Background/Question/Methods The Northern Mountains of Oaxaca (NMO) in Mexico harbor the largest area of Tropical Montane Cloud Forest (TMCF) in that country. TMCF are fundamental ecosystems in terms of biodiversity, water and carbon cycling. Forest loss in the region has increased in the last five years. Agricultural expansion is one of the most common causes of forest clearing in the tropics resulting in biomass, habitat, and biodiversity loss. However, in the NMO shifting agriculture is still very common, allowing forest re-growth after plot abandonment. Moreover, landscapes with different levels of forest and agriculture integration create a gradient of agricultural intensification. In this project we aim to answer the following questions: How does aboveground biomass and biodiversity change along a landscape intensification gradient in TMCF, and how accurately can satellite remote sensing techniques estimate such ecological effects at the landscape and regional scales? For answering these questions, we used ground-based data retrieved from the Mexican National Inventory of Forests and Soil carried out between 2009 and 2014, and remote sensing products and images (Landsat 5 and 7) for estimating landscape intensification, and calculating age of 310 plots sampled in TMCF in the NMO. Variables such as tree density, Lorey’s height, aboveground biomass, and biodiversity indices were compared to the level of landscape intensification and plots’ age. Plot age was estimated looking for the most recent abrupt change in each plot’s NDVI time series. Abrupt changes were detected with the R package Breaks For Additive Season and Trend (BFAST). Results/Conclusions Our preliminary results show, first, that biomass and biodiversity do not exhibit the same relationship with landscape intensification. Second, that it is possible to estimate an approximate date of forest stands with remote sensing techniques in tropical regions. We found a positive relation between age and all forest structure variables. Aboveground biomass, canopy cover, tree height, tree number and density, increased with plot age. The stronger relationship being that between age and Lorey’s height (p = 0.007), indicating this could be a good variable for analyzing forest structure changes through time after disturbance at regional scales. Third, biodiversity did not show a significant relationship with age. However, older plots showed larger variance in species richness and biodiversity indices. These results must be further analyzed. Future directions include: (i) scaling up the relationship between remote sensing variables, structure and composition to regional scales; (ii) analyzing how landscape composition impacts forest succession.