Using phenocams to monitor our changing earth: Toward a global phenocam network

Frontiers in Ecology and the Environment

Volumn 14, Issue 2, 2016, Pages 84-93

  Brown, Tim B ^a   Hultine, Kevin R ^b   Steltzer, Heidi ^c   Denny, Ellen G ^d   Denslow, Michael W ^e   Granados, Joel ^f   Henderson, Sandra ^g   Moore, David ^d   Nagai, Shin ^h   Sanclements, Michael ^g   Sánchez Azofeifa, Arturo ⁱ   Sonnentag, Oliver ^j   Tazik, David ^g   Richardson, Andrew D ^k  

^a AUSTRALIAN NATIONAL UNIVERSITY   (Australia)

^b DESERT BOTANICAL GARDEN   (United States)

^c FORT LEWIS COLLEGE   (United States)

^d UNIVERSITY OF ARIZONA   (United States)

^e APPALACHIAN STATE UNIVERSITY   (United States)

^f IT UNIVERSITY OF COPENHAGEN   (Denmark)

^g NATIONAL ECOLOGICAL OBSERVATORY NETWORK   (United States)

^h JAPAN AGENCY FOR MARINE EARTH SCIENCE AND TECHNOLOGY   (Japan)

ⁱ UNIVERSITY OF ALBERTA   (Canada)

^j UNIVERSITÉ DE MONTRÉAL   (Canada)

^k HARVARD UNIVERSITY   (United States)

more..

Author keywords

[No Author keywords available]

Indexed keywords

BIOSPHERE; DIGITAL MAPPING; ENVIRONMENTAL CHANGE; ENVIRONMENTAL DISTURBANCE; ENVIRONMENTAL MONITORING; EUROPEAN UNION; FIRE; MONITORING SYSTEM; OBSERVATORY; PHENOLOGY; PHOTOGRAPHY; POLICY DEVELOPMENT; SNOW COVER; VEGETATION MAPPING; VISUALIZATION;

EUROPE; UNITED STATES;

EID: 84959314637     PISSN: 15409295     EISSN: 15409309     Source Type: Journal    
DOI: 10.1002/fee.1222     Document Type: Review

References (47)

