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

Future suburban development and the environmental implications of lawns: A case study in New England, USA

Lawns cover more land than irrigated corn in the United States according to the most recent estimates (Milesi et al. 2009). The associated ecological ramifications-such as habitat fragmentation, water quality and availability-may be far-reaching. The way lawns are maintained, especially intensive fertilization and watering, also presents risks for water use and quality, nutrient cycling, urban climate regimes, and even human health. However, the lack of broad-extent, high-resolution land cover data has limited the ability of researchers to measure or project the extent of lawns. In this chapter, we first produce a high resolution (0.5 m) land-cover classification to quantify existing lawn extent for the year 2005 in the Plum Island Ecosystem (PIE), a collection of 26 suburban towns northeast of Boston, MA, USA. We then use this dataset in conjunction with the GEOMOD land-change model to project lawn extent under two scenarios of urban growth for the year 2030. We find that in 2005, 76 km2 of lawn “defined as grass on residential land “existed in the PIE study region. Under a Current Trends scenario, we project residential lawns may increase by 7.0 % to 81 km 2, while under a Smart Growth scenario we project a 1.6 % increase to 77 km2. We estimate this could result in up to 61 million additional liters of annual water use under the Current Trends scenario, and 14 million under Smart Growth, putting additional stress on utilities that already face regular water shortages

A Multi-Criteria Geographic Information Systems Approach for the Measurement of Vulnerability to Climate Change

A flexible procedure for the development of a multi-criteria composite index to measure relative vulnerability under future climate change scenarios is presented. The composite index is developed using the Weighted Ordered Weighted Average (WOWA) aggregation technique which enables the selection of different levels of trade-off, which controls the degree to which indicators are able to average out others. We explore this approach in an illustrative case study of the United States (US), using future projections of widely available indicators quantifying flood vulnerability under two scenarios of climate change. The results are mapped for two future time intervals for each climate scenario, highlighting areas that may exhibit higher future vulnerability to flooding events. Based on a Monte Carlo robustness analysis, we find that the WOWA aggregation technique can provide a more flexible and potentially robust option for the construction of vulnerability indices than traditionally used approaches such as Weighted Linear Combinations (WLC). This information was used to develop a proof-of-concept vulnerability assessment to climate change impacts for the US Army Corps of Engineers. Lessons learned in this study informed the climate change screening analysis currently under way

Data from: Estimating the reproducibility of psychological science

This record contains the underlying research data for the publication "Estimating the reproducibility of psychological science" and the full-text is available from: https://ink.library.smu.edu.sg/lkcsb_research/5257Reproducibility is a defining feature of science, but the extent to which it characterizes current research is unknown. We conducted replications of 100 experimental and correlational studies published in three psychology journals using high-powered designs and original materials when available. Replication effects were half the magnitude of original effects, representing a substantial decline. Ninety-seven percent of original studies had statistically significant results. Thirty-six percent of replications had statistically significant results; 47% of original effect sizes were in the 95% confidence interval of the replication effect size; 39% of effects were subjectively rated to have replicated the original result; and if no bias in original results is assumed, combining original and replication results left 68% with statistically significant effects. Correlational tests suggest that replication success was better predicted by the strength of original evidence than by characteristics of the original and replication teams