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.
Highlights
Land cover information is important for climate change studies and understanding complex interactions between human activities and global change [1,2,3,4,5,6]
The land cover classification results (Figure 3) show that spatial distribution of land cover types is consistently achieved using maximum likelihood classifier (MLC) and support vector machine (SVM) classifiers with only Operational Land Imager (OLI) spectral data, the OLI spectral data integrated with phenological features and statistical temporal features extracted from time series MODIS
The main improvement achieved by using phenological features or statistical temporal features was that vegetation types could be more effectively identified
Summary
Land cover information is important for climate change studies and understanding complex interactions between human activities and global change [1,2,3,4,5,6]. The phenological features-based approach uses time series vegetation index data to monitor the dynamic changes in vegetation growing cycles, and different vegetation types can be distinguished based on their unique phenological signature [19]. Phenological features such as the beginning and ending dates of the growing season and the season’s length can be extracted from time series vegetation index data, and the TIMESAT software is a popular tool for extracting phenological features from time series data [20,21]. The phenological features can be used to improve land cover classification accuracy, especially for vegetation classification, because different vegetation types have unique growing characteristics
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