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| DOI: | 10.1007/s00267-007-9032-z |
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| Web URL(s): | https://link.springer.com/article/10.1007/s00267-007-9032-z/fulltext.html
Last checked: 10/05/2017
Access conditions: Item is within a limited-access website
https://link.springer.com/content/pdf/10.1007%2Fs00267-007-9032-z.pdf
Last checked: 10/05/2017
Requires: PDF Reader
Access conditions: Item is within a limited-access wbesite |
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| Publication Type:
| Refereed |
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| Author(s): | Zhou, Weiqi;
Troy, Austin;
Grove, Morgan |
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| Author Affiliation: | Zhou and Troy: Rubenstein School of Environment and Natural Resources, University of Vermont, George D. Aiken Center, Burlington; Grove: Northeastern Research Station, USDA Forest Service, South Burlington, VT |
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| Title: | Modeling residential lawn fertilization practices: Integrating high resolution remote sensing with socioeconomic data |
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| Source: | Environmental Management. Vol. 41, No. 5, May 2008, p. 742-752. |
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| Publishing Information: | New York: Springer-Verlag New York Inc. |
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| # of Pages: | 11 |
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| Related Web URL: | https://link.springer.com/article/10.1007%2Fs00267-007-9032-z
Last checked: 10/05/2017
Notes: Abstract only |
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| Keywords: | TIC Keywords: Fertilization program; Fertilization rates; Lawn maintenance; Models; Nitrogen fertilization; Regional variation; Remote sensing; Urban habitat; Water management
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| Abstract/Contents: | "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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| Language: | English |
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| References: | 32 |
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| Note: | Pictures, b/w
Figures
Tables
Graphs |
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| ASA/CSSA/SSSA Citation (Crop Science-Like - may be incomplete):
Zhou, W., A. Troy, and M. Grove. 2008. Modeling residential lawn fertilization practices: Integrating high resolution remote sensing with socioeconomic data. Environ. Manage. 41(5):p. 742-752. |
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| DOI: 10.1007/s00267-007-9032-z |
| Web URL(s):
https://link.springer.com/article/10.1007/s00267-007-9032-z/fulltext.html
Last checked: 10/05/2017
Access conditions: Item is within a limited-access website
https://link.springer.com/content/pdf/10.1007%2Fs00267-007-9032-z.pdf
Last checked: 10/05/2017
Requires: PDF Reader
Access conditions: Item is within a limited-access wbesite |
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MSU catalog number: b2204501 |
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