DOI: 10.1016/j.jag.2015.12.007
Scopus记录号: 2-s2.0-84988697081
论文题名: Mapping the distributions of C3 and C4 grasses in the mixed-grass prairies of southwest Oklahoma using the Random Forest classification algorithm
作者: Yan D ; , de Beurs K ; M
刊名: International Journal of Applied Earth Observation and Geoinformation
ISSN: 15698432
出版年: 2016
卷: 47 起始页码: 125
结束页码: 138
语种: 英语
英文关键词: Historical land cover mapping
; Mixed-grass prairies
; Random Forest classifier
; Spatial pattern analysis
Scopus关键词: accuracy assessment
; algorithm
; C3 plant
; C4 plant
; environmental factor
; grass
; image classification
; land cover
; pixel
; prairie
; spatial distribution
; vegetation mapping
; Oklahoma [United States]
; United States
; Poaceae
英文摘要: The objective of this paper is to demonstrate a new method to map the distributions of C3 and C4 grasses at 30 m resolution and over a 25-year period of time (1988–2013) by combining the Random Forest (RF) classification algorithm and patch stable areas identified using the spatial pattern analysis software FRAGSTATS. Predictor variables for RF classifications consisted of ten spectral variables, four soil edaphic variables and three topographic variables. We provided a confidence score in terms of obtaining pure land cover at each pixel location by retrieving the classification tree votes. Classification accuracy assessments and predictor variable importance evaluations were conducted based on a repeated stratified sampling approach. Results show that patch stable areas obtained from larger patches are more appropriate to be used as sample data pools to train and validate RF classifiers for historical land cover mapping purposes and it is more reasonable to use patch stable areas as sample pools to map land cover in a year closer to the present rather than years further back in time. The percentage of obtained high confidence prediction pixels across the study area ranges from 71.18% in 1988 to 73.48% in 2013. The repeated stratified sampling approach is necessary in terms of reducing the positive bias in the estimated classification accuracy caused by the possible selections of training and validation pixels from the same patch stable areas. The RF classification algorithm was able to identify the important environmental factors affecting the distributions of C3 and C4 grasses in our study area such as elevation, soil pH, soil organic matter and soil texture. © 2015 Elsevier B.V. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/80117
Appears in Collections: 气候变化事实与影响
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作者单位: Department of Geography and Environmental Sustainability, The University of Oklahoma, 100 East Boyd Street, SEC Suite 510, Norman, OK, United States
Recommended Citation:
Yan D,, de Beurs K,M. Mapping the distributions of C3 and C4 grasses in the mixed-grass prairies of southwest Oklahoma using the Random Forest classification algorithm[J]. International Journal of Applied Earth Observation and Geoinformation,2016-01-01,47