Fall 2005 and Spring 2006, Legacy Program: Migratory Bird Monitoring Using Automated Acoustic and Internet Technologies

Abstract

Cornell Laboratory of Ornithology (CLO) developed digital autonomous recording units (ARUs) that record mp3 and binary (BIN) sound files for periods of up to 6 weeks in duration. We address the limiting factors of observers monitoring birds acoustically and of protocols monitoring birds that may be missed by traditional observation methods and provide solutions and sample data that enhance DoD's capacity to monitor avian resources on and around DoD lands and analysis and summary of these data. We also examine ARU reliability, applicability to tasks, and recording quality. We tested all devices with the planned application of this technology: to monitor acoustically species that vocalize infrequently, to improving accuracy of existing census methods, to produce acoustic datasets for training purposes, and to monitor flight-calls of migrant birds for predicting migration and stopover use on DoD installations. We collected over 27,000 hours of data in fall 2005 and spring 2006, and we have successfully stored, processed, and initiated analysis of this information. We outline problems and constraints we encountered in developing and applying hardware and software technologies. We indicate future areas to improve our data collection and analysis, to expand our research, and to form partnership that will further bolster the use of this technology.

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Document Details

Document Type
Technical Report
Publication Date
Apr 01, 2007
Accession Number
ADA548364

Entities

People

  • Kenneth Rosenberg

Organizations

  • Cornell University

Tags

Communities of Interest

  • Autonomy
  • Energy and Power Technologies
  • Materials and Manufacturing Processes
  • Space

DTIC Thesaurus Topics

  • Birds
  • Data Sets
  • Department Of Defense
  • Detection
  • Detectors
  • Habitats
  • Medical Personnel
  • Operating Systems
  • Test Methods
  • United States Military Academy
  • Wildlife

Readers

  • Computer Science/Computer Engineering/Data Science/Digital Signal Processing.
  • Marine Mammal Biology
  • Wetland-Land-Environmental Management.