Downed logs on the forest floor provide habitat for species, fuel for forest fires, and function as a key component of forest nutrient cycling and carbon storage. Ground-based field surveying is a conventional method for mapping and characterizing downed logs but is limited. In addition, optical remote sensing methods have not been able to map these ground targets due to the lack of optical sensor penetrability into the forest canopy and limited sensor spectral and spatial resolutions. Lidar (light detection and ranging) sensors have become a more viable and common data source in forest science for detailed mapping of forest structure. This study evaluates the utility of discrete, multiple return airborne lidar-derived data for image object segmentation and classification of downed logs in a disturbed forested landscape and the efficiency of rule-based object-based image analysis (OBIA) and classification algorithms. Downed log objects were successfully delineated and classified from lidar derived metrics using an OBIA framework. 73% of digitized downed logs were completely or partially classified correctly. Over classification occurred in areas with large numbers of logs clustered in close proximity to one another and in areas with vegetation and tree canopy. The OBIA methods were found to be effective but inefficient in terms of automation and analyst's time in the delineation and classification of downed logs in the lidar data.

We estimated associations between neighborhood supermarket gain or loss and glycemic control (assessed by glycated hemoglobin (HbA1c) values) in patients from the Kaiser Permanente Northern California Diabetes Registry (n = 434,806 person-years; 2007-2010). Annual clinical measures were linked to metrics from a geographic information system for each patient's address of longest residence. We estimated the association between change in supermarket presence (gain, loss, or no change) and change in HbA1c value, adjusting for individual- and area-level attributes and according to baseline glycemic control (near normal, <6.5%; good, 6.5%-7.9%; moderate, 8.0%-8.9%; and poor, ≥9.0%). Supermarket loss was associated with worse HbA1c trajectories for those with good, moderate, and poor glycemic control at baseline, while supermarket gain was associated with marginally better HbA1c outcomes only among patients with near normal HbA1c values at baseline. Patients with the poorest baseline HbA1c values (≥9.0%) had the worst associated changes in glycemic control following either supermarket loss or gain. Differences were not clinically meaningful relative to no change in supermarket presence. For patients with type 2 diabetes mellitus, gaining neighborhood supermarket presence did not benefit glycemic control in a substantive way. The significance of supermarket changes on health depends on a complex interaction of resident, neighborhood, and store characteristics.

Measures of accessibility have long been an important metric in regional transportation planning and modeling. However, new methods are needed to provide computationally efficient, multiscale, free, transparent, and customizable tools that harness open and disparate sources of transportation network data at fine spatial resolution over large geographic extents. This research presents a new open source tool, UrbanAccess, which uses a generalized and scalable methodology to measure transit accessibility with a multimodal network comprising both pedestrian and operational schedule transit networks at a fine spatial scale over large metropolitan extents. A typical use for this tool is illustrated in a case study that characterizes regional transit accessibility in the San Francisco Bay Area in California.

Abstract:The benefits of terrestrial remote sensing in the environmental sciences are clear across a range of applications, and increasingly remote sensing analyses are being integrated into public health research. This integration has largely been in two areas: first, through the inclusion of continuous remote sensing products such as normalized difference vegetation index (NDVI) or moisture indices to answer large-area questions associated with the epidemiology of vector-borne diseases or other health exposures; and second, through image classification to map discrete landscape patches that provide habitat to disease-vectors or that promote poor health. In this second arena, new improvements in object-based image analysis (or "OBIA") can provide advantages for public health research. Rather than classifying each pixel based on its spectral content alone, the OBIA approach first segments an image into objects, or segments, based on spatially connected pixels with similar spectral properties, and then these objects are classified based on their spectral, spatial and contextual attributes as well as by their interrelations across scales. The approach can lead to increases in classification accuracy, and it can also develop multi-scale topologies between objects that can be utilized to help understand human-disease-health systems. This paper provides a brief review of what has been done in the public health literature with continuous and discrete mapping, and then highlights the key concepts in OBIA that could be more of use to public health researchers interested in integrating remote sensing into their work.

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We examined whether residing within 2 miles of a new supermarket opening was longitudinally associated with a change in body mass index (BMI). We identified 12 new supermarkets that opened between 2009–2010 in 8 neighborhoods. Using the Kaiser Permanente Northern California Diabetes Registry, we identified members with type 2 diabetes residing continuously in any of these neighborhoods 12 months prior to the first supermarket opening until 10 months following the opening of the last supermarket. Exposure was defined as a reduction (yes/no) in travel distance to the nearest supermarket as a result of a new supermarket opening. First difference regression models were used to estimate the impact of reduced supermarket distance on BMI, adjusting for longitudinal changes in patient and neighborhood characteristics. Among patients in the exposed group, new supermarket openings reduced travel distance to the nearest supermarket by 0.7 miles on average. However, reduced distance to nearest supermarket was not associated with BMI changes. Overall, we found no evidence that reduced supermarket distance was associated with reduced levels of obesity for residents with type 2 diabetes.

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Associations between neighborhood food environment and adult body mass index (BMI; weight (kg)/height (m)2) derived using cross-sectional or longitudinal random-effects models may be biased due to unmeasured confounding and measurement and methodological limitations. In this study, we assessed the within-individual association between change in food environment from 2006 to 2011 and change in BMI among adults with type 2 diabetes using clinical data from the Kaiser Permanente Diabetes Registry collected from 2007 to 2011. Healthy food environment was measured using the kernel density of healthful food venues. Fixed-effects models with a 1-year-lagged BMI were estimated. Separate models were fitted for persons who moved and those who did not. Sensitivity analysis using different lag times and kernel density bandwidths were tested to establish the consistency of findings. On average, patients lost 1 pound (0.45 kg) for each standard-deviation improvement in their food environment. This relationship held for persons who remained in the same location throughout the 5-year study period but not among persons who moved. Proximity to food venues that promote nutritious foods alone may not translate into clinically meaningful diet-related health changes. Community-level policies for improving the food environment need multifaceted strategies to invoke clinically meaningful change in BMI among adult patients with diabetes.