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A comparison of change detection measurements using object-based and pixel-based classification methods on western juniper dominated woodlands in eastern Oregon

Department of Plant and Wildlife Sciences, 4105A LSB Brigham Young University, Provo, UT 84604, USA

Special Issues: Applications of remote sensing and Geographic Information Systems in environmental monitoring

Encroachment of pinyon (Pinus spp) and juniper (Juniperus spp.) woodlands in western North America is considered detrimental due to its effects on ecohydrology, plant community structure, and soil stability. Management plans at the federal, state, and private level often include juniper removal for improving habitat of sensitive species and maintaining sustainable ecosystem processes. Remote sensing has become a useful tool in determining changes in juniper woodland structure because of its uses in comparing archived historic imagery with newly available multispectral images to provide information on changes that are no longer detectable by field measurements. Change in western juniper (J. occidentalis) cover was detected following juniper removal treatments between 1995 and 2011 using panchromatic 1-meter NAIP and 4-band 1-meter NAIP imagery, respectively. Image classification was conducted using remotely sensed images taken at the Roaring Springs Ranch in southeastern Oregon. Feature Analyst for ArcGIS (object-based extraction) and a supervised classification with ENVI 5.2 (pixel-based extraction) were used to delineate juniper canopy cover. Image classification accuracy was calculated using an Accuracy Assessment and Kappa Statistic. Both methods showed approximately a 76% decrease in western juniper cover, although differing in total canopy cover area, with object-based classification being more accurate. Classification results for the 2011 imagery were much more accurate (0.99 Kappa statistic) because of its low juniper density and the presence of an infrared band. The development of methods for detecting change in juniper cover can lead to more accurate and efficient data acquisition and subsequently improved land management and monitoring practices. These data can subsequently be used to assess and quantify juniper invasion and succession, potential ecological impacts, and plant community resilience.
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Keywords western juniper; object-based image classification; pixel-based image classification; remote sensing; juniper encroachment

Citation: Ryan G. Howell, Steven L. Petersen. A comparison of change detection measurements using object-based and pixel-based classification methods on western juniper dominated woodlands in eastern Oregon. AIMS Environmental Science, 2017, 4(2): 348-357. doi: 10.3934/environsci.2017.2.348


