Applying Big Data Technology To Remote Sensing For Species Identification

 Understanding the processes governing ecological systems from local to global scales is crucial to determining how they will respond to and influence environmental, economical and geopolitical issues such as climate change, invasive species, fire hazards, and land use change. To collect the data necessary to model ecological processes across scales the National Ecological Observatory Network (NEON) was built starting in 2012 to conduct intensive monitoring and measurements across the United States. Hundreds of ecological and environmental data products ranging from small local samples to large scale remote sensing using aircraft will be monitored across over 81 different observatory sites. The volume, velocity, and variety of data generated by this effort is far greater than anything being currently collected or analyzed by ecologists. Therefore maximizing the knowledge gained from this data will require bridging the gap between different disciplines including ecology, computer science, statistics, and data science. To help develop interdisciplinary approaches to working with and understanding these data, we propose an applied, multidisciplinary, multi-modal, big data challenge to NIST Data Science Evaluation (DSE) series to be used as a stepping stone, with an initial focus on using a combination of airborne remote sensing data and field measurements of forests to characterize the structure of the plant community at large scales.

NEON sites across the United States