Estimating pinyon and juniper cover across Utah using NAIP imagery

Darrell B. Roundy; April Hulet; Bruce A. Roundy; Ryan R. Jensen; Jordan B. Hinkle; Leann Crook; Steven L. Petersen; Darrell B. Roundy; April Hulet; Bruce A. Roundy; Ryan R. Jensen; Jordan B. Hinkle; Leann Crook; Steven L. Petersen

doi:10.3934/environsci.2016.4.765

AIMS Environmental Science

2016, Volume 3, Issue 4: 765-777. doi: 10.3934/environsci.2016.4.765

Previous Article Next Article

Research article Special Issues

Estimating pinyon and juniper cover across Utah using NAIP imagery

1.
Department of Plant and Wildlife Sciences, Brigham Young University, Provo, UT 84602, USA
2.
Department of Forest, Rangeland, and Fire Sciences, University of Idaho, Moscow, ID, 83844-1133, USA
3.
Department of Geography, Brigham Young University, Provo, UT, 84602, USA
4.
Research Assistant, Chandler, AZ, 85244, USA
5.
Research Assistant, Madison, WI, 53713, USA

Received: 15 August 2016 Accepted: 09 November 2016 Published: 17 November 2016

Expansion of Pinus L. (pinyon) and Juniperus L. (juniper) (P-J) trees into sagebrush (Artemisia L.) steppe communities can lead to negative effects on hydrology, loss of wildlife habitat, and a decrease in desirable understory vegetation. Tree reduction treatments are often implemented to mitigate these negative effects. In order to prioritize and effectively plan these treatments, rapid, accurate, and inexpensive methods are needed to estimate tree canopy cover at the landscape scale. We used object based image analysis (OBIA) software (Feature Analyst^TMfor ArcMap 10.1^®, ENVI Feature Extraction^®, and Trimble eCognition Developer 8.2^®) to extract tree canopy cover using NAIP (National Agricultural Imagery Program) imagery. We then compared our extractions with ground measured tree canopy cover (crown diameter and line point intercept) on 309 plots across 44 sites in Utah. Extraction methods did not consistently over- or under-estimate ground measured P-J canopy cover except where tree cover was >45%. Estimates of tree canopy cover using OBIA techniques were strongly correlated with estimates using the crown diameter method (r = 0.93 for ENVI, 0.91 for Feature Analyst^TM, and 0.92 for eCognition). Tree cover estimates using OBIA techniques had lower correlations with tree cover measurements using the line-point intercept method (r = 0.85 for ENVI, 0.83 for Feature Analyst^TM, and 0.83 for eCognition). All software packages accurately and inexpensively extracted P-J canopy cover from NAIP imagery when the imagery was not blurred, and when P-J cover was not mixed with Amelanchier alnifolia (Utah serviceberry) and Quercus gambelii (Gambel’s oak), which had similar spectral values as P-J.
- Object-based image analysis,
- canopy cover,
- National Agriculture Imagery Program,
- eCognition,
- Feature Analyst^TM,
- ENVI Feature Extraction
Citation: Darrell B. Roundy, April Hulet, Bruce A. Roundy, Ryan R. Jensen, Jordan B. Hinkle, Leann Crook, Steven L. Petersen. Estimating pinyon and juniper cover across Utah using NAIP imagery[J]. AIMS Environmental Science, 2016, 3(4): 765-777. doi: 10.3934/environsci.2016.4.765

Related Papers:

