With the rapid development of the LiDAR (Light Detection and Ranging) remote sensing technology in the past decade, LiDAR sensing systems have become an important source for acquiring environmental data. The LiDAR system can be equipped on an aircraft to collect geographic information in a wide area. One characteristic of the LiDAR system is that it usually produces huge volumes of data. Thus, how to efficiently manage, store, process and visualize the LiDAR data sources has become an important and challenging research issue in the spatial database community. In this paper, we propose a distributed algorithm to process a remarkable spatial query (i.e., range queries) over massive LiDAR data points. Different from existing range query processing approaches which assume all data points are stored in a centralized server, our method adopts a decentralized fashion. Our query processing system is a master/slave architecture. A large data set is split into smaller partitions that are distributed among several slave machines. Therefore, each slave machine only process a small part of data points. We also develop index structures over LiDAR data sets to further enhance the efficiency of query processing. Our performance study proves the efficiency of the design.