Using object-based image analysis to map commercial poultry operations from high resolution imagery to support animal health outbreaks and events

Abstract

Precise locations of commercial poultry operations are important to planning and response for animal health outbreaks and events. These data are not available nationally or uniformly in the United States. This project uses machine learning capabilities to identify and map commercial poultry operations from aerial imagery in seven south-eastern states in the United States. The output protocol uses an Object-Based Image Analysis (OBIA) approach, which identifies objects based on spectral signatures combined with spatial, contextual, and textural information. The protocol is a semi-automated and user-assisted process, meaning that the object identification routines require minimal user inputs or expertise. Using the protocol, we produced locations of likely commercial poultry operations in up to two counties in one workday, about two times faster than manual digitisation. The resulting datasets provide an estimate of the number and geographic distribution of commercial poultry operations to assist outbreak response by augmenting available knowledge in affected areas.

Dimensions

Altmetric

PlumX Metrics

Downloads

Download data is not yet available.

Author Biography

Christopher Burdett, Department of Biology, Colorado State University

  

References

Abdel-Wahab AM, Al-Harthy AA, 2012. 3D analysis for Airborne Light Detection and Ranging (LIDAR) data for east of Jeddah Province by using ArcGIS. World Applied Services Journal 19 (7) 1057-1065.

Bell Jr. DW, Weaver W (eds), 2002. Commercial chicken meat and egg production, Kluwer Academic Publishers, 1265 pp. DOI: https://doi.org/10.1007/978-1-4615-0811-3

Blaschke T, 2010. Object based image analysis for remote sensing. ISPRS Journal of Photogrammetry and Remote Sensing 65(1), 2-16. DOI: https://doi.org/10.1016/j.isprsjprs.2009.06.004

Blaschke, T., Lang, S., Lorup, E., Strobl, J. & Zeil, P., (2000). Object-oriented Image Processing in an Integrated GIS/Remote Sensing Environment and Perspectives for Environmental Applications. In: Cremers, A. B. & Greve, K. (Hrsg.), Umweltinformatik ’00 Umweltinformation für Planung, Politik und Öffentlichkeit. Marburg: Metropolis.

Blundell S, Opitz D, Morris M, Rao R, 2008. Feature Analyst V5.0. ASPRS 2008 Annual Conference, 1-9.

Burdett CL, Kraus B, Garza S, Miller R, Bjork K, 2015. Simulating the Distribution of Individual Livestock Farms and Their Populations in the United States: An Example Using Domestic Swine (Sus scrofa domesticus) Farms. PLOS ONE 10(11)(e0140338) 1-13. DOI: https://doi.org/10.1371/journal.pone.0140338

Burnham KP, Anderson DR, 2002. Model Selection and Multimodel Inference, 2nd ed., Springer-Verlad New York, 454 pp.

Caggiano M, Tinkham WT, Hoffman C, Cheng AS, Hawbaker TJ, 2016. High resolution mapping of development in the wildland-urban interface using object based image extraction. Heliyon 2(10), e00174, 1-19. DOI: https://doi.org/10.1016/j.heliyon.2016.e00174

Coates PS, Gustafson B, Roth CL, Chenaille MP, Ricca MA, Mauch K, Sanchez-Chopitea E, Kroger TJ, Perry WM, Casazza ML, 2017. Using object-based image analysis to conduct high-resolution conifer extraction at regional spatial scales. U.S. Geological Survey, U.S. Department of the Interior, Open-File Report 2017-1093, 40 pp. DOI: https://doi.org/10.3133/ofr20171093

Environmental Systems Research Institute, 2016. ArcGIS Desktop. Redlands, CA.

Fairchild B, 2005. Basic Introduction to Broiler House Environmental Control. Available from: The Poultry Site, https://thepoultrysite.com/articles/basic-introduction-to-broiler-housing-environmental-control. Last accessed on March 19, 2020.

Freire S, Santos T, Navarro A, Soares F, Silva JD, Afonso N, Fonseca A, Tenedório J, 2014. Introducing mapping standards in the quality assessment of buildings extracted from very high resolution satellite imagery. ISPRS Journal of Photogrammetry and Remote Sensing 90, 1-9. DOI: https://doi.org/10.1016/j.isprsjprs.2013.12.009

Hamilton R, Megown K, Lachowski H, Campbell R, 2006. Mapping Russian olive: Using remote sensing to map an invasive tree. U.S. Department of Agriculture, U.S. Forest Service, Remote Sensing Applications Center, RSAC-0087-RPT1.

