Deforestation is a major problem in ecosystem degradation and one of the main sources of carbon emission to the atmosphere. The use of multi-temporal satellite remote sensing has proven to be effective means to monitor forest conditions on global scale. The effectiveness of the utilization of remotely sensed images will depend on the analysis model and parameter selection procedure to provide information that meets the requirements of deforestation monitoring. Here we demonstrate the ability of semantic computing for analyzing satellite images that applied to interpreting tropical deforestation. The typical semantic computing works for interpreting numerical or textual data. It still remains challenging for utilizing remote sensing images for environmental monitoring in semantic computing since the data is presented in interval value. Therefore, we proposed interval matrix decomposition for automatically generate semantic projection that address uncertain value in environmental parameters. In this study, independent dimensions of Landsat Thematic Mapper imagery (Landsat-8) and The Phased Array Type L-band SAR-2 (PALSAR-2) were combined to create an integrated interpretation of environmental condition for a study area in the deforestation zone of global tropical forest. We proposed essential semantic interval-dimensions derived from heterogonous satellite images, combination of L-Band SAR and optical, namely: HV gamma-naught, red, green, blue, NIR, SWIR channel; and combinational features: Soil temperature, soil moisture, temporal change density, temporal change velocity, shape and texture. Afterward, semantic computing is employed as analysis model to explain significant knowledge of deforestation activity. The experimental result shows that integrated independent dimensions from both the optical and SAR domains, has potential for presenting for aspect-based deforestation assessment and to enable the design of robust forest monitoring systems.