There are two major challenges for a high-performance remote-sensing database. First, it must provide low-latency retrieval of very large volumes of spatio-temporal data. This requires effective declustering and placement of a multi-dimensional dataset onto a large disk farm. Second, the order of magnitude reduction in data-size due to post-processing makes it imperative, from a performance perspective, that the postprocessing be done on the machine that holds the data. This requires careful coordination of computation and data retrieval. This paper describes the design, implementation and evaluation of {\em Titan}, a parallel shared-nothing database designed for handling remote-sensing data. The computational platform for Titan is a 16-processor IBM SP-2 with four fast disks attached to each processor. Titan is currently operational and contains about 24~GB of data from the Advanced Very High Resolution Radiometer (AVHRR) on the NOAA-7 satellite. The experimental results show that Titan provides good performance for global queries, and interactive response times for local queries. (Also cross-referenced as UMIACS-TR-96-67)