DOI: 10.1016/j.jag.2013.11.005
Scopus记录号: 2-s2.0-84897417291
论文题名: Mapping tillage operations over a peri-urban region using combined SPOT4 and ASAR/ENVISAT images
作者: Vaudour E ; , Baghdadi N ; , Gilliot J ; M
刊名: International Journal of Applied Earth Observation and Geoinformation
ISSN: 15698432
出版年: 2014
卷: 28, 期: 1 起始页码: 43
结束页码: 59
语种: 英语
英文关键词: Bare agricultural soils
; Mapping
; Soil roughness
; SPOT/ASAR synergy
; SVM
; Tillage operations
Scopus关键词: agricultural soil
; agriculture
; bare soil
; Envisat-1
; land use
; mapping
; periurban area
; roughness
; soil property
; SPOT
; synthetic aperture radar
; tillage
; urban region
; France
; Ile de France
; Paris
; Ville de Paris
英文摘要: This study aimed at assessing the potential of combining synchronous SPOT4 and ENVISAT/ASAR images (HH and HV polarizations) for mapping tillage operations (TOs) of bare agricultural fields over a periurban area characterized by conventional tillage system in the western suburbs of Paris (France). The reference spatial units for spatial modeling are 57 within-field areas named "reference zones" (RZs) homogeneous for their soil properties, constructed in the vicinity of 57 roughness measurement locations, spread across 20 agricultural fields for which TOs were known. The total RZ dataset was half dedicated to successive random selections of training/validating RZs, the remaining half (29 RZs) being kept for validating the final map results. Five supervised per-pixels classifiers were used in order to map 2 TOs classes (seedbed&harrowed and late winter plough) in addition to 4 landuse classes (forest, urban, crops and grass, water bodies): support vector machine with polynomial kernel (pSVM), SVM with radial basis kernel (rSVM), artificial neural network (ANN), Maximum Likelihood (ML), and regression tree (RT). All 5 classifiers were implemented in a bootstrapping approach in order to assess the uncertainty of map results. The best results were obtained with pSVM for the SPOT4/ASAR pair with producer's and user's mean validation accuracies (PmVA/UmVA) of 91.7%/89.8% and 73.2%/73.3% for seedbed&harrowed and late winter plough conditions, respectively. Whatever classifier, the SPOT4/ASAR pair appeared to perform better than each of the single images, particularly for late winter plough: PmVA/UmVA of 61.6%/53.0% for the single SPOT4 image; 0%/6% for the single ASAR image. About 73% of the validation agricultural fields (79% of the RZs) were correctly predicted in terms of TOs in the best pSVM-derived final map. Final map results could be improved through masking non-agricultural areas with land use identification system layer prior to classifying images. Such knowledge of agricultural operations is likely to facilitate the mapping of agricultural systems which otherwise proceed from time-consuming surveys to farmers. © 2013 Elsevier B.V. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/79660
Appears in Collections: 气候变化事实与影响
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作者单位: AgroParisTech, UMR 1091 INRA/AgroParisTech Environnement et Grandes Cultures, Equipe Sol, avenue Lucien Brétignières, F-78850 Thiverval-Grignon, France; INRA, UMR 1091 INRA/AgroParisTech 'Environnement et Grandes Cultures', Equipe Sol, avenue Lucien Brétignières, F-78850 Thiverval-Grignon, France; IRSTEA, UMR TETIS, 500 rue Jean-François Breton, BP 5095, F-34093 Montpellier Cedex, France
Recommended Citation:
Vaudour E,, Baghdadi N,, Gilliot J,et al. Mapping tillage operations over a peri-urban region using combined SPOT4 and ASAR/ENVISAT images[J]. International Journal of Applied Earth Observation and Geoinformation,2014-01-01,28(1)