1. Ahrends HE, Etzold S, Kutsch WL, et al. 2009. Tree phenology and carbon dioxide fluxes: use of digital photography for process-based interpretation at the ecosystem scale. Clim Res 39:261-74.
2. Anderson K and Gaston KJ. 2013. Lightweight unmanned aerial vehicles will revolutionize spatial ecology. Front Ecol Environ 11:138-46.
3. Bellard C, Bertelsmeier C, Leadley P, et al. 2012. Impacts of climate change on the future of biodiversity. Ecol Lett 15:365-77.
4. Brown TB, Zimmermann CL, Panneton W, et al. 2012. Highresolution, time-lapse imaging for ecosystem-scale phenotyping in the field. In: Normanly J (Ed). High throughput phenotyping in plants. Methods in molecular biology. New York, NY: Springer-Verlag.
5. Burgess SSO, Kranz ML, Turner NE, et al. 2010. Harnessing wireless sensor technologies to advance forest ecology and agricultural research. Agr Forest Meteorol 150:30-37.
6. Ciais P, Sabine C, Bala G, et al. 2013. Carbon and other biogeochemical cycles. In: Climate change 2013: the physical science basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge, UK: Cambridge University Press.
7. Crimmins MA and Crimmins TM. 2008. Monitoring plant phenology using digital repeat photography. Environ Manage 41:949-58.
8. Denny EG, Gerst KL, Miller-Rushing AJ, et al. 2014. Standardized phenology monitoring methods to track plant and animal activity for science and resource management applications. Int J Biometeorol 58:591-601.
9. Filippa G, Cremonese E, Migliavacca M, et al. nd. Phenopix R package - analysis of phenological digital imagery. https://r-forge.r-project.org/projects/phenopix. Viewed 15 Dec. 2015.
10. Fisch KM, Meißner T, Gioia L, et al. 2015. Omics pipe: a computational framework for reproducible multi-omics data analysis. Bioinformatics 31:1724-28.
11. Gillespie AR, Kahle AB, and Walker RE. 1987. Color enhancement of highly correlated images. II. Channel ratio and "chromaticity" transformation techniques. Remote Sens Environ 22:343-65.
12. Graham EA, Riordan EC, Yuen EM, et al. 2010. Public internetconnected cameras used as a cross-continental ground-based plant phenology monitoring system. Glob Change Biol 16:3014-23.
13. Granados JA, Graham EA, Bonnet P, et al. 2013. EcoIP: an open source image analysis toolkit to identify different stages of plant phenology for multiple species with pan-tilt-zoom cameras. Ecol Inform 15:58-65.
14. Hill A, Guralnick R, Smith A, et al. 2012. The notes from nature tool for unlocking biodiversity records from museum records through citizen science. Zookeys 209:219-33.
15. Hufkens K, Friedl MA, Keenan TF, et al. 2012. Ecological impacts of a widespread frost event following early spring leaf-out. Glob Change Biol 18:2365-77.
16. Inoue T, Nagai S, Kobayashi H, and Koizumi H. 2015. Utilization of ground-based digital photography for the evaluation of seasonal changes in the aboveground green biomass and foliage phenology in a grassland ecosystem. Ecol Inform 25:1-9.
17. Inoue T, Nagai S, Saitoh TM, et al. 2014. Detection of the different characteristics of year-to-year variation in foliage phenology among deciduous broad-leaved tree species by using daily continuous canopy surface images. Ecol Inform 22:58-68.
18. Jacobs N, Burgin W, Fridrich N, et al. 2009. The global network of outdoor webcams: properties and applications. In: ACM International Conference on Advances in Geographic Information Systems (SIGSPATIAL GIS). 4-6 Nov. 2009; Seattle, WA: ACM Press.
19. Julitta T, Cremonese E, Migliavacca M, et al. 2014. Using digital camera images to analyse snowmelt and phenology of a subalpine grassland. Agr Forest Meteorol 198:116-25.
20. Keller M, Schimel DS, Hargrove WW, and Hoffman FM. 2008. A continental strategy for the National Ecological Observatory Network. Front Ecol Environ 6:282-84.
21. Koh LP and Wich SA. 2012. Dawn of drone ecology: low-cost autonomous aerial vehicles for conservation. Trop Conserv Sci 5:121-32.
22. Lobell DB, Schlenker W, and Costa-Roberts J. 2011. Climate trends and global crop production since 1980. Science 333:616-20.
23. Macfarlane C, Ryu Y, Ogden GN, and Sonnentag O. 2014. Digital canopy photography: exposed and in the raw. Agr Forest Meteorol 197:244-53.
24. Martin-Brualla R, Gallup D, and Seitz SM. 2015. Time-lapse mining from internet photos. ACM T Graphic 34:62.