Abstract

Expansion of Pinus L. (pinyon) and Juniperus L. (juniper) (P-J) trees into sagebrush (Artemisia L.) steppe communities can lead to negative effects on hydrology, loss of wildlife habitat, and a decrease in desirable understory vegetation. Tree reduction treatments are often implemented to mitigate these negative effects. In order to prioritize and effectively plan these treatments, rapid, accurate, and inexpensive methods are needed to estimate tree canopy cover at the landscape scale. We used object based image analysis (OBIA) software (Feature Analyst^TMfor ArcMap 10.1^®, ENVI Feature Extraction^®, and Trimble eCognition Developer 8.2^®) to extract tree canopy cover using NAIP (National Agricultural Imagery Program) imagery. We then compared our extractions with ground measured tree canopy cover (crown diameter and line point intercept) on 309 plots across 44 sites in Utah. Extraction methods did not consistently over- or under-estimate ground measured P-J canopy cover except where tree cover was >45%. Estimates of tree canopy cover using OBIA techniques were strongly correlated with estimates using the crown diameter method (r = 0.93 for ENVI, 0.91 for Feature Analyst^TM, and 0.92 for eCognition). Tree cover estimates using OBIA techniques had lower correlations with tree cover measurements using the line-point intercept method (r = 0.85 for ENVI, 0.83 for Feature Analyst^TM, and 0.83 for eCognition). All software packages accurately and inexpensively extracted P-J canopy cover from NAIP imagery when the imagery was not blurred, and when P-J cover was not mixed with Amelanchier alnifolia (Utah serviceberry) and Quercus gambelii (Gambel’s oak), which had similar spectral values as P-J.

