Using Landsat 8 imagery to determine a threshold for land cover change: a simulation approach

D. Conner Gay, Michael Crosby, Jason J. Holderieath, T. Eric McConnell


Satellite data is often employed to assess land use/land cover changes, particularly over larger areas. However, little attention is given to how much area can change before a given land use/cover classification is detected using satellite data. This is an important consideration, particularly in the use of image classifications to assess best management practices (BMPs). To determine these changes, and their corresponding impacts on land cover classification, Landsat 8 data was acquired and an area selected where two land cover classes meet (i.e., forest and field). The Landsat pixels were subset into 900 one square meter (1 m2) pixels and the average pixel values for grass were utilized to simulate tree/forest removal. The objective is to determine how many pixels would be converted from forest to field before an unsupervised classification detected the change. Approximately 25 percent of the area changed before one Landsat pixel (30m) changed classes and 43 % of pixels changed before a row, representing a streamside management zone (SMZ), changed. This indicates that image resolution should be considered when using satellite imagery to assess BMPs/land cover changes.


Landsat 8; Change detection

Full Text:



Corona, P. 2016. Consolidating new paradigms in large- scale monitoring and assessment of forest ecosystems. Environmental Research 144:8-14.

Cristan, R. W.M. Aust, M.C. Bolding, S.M. Barrett, and J.F. Munsell. 2016. Status of state forestry best man- agement practices for the southeastern United States. In Proceedings of the 18 th Biennial Southern Silvicul- tural Research Conference. e-Gen. Tech. Rep. SRS- 212. Asheville, NC: U.S. Department of Agriculture Forest Service, Southern Research Station.

Georgia Forestry Commission (GFC). 2019. Results of Georgia’s 2019 Silvicultural Best Management Prac- tices Implementation and Compliance Survey. Avail- able at Ac- cessed Oct. 20, 2020.

Goetz, S.J. 2006. Remote sensing of riparian buffers: past progress and future prospects. Journal of the American Water Resources Association. 42(1): 133- 143.

Gregory, S., F. Swanson, W. McKee, and K. Cummins. 1991. An ecosystem perspective of riparian zones. Bio- science. 41: 540-551.

Johansen, K., D. Tiede, T. Blaschke, L.A. Arroyo, and S. Phinn. 2011. Automatic geographic object based mapping of streambed and riparian zone extent from LiDAR data in a temperate rural urban environment, Australia. Remote Sensing. 3: 1139-1156. Klemas, V. 2014. Remote sensing of riparian and wet- land buffers: an overview. Journal of Coastal Re- search. 30(5): 869-880.

Lemoine, D., J.P. Evans, and C.K. Smith. 2006. A landscape-level Geographic Information System (GIS) analysis of streamside management zones on the Cum- berland Plateau. Journal of Forestry. 104(4): 125-131.

MacLachlan, A., G. Roberts, E. Biggs, and B. Boruff. 2017. Subpixel land-cover classification for improved urban area estimates using Landsat. International Journal of Remote Sensing. 38(20): 5763-5792.

McConnell, T.E., M.K. Crosby, J.J. Holderieath, and C.L. VanderSchaaf. 2020. Financial assessment of fu- ture stand conditions required to recover the oppor- tunity costs of a North Louisiana streamside manage- ment zone. Forest Products Journal 70(1): 39-49.

McConnell, T.E., C.L. VanderSchaaf, J.J. Holderieath, and M.K. Crosby. 2019. Adequacy of timber tres- pass civil awards: a Louisiana case study. Journal of Forestry. 117(6): 533-542.

Mississippi Forestry Commission (MFC). 2019. 2019 BMP implementation survey: Mississippi’s volun- tary silvicultural best management practices im- plementation monitoring program. Available at: Accessed 19 November 2019.

Narumalani, S., Y. Zhou, and J.R. Jensen. 1997. Ap- plication of remote sensing and geographic informa- tion systems to the delineation and analysis of ripar- ian buffer zones. Aquatic Botany. 58: 393-409.

Natural Resources Conservation Service (NRCS). 2013. Riparian forest buffer 391-Louisiana Streamside Management Zone Technical Note. https://efotg- Tech - Notet Final Draft.pdf. Accessed 19 Nov. 2019.

Pennington, D.N., J. Hansel, and R.B. Blair. 2008. The conservation value of urban riparian areas for land- birds during spring migration: land cover, scale, and vegetation effects. Biological Conservation. 141:1235- 1248.

Phiri, D. and J. Morgenroth. 2017. Developments in Landsat land cover classification methods: a review. Remote Sensing. 9:967.

Powell, R., D. Roberts, P. Dennison, and L. Hess. 2007. Sub-pixel mapping of urban land cover using multi- ple endmember spectral mixture analysis: Manaus, Brazil. Remote Sensing of Environment. 106(2): 253-267

Sugden, B.D., R. Steiner, and J.E. Jones. 2019. Stream- side management zone effectiveness for water temper- ature control in Western Montana. International Jour- nal of Forest Engineering. 30(2): 87-98.

Van Looy, K., T. Tormos, Y. Souchon, and D. Gilvear. 2017. Analyzing riparian zone ecosystem services bun- dles to instruct river management. International Jour- nal of Biodiversity Sciences, Ecosystem Services, & Management. 13(1): 330-341.

Wasser, L., L. Chasmer, R. Day, and A. Taylor. 2015. Quantifying land use effects on forested riparian buffer vegetation structure using LiDAR data. Ecosphere. 6(1): 10.

Williams, T.M., D.J. Lipscomb, and C.J. Post. 2004. Defining streamside management zones or riparian buffers. In Proceedings of the 12 th Biennial Southern Silvicultural Research Conference. Gen. Tech. Rep. SRS-71. Asheville, NC: U.S. Department of Agricul- ture Forest Service, Southern Research Station. 594p.

Wu, CS, L. Teeter, R. Abt, and D. Hicks. 1996. Assessing the economic effects of streamside management zones on the forestry sector. In Proceedings of the annual meeting of the Southern Forest Economics Workers.

Yang, S., J. Bai, C. Zhao, H. Lou, C. Zhang, Y. Guan, Y. Zhang, Z. Wang, and Z. Yu. 2018. The assessment of the changes of biomass and riparian buffer width in the terminal reservoir under the impact of the South- to-North water diversion project in China. Ecological Indicators. 85: 932-943.


  • There are currently no refbacks.


© 2008 Mathematical and Computational Forestry & Natural-Resource Sciences