globalchange  > 过去全球变化的重建
DOI: 10.1371/journal.pone.0117551
论文题名:
Census Parcels Cropping System Classification from Multitemporal Remote Imagery: A Proposed Universal Methodology
作者: Luis García-Torres; Juan J. Caballero-Novella; David Gómez-Candón; José Manuel Peña
刊名: PLOS ONE
ISSN: 1932-6203
出版年: 2015
发表日期: 2015-2-17
卷: 10, 期:2
语种: 英语
英文关键词: Cereal crops ; Orchards ; Decision trees ; Cotton ; Land use ; Forecasting ; Image processing ; Maize
英文摘要: A procedure named CROPCLASS was developed to semi-automate census parcel crop assessment in any agricultural area using multitemporal remote images. For each area, CROPCLASS consists of a) a definition of census parcels through vector files in all of the images; b) the extraction of spectral bands (SB) and key vegetation index (VI) average values for each parcel and image; c) the conformation of a matrix data (MD) of the extracted information; d) the classification of MD decision trees (DT) and Structured Query Language (SQL) crop predictive model definition also based on preliminary land-use ground-truth work in a reduced number of parcels; and e) the implementation of predictive models to classify unidentified parcels land uses. The software named CROPCLASS-2.0 was developed to semi-automatically perform the described procedure in an economically feasible manner. The CROPCLASS methodology was validated using seven GeoEye-1 satellite images that were taken over the LaVentilla area (Southern Spain) from April to October 2010 at 3- to 4-week intervals. The studied region was visited every 3 weeks, identifying 12 crops and others land uses in 311 parcels. The DT training models for each cropping system were assessed at a 95% to 100% overall accuracy (OA) for each crop within its corresponding cropping systems. The DT training models that were used to directly identify the individual crops were assessed with 80.7% OA, with a user accuracy of approximately 80% or higher for most crops. Generally, the DT model accuracy was similar using the seven images that were taken at approximately one-month intervals or a set of three images that were taken during early spring, summer and autumn, or set of two images that were taken at about 2 to 3 months interval. The classification of the unidentified parcels for the individual crops was achieved with an OA of 79.5%.
URL: http://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0117551&type=printable
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资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/21428
Appears in Collections:过去全球变化的重建
影响、适应和脆弱性
科学计划与规划
气候变化与战略
全球变化的国际研究计划
气候减缓与适应
气候变化事实与影响

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作者单位: Institute for Sustainable Agriculture, Spanish Council for Scientific Research (CSIC), Cordoba, Spain;Institute for Sustainable Agriculture, Spanish Council for Scientific Research (CSIC), Cordoba, Spain;Institute for Sustainable Agriculture, Spanish Council for Scientific Research (CSIC), Cordoba, Spain;Institute for Sustainable Agriculture, Spanish Council for Scientific Research (CSIC), Cordoba, Spain

Recommended Citation:
Luis García-Torres,Juan J. Caballero-Novella,David Gómez-Candón,et al. Census Parcels Cropping System Classification from Multitemporal Remote Imagery: A Proposed Universal Methodology[J]. PLOS ONE,2015-01-01,10(2)
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