Monitoring reforestation projects in dense forested areas can be a cumbersome operation if done by means of ground measurements. While remote sensing techniques could simplify forest monitoring, traditional remote sensing platforms have shown to be costly and low in flexibility. In recent years, the technology behind Unmanned Aerial Vehicles (UAV) and mountable sensors has advanced enormously, lowering the cost of data collection and delivering very-high resolution image-products (cm to dm pixel-size). This paper focusses on the use of automated OBIA processing of UAV-derived data to assist reforestation management. The study site is a reforestation project of Mindo Cloud, in the north of Ecuador. UAV-image data is available for 24 terrains in three different strata (montane, pre-montane and coastal biotopes) at two different flight altitudes. Ground measurements with respect to tree location, tree height, diameter and species are available in sub-plots on each terrain. Six different species have been planted at the start of the reforestation project. This paper evaluates the effects of UAV image quality and orthophoto-processing artefacts on the OBIA process. The raw UAV data is processed using Agisoft Photoscan, which results in a 3D-model and a rectified (RGB) orthophoto of the terrain. These outputs are used in an OBIA process to segment the image. Preliminary segmentation results for tree crown delineation through multi-resolution segmentation are promising and form a good basis to allow differentiating species based on spectral (RGB) responses and textures. While the advantages of operational application of UAV data in reforestation management are numerous (very high spatial resolution, potential for high temporal resolution, insensitivity for cloud cover, potential for 3D image generation, relatively low cost ...), this technology also has its limitations (low spectral resolution, low software automation, sensitivity to atmospheric conditions ...). Another disadvantage is the poor geometric and radiometric performance, which results in image artefacts and image blurs on the UAV-orthophoto after the rectification process. In this paper, we discuss how these artefacts affect the performance of the segmentation process and the level of consistency we can expect compared with traditional RS products.