Rangeland monitoring using remote sensing: comparison of cover estimates from field measurements and image analysis

Ammon Boswell; Steven Petersen; Bruce Roundy; Ryan Jensen; Danny Summers; April Hulet; Ammon Boswell; Steven Petersen; Bruce Roundy; Ryan Jensen; Danny Summers; April Hulet

doi:10.3934/environsci.2017.1.1

AIMS Environmental Science

2017, Volume 4, Issue 1: 1-16. doi: 10.3934/environsci.2017.1.1

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Rangeland monitoring using remote sensing: comparison of cover estimates from field measurements and image analysis

1.
Natural Resource Conservation Service, Monticello, Utah, 84535, USA
2.
Department of Plant and Wildlife Sciences, Brigham Young University, Provo, Utah, 84602, USA
3.
Department of Geography, Brigham Young University, Provo, Utah, 84602, USA
4.
Great Basin Research Center, Utah Division of Wildlife Resources, Ephraim, Utah, 84627, USA
5.
Department of Forest, Rangeland, and Fire Sciences, University of Idaho, Moscow, Idaho, 83843, USA

Received: 23 August 2016 Accepted: 04 January 2017 Published: 06 January 2017

Rangeland monitoring is important for evaluating and assessing semi-arid plant communities. Remote sensing provides an effective tool for rapidly and accurately assessing rangeland vegetation and other surface attributes such as bare soil and rock. The purpose of this study was to evaluate the efficacy of remote sensing as a surrogate for field-based sampling techniques in detecting ground cover features (i.e., trees, shrubs, herbaceous cover, litter, surface), and comparing results with field-based measurements collected by the Utah Division of Wildlife Resources Range Trent Program. In the field, five 152 m long transects were used to sample plant, litter, rock, and bare-ground cover using the Daubenmire ocular estimate method. At the same location of each field plot, a 4-band (R,G,B,NIR), 25 cm pixel resolution, remotely sensed image was taken from a fixed-wing aircraft. Each image was spectrally classified producing 4 cover classes (tree, shrub, herbaceous, surface). No significant differences were detected between canopy cover collected remotely and in the field for tree (P = 0.652), shrub (P = 0.800), and herbaceous vegetation (P = 0.258). Surface cover was higher in field plots (P < 0.001), likely in response to the methods used to sample surface features by field crews. Accurately classifying vegetation and other features from remote sensed information can improve the efficiency of collecting vegetation and surface data. This information can also be used to improve data collection frequency for rangeland monitoring and to efficiently quantify ecological succession patterns.
- Image classification,
- canopy cover,
- rangeland monitoring,
- remote sensing
Citation: Ammon Boswell, Steven Petersen, Bruce Roundy, Ryan Jensen, Danny Summers, April Hulet. Rangeland monitoring using remote sensing: comparison of cover estimates from field measurements and image analysis[J]. AIMS Environmental Science, 2017, 4(1): 1-16. doi: 10.3934/environsci.2017.1.1

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Abstract

Rangeland monitoring is important for evaluating and assessing semi-arid plant communities. Remote sensing provides an effective tool for rapidly and accurately assessing rangeland vegetation and other surface attributes such as bare soil and rock. The purpose of this study was to evaluate the efficacy of remote sensing as a surrogate for field-based sampling techniques in detecting ground cover features (i.e., trees, shrubs, herbaceous cover, litter, surface), and comparing results with field-based measurements collected by the Utah Division of Wildlife Resources Range Trent Program. In the field, five 152 m long transects were used to sample plant, litter, rock, and bare-ground cover using the Daubenmire ocular estimate method. At the same location of each field plot, a 4-band (R,G,B,NIR), 25 cm pixel resolution, remotely sensed image was taken from a fixed-wing aircraft. Each image was spectrally classified producing 4 cover classes (tree, shrub, herbaceous, surface). No significant differences were detected between canopy cover collected remotely and in the field for tree (P = 0.652), shrub (P = 0.800), and herbaceous vegetation (P = 0.258). Surface cover was higher in field plots (P < 0.001), likely in response to the methods used to sample surface features by field crews. Accurately classifying vegetation and other features from remote sensed information can improve the efficiency of collecting vegetation and surface data. This information can also be used to improve data collection frequency for rangeland monitoring and to efficiently quantify ecological succession patterns.

