In-situ co-axial meltpool monitoring has become a popular tool for digitising the laser powder bed fusion (L-PBF) process , providing baseline data for certification. Each layer produces an image where the pixel position represents the laser coordinates and the pixel intensity denotes the sensor response. The 3D image stacks represent the infrared emission during the manufacturing of the physical component. However, interpreting monitoring data remains a challenge. To address this issue, this study evaluates the performance of a near-infrared photodiode in detecting typical geometrical features such as porosity and overhanging structures ranging from the micro-to-meso scale. Monitoring data is highly sensitive to heat accumulation around overhanging structures and can quantify dross formation based on hotspots. Cold spots, which represent a lack of fusion porosity at scan track intersections, can indicate a probability of defect formation. However, the sensitivity and predictive value of monitoring data for porosity are low due to the healing of defects in subsequent layers. Local process variables, such as the scan strategy and part orientation, significantly influence dross and hot spot formation. This study shows the potential of NIR photodiodes in deriving metrics for in-line certification of L-PBF components, leading to improved process control and quality assurance.