ABSTRACT
Motivated by the needs of precise forest inventory and real-time surveillance for ecosystem management, in this paper we present GreenOrbs [2], a wireless sensor network system and its application for canopy closure estimates. Both the hardware and software designs of GreenOrbs are tailored for sensing in wild environments without human supervision, including a firm weatherproof enclosure of sensor motes and a light-weight mechanism for node state monitoring and data collection. By incorporating a pre-deployment training process as well as a distributed calibration method, the estimates of canopy closure stay accurate and consistent against uncertain sensory data and dynamic environments. We have implemented a prototype system of GreenOrbs and carried out multiple rounds of deployments. The evaluation results demonstrate that GreenOrbs outperforms the conventional approaches for canopy closure estimates. Some early experiences are reported in this paper.
- FRA 2000 On Definitions of Forest and Forest Change. http://www.fao.org/docrep/006/ad665e/ad665e00.htm.Google Scholar
- GreenOrbs. http://www.greenorbs.org.Google Scholar
- Si photodiode S1087/S1133 Series.Google Scholar
- G. Barrenetxea and G. Schaefer. The hitchhiker's guide to successful wireless sensor network deployments. In Proceedings of the 6th ACM conference on Embedded network sensor systems, pages 43--56. ACM New York, NY, USA, 2008. Google ScholarDigital Library
- F. Bunnell and D. Vales. Comparison of methods for estimating forest overstory cover: differences among techniques. Canadian Journal of Forest Research, 20(1):101--107, 1990.Google ScholarCross Ref
- A. Cescatti. Modelling the radiative transfer in discontinuous canopies of asymmetric crowns. II. Model testing and application in a Norway spruce stand. Ecological Modelling, 101(2--3):275--284, 1997.Google Scholar
- P. Dutta, J. Hui, J. Jeong, S. Kim, C. Sharp, J. Taneja, G. Tolle, K. Whitehouse, and D. Culler. Trio: Enabling sustainable and scalable outdoor wireless sensor network deployments. In Proceedings of the 5th international conference on Information processing in sensor networks, pages 407--415. ACM New York, NY, USA, 2006. Google ScholarDigital Library
- S. Englund, J. O'Brien, and D. Clark. Evaluation of digital and film hemispherical photography and spherical densiometry for measuring forest light environments. Canadian Journal of Forest Research, 30(12):1999--2005, 2000.Google ScholarCross Ref
- A. Fiala, S. Garman, and A. Gray. Comparison of five canopy cover estimation techniques in the western Oregon Cascades. Forest Ecology and Management, 232(1--3):188--197, 2006.Google Scholar
- R. Grumbine. What is ecosystem management? Conservation Biology, 8(1):27--38, 1994.Google ScholarCross Ref
- F. James. Monte Carlo theory and practice. Reports on Progress in Physics, 43(9):1145--1189, 1980.Google ScholarCross Ref
- J. Kang, Y. Zhang, and B. Nath. TARA: Topology-Aware Resource Adaptation to Alleviate Congestion in Sensor Networks. IEEE Transactions on Parallel and Distributed Systems, 18(7):919, 2007. Google ScholarDigital Library
- L. Korhonen, K. Korhonen, M. Rautiainen, and P. Stenberg. Estimation of forest canopy cover: a comparison of field measurement techniques. Silva Fennica, 40(4):577, 2006.Google ScholarCross Ref
- M. Li and Y. Liu. Underground structure monitoring with wireless sensor networks. In Proceedings of the 6th international conference on Information processing in sensor networks, pages 69--78. ACM New York, NY, USA, 2007. Google ScholarDigital Library
- A. Mainwaring, J. Polastre, R. Szewczyk, D. Culler, and J. Anderson. Wireless sensor networks for habitat monitoring.Google Scholar
- D. Moore. The basic practice of statistics. WH Freeman, 2004. Google ScholarDigital Library
- J. Polastre, R. Szewczyk, and D. Culler. Telos: Enabling ultra-low power wireless research. In Proceedings of the 4th international symposium on Information processing in sensor networks. IEEE Press Piscataway, NJ, USA, 2005. Google ScholarDigital Library
- V. Ravelomanana. Optimal initialization and gossiping algorithms for random radio networks. IEEE Transactions on Parallel and Distributed Systems, 18(1):17--28, 2007. Google ScholarDigital Library
- L. Selavo, A. Wood, Q. Cao, T. Sookoor, H. Liu, A. Srinivasan, Y. Wu, W. Kang, J. Stankovic, D. Young, et al. LUSTER: wireless sensor network for environmental research. In Proceedings of the 5th international conference on Embedded networked sensor systems, pages 103--116. ACM New York, NY, USA, 2007. Google ScholarDigital Library
- G. Tolle, J. Polastre, R. Szewczyk, D. Culler, N. Turner, K. Tu, S. Burgess, T. Dawson, P. Buonadonna, D. Gay, et al. A macroscope in the redwoods. In Proceedings of the 3rd international conference on Embedded networked sensor systems, pages 51--63. ACM New York, NY, USA, 2005. Google ScholarDigital Library
- P. Vicaire, T. He, Q. Cao, T. Yan, G. Zhou, L. Gu, L. Luo, R. Stoleru, J. Stankovic, and T. Abdelzaher. Achieving long-term surveillance in vigilnet. In Proceedings of the 25th IEEE Conference on Computer Communications, 2006.Google Scholar
- G. Werner-Allen, K. Lorincz, J. Johnson, J. Lees, and M. Welsh. Fidelity and yield in a volcano monitoring sensor network. In Proceedings of OSDI, 2006. Google ScholarDigital Library
- N. Xu, S. Rangwala, K. Chintalapudi, D. Ganesan, A. Broad, R. Govindan, and D. Estrin. A wireless sensor network for structural monitoring. In Proceedings of the 2nd international conference on Embedded networked sensor systems, pages 13--24. ACM New York, NY, USA, 2004. Google ScholarDigital Library
- L. Zhang, H. Yu, H. Yang, and Z. Zhang. Theoretical Research on a Model for Predicting the Shadow Boundary of an Individual Conical Crown on a Slope. ACTA ECOLOGICA SINICA, 26(010):3317--3323, 2006.Google Scholar
Index Terms
- Canopy closure estimates with GreenOrbs: sustainable sensing in the forest
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