Classification mapping of salt marsh vegetation by flexible monthly NDVI time-series using Landsat imagery

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

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 Spa...

Time-Series Classification of High-Temporal Resolution AVHRR NDVI Imagery of Mexico

Time-series data from wide-field sensors, acquired for the period of a growing season or longer, capitalize on phenological changes in vegetation and make it possible to identify vegetated land cover types in greater detail. Our objective was to examine the utility of time-series data to rapidly update maps of vegetation condition and land cover change in Mexico as an input to biodiversity mode...

Interpretation of Tropical Vegetation Using Landsat Etm+ Imagery

Monitoring of tropical forest is important as it plays a vital role in Global biodiversity and terrestrial carbon dynamics. Remote sensing can be effectively used to monitor the tropical vegetation. However, an operational tool for interpretation and monitoring of tropical forest has not yet well developed. Therefore, the current study aimed to generate an interpretation key and a spectral libr...

Classification of EEG-based motor imagery BCI by using ECOC

AbstractAccuracy in identifying the subjects’ intentions for moving their different limbs from EEG signals is regarded as an important factor in the studies related to BCI. In fact, the complexity of motor-imagination and low amount of signal-to-noise ratio for EEG signal makes this identification as a difficult task. In order to overcome these complexities, many techniques such as variou...

Monitoring Agricultural Biomass Using NDVI Time Series