期刊
IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING
卷 7, 期 8, 页码 3428-3439出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/JSTARS.2014.2347203
关键词
Biodiversity; classification; habitats directive (HabDir); multitemporal; NATURA 2000; phenology; RapidEye; seminatural grassland vegetation; significance analysis; support vector machine (SVM); time series; vegetation indices
类别
资金
- German National Space Agency DLR (Deutsches Zentrum fur Luft und Raumfahrt e. V.) on behalf of the German Federal Ministry of Economy and Technology based on the Bundestag [50EE0923]
- CARE-X project
The amount of images used in multitemporal classification studies has greatly increased along with enhanced temporal sensor capacities. Handling large intra-annual time series leads to the question of how the selection of image acquisition dates could be optimized. In this study, an empirical approach for evaluating the relative classification power of single acquisition dates is introduced for the differentiation of seminatural grassland vegetation. The main question is how many acquisitions from which phenological origins are preferable to achieve a certain classification accuracy target. The tested time series contains 24 single RapidEye scenes from 2009 to 2011. The vegetation index composites of these images were iteratively classified into different combinations of acquisition dates using the support vector machine (SVM) algorithm. The subsequent results were tested for significant accuracy improvements over single acquisition dates. These acquisition dates are subsumed under phenological seasons to evaluate adequate temporal acquisition windows. The results show that a three-scene composite reaches more than 0.8 overall accuracy (OAA). The best tradeoff amount between number of acquisition dates and classification accuracy is achieved using a seven-scene NDVI composite. The most important season for the differentiation of seminatural grassland is early summer (ESu). Full spring (FuS), late summer (LSu), and midsummer (MSu) can also be identified as influential temporal windows for data acquisition.
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