINS) and the Lake Meredith National Recreation Area (LM-NRA), to provide information for NPS oil and gas management plans. Because NVCS landcover classifications did not exist for these two areas prior to this study, we created landcover classes, through intensive ground and aerial reconnaissance, that characterized the general landscape features and at the same time complied with NVCS guidelines. The created landcover classes were useful for the resource management and were conducive to classification with optical remote sensing systems, such as the Landsat Thematic Mapper (TM). In the LMNRA, topographic elevation data were added to the TM data to reduce confusion between cliff, high plains, and forest classes. Classification accuracies (kappa statistics) of 89.9% (0.89) and 88.2% (0.87) in PINS and LMNRA, respectively, verified that the two NPS landholdings were adequately mapped with TM data. Improved sensor systems with higher spectral and spatial resolutions will ultimately refine the broad classes defined in this classification; however, the landcover classifications created in this study have already provided valuable information for the management of both NPS lands. Habitat information provided by the classifications has aided in the placement of inventory and monitoring plots, has assisted oil and gas operators by providing information on sensitive habitats, and has allowed park managers to better use resources when fighting wildland fires and in protecting visitors and the infrastructure of NPS lands.

Additional Publication Details

Publication type:
Article
Publication Subtype:
Journal Article
Title:
The National Vegetation Classification Standard applied to the remote sensing classification of two semiarid environments
Series title:
Environmental Management
DOI:
10.1007/s00267-001-0048-5
Volume
29
Issue:
5
Year Published:
2002
Language:
English
Larger Work Type:
Article
Larger Work Subtype:
Journal Article
First page:
703
Last page:
715
Number of Pages:
13