Determining vegetation dynamics through the enhanced vegetation index and meteorological variables for the ribeirão Cachimbal basin, Rio de Janeiro-Brazil

Abstract

ABSTRACT:The objective of this study was to analyze a spatiotemporal study of the vegetation dynamics of the hydrographic basin of the Ribeirão Cachimbal in Rio de Janeiro (Brazil), based on Moderate-Resolution Imaging Spectroradiometer imagery acquired by the TERRA satellite. A total of 23 images were used for each year of El Niño (2005 and2015), 250-m-resolution images from collection 006 of the 16-day Enhanced Vegetation Index (MOD13Q1) product were used. Daily rainfall and temperature data were obtained from a conventional meteorological station at Resende (Rio de Janeiro State), which were made available by the National Institute for Meteorology of Brazil. Simple linear regression analysis was performed to evaluate the dependence of the temporal series of vegetation as a function of the daily series of rainfall and temperature in terms of the significance of their correlation coefficients. Multivariate analysis of the main components was also undertaken. The results of the simple linear regression between the vegetation index and meteorological variables (temperature and rainfall) were significant in the respective years (p-value < 5%), except for rainfall in 2015, which presented a value of 0.06 (p-value < 25%). Observing the trend, both years (2005 and 2015 showed an increase in vegetation in the study area (Z = 0.37 and 0.24, respectively). By quantifying the values of the respective vegetation classes, it was possible to verify that a reduction of 40% had occurred in areas with dense vegetation coverage by 2015. The vegetation dynamics of the Ribeirão Cachimbal basin are influenced by rainfall and temperature variables and they have greatest correlation in spring and summer.

Temporal integration of remote‐sensing land cover maps to identify crop rotation patterns in a semiarid region of Argentina

“…Several authors analyzed the correspondence between the dynamics of ecosystems with crop patterns obtained from studies longer than 1 yr (Volante et al., 2005; Baldi & Paruelo, 2008; Baeza et al., 2014 ; Sahajpal et al., 2014; Dos Santos et al., 2018). However, the time frame of these analyses usually encompasses a few years, hindering the possibility of observing recurrent cycles of crop rotations.…”