  • 1. Tausch RJ, West NE, Nabi AA (1981) Tree age and dominance patterns in Great Basin pinyon-juniper woodlands. J Range Manage, 259-264.
  • 2. Miller RF, Wigand PE (1994) Holocene changes in semiarid pinyon-juniper woodlands. BioScience 44: 465-474.    
  • 3. Miller RF, Rose JA (1995) Historic expansion of Juniperus occidentalis (western juniper) in southeastern Oregon. The Great Basin Naturalist, 37-45.
  • 4. Romme WH, Allen CD, Bailey JD, et al. (2009) Historical and modern disturbance regimes, stand structures, and landscape dynamics in pinon–juniper vegetation of the western United States. Rangeland Ecol Manage 62: 203-222    
  • 5. Madsen MD, Zvirzdin DL, Davis BD, et al. (2011) Feature extraction techniques for measuring pinon and juniper tree cover and density, and comparison with field-based management surveys. Environ manage 47: 766-776.    
  • 6. Anderson JJ, Cobb NS (2004) Tree cover discrimination in panchromatic aerial imagery of pinyon-juniper woodlands. Photogramm Eng Rem S 70: 1063-1068.    
  • 7. Miller RF, Tausch RJ, The role of fire in pinyon and juniper woodlands: a descriptive analysis. Proceedings of the invasive species workshop: the role of fire in the control and spread of invasive species. Tall Timbers Research Station Miscellaneous Publication; 2001. 15-30.
  • 8. Belsky AJ (1996) Viewpoint: Western juniper expansion: Is it a threat to arid northwestern ecosystems? J Range Manage, 53-59.
  • 9. Burkhardt JW, Tisdale EW (1976) Causes of juniper invasion in southwestern Idaho. Ecology: 472-484.
  • 10. Ellison L (1960) Influence of grazing on plant succession of rangelands. The Bot Review 26: 1-78.    
  • 11. Miller RF, Wigand PE (1994) Holocene changes in semiarid pinyon-juniper woodlands. BioScience 44: 465-474.    
  • 12. Young JA, Evans RA (1981) Demography and fire history of a western juniper stand. J Range Manage, 501-506.
  • 13. Bates JD, Miller RF, Svejcar T (2005). Long-term successional trends following western juniper cutting. Rangeland Ecol Manage 58: 533-541.
  • 14. Buckhouse JC, Mattison JL (1980) Potential soil erosion of selected habitat types in the high desert region of central Oregon. J Range Manage, 282-285.
  • 15. Davies KW, Boyd CS, Beck JL, et al. (2011) Saving the sagebrush sea: an ecosystem conservation plan for big sagebrush plant communities. Biol Conserv 144: 2573-2584.    
  • 16. Lajtha K, Getz J (1993) Photosynthesis and water-use efficiency in pinyon-juniper communities along an elevation gradient in northern New Mexico. Oecologia 94: 95-101.    
  • 17. Miller RF, Svejcar TJ, Rose JA (2000) Impacts of western juniper on plant community composition and structure. J Range Manage, 574-585.
  • 18. Tilman D (1987) Secondary succession and the pattern of plant dominance along experimental nitrogen gradients. Ecol monogr 57: 189-214.
  • 19. Wall TG, Miller RF, Svejcar TJ (2001) Juniper encroachment into aspen in the Northwest Great Basin. J Range Manage, 691-698.
  • 20. Schmidt KM, Menakis JP, Hardy CC, et al. (2002) Development of coarse-scale spatial data for wildland fire and fuel management.
  • 21. Knick ST, Hanser SE, Leu M (2014) Ecological scale of bird community response to pinon-juniper removal. Rangeland Ecol Manage 67: 553-562.    
  • 22. Hansen AJ, Knight RL, Marzluff JM, et al. (2005) Effects of exurban development on biodiversity: patterns, mechanisms, and research needs. Ecol Appl 15: 1893-1905.    
  • 23. Petersen SL, Stringham TK (2008) Infiltration, runoff, and sediment yield in response to western juniper encroachment in southeast Oregon. Rangeland Ecol Manage 61: 74-81.    
  • 24. Bates JD, Miller RF, Svejcar TJ (2000) Understory dynamics in cut and uncut western juniper woodlands. J Range Manage, 119-126.
  • 25. Severson KE (1986) Woody plant reestablishment in modified pinyon-juniper woodlands, New Mexico. J Range Manage, 438-442.
  • 26. Allen CD, Breshears DD (1998) Drought-induced shift of a forest–woodland ecotone: rapid landscape response to climate variation. P Natl Acad Sci 95: 14839-14842.    
  • 27. Campbell RB Jr., Monsen SB, Stevens R, 1999. Ecology and management of pinyon-juniper communities within the Interior West: Overview of the "Ecological Restoration" session of the symposium (Monsen S.B. and R. Stevens, editors), Proceedings: Ecology and management of pinyon-juniper communities within the Interior West, 15–18 September, 1997, Provo, UT, (Rocky Mountain Research Station, Provo, Utah), 271-277.
  • 28. Creque JA, West NE, Dobrowolski JP, Methods in historical ecology: a case study of Tintic Valley, Utah. SB Monsen and R. Stevens (compilers). Proceedings: ecology and management of pinyon–juniper communities within the Interior West. Proceedings RMRSP-9, US Department of Agriculture, Forest Service, Rocky Mountain Research Station, Ogden, UT, 1999. 121-137.
  • 29. Roundy DB, Estimating Pinyon and Juniper Cover Across Utah Using NAIP Imagery. [master's thesis]. Brigham Young University; 2015.
  • 30. Booth DT, Tueller PT (2003) Rangeland monitoring using remote sensing. Arid Land Res Manag 17: 455-467.    
  • 31. West NE (1999) Accounting for rangeland resources over entire landscapes. In Proceedings of the VI International Rangeland Congress. Aitkenvale, Queensland 4814: 726-736.
  • 32. Pellant M, Shaver P, Spaeth K (1999) Field test of a prototype rangeland inventory procedure in the western USA. In Proceedings of the VI International Rangeland Congress, People and Rangelands, Building the Future 2: 766-767.
  • 33. Noss RF (1999) Assessing and monitoring forest biodiversity: a suggested framework and indicators. Forest ecol manag 115: 135-146.    
  • 34. Hulet A, Roundy BA, Petersen SL, et al. (2013) Assessing the relationship between ground measurements and object-based image analysis of land cover classes in pinyon and juniper woodlands. Photogramm Eng Rem S 79: 799-808.    
  • 35. Davies KW, Petersen SL, Johnson DD, et al. (2010) Estimating juniper cover from National Agriculture Imagery Program (NAIP) imagery and evaluating relationships between potential cover and environmental variables. Rangeland Ecol Manage 63: 630-637.    
  • 36. Cihlar J, Xiao Q, Chen J, et al. (1998) Classification by progressive generalization: A new automated methodology for remote sensing multichannel data. Int J Remote Sens 19: 2685-2704.    
  • 37. Meneguzzo DM, Liknes GC, Nelson MD (2013) Mapping trees outside forests using high-resolution aerial imagery: a comparison of pixel-and object-based classification approaches. Environ monit assess 185: 6261-6275.    
  • 38. Viera AJ, Garrett JM (2005) Understanding interobserver agreement: the kappa statistic. Fam Med 37: 360-363.
  • 39. Blaschke T (2010) Object based image analysis for remote sensing. ISPRS J photogramm 65: 2-16.
  • 40. Franklin SE, Dickson EE, Approaches for monitoring landscape composition and pattern using remote sensing. Monitoring forest biodiversity in Alberta: program framework, Alberta Forest Biodiversity Monitoring Program Technical Report. 1999.
  • 41. Noss RF (1990) Indicators for monitoring biodiversity: a hierarchical approach. Conserv boil 4: 355-364.
  • 42. Hulet A, Roundy BA, Petersen SL, et al. (2014) Utilizing national agriculture imagery program data to estimate tree cover and biomass of pinon and juniper woodlands. Rangeland Ecol Manage 67: 563-572.    
  • 43. Heller RC, Doverspike GE, Aldrich RC (1964) Identification of tree species on large-scale panchromatic and color aerial photographs. US Department of Agriculture, Forest Service.
  • 44. Congalton RG (1991) A review of assessing the accuracy of classifications of remotely sensed data. Remote sens environ 37: 35-46.
  • 45. Smits PC, Dellepiane SG, Schowengerdt RA (1999) Quality assessment of image classification algorithms for land-cover mapping: a review and a proposal for a cost-based approach. Int J remote sens 20: 1461-1486.    


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  • 1. Nicole Durfee, Carlos Ochoa, Ricardo Mata-Gonzalez, The Use of Low-Altitude UAV Imagery to Assess Western Juniper Density and Canopy Cover in Treated and Untreated Stands, Forests, 2019, 10, 4, 296, 10.3390/f10040296

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Copyright Info: 2017, Ryan G. Howell, et al., licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution Licese (http://creativecommons.org/licenses/by/4.0)

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