Globally, land use change has consistently resulted in greater losses than gains in aboveground biomass (AGB). Forest fragmentation is a primary driver of biodiversity loss and the depletion of natural capital. Measuring landscape characteristics and analyzing changes in forest landscape patterns are essential for accounting for the contributions of forest ecosystems to the economy and human well-being. This study predicts national forest distribution for 2036 and 2054 using a Cellular Automata (CA) system and assesses ecosystem conditions through landscape metrics at the patch, class, and landscape levels. We calculated 130 metrics and applied a Variance Threshold method to remove features with low variance, testing different thresholds. The first filtered-out metrics were further analysed through Principal Component Analysis combined with a Feature Importance technique to select and rank the top 10 indicators: effective mesh size, splitting index, mean radius of gyration, largest patch index, mean core area, core area percentage, Simpson's evenness index, mutual information, Simpson's diversity index, and mean contiguity index. The eleventh selected indicator is the AGB density, a structural measurement for ecosystem condition and a proxy for forest carbon storage and sequestration assessments. From 2000 to 2018, the national AGB forest carbon stock decreased from 131.5 to 91.3 Megatons (Mt) with expected values for 2036 and 2054 being 71.8 and 55.3 Mt., respectively. Landscape measurements quantitatively describe forest dynamics, providing insights into the structure, configuration, and changes characterizing landscape evolution. This research underscores the capability of CA models to map large-scale forest resources and predict future development scenarios, offering useful information for conservation and environmental management decisions. Additionally, it provides measurements to support Ecosystem Accounting by assessing forest extent and indicators of its conditions.