Suchergebnisse

There is a wide availability of methods and techniques for classification of data from remote sensing images. However, one of the biggest challenges is to identify whether the applied method is really effective for the thematic mapping the terrain features. Thus, the aim of this work was to provide a comparative analysis involving data classification methods for mapping forest cover using orbital images from the Landsat 8 satellite. The applied method consisted of pre-processing the images, calculating the NDVI image, performing the infrared image composition and principal component analysis (PCA). The maximum likelihood classification method (MAXVER) was used to delimit the vegetation cover applied to the three types of databases. To validate the classification results, field data, Kappa analysis and pixel-by-pixel analysis were applied. The results pointed out that the NDVI method showed the least general similarity regarding to the reference data used for validation from the MAPBIOMAS project. It was possible to identify similar results in relation to the delimitation of forest cover. The results allowed identifying that the several methodologies available for classification of vegetation are of great value for the thematic mapping of forest resources. In addition, we conclude that the PCA showed the best capacity for delimiting the vegetation cover in the study region, closely followed by infrared composition, and the NDVI was the least accurate.

The development of several time series analysis programs using satellite images has provided many applications based on resources from geostatistics field. Currently, the use of statistical tests applied to vegetation indexes has enabled the analysis of different natural phenomena, such as drought events in watershed areas. The objective of this article is to provide a comparative analysis between NDVI and EVI vegetation index data made available by MOD13Q1 project of MODIS sensor for drought mapping using vegetation condition index (VCI) in the Serra Azul stream sub-basin, MG. The methodology adopted the Cox-Stuart statistical test for seasonality analysis and Pearson's linear correlation to verify the influence of different indexes on delimitation of drought in a watershed. The results indicated the NDVI vegetation index as more efficient than EVI in spatial characterization of studied watershed region, mainly in identification of seasonality. The VCI proved to be highly feasible for monitoring drought in study period between 2013 and 2018, allowing the effective delimitation of drought conditions in the Serra Azul stream sub-basin. In addition, the effectiveness of MODIS sensor data in characterizing drought events that affected the study area was proven.