References

[1]	Schalau J (2010) Rangeland monitoring: Selecting key areas, The University of Arizona Cooperative Extension, p 1-3.
[2]	Holechek JL, Pieper RD, Herbel CH (2004) Rangeland Management: Principles and Practices. 5th Edition, Prentice Hall, Englewood Cliffs, New Jersey, USA.
[3]	Godınez-Alvarez H, Herrick JE, Mattocks M, et al. (2009) Comparison of three vegetation monitoring methods: Their relative utility for ecological assessment and monitoring. Ecol Indic 9: 1001-1008. doi: 10.1016/j.ecolind.2008.11.011
[4]	National Research Council (NRC) (2000). Ecological indicators for the Nation, National Academy Press. Washington, DC.
[5]	Miller RF, Tausch RJ (2001) 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, Fire conference 2000: The First National Congress on Fire Ecology, Prevention, and Management (K.E.M. Galley and T.P.Wilson, editors) Miscellaneous Publication No. 11, Tall Timbers Research Station, Tallahassee, FL. pp 15-30.
[6]	Stringham TK, Krueger WC, Shaver PL (2003) State and transition modeling: An ecological process approach. J Range Manage 56: 106-113.
[7]	Herrick JE, Brown JR, Tugel AJ, et al. (2002) Application of soil quality to monitoring and management: Paradigms from rangeland ecology. Agron J 94: 3-11. doi: 10.2134/agronj2002.0003
[8]	Booth DT, Tueller PT (2003) Rangeland monitoring using remote sensing. Arid Land Res Manag 17: 455-478.
[9]	Booth DT, Cox SE, Meikle T, et al. (2008) Ground-cover measurements: Assessing correlation among aerial and ground-based methods. Environ Manag 42: 1091-1100.
[10]	National Research Council (NRC) (1994) Rangelands Health. New Methods to Classify Inventory and Monitor Rangelands, National Academy Press, Washington, DC.
[11]	Society for Range Management Task Group on Unity in Concepts and Terminology (1995) New concepts for assessment of rangeland condition. J Range Manage 48: 271-282.
[12]	Fox DM, Bryan RB, Price AG (1997) The influence of slope angle on final infiltration rate for interrill conditions. Geoderma 80: 181-194. doi: 10.1016/S0016-7061(97)00075-X
[13]	Briske DD, Bestelmeyer BT, Stringham TK, et al. (2008) Recommendations for development of resilience-based state-and-transition models. Rangeland Ecol Manag 61: 359-367.
[14]	Chambers JC, Bradley BA, Brown CS, et al. (2007) Resilience to stress and disturbance, and resistance to Bromus tectorum L. invasion in cold desert shrublands of Western North America. Ecosystems 17: 360-375.
[15]	Pierson FB, Spaeth KE, Weltz MA, et al. (2002) Hydrologic response of diverse western rangelands. J Range Manage 55: 558-570.
[16]	Gunnell K, Lane J, Cox J (2011) Publ. No. 12–15 Utah Big Game Range Trend Studies. Utah Dept. of Natural Resources, Division of Wildlife. Resources, Salt Lake City, Utah.
[17]	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.
[18]	Petersen SL, Stringham TK (2008) Development of GIS-based models to predict plant community structure in relation to western juniper establishment. Forest Ecol Manag 256: 981-989. doi: 10.1016/j.foreco.2008.05.058
[19]	Krebs CJ (1999) Ecological Methodology, 2nd Edition, Published by Addison-Welsey.
[20]	Booth DT, Cox SE, Meikle TW, et al. (2006) The accuracy of ground-cover measurements. Rangeland Ecol Manag 59: 179-188. doi: 10.2111/05-069R1.1
[21]	Laliberte E, Norton DA, Tylianakis JM, et al. (2010) Comparison of two sampling methods for quantifying changes in vegetation composition under rangeland development. Rangeland Ecol Manag 63: 537-545. doi: 10.2111/REM-D-09-00156.1
[22]	Moffet CA (2009) Agreement between measurements of shrub cover using ground-based methods and very large scale aerial imagery. Rangeland Ecol Manag 62: 268-277. doi: 10.2111/08-244R.1