Handan-Nader C, Ho DE, 2019. Deep learning to map concentrated animal feeding operations, Nature Sustainability 2:298-306. DOI: https://doi.org/10.1038/s41893-019-0246-x

Johnson KK, Seeger RM, Marsh TL, 2016. Local Economies and Highly Pathogenic Avian Influenza. Choices. Quarter 2. Available online: https://www.choicesmagazine.org/choices-magazine/theme-articles/economic-consequences-of-highly-pathogenic-avian-influenza/local-economies-and-highly-pathogenic-avian-influenza

Miller JE, Nelson SAC, Hess GR, 2009. An Object Extraction Approach for Impervious Surface Classification with Very-High-Resolution Imagery. The Professional Geographer 61(2), 250-264. DOI: https://doi.org/10.1080/00330120902742920

Overwatch Systems, Ltd., 2010a. Feature Analyst 5.0 for ArcGIS reference manual. Textron Systems, 354 pp.

Overwatch Systems, Ltd., 2010b. Feature Analyst extension. Sterling, VA, USA

Opitz D, Blundell S, 2008. Object recognition and image segmentation: the Feature Analyst approach Ch. 2.3 in Object-Based Image Analysis. Series: Lecture Notes in Geoinformation and Cartography. Blaschke T, Lang S, Hay G (eds), Springer-Verlag Berlin Heidelberg, 15 pp.

Seni G, Elder FL, 2010. Ensemble methods in data mining: Improving accuracy through combining predictions. Synthesis Lectures on Data Mining and Knowledge Discovery. Morgan and Claypool Publishers, 127 pp. DOI: https://doi.org/10.2200/S00240ED1V01Y200912DMK002

Spröhnle K, Fuchs E-M, Pelizari PA, 2017. Object-based analysis and fusion of optical and SAR satellite data for dwelling detection in refugee camps. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 10(5), 1780-1791. DOI: https://doi.org/10.1109/JSTARS.2017.2664982

Spröhnle K, Tiede D, Schoepfer E, Füreder P, Svanberg A, Rost T, 2014. Earth observation-based dwelling detection approaches in a highly complex refugee camp environment - a comparative study. Remote Sensing 6, 9277-9297. DOI: https://doi.org/10.3390/rs6109277

USDA, Farm Service Agency, 2019. NAIP Imagery. Available from: https://www.fsa.usda.gov/programs-and-services/aerial-photography/imagery-programs/naip-imagery/. Last accessed on March 13, 2020.

USDA, National Agricultural Statistics Service, Census of Agriculture, 2015a. 2012 Census of Agriculture Highlights: Poultry and Egg Production. Available from: https://www.nass.usda.gov/Publications/Highlights/2015/Poultry_and_Egg_Production.pdf. Last accessed on March 13, 2020.

USDA, National Agricultural Statistics Service, Census of Agriculture, 2015b. 2012 Census Volume 1, Chapter 2, County Level. Available from: https://www.nass.usda.gov/Publications/AgCensus/2012/Full_Report/Volume_1,_Chapter_2_County_Level/. Last accessed on March 13, 2020.

Textron Systems, Veteto W, 2017. personal communication.

Zhang C, Smith M, Fang C, 2017. Evaluation of Goddard's LiDAR, hyperspectral, and thermal data products for mapping urban land-cover types. GIScience & Remote Sensing 55(1), 90-109. DOI: https://doi.org/10.1080/15481603.2017.1364837

Published
2020-12-10
Info
Issue
Section
Original Articles
Keywords:
OBIA, machine learning, USDA, Feature Analyst, poultry.
Statistics
  • Abstract views: 895

  • PDF: 174
How to Cite
Maroney, S., McCool-Eye, M., Fox, A., & Burdett, C. (2020). Using object-based image analysis to map commercial poultry operations from high resolution imagery to support animal health outbreaks and events. Geospatial Health, 15(2). https://doi.org/10.4081/gh.2020.919