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Modeling Residential Lawn Fertilization Practices: Integrating High Resolution Remote Sensing with Socioeconomic Data

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Abstract

This article investigates how remotely sensed lawn characteristics, such as parcel lawn area and parcel lawn greenness, combined with household characteristics, can be used to predict household lawn fertilization practices on private residential lands. This study involves two watersheds, Glyndon and Baisman’s Run, in Baltimore County, Maryland, USA. Parcel lawn area and lawn greenness were derived from high-resolution aerial imagery using an object-oriented classification approach. Four indicators of household characteristics, including lot size, square footage of the house, housing value, and housing age were obtained from a property database. Residential lawn care survey data combined with remotely sensed parcel lawn area and greenness data were used to estimate two measures of household lawn fertilization practices, household annual fertilizer nitrogen application amount (N_yr) and household annual fertilizer nitrogen application rate (N_ha_yr). Using multiple regression with multi-model inferential procedures, we found that a combination of parcel lawn area and parcel lawn greenness best predicts N_yr, whereas a combination of parcel lawn greenness and lot size best predicts variation in N_ha_yr. Our analyses show that household fertilization practices can be effectively predicted by remotely sensed lawn indices and household characteristics. This has significant implications for urban watershed managers and modelers.

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Acknowledgments

This research was funded by the Northern Research Station, USDA Forest Service, and the National Science Foundation LTER program (grant DEB- 042376). Thanks are due to Neely Law for providing the lawn fertilization survey data. The authors would also like to thank the reviewers for their constructive comments and suggestions.

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Authors and Affiliations

  1. Rubenstein School of Environment and Natural Resources, University of Vermont, George D. Aiken Center, 81 Carrigan Drive, Burlington, VT, 05405, USA

    Weiqi Zhou & Austin Troy

  2. Northeastern Research Station, USDA Forest Service, South Burlington, VT, 05403, USA

    Morgan Grove

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  1. Weiqi Zhou
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  2. Austin Troy
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  3. Morgan Grove
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Correspondence to Weiqi Zhou.

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Zhou, W., Troy, A. & Grove, M. Modeling Residential Lawn Fertilization Practices: Integrating High Resolution Remote Sensing with Socioeconomic Data. Environmental Management 41, 742–752 (2008). https://doi.org/10.1007/s00267-007-9032-z

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  • DOI: https://doi.org/10.1007/s00267-007-9032-z

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