In remote sensing studies, the photosynthetically active radiation absorbed by chlorophyll in the green leaves of vegetation canopies is measured using Red and Near-Infra Red bands. The Normalized Difference Vegetation Index (NDVI) is one of the most commonly used vegetation indices that are generally obtained from a calculation of the above mentioned bands; it presents a decent surrogate measures of the physiologically functioning surface greenness level. In this study, the latest version of the GIMMS NDVI data set, between the period of January 1982 and December 2015, were used to classify the global vegetation areas into five main categories (i.e. Agriculture Areas, Boreal Forests, Deciduous Forests, Evergreen and Tropical Forests, and Other Vegetation), using a simple and straight-forward method of classification, sumamed Global Vegetation Types Classification (GVTC). The total accuracy of the model reached 90.4% with a kappa value of 87.1%. In each category, a trend analysis has been carried out at both global and continental levels. The objective was to highlight the changes within each category, throughout the past thirty-four years. Results show that Agriculture Areas are increasing worldwide, with a huge upsurge observed since 2011 coinciding with a remarkable decrease in Boreal Forests. Changes in vegetation's classes, between 1982 and 2015, were more pronounceable in continents such as Asia, America and Africa; Europe and Oceania showed limited variations throughout this same period. Following these results, regional policies should be reformed and mitigation plans should be established in order to maintain a sustainable development of the global vegetation lands. The GVTC could be implemented with higher spatial resolution imageries for more local-based assessments.