Automated annual cropland mapping using knowledge-based temporal features
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- 作者：Franç ; ois Waldner ; francois.waldner@uclouvain.be" class="auth_mail" title="E-mail the corresponding author ; Guadalupe Sepulcre Canto ; Pierre Defourny
- 关键词：Cropland ; Classification ; Near real time ; Generalization ; Thematic uncertainty
- 刊名：ISPRS Journal of Photogrammetry and Remote Sensing
- 出版年：2015
- 出版时间：December 2015
- 年：2015
- 卷：110
- 期：Complete
- 页码：1-13
- 全文大小：5646 K
文摘
Global, timely, accurate and cost-effective cropland mapping is a prerequisite for reliable crop condition monitoring. This article presented a simple and comprehensive methodology capable to meet the requirements of operational cropland mapping by proposing (1) five knowledge-based temporal features that remain stable over time, (2) a cleaning method that discards misleading pixels from a baseline land cover map and (3) a classifier that delivers high accuracy cropland maps (g" data-mathURL="/science?_ob=MathURL&_method=retrieve&_eid=1-s2.0-S0924271615002233&_mathId=si3.gif&_user=111111111&_pii=S0924271615002233&_rdoc=1&_issn=09242716&md5=975d609e1b6f5b4e7447e6dde2b860e2" title="Click to view the MathML source">>80%). This was demonstrated over four contrasted agrosystems in Argentina, Belgium, China and Ukraine. It was found that the quality and accuracy of the baseline impact more the certainty of the classification rather than the classification output itself. In addition, it was shown that interpolation of the knowledge-based features increases the stability of the classifier allowing for its re-use from year to year without recalibration. Hence, the method shows potential for application at larger scale as well as for delivering cropland map in near real time.