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Land Cover Classification of Landsat Data with Phenological 
Features Extracted from Time Series MODIS NDVI Data

 

Kun Jia ^1,*, Shunlin Liang ^1,2, Xiangqin Wei ^3,4, Yunjun Yao ¹, Yingru Su⁵, Bo Jiang ¹and Xiaoxia Wang ¹

 

1 State Key Laboratory of Remote Sensing Science, College of Global Change and Earth System Science, Beijing Normal University, Beijing 100875, China

2 Department of Geographical Sciences, University of Maryland, College Park, MD 20742, USA

3 Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China

4 School of Chinese Academy of Sciences, Beijing 100049, China

5 North China Institute of Aerospace Engineering, Langfang 065000, China

 

ABSTRACT

Temporal-related features are important for improving land cover classification accuracy using remote sensing data. This study investigated the efficacy of phenological features extracted from time series MODIS Normalized Difference Vegetation Index (NDVI) data in improving the land cover classification accuracy of Landsat data. The MODIS NDVI data were first fused with Landsat data via the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) algorithm to obtain NDVI data at the Landsat spatial resolution. Next, phenological features, including the beginning and ending dates of the growing season, the length of the growing season, seasonal amplitude, and the maximum fitted NDVI value, were extracted from the fused time series NDVI data using the TIMESAT tool. The extracted data were integrated with the spectral data of the Landsat data to improve classification accuracy using a maximum likelihood classifier (MLC) and support vector machine (SVM) classifier. The results indicated that phenological features had a statistically significant effect on improving the land cover classification accuracy of single Landsat data (an approximately 3% increase in overall classification accuracy), especially for vegetation type discrimination. However, the phenological features did not improve on statistical measures including the maximum, the minimum, the mean, and the standard deviation values of the time series NDVI dataset, especially for human-managed vegetation types. Regarding different classifiers, SVM could achieve better classification accuracy than the traditional MLC classifier, but the improvement in accuracy obtained using advanced classifiers was inferior to that achieved by involving the temporally derived features for land cover classification.

 

Keywords: land cover; phenological features; classification; remote sensing; fusing

 

PUBLISHED BY: REMOTE SENSING, 2014, 6 (11): 11518-11532

 

SOURCE:  http://www.mdpi.com/2072-4292/6/11/11518