This study provides the use of a near-infrared (NIR) sensor with an appropriate gimbal mounted on a lightweight Unmanned Aerial Vehicle (UAV) to operate remote sensing acquisitions of oil palm trees between the age of 9 to 15 years, at low altitudes. UAVs and NIR sensor calibrations and corrections were used in this study to develop a correction approach for obtaining accurate remote sensing images that may be used to monitor oil palms. The major aspects of calibration required for quality control of the flight data are UAV attitude flight information such as heading, pitch, and yaw. Compared to the previous system, which is based on a gyroscopic instrument, this flight information delivers superior precision and reliability. Geometric and radiometric sensor corrections and calibrations were carried out. After correcting undesired and sensor fault features, reliability image data analysis was created. As a new approach, the Improved Normalized Difference Vegetation Index (INDVI) algorithm uses the red and green channels as reflectance and the blue channel as absorption (400-1100 nm region), where healthy plants normally reflect green light in NIR and visible light, as opposed to the traditional NDVI, which only uses the near-infrared and red channels where the processing image is converted using ImageJ software. The results only discuss whether an area is rich in biomass or has less vegetation, which influences the results because INDVI and NDVI are two different indices.