DOI: 10.1016/j.jag.2014.07.002
Scopus记录号: 2-s2.0-84908440807
论文题名: A support vector machine to identify irrigated crop types using time-series Landsat NDVI data
作者: Zheng B ; , Myint S ; W ; , Thenkabail P ; S ; , Aggarwal R ; M
刊名: International Journal of Applied Earth Observation and Geoinformation
ISSN: 15698432
出版年: 2015
卷: 34, 期: 1 起始页码: 103
结束页码: 112
语种: 英语
英文关键词: Crop classification
; Landsat
; NDVI
; Support vector machines
; SVM
Scopus关键词: accuracy assessment
; agricultural land
; arid region
; cropping practice
; identification method
; image classification
; irrigation
; Landsat
; NDVI
; satellite data
; satellite imagery
; semiarid region
; time series
; vegetation classification
英文摘要: Site-specific information of crop types is required for many agro-environmental assessments. The study investigated the potential of support vector machines (SVMs) in discriminating various crop types in a complex cropping system in the Phoenix Active Management Area. We applied SVMs to Landsat time-series Normalized Difference Vegetation Index (NDVI) data using training datasets selected by two different approaches: Stratified random approach and intelligent selection approach using local knowledge. The SVM models effectively classified nine major crop types with overall accuracies of >86% for both training datasets. Our results showed that the intelligent selection approach was able to reduce the training set size and achieved higher overall classification accuracy than the stratified random approach. The intelligent selection approach is particularly useful when the availability of reference data is limited and unbalanced among different classes. The study demonstrated the potential of utilizing multi-temporal Landsat imagery to systematically monitor crop types and cropping patterns over time in arid and semi-arid regions. © 2014 Elsevier B.V. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/79468
Appears in Collections: 气候变化事实与影响
There are no files associated with this item.
作者单位: School of Geographical Sciences and Urban Planning, Arizona State University, Tempe, AZ, United States; United States Geological Survey (USGS), 2255 N Gemini Dr., Flagstaff, AZ, United States; School of Sustainability, Arizona State University, Tempe, AZ, United States
Recommended Citation:
Zheng B,, Myint S,W,et al. A support vector machine to identify irrigated crop types using time-series Landsat NDVI data[J]. International Journal of Applied Earth Observation and Geoinformation,2015-01-01,34(1)