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7 November 2008 Endmembers extraction of wheat based on time series of MODIS-NDVI and TM samples data
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Proceedings Volume 7147, Geoinformatics 2008 and Joint Conference on GIS and Built Environment: Classification of Remote Sensing Images; 71470G (2008) https://doi.org/10.1117/12.813216
Event: Geoinformatics 2008 and Joint Conference on GIS and Built Environment: Geo-Simulation and Virtual GIS Environments, 2008, Guangzhou, China
Abstract
Knowledge of the area and distribution of cropland is important for land management and land security. Low spatial resolution imagery is one of the important remote sensing data source in the study of the large extent cropland. There exist many mixed pixels and effective method that should be improved to deal with them. In this paper, linear mixing model was used to unmix the time series of MODIS-NDVI data. The emphasis was the identification and extraction of endmembers, which represent the spectral characteristics of the single pure land cover types. A new endmembers extraction algorithm based on the temporal series of MODIS-NDVI and TM sample data was presented in this paper. We used the effective endmembers to linear spectral mixture model to achieve the wheat area in the study area. Regarding the classification of TM as the reference data, we evaluated the classification results and found wheat distribution's region accuracy and pixel accuracy reach to 92.9% and 0.837 respectively, which were higher than the clarification result based on the endmembers from MODIS-NDVI pixel purity index analysis or from classifications of TM data. This shew that our endmembers extraction algorithmwas available and effective, which helped to improve monitoring accuracy of large scope and distribution of vegetation.
© (2008) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Xulong Liu, Kaiwen Zhong, Wanxia Liu, and Yaozhong Pan "Endmembers extraction of wheat based on time series of MODIS-NDVI and TM samples data", Proc. SPIE 7147, Geoinformatics 2008 and Joint Conference on GIS and Built Environment: Classification of Remote Sensing Images, 71470G (7 November 2008); https://doi.org/10.1117/12.813216
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