[23]	Afinowicz JD, Munster CL, Wilcox BP, et al. (2005) A process for assessing wooded plant cover by remote sensing. Rangeland Ecol Manag 58: 184-190.
[24]	Booth DT, Cox SE (2008) Image-based monitoring to measure ecological change in rangeland. Front Ecol Environ 6: 185-190.
[25]	Hunt ER Jr, Everitt JH, Ritchie, JC, et al. (2003) Application and research using remote sensing for rangeland management. Photogramm Eng Rem S 69: 675-693.
[26]	Petersen SL, Stringham TK, Laliberte AS (2005) Classification of willow species using large-scale aerial photography. Rangeland Ecol Manag 58: 582-587.
[27]	Clark PE, Seyfried MS, Harris B (2001) Intermountain plant community classification using Landsat TM and SPOT HRV data. J Range Manage 54: 152-160.
[28]	Congalton RG (2001) Accuracy assessment and validation of remotely sensed and other spatial information. Int J Wildland Fire 10: 321-328. doi: 10.1071/WF01031
[29]	Jensen JR (2005) Introductory Digital Image Processing: A remote sensing perspective. Prentice Hall, New Jersey, p. 526.
[30]	Landis J, Koch G (1977) The measurement of observer agreement for categorical data. Biometrics 33: 159-174. doi: 10.2307/2529310
[31]	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. doi: 10.14358/PERS.79.9.799
[32]	Herrick JE (2000) Soil quality: an indicator of sustainable land management? Appl Soil Ecol 15: 75-83. doi: 10.1016/S0929-1393(00)00073-1
[33]	Havstad KM, Herrick JE (2003) Long-Term Ecological Monitoring. Arid Land Res Manage 17: 389-400.
[34]	Booth DT, Glenn D, Keating B, et al. (2003) Monitoring rangelands with very-large scale aerial imagery. Paper 74 in Proceedings of the VII International Rangeland Congress.
[35]	Bennett LT, Judd TS, Adams MA (2000) Close-range vertical photography for measuring cover changes in perennial grasslands. J Range Manage 53: 634-641.
[36]	Louhaichi M, Johnson DE (2001) Spatially located platform and aerial photography for documentation of grazing impacts on wheat. Geocarta International 16: 63-68. doi: 10.1080/10106040108542205
[37]	Petersen SL, Stringham TK (2008) Infiltration, runoff, and sediment yield in response to western juniper encroachment in Southeast Oregon. Rangeland Ecol Manag 61: 74-81. doi: 10.2111/07-070R.1
[38]	Madsen MD, Zvirzdin DL, Davis BD, et al. (2010) Feature extraction techniques for measuring pinon and juniper tree cover and density, and comparison with field-based management surveys. Environ Manag 47: 766-776.
[39]	Sankey TT, Germino MJ (2008) Assessment of juniper encroachment with the use of satellite imagery and geospatial data. Rangeland Ecol Manag 61: 412-418. doi: 10.2111/07-141.1
[40]	Platt RV, Schoennagel T (2009) An object-oriented approach to assessing changes in tree cover in the Colorado Front Range 1938–1999. Forest Ecolo Manag 258: 1342-1349. doi: 10.1016/j.foreco.2009.06.039
[41]	Roundy BA, Miller RF, Tausch RJ, et al. (2014) Understory cover responses to pinon-juniper treatments across tree dominance gradients in the Great Basin. Rangeland Ecol Manag 67: 482-494.
[42]	Hulet A, Roundy BA, Petersen SL, et al. (2014) An object-based image analysis of pinyon and juniper woodlands treated to reduce fuels. Environ Manag 53: 660-671. doi: 10.1007/s00267-013-0227-1
[43]	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.
[44]	Davies KW, Petersen SL, Johnson DD, et al. (2010) Estimating juniper cover from national agriculture imagery program (NAIP) imagery and evaluating the relationship between potential cover and environmental variables. Rangeland Ecol Manag 63: 630-637.
[45]	Greenwood DL, Weisberg PJ (2009) GIS-Based modeling of pinyon-juniper woodland structure in the Great Basin. Forest Sci 55: 1-12.

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