Modeling Residential Lawn Fertilization Practices: Integrating High Resolution Remote Sensing with Socioeconomic Data |
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Authors: | Weiqi Zhou Austin Troy Morgan Grove |
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Institution: | (1) Rubenstein School of Environment and Natural Resources, University of Vermont, George D. Aiken Center, 81 Carrigan Drive, Burlington, VT 05405, USA;(2) Northeastern Research Station, USDA Forest Service, South Burlington, VT 05403, USA |
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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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Keywords: | Lawn fertilization Lawn greenness Remote sensing Socioeconomic characteristics Modeling Object-oriented classification LTER |
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