Using Fine Resolution Orthoimagery and Spatial Interpolation to Rapidly Map Turf Grass in Suburban Massachusetts

This paper explores the use of spatial interpolative methods in conjunction with object based image analysis to estimate turf grass land cover quantity and allocation in Greater Boston, Massachusetts, USA. The goal is to learn how accurately turf grass can be estimated if only a limited portion of the study area is mapped. First, turf grass land cover is mapped at the 0.5 m resolution across the entire Plum Island Ecosystems (PIE) Long Term Ecological Research (LTER) site, a 1143-km2 area. Second, the turf grass map is aggregated into 120 m cells (N = 84,661). Third, a random sample of these 120 m cells are selected to generate an estimate of the unselected cells using four estimation methods - Inverse Distance Weighting, Kriging, Polygonal Interpolation, and Mean Estimation. The difference between known and estimated values is recorded using 120 m cell and census block group stratifications. This process is repeated 500 times for sample sizes of 2.5%, 5.0%, 7.5% and 10.0% of the study area, for a total of 2000 iterations. The average error statistics are reported by sample size, strata, and estimation method. Inverse distance weighting performed best in terms of total error across all sample sizes. It was found that by mapping only 2.5% of the study area, all four methods outperformed a recently published approach to estimating turf grass in terms of overall error

A growing concern? Examining the influence of lawn size on residential water use in suburban Boston, MA, USA

In the US, households devote a considerable share of their annual water use to outdoor purposes. Existing literature suggests that residential lawns are a major driver of this outdoor use, especially in suburban settings. Yet this has not been tested using a broad-scope, fine-scale, and spatially explicit dataset. This paper presents a spatially explicit analysis of the relationship between household lawns and water use in suburban Boston for the year 2005, and extrapolates this relationship to the year 2030 under different scenarios of (sub)urban growth. We examine this relationship by employing two novel datasets: a 0.5. m resolution land cover classification of the town of Ipswich, MA and a town-wide household-scale monthly water billing dataset. Two scenarios of (sub)urban development in 2030 are explored, representing current trends and smart growth assumptions, using the land change model GEOMOD. Expected total annual residential water use is calculated for each scenario by extrapolating the relationship between household characteristics and water use from 2005 to 2030. We find that lawn cover, living unit density, and the number of bathrooms can explain 90% of the variation in annual residential water use. We estimate that Ipswich, MA could save 46 million liters of residential water use (a reduction of 5%) by pursuing a smart growth strategy. These modest savings are notable as they are achieved strictly through a densification approach to development i.e., the scenario includes no demand side management. © 2013 Elsevier B.V

A growing concern? Examining the influence of lawn size on residential water use in suburban Boston, MA, USA

In the US, households devote a considerable share of their annual water use to outdoor purposes. Existing literature suggests that residential lawns are a major driver of this outdoor use, especially in suburban settings. Yet this has not been tested using a broad-scope, fine-scale, and spatially explicit dataset. This paper presents a spatially explicit analysis of the relationship between household lawns and water use in suburban Boston for the year 2005, and extrapolates this relationship to the year 2030 under different scenarios of (sub)urban growth. We examine this relationship by employing two novel datasets: a 0.5. m resolution land cover classification of the town of Ipswich, MA and a town-wide household-scale monthly water billing dataset. Two scenarios of (sub)urban development in 2030 are explored, representing current trends and smart growth assumptions, using the land change model GEOMOD. Expected total annual residential water use is calculated for each scenario by extrapolating the relationship between household characteristics and water use from 2005 to 2030. We find that lawn cover, living unit density, and the number of bathrooms can explain 90% of the variation in annual residential water use. We estimate that Ipswich, MA could save 46 million liters of residential water use (a reduction of 5%) by pursuing a smart growth strategy. These modest savings are notable as they are achieved strictly through a densification approach to development i.e., the scenario includes no demand side management. © 2013 Elsevier B.V