25. Meeker M. 2014. Internet trends 2014. 2014 Code Conference. www.kpcb.com/internet-trends. Viewed 7 Jan 2016.
26. Morisette JT, Richardson AD, Knapp AK, et al. 2009. Tracking the rhythm of the seasons in the face of global change: phenological research in the 21st century. Front Ecol Environ 7:253-60.
27. Morris DE, Boyd DS, Crowe JA, et al. 2013. Exploring the potential for automatic extraction of vegetation phenological metrics from traffic webcams. Remote Sensing 5:2200-18.
28. Nagler PL, Pearlstein S, Glenn EP, et al. 2014. Rapid dispersal of saltcedar (Tamarix spp) biocontrol beetles (Diorhabda carinulata) on a desert river detected by phenocams, MODIS imagery and ground observations. Remote Sens Environ 140:206-19.
29. Nasahara KN and Nagai S. 2015. Review: development of an in situ observation network for terrestrial ecological remote sensing: the Phenological Eyes Network (PEN). Ecol Res 30:211-23.
30. Newman G, Wiggins A, Crall A, et al. 2012. The future of citizen science: emerging technologies and shifting paradigms. Front Ecol Environ 10:298-304.
31. Nijland W, de Jong R, de Jong SM, et al. 2014. Monitoring plant condition and phenology using infrared sensitive consumer grade digital cameras. Agr Forest Meteorol 184:98-106.
32. Nourbakhsh IR and Sargent R. 2010. The Fine International Conference on Gigapixel Imaging for Science. In: Nourbakhsh IR and Sargent R (Eds). Proceedings of the Fine International Conference on Gigapixel Imaging for Science; 11-13 Nov. 2010; Pittsburgh, PA. Pittsburgh, PA: Carnegie Mellon University.
33. Petach AR, Toomey M, Aubrecht DM, and Richardson AD. 2014. Monitoring vegetation phenology using an infrared-enabled security camera. Agr Forest Meteorol 195-196:143-51.
34. Raddick M, Bracey G, Gay PL, et al. 2013. Galaxy zoo: motivations of citizen scientists. Astron Educ Rev 12.
35. Rankine CJ, Sanchez-Azofeifa GA, and MacGregor MH. 2014. Seasonal wireless sensor network link performance in boreal forest phenology monitoring. In: 2014 Eleventh Annual IEEE International Conference on Sensing, Communication, and Networking (SECON). 30 Jun-3 Jul. 2014; Singapore. doi:10.1109/SAHCN.2014.6990366.
36. Richardson AD, Braswell BH, Hollinger DY, et al. 2009. Nearsurface remote sensing of spatial and temporal variation in canopy phenology. Ecol Appl 19:1417-28.
37. Richardson AD, Jenkins JP, Braswell BH, et al. 2007. Use of digital webcam images to track spring green-up in a deciduous broadleaf forest. Oecologia 152:323-34.
38. Richardson AD, Keenan TF, Migliavacca M, et al. 2013a. Climate change, phenology, and phenological control of vegetation feedbacks to the climate system. Agr Forest Meteorol 169:156-73.
39. Richardson AD, Klosterman S, and Toomey M. 2013b. Nearsurface sensor-derived phenology. In: Schwartz MD (Ed). Phenology: an integrative environmental science. Dordrecht, the Netherlands: Springer.
40. Ryu Y, Verfaillie J, Macfarlane C, et al. 2012. Continuous observation of tree leaf area index at ecosystem scale using upward-pointing digital cameras. Remote Sens Environ 126:116-25.
41. Sonnentag O, Hufkens K, Teshera-Sterne C, et al. 2012. Digital repeat photography for phenological research in forest ecosystems. Agr Forest Meteorol 152:159-77.
42. Toomey M, Friedl MA, Frolking S, et al. 2015. Greenness indices from digital cameras predict the timing and seasonal dynamics of canopy-scale photosynthesis. Ecol Appl 25:99-115.
43. Webb RH. 2010. Repeat photography: methods and applications in the natural sciences. Washington, DC: Island Press.
44. West R, Halley A, O'Neil-Dunne J, et al. 2013. Collaborative imaging of urban forest dynamics: augmenting re-photography to visualize changes over time. In: Dolinsky M and McDowall IE (Eds). The engineering reality of virtual reality. Proc SPIE; doi:10.1117/12.2008109.
45. White MA, de Beurs KM, Didan K, et al. 2009. Intercomparison, interpretation, and assessment of spring phenology in North America estimated from remote sensing for 1982-2006. Glob Change Biol 15:2335-59.
46. Whitham TG, Bailey JK, Schweitzer JA, et al. 2006. A framework for community and ecosystem genetics: from genes to ecosystems. Nat Rev Genet 7:510-23.
47. Wingate L, Ogée J, Cremonese E, et al. 2015. Interpreting canopy development and physiology using the EUROPhen camera network at flux sites. Biogeosciences 12:7979-8034.