Phenological-metric algorithm for mapping soybean in savanna biome in Brazil

Abstract

Agricultural expansion in Brazil is still intense for commodities (such soybeans and corn), mostly cultivated over large portions of the Cerrado biome. Therefore, the development and application of techniques based on remote sensing to map crop areas at a regional level, in a dynamic and more precise way is urgently necessary. In this context, the objective of this study is the improvement of techniques for mapping soybean crops in Brazil, through an analysis of the Centro Goiano mesoregion of Goiás state (a core area of Cerrado), using a time series of Enhanced Vegetation Index (EVI) images provided by TERRA/MODIS orbital sensor, in a test period between 2002 and 2010. Despite their proven quality, MODIS EVI images already contain atmospheric interferences inherent to the acquisition process, such as the presence of clouds. Thus, a set of methods to minimize such artifacts was applied to the data of this study. In general, the methodological procedures comprise of (1) the application of the pixel reliability band aiming to remove pixels contaminated by clouds; (2) the use of contaminated pixel estimates (excluded from the time series); (3) application of an interpolation filter to fill the void pixels in each scene, obtaining continuous and smoothed spectral-temporal profiles for each land use classes; and (4) the classification of agricultural areas using a specific algorithm for crops in the Cerrado region of Goiás. The areas reconstituted in the images matched neighboring pixels, maintaining good coherence with the original data. Likewise, areas mapped with soybeans had a high correlation with official IBGE census data, with a global accuracy value of 78%, and Pearson Correlation coefficient of 0.64. The application of this technique to other imagery sensors (such as RapidEye, Landsat 8 and Sentinel 2) is highly encouraged due a better spatial and temporal resolution (when applied together in a temporal image cube), ensuring more efficient crop monitoring in Brazil.

Habitat Partitioning and Overlap by Large Lacertid Lizards in Southern Europe

“…The EVI data were obtained for the 2009-2019 period from the high-resolution (250 m pixel -1 ) MODIS Collection EVI composite images [25]. The MODIS data were first checked to remove atmospheric artefacts, and then used to generate a series of variables describing the seasonal variability among habitats [26] including: the mean value (EVImean), the coefficient of variation (EVIcv), and the range (EVIrange), considering the inter-annual (mean value for 10 years) and spatial variability (mean value for 50 points, generated randomly within a maximum radius of 5 km). This larger area assessed the effect of the environment around the core habitat where specimens were found and took into account that species occurrence is sustained by isolated suitable habitat patch, but also by the interconnection of habitat patches that support the entire population [27].…”