Canopy closure estimates with GreenOrbs: sustainable sensing in the forest

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.

[1]  F. James,et al.  Monte Carlo theory and practice , 1980 .

[2]  Fred L. Bunnell,et al.  Comparison of methods for estimating forest overstory cover: differences among techniques. , 1990 .

[3]  S. Kim,et al.  Trio: enabling sustainable and scalable outdoor wireless sensor network deployments , 2006, 2006 5th International Conference on Information Processing in Sensor Networks.

[4]  M. Rautiainen,et al.  Estimation of forest canopy cover: A comparison of field measurement techniques , 2006 .

[5]  John Anderson,et al.  Wireless sensor networks for habitat monitoring , 2002, WSNA '02.

[6]  David S. Moore,et al.  The Basic Practice of Statistics [With CDROM] , 1999 .

[7]  Gang Zhou,et al.  Achieving Long-Term Surveillance in VigilNet , 2006, INFOCOM.

[8]  François Ingelrest,et al.  The hitchhiker's guide to successful wireless sensor network deployments , 2008, SenSys '08.

[9]  Yu Hai Theoretical research on a model for predicting the shadow boundary of an individual conical crown on a slope , 2006 .

[10]  Vlady Ravelomanana,et al.  Optimal Initialization and Gossiping Algorithms for Random Radio Networks , 2007, IEEE Transactions on Parallel and Distributed Systems.

[11]  David E. Culler,et al.  Telos: enabling ultra-low power wireless research , 2005, IPSN 2005. Fourth International Symposium on Information Processing in Sensor Networks, 2005..

[12]  Zhang Yanyong,et al.  TARA: Topology-Aware Resource Adaptation to Alleviate Congestion in Sensor Networks , 2007 .

[13]  Alessandro Cescatti,et al.  Modelling the radiative transfer in discontinuous canopies of asymmetric crowns. II. Model testing and application in a Norway spruce stand , 1997 .

[14]  Matt Welsh,et al.  Fidelity and yield in a volcano monitoring sensor network , 2006, OSDI '06.

[15]  John A. Stankovic,et al.  LUSTER: wireless sensor network for environmental research , 2007, SenSys '07.

[16]  Yunhao Liu,et al.  Underground Structure Monitoring with Wireless Sensor Networks , 2007, 2007 6th International Symposium on Information Processing in Sensor Networks.

[17]  Deborah Estrin,et al.  A wireless sensor network For structural monitoring , 2004, SenSys '04.

[18]  G. G. Stokes "J." , 1890, The New Yale Book of Quotations.

[19]  A. Cescatti Modelling the radiative transfer in discontinuous canopies of asymmetric crowns. I. Model structure and algorithms , 1997 .

[20]  S. Garman,et al.  Comparison of five canopy cover estimation techniques in the western Oregon Cascades , 2006 .

[21]  D. Clark,et al.  Evaluation of digital and film hemispherical photography and spherical densiometry for measuring forest light environments. , 2000 .

[22]  Wei Hong,et al.  A macroscope in the redwoods , 2005, SenSys '05.

[23]  R. E. Grumbine What Is Ecosystem Management , 1994 .