Abstract Assessing tree biomass is essential for observing carbon stock and forest biodiversity which are an important indicator in climate change monitoring. The most accurate assessment involved ground data collection, including its data processing. In certain condition, it is extremely challenging, due to the difficulties of accessing dense forest and variation of terrain, tedious and time-consuming. Therefore, due to these limitations, remote sensing might become a better approach in measuring this information. The focus of this study is to estimate the tree stump height for biomass estimation after selective logging practices. In this study, we utilize remotely sensed canopy height model (CHM) derived from Unmanned Aerial Vehicle (UAV) to quantify tree stump height after felling logs at a local scale. This study aims to investigate the feasibility of utilizing UAV imagery to derive a canopy height model (CHM) for preparing parameters in assessing timber tree biomass. CHM is the reference surface to derive statistics that will be used to estimate the forest variables. Data was obtained through UAV which flown at the logging compartment in Ulu Jelai Forest Reserve, Pahang, Malaysia. The estimated stump height obtained from this technique was compared with a measured stump on the ground. Based on scatterplot regression, it showed a significant relationship with a strong coefficient, R² of 0.8368. At this stage of the study, the performance of the result was not assessed since it is an only preliminary result and the study only focused on producing CHM for stump height estimation using the UAV platform only.