AI Article Synopsis
- The study classified vegetation cover in Rio Grande do Sul by grouping pixels with similar temporal NDVI patterns from GIMMS and MODIS data.
- K means cluster analysis was used on monthly NDVI data from 2000 to 2008, with results validated through Landsat images, revealing that both GIMMS and MODIS can define similar vegetation regions, albeit with MODIS offering more detail.
- Findings demonstrated a seasonal NDVI pattern and highlighted the potential for linear calibration corrections between datasets, which could aid in creating comprehensive time series for analyzing land cover changes.
Article Abstract
This study aimed to classify the homogeneous regions of vegetation cover, which occur in Rio Grande do Sul, formed by clustering of pixels with same pattern of temporal variability of the Normalized Difference Vegetation Index (NDVI) of AVHRR GIMMS and MODIS series and to compare their temporal dynamics. We use K means cluster analysis for defining homogeneous regions, based on the temporal variability of GIMMS (8 km spatial resolution) and MODIS (1 km spatial resolution) NDVI data sets, using monthly images mean from 2000 to 2008 (overlapping period); and we analyzed the annual pattern of NDVI. Accuracy assessment was done with Landsat images. The results show that the temporal variability of GIMMS and MODIS NDVI allows to delimit similar homogeneous regions in order to mapping the main vegetation cover. MODIS series shows a greater detail in the definition of the regions, but with compatibility with those generated by GIMMS. The temporal dynamics show a typical seasonal pattern, with variations of NDVI amplitude between the groups, that allow to monitor phenological changes. The deviations from calibration between times series are linear, which would facilitate a correction in order to construct a long synthetic time series for studies of land cover change.
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| http://dx.doi.org/10.1590/0001-3765202120201278 | DOI Listing |
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