Leveraging unmanned aerial vehicles (UAVs) for access and high altitude platform stations (HAPSs) for data backhaul to construct the Air-Ground Integrated Network (AGIN), is a feasible solution to achieve seamless network coverage for remote IoT devices in future 6G era. However, since the number of terminals increases exponentially, it is essential to improve spectrum efficiency and throughput of the system. In addition, the limited on-board energy storage of UAVs and finite battery of IoT terminals make the system energy efficiency (EE) a new concern. To cope with the above mentioned challenges, in this study we first put forward a clustered-NOMA (C-NOMA)-enabled heterogeneous AGIN model for remote areas including one HAPS for backhaul and multiple UAVs for access, where C-NOMA is used to obtain both improved system throughput and terminal complexity. Then, we study the joint UAV trajectory plan and resource allocation problem in order to maximize the system EE. Since it is a mixed integer nonlinear programming (MINLP) issue coupled with transmission power, subchannel allocation, UAV trajectory and speed control, this problem is decoupled into two subproblems and solved iteratively. For the first one, the optimal channel and power strategy is obtained by our subchannel allocation and power control for AGIN EE (SAPAE) maximum algorithm according to Lagrange dual decomposition method. For the second, for transforming the nonconvex problem into a convex one, we provide the successive convex approximation-based UAV trajectory and speed optimization for AGIN EE (SUTSAE) maximum algorithm, and then the near-optimal UAV trajectory and flight speed are obtained. We conduct extensive experiments to compare with other benchmark methods. It is shown that the proposed approach is better in EE and spectrum efficiency.