Traditional forest surveys have long relied on labor-intensive on-foot assessments conducted by specialized teams. However, recent advancements in Deep Learning-based multi-class Object detection and Autonomous Unmanned Aerial Vehicles (UAVs) for Aerial Survey present opportunities for automation. In this study, we introduce a novel Attention Block called Hybrid Pooling Efficient Channel Attention (HyP-ECA), which is integrated into the architecture of YOLOv8, resulting in HyP-ECA-YOLO. To address the lack of aerial datasets for tree species detection, we curated the Multi-Tree species aerial detection (MTAD) dataset by consolidating and re-annotating open-source aerial datasets covering five types of trees. HyP-ECA YOLO achieves a mean Average Precision (mAP:50) of 87.9% on our dataset. We validate our Aerial Data collection Survey process through a virtual Software In The Loop (SITL) simulation environment using ROS and Gazebo. Open Drone Map is then utilized for post-processing, enabling map creation and dataset generation.