Disaggregation of Green Space Access, Walkability, and Behavioral Risk Factor Data for Precise Estimation of Local Population Characteristics.

BACKGROUND: Social and Environmental Determinants of Health (SEDH) provide us with a conceptual framework to gain insights into possible associations among different human behaviors and the corresponding health outcomes that take place often in and around complex built environments. Developing better built environments requires an understanding of those aspects of a community that are most likely to have a measurable impact on the target SEDH. Yet data on local characteristics at suitable spatial scales are often unavailable. We aim to address this issue by application of different data disaggregation methods. METHODS: We applied different approaches to data disaggregation to obtain small area estimates of key behavioral risk factors, as well as geospatial measures of green space access and walkability for each zip code of Allegheny County in southwestern Pennsylvania. RESULTS: Tables and maps of local characteristics revealed their overall spatial distribution along with disparities therein across the county. While the top ranked zip codes by behavioral estimates generally have higher than the county's median individual income, this does not lead them to have higher than its median green space access or walkability. CONCLUSION: We demonstrated the utility of data disaggregation for addressing complex questions involving community-specific behavioral attributes and built environments with precision and rigor, which is especially useful for a diverse population. Thus, different types of data, when comparable at a common local scale, can provide key integrative insights for researchers and policymakers.

Variation the in relationship between urban tree canopy and air temperature reduction under a range of daily weather conditions.

Mitigating heat is a vital ecosystem service of trees, particularly with climate change. Land surface temperature measures captured at a single time of day (in the morning) dominate the urban heat island literature. Less is known about how local tree canopy and impervious surface regulate air temperature throughout the day, and/or across many days with varied weather conditions, including cloud cover. We use bike-mounted air temperature sensors throughout the day in New Haven, Connecticut, USA, from 2019 to 2021 and generalized additive mixed models across 156 rides to estimate the daily variation in cooling benefits associated with tree canopy cover, and warming from impervious surface cover in 90 m buffers surrounding bike observations. Cooling is inferred by subtracting the bicycle-observed temperature from a reference station. The cooling benefits from tree canopy cover were strongest in the midday (11:00-14:00, -1.62 °C), afternoon (14:00-17:00, -1.19 °C), and morning (8:00-11:00, -1.15 °C) on clear days. The cooling effect was comparatively smaller on cloudy mornings -0.92 °C and afternoons -0.51 °C. Warming from impervious surfaces was most pronounced in the evening (17:00-20:00, 1.11 °C) irrespective of clouds, and during cloudy nights (20:00-23:00) and cloudy mornings 1.03 °C 95 % CI [1.03, 1.04]. Among the hottest observed days (top 25th percentile of reference station daily maxima), tree canopy was associated with lower temperatures on clear afternoons -1.78 °C [-1.78, -1.78], cloudy midday -1.17 °C [-1.19, -1.15], clear midday -1.12 °C [-1.12, -1.11]. We add a broader spectrum of weather conditions by explicitly including clouds, and greater temporal resolution by measuring throughout the day to bike-based urban heat research. Future mobile sampling campaigns may broaden the spatial extent with more environmental variation, representing an opportunity for public science and engagement.

Canopy composition drives variability in urban growing season length more than the heat island effect.

The elevated heat of urban areas compared to their surroundings makes humid temperate cities a useful preview of future climate effects on natural forest phenology. The utility of this proxy rests on the expectation that trees in urban areas alter their phenology in response to warmer site conditions in spring and fall. However, it is possible that apparent lengthening of the growing season is instead governed by human-driven tree species selection and plant functional type (PFT; trees, shrubs, turfgrass) heterogeneity typical of managed landscapes. Without the use of highly spatially and temporally resolved remote sensing data, the roles of tree taxonomy and local site characteristics (e.g., impervious cover) in controlling phenology remain confounded. To understand the drivers of earlier start of season (SOS) and later end of season (EOS) among urban trees, we estimated individual tree phenology using >130 high-resolution satellite images per year (2018-2020) for ~10,000 species-labeled trees in Washington, DC. We found that species identity alone accounted for 4× more variability in the timing of SOS and EOS compared with a tree's planting location characteristics. Additionally, the urban mix of PFTs may be more responsible for apparent advances in SOS (by between 1.8 ± 1.3 and 3.5 ± 1.3 days) than heat per se. The results of this study caution against associating longer growing seasons in cities-observed in moderate to coarse resolution remote sensing imagery-to within-species phenological plasticity and demonstrate the power of high-resolution satellite data for tracking tree phenology in biodiverse environments.

Capturing coupled riparian and coastal disturbance from industrial mining using cloud-resilient satellite time series analysis.

The socio-ecological impacts of large scale resource extraction are frequently underreported in underdeveloped regions. The open-pit Grasberg mine in Papua, Indonesia, is one of the world's largest copper and gold extraction operations. Grasberg mine tailings are discharged into the lowland Ajkwa River deposition area (ADA) leading to forest inundation and degradation of water bodies critical to indigenous peoples. The extent of the changes and temporal linkages with mining activities are difficult to establish given restricted access to the region and persistent cloud cover. Here, we introduce remote sensing methods to "peer through" atmospheric contamination using a dense Landsat time series to simultaneously quantify forest loss and increases in estuarial suspended particulate matter (SPM) concentration. We identified 138 km2 of forest loss between 1987 and 2014, an area >42 times larger than the mine itself. Between 1987 and 1998, the rate of disturbance was highly correlated (Pearson's r = 0.96) with mining activity. Following mine expansion and levee construction along the ADA in the mid-1990s, we recorded significantly (p < 0.05) higher SPM in the Ajkwa Estuary compared to neighboring estuaries. This research provides a means to quantify multiple modes of ecological damage from mine waste disposal or other disturbance events.