This work synthesizes results from the application of land cover classification techniques and probability sampling of satellite imagery for estimating forest extent and deforestation in Lake Maracaibo Basin (Venezuela and Colombia). A forest map was produced using a semi-automated supervised classification routine on MODIS 8-day 500-m imagery acquired in January 2010. Results show that forests occupy 29,710 km2 which represents 38% of the basin's total terrestrial landmass. From this extent, 61% belongs to Venezuela and 39% falls within the Colombian region. Findings indicate a drastic decrease in forest cover as a result of anthropogenic agricultural and urban expansion, especially when compared to its potential extent within the ‘Maracaibo dry forests’ and the ‘Venezuelan Andean montane forests’ ecoregions. Using time series of Landsat imagery, deforestation rates for the 1985–2010 time period were calculated. The analysis was performed on 24 samples blocks of 10 × 10 km2 randomly allocated within previously defined change probability strata. The general spatial distribution of deforestation rates was predicted by a simple regression model between sample blocks and prior change probabilities at the basin scale. Our results indicate that deforestation was low (<0.5%/y) in 85% of the basin, with highly focalized deforestation fronts (intermediate-to-high rates, <2.5%/y) in three regions: a) the Motatán river sub-basin in the Eastern Cordillera, b) the lower slopes of the Catatumbo river sub-basin and c) the submontane regions of the Apón and Santa Ana river sub-basins. The results of this paper lead the way for understanding current patterns in socioeconomic drivers of forest clearing in Lake Maracaibo Basin. The study also demonstrates the feasibility of using alternatives methods to the time-consuming and financially unsustainable methods traditionally used at national and sub-national scale in Venezuela and other Latin American countries.