A semi-ellipsoid-model based fuzzy classifier to map grassland in Inner Mongolia,China |
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| Institution: | 1. Institute of Solid State Chemistry and Mechanochemistry SB RAS, Kutateladze-street 18, 630128 Novosibirsk, Russia;2. Novosibirsk State Technical University, Karl Marx prospect 20, 630073 Novosibirsk, Russia;1. Key Laboratory of Desert and Desertification/Dunhuang Gobi and Desert Research Station, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou, 730000, China;2. Institute of Desertification Studies, Chinese Academy of Forestry, Beijing, 100091, China;1. GROW School for Oncology and Developmental Biology, Maastricht University Medical Centre, P.O. Box 616, 6200 MD, Maastricht, The Netherlands;2. Department of Radiology, The Netherlands Cancer Institute, P.O. Box 90203, 1006 BE, Amsterdam, The Netherlands;3. Department of Radiology, Maastricht University Medical Center, P.O. Box 6200, 6202 AZ, Maastricht, The Netherlands;4. Department of Surgery, Maastricht University Medical Center, P.O. Box 6200, 6202 AZ, Maastricht, The Netherlands;5. Faculty of Health, Medicine and Life Sciences, Maastricht University, P.O. Box 6200, 6202 AZ, Maastricht, The Netherlands;6. Department of Surgery, The Netherlands Cancer Institute, PO Box 90203, 1066 CX, Amsterdam, The Netherlands;1. Henan Province Key Laboratory of New Optoelectronic Functional Materials, College of Chemistry and Chemical Engineering, Anyang Normal University, Anyang, Henan, 455002, PR China;2. College of Chemistry and Molecular Engineering, Zhengzhou University, Zhengzhou, Henan, 450001, PR China |
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| Abstract: | Remote sensing techniques offer effective means for mapping plant communities. However, mapping grassland with fine vegetative classes over large areas has been challenging for either the coarse resolutions of remotely sensed images or the high costs of acquiring images with high-resolutions. An improved hybrid-fuzzy-classifier (HFC) derived from a semi-ellipsoid-model (SEM) is developed in this paper to achieve higher accuracy for classifying grasslands with Landsat images. The Xilin River Basin, Inner Mongolia, China, is chosen as the study area, because an acceptable volume of ground truthing data was previously collected by multiple research communities. The accuracy assessment is based on the comparison of the classification outcomes from four types of image sets: (1) Landsat ETM+ August 14, 2004, (2) Landsat TM August 12, 2009, (3) the fused images of ETM+ with CBERS, and (4) TM with CBERS, respectively, and by three classifiers, the proposed HFC-SEM, the tetragonal pyramid model (TPM) based HFC, and the support vector machine method. In all twelve classification experiments, the HFC-SEM classifier had the best overall accuracy statistics. This finding indicates that the medium resolution Landsat images can be used to map grassland vegetation with good vegetative detail when the proper classifier is applied. |
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| Keywords: | Fuzzy classifier Grassland classification Landsat Semi-ellipsoid-model Tetragonal pyramid model Image fusion |
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