Biological diversity is decreasing at an alarming rate worldwide. Improved ecosystem monitoring can help detect problems in time to intervene. Earth orbiting satellites collecting terabytes of imagery daily, can support effective monitoring of many habitats. Data parallelism is ideal for many automated image analysis algorithms, but less natural for the complex spatial structure of most ecosystems. This paper presents a coarse-to-fine processing framework: based on a set of spatial transformations, that compact disconnected regions to achieve more efficient nested data parallelism. Experiments with a montane island ecosystem in southeast Arizona use Landsat TM data to characterize the processing framework the spatial transformations, and the feature extraction algorithms.>

The transfer of digital images between data archives and scientific workstations is likely to consume a significant amount of network bandwidth in the very near future. This paper examines the image transfer problem from an end-to-end perspective, that is, it describes a complete image transfer protocol that takes into account both the nature of digital imagery and the properties of the underlying network. Specifically, it describes a simple algorithm for encoding images into network packets in such a way that the receiver can recover from dropped packets without requiring the sender to retransmit them. Avoiding retransmissions has the advantages of improving response time and eliminating the need to buffer data at the sender.