Neighborhood Greenspace and Cognition: The Cardiovascular Health Study

Access to greenspace has been positively associated with cognition among older adults, however prior research has been limited in temporal and geographic scope. We evaluated associations between neighborhood greenspace and incidence of dementia and change in cognitive functioning using a longitudinal sample of non-demented adults (n=2,465) from the Cardiovascular Health and Cognition Study. Percent greenness (1-km radial buffers) was derived from the National Land Cover Dataset. Cognitive function was measured using the Mini-Mental State Examination (3MSE) and dementia status was clinically adjudicated. Cox proportional hazard and logistics regression analyses were used to examine associations of baseline greenness with risk of incident dementia and risk of mild cognitive impairment, respectively. Generalized linear mixed models accounting for within-subject correlations were used to examine the association between greenspace in the neighborhood at baseline and 3MSE score (1991-1999). Ongoing results will be presented, along with modifiers and mediators of associations.

Connections Between Daily Greenness Exposure and Health Outcomes

A compelling body of research demonstrates that exposure to nature, especially trees, is beneficial to human health. We know little, however, about the extent to which understory vegetation that does not reach the height of trees, impacts human health. An additional gap in our knowledge concerns the extent to which daily variations in exposure to various forms of vegetation are related to human health outcomes. Many previous findings describing such connections were achieved in laboratory settings or through semi-controlled experiments, which do not reflect the dynamic variations of people’s daily exposure to nature. Thus, we conducted an online survey to address these questions. We used the National Land Cover Dataset 2011 and Google Street View images to estimate participants’ daily exposure to nature, and two standard questionnaires (General Health SF-12 and the Perceived Stress Scale) to assess health. Results show that greater exposure to trees in daily life is associated with better health outcomes. Specifically, higher neighborhood concentrations of tree canopy are related to better physical health, overall health and an increased capacity to control stress. In contrast, the results exploring the health associations of understory vegetation were inconsistent. In most cases, understory vegetation had a negative relationship with stress and mental health measures.

Analysis and expert assessment of the semantic similarity between land cover classes

Products of CORINE Land Cover (CLC), the National Land Cover Dataset (NLCD), the FAO/UNEP Land Cover Classification System (LCCS), etc. currently provide an important source of information used for the assessment of issues such as landscape change, landscape fragmentation and the planning of urbanization. Assuming that the data from these various databases are often used in searching for solutions to environmental problems, it is necessary to know which classes of different databases exist and to what extent they are similar, i.e. their possible compatibility and interchangeability. An expert assessment of the similarity between the CLC and NLCD 1992 nomenclatures is presented. Such a similarity assessment in comparison with the ‘geometric model’, the ‘feature model’ and the ‘network model’ is not frequently used. The results obtained show the similarity of assessments completed by four experts who marked the degree of similarity between the compared land cover classes by 1 (almost similar classes), 0.5 (partially similar classes) and 0 (not similar classes). Four experts agreed on assigning 1 in only three cases; 0.5 was given 33 times. A single expert assigned 0.5 a total of 17 times. Results confirmed that the CLC and NLCD nomenclatures are not very similar.

Quantifying river form variations in the Mississippi Basin using remotely sensed imagery

Geographic variations in river form are often estimated using the framework of downstream hydraulic geometry (DHG), which links spatial changes in discharge to channel width, depth, and velocity through power-law models. These empirical relationships are derived from limited in situ data and do not capture the full variability in channel form. Here, we present a dataset of 1.2 × 106 river widths in the Mississippi Basin measured from the Landsat-derived National Land Cover Dataset that characterizes width variability observationally. We construct DHG for the Mississippi drainage by linking DEM-estimated discharge values to each width measurement. Well-developed DHG exists over the entire Mississippi Basin, though individual sub-basins vary substantially from existing width-discharge scaling. Comparison of depth predictions from traditional depth–discharge relationships with a new model incorporating width into the DHG framework shows that including width improves depth estimates by, on average, 24%. Results suggest that channel geometry derived from remotely sensed imagery better characterizes variability in river form than do the assumptions of DHG.