References

[1]	Miller RF, Tausch RJ (2001) The role of fire in pinyon and juniper woodlands: a descriptive analysis. In: K. E. M. Galley and T. P. Wilson [eds]. Proceedings of the Invasive Species Workshop: the Role of Fire in the Control and Spread of Invasive Species. Fire Conference 2000: the First National Congress on Fire Ecology, Prevention, and Management. Miscellaneous Publication No. 11, Tall Timber Research Station, Tallahassee, FL, USA. p. 15-30.
[2]	Miller RF, Bates JD, Svejcar TJ, et al. (2005) Biology, ecology, and management of western juniper. Corvallis, OR, USA: Oregon State University, Technical Bulletin 152. 77 p.
[3]	Miller RF, Svejcar TJ, Rose JA (2000) Impacts of western juniper on plant community composition and structure. J Range Manage 53: 574-585.
[4]	Bates JD, Miller RF, Svejcar TJ (2005) Long-term successional trends following western juniper cutting. Rangeland Ecol Manag 58: 533-541.
[5]	Pierson FB, Williams CJ, Kormos PR, et al. (2010) Hydrologic vulnerability of sagebrush steppe following pinyon and juniper encroachment. Rangeland Ecol Manag 63: 614-629.
[6]	Roundy BA, Miller RF, Tausch RJ, et al. (2014) Understory cover responses to piñon-juniper treatments across tree cover gradients in the Great Basin. Rangeland Ecol Manag 67: 482-494. doi: 10.2111/REM-D-13-00018.1
[7]	Knick ST, Hanser SE, Leu M (2014) Ecological Scale of Bird Community Response to Pinyon-Juniper Removal. Rangeland Ecol Manag 67: 553-562. doi: 10.2111/REM-D-13-00023.1
[8]	Gruell GE (1999) Historical and modern roles of fire in pinyon-juniper. In: Steve B. Monsen and Richard Stevens [EDS]. Proceedings: Ecology and management of pinyon-juniper communities within the Interior West; 15-18 September 1997; Brigham Young University, Provo, Utah, USA. Ogden, UT, USA: U.S. Department of Agriculture, Forest Service, Rocky Mountain Research Station, RMRS-P-9. p. 24-28.
[9]	Miller RF, Ratchford J, Roundy BA, et al. (2014) Response of conifer-encroached shrublands in the Great Basin to prescribed fire and mechanical treatments. Rangeland Ecol Manag 67: 468-481. doi: 10.2111/REM-D-13-00003.1
[10]	Young KR, Roundy BA, Eggett DL (2014) Mechanical mastication of Utah juniper encroaching sagebrush steppe increases inorganic soil N. Appl Environ Soil Sci, 2014.
[11]	Young KR, Roundy BA, Bunting SC, et al. (2015) Utah juniper and two- needle piñon reduction alters fuel loads. Int J Wildland Fire 24: 236-248.
[12]	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.
[13]	Hulet A, Roundy BA, Petersen SL, et al. (2013) An object-based image analysis of pinyon and juniper woodlands treated to reduce fuels. Environ Manag 53: 660-671.
[14]	Hulet A, Roundy BA, Petersen SL, et al. (2014) Cover estimations using object-based image analysis rule sets developed across multiple scales in pinyon and juniper woodlands. Rangeland Ecol Manag 67: 318-327.
[15]	Madsen MD, Zvirzdin DL, Davis BD, et al. (2011) Feature extraction techniques for measuring pińon and juniper tree cover and density, and comparison with field-based management surveys. Environ Manag 47: 766-776.
[16]	Hulet A, Roundy BA, Petersen SL, et al. (2014) Utilizing NAIP imagery to estimate tree cover and biomass of pinyon and juniper woodlands. Rangeland Ecol Manag 67: 563-572.
[17]	Weisberg PJ, Lingua E, Pillai RB (2007) Spatial patterns on pinyon-juniper woodland expansion in Central Nevada. Rangeland Ecol Manag 60: 115-124.
[18]	Bybee J, Roundy BA, Young KR, et al. (2016) Vegetation response to pinon and juniper tree shredding. Rangeland Ecol Manag 69: 224-234.
[19]	Booth DT, Cox SE, Fifield C, et al. (2005) Image analysis compared with other methods for measuring ground cover. Arid Land Res Manag 19: 91-100.
[20]	Ko D, Bristow N, Greenwood D, et al. (2009) Canopy cover estimation in semiarid woodlands: comparison of field-based and remote sensing methods. Forest Sci 55: 132-141.
[21]	Laliberte AS, Fredrickson EL, Rango A (2007) Combining decision trees with hierarchical object-oriented image analysis for mapping arid rangelands. Photogramm Eng Rem S 73: 197-207. doi: 10.14358/PERS.73.2.197
[22]	Laliberte AS, Rango A, Herrick JE, et al. (2007) An object-based image analysis approach for determining fractional cover of senescent and green vegetation with digital plot photography. J Arid Environ 69: 1-14. doi: 10.1016/j.jaridenv.2006.08.016
[23]	Laliberte AS, Rango A (2009) Texture and scale in object-based analysis of subdecimeter resolution unmanned aerial vehicle (UAV) imagery. IEEE T Geosci Remote 47: 761-770. doi: 10.1109/TGRS.2008.2009355
[24]	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 Manag 63: 630-637.
[25]	Visual Information Solutions (2008) ENVI Feature Extraction Module User’s Guide. Available from: http://www.exelisvis.com/portals/0/pdfs/envi/feature_extraction_module.pdf.
[26]	Blundell S, Opitz D, Morris M, et al. (2008) Feature Analyst V5.0. Paper presented at The ASPRS Annual Conference, Portland, Oregon, USA.
[27]	Trimble (2011) eCognition Developer 8.64.1 reference book, version 8.64.1, Trimble Germany GmbH, München, Germany.
[28]	McIver JD, Brunson M (2014) Multidisciplinary, multisite evaluation of alternative sagebrush steppe restoration treatments: the SageSTEP project. Rangeland Ecol Manag 67: 435-439.
[29]	Bybee J, Roundy BA, Young KR, Hulet A, Roundy DB, Crook L, Aanderud Z, Eggett D, Cline NL (2016) Vegetation response to pinon and juniper tree shredding. Rangeland Ecol Manag 69: 224-234.
[30]	Baatz M, Schäpe A (2000) Multiresolution segmentation—an optimization approach for high quality multi-scale image segmentation. In: J. Strobl, et al. [eds]. Angewandte Geographische Informationsverarbeitung XII. Wichmann, Heidelberg, Germany. p. 12-23.
[31]	Congalton RG (2001) Accuracy assessment and validation of remotely sensed and other spatial information. Int J Wildland Fire 10: 321-328.
[32]	Landis J, Koch G (1977) The measurement of observer agreement for categorical data. Biometrics 33: 159-174.

Reader Comments

Your name:*

Email:*
© 2016 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)