Hierarchical object-based classification of ultra-high-resolution digital mapping camera (DMC) imagery for rangeland mapping and assessment

A. S. Lalibertea; D. M. Browningb; J. E. Herrickb; P. Gronemeyera

doi:10.1080/14498596.2010.487853

Hierarchical object-based classification of ultra-high-resolution digital mapping camera (DMC) imagery for rangeland mapping and assessment

A. S. Lalibertea
, D. M. Browningb
, J. E. Herrickb
, P. Gronemeyera

Research output: Contribution to journal › Article › peer-review

Abstract

Ultra-high-resolution digital aerial imagery has great potential to complement or replace ground measurements of vegetation cover for rangeland monitoring and assessment. This research investigated object-based image analysis (OBIA) techniques for classifying vegetation in southwestern USA arid rangelands with 4 cm resolution digital aerial imagery. We obtained high r-square values for the regressions relating ground- to imagebased measures of percent cover (r-square values: 0.82-0.92). OBIA enabled us to automate the classification process and demonstrated potential for quantifying fine-scale land cover attributes with ultra-high-resolution imagery. This approach exhibits promise for nationwide application for monitoring grazing lands.

Original language	English (US)
Pages (from-to)	101-115
Number of pages	15
Journal	Journal of Spatial Science
Volume	55
Issue number	1
DOIs	https://doi.org/10.1080/14498596.2010.487853
State	Published - Jun 2010
Externally published	Yes

Keywords

Aerial photography
Object-based image analysis
Vegetation classification
Very high resolution rangelands

ASJC Scopus subject areas

Geography, Planning and Development
General Energy
Atmospheric Science

Online availability

10.1080/14498596.2010.487853

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title = "Hierarchical object-based classification of ultra-high-resolution digital mapping camera (DMC) imagery for rangeland mapping and assessment",

abstract = "Ultra-high-resolution digital aerial imagery has great potential to complement or replace ground measurements of vegetation cover for rangeland monitoring and assessment. This research investigated object-based image analysis (OBIA) techniques for classifying vegetation in southwestern USA arid rangelands with 4 cm resolution digital aerial imagery. We obtained high r-square values for the regressions relating ground- to imagebased measures of percent cover (r-square values: 0.82-0.92). OBIA enabled us to automate the classification process and demonstrated potential for quantifying fine-scale land cover attributes with ultra-high-resolution imagery. This approach exhibits promise for nationwide application for monitoring grazing lands.",

keywords = "Aerial photography, Object-based image analysis, Vegetation classification, Very high resolution rangelands",

author = "Lalibertea, \{A. S.\} and Browningb, \{D. M.\} and Herrickb, \{J. E.\} and P. Gronemeyera",

note = "This research was funded by the USDA Natural Resources Conservation Service in support of the Conservation Effects Assessment Project, and by the USDA Agricultural Research Service and the National Science Foundation Long-Term Ecological Research Program, Jornada Basin V: Landscape Linkages in Arid and Semiarid Ecosystems. The authors would like to thank Debra Peters and John Anderson for access to the LTER NPP sites, and Hector Godinez-Alvarez, Michelle Mattocks, David Toledo and Justin Van Zee for providing assistance with field data collection. Additional thanks go to the three anonymous reviewers.",

year = "2010",

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doi = "10.1080/14498596.2010.487853",

language = "English (US)",

volume = "55",

pages = "101--115",

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publisher = "Mapping Sciences Institute Australia",

number = "1",

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AU - Lalibertea, A. S.

AU - Browningb, D. M.

AU - Herrickb, J. E.

AU - Gronemeyera, P.

N1 - This research was funded by the USDA Natural Resources Conservation Service in support of the Conservation Effects Assessment Project, and by the USDA Agricultural Research Service and the National Science Foundation Long-Term Ecological Research Program, Jornada Basin V: Landscape Linkages in Arid and Semiarid Ecosystems. The authors would like to thank Debra Peters and John Anderson for access to the LTER NPP sites, and Hector Godinez-Alvarez, Michelle Mattocks, David Toledo and Justin Van Zee for providing assistance with field data collection. Additional thanks go to the three anonymous reviewers.

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N2 - Ultra-high-resolution digital aerial imagery has great potential to complement or replace ground measurements of vegetation cover for rangeland monitoring and assessment. This research investigated object-based image analysis (OBIA) techniques for classifying vegetation in southwestern USA arid rangelands with 4 cm resolution digital aerial imagery. We obtained high r-square values for the regressions relating ground- to imagebased measures of percent cover (r-square values: 0.82-0.92). OBIA enabled us to automate the classification process and demonstrated potential for quantifying fine-scale land cover attributes with ultra-high-resolution imagery. This approach exhibits promise for nationwide application for monitoring grazing lands.

AB - Ultra-high-resolution digital aerial imagery has great potential to complement or replace ground measurements of vegetation cover for rangeland monitoring and assessment. This research investigated object-based image analysis (OBIA) techniques for classifying vegetation in southwestern USA arid rangelands with 4 cm resolution digital aerial imagery. We obtained high r-square values for the regressions relating ground- to imagebased measures of percent cover (r-square values: 0.82-0.92). OBIA enabled us to automate the classification process and demonstrated potential for quantifying fine-scale land cover attributes with ultra-high-resolution imagery. This approach exhibits promise for nationwide application for monitoring grazing lands.

KW - Aerial photography

KW - Object-based image analysis

KW - Vegetation classification

KW - Very high resolution rangelands

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Hierarchical object-based classification of ultra-high-resolution digital mapping camera (DMC) imagery for rangeland mapping and assessment

Abstract

Keywords

ASJC Scopus subject areas

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