Resolution Land Cover Data Research Articles

Application of Hydrological and Sediment Modeling with Limited Data in the Abbay (Upper Blue Nile) Basin, Ethiopia

In most developing countries, biophysical data are scarce, which hinders evidence-based watershed planning and management. To use the scarce data for resource development applications, special techniques are required. Thus, the primary goal of this study was to estimate sediment yield and identify erosion hotspot areas of the Andasa watershed with limited sediment concentration records. The hydrological simulation used meteorological, hydrological, suspended sediment concentration, 12.5 m Digital Elevation Model (DEM), 250 m resolution African Soil Information Service (AfSIS) soil, and 30 m resolution land-cover data. Using the limited sediment concentration data, a sediment rating curve was developed to estimate the sediment yield from discharge. The physical-based Soil and Water Assessment Tool (SWAT) model was employed to simulate streamflow and sediment yield in a monthly time step. The result shows that SWAT predicted streamflow with a coefficient of determination (R2) of 0.88 and 0.81, Nash–Sutcliffe Efficiency (NSE) of 0.88 and 0.80, and percent of bias (PBIAS) of 6.4 and 9.9 during calibration and validation periods, respectively. Similarly, during calibration and validation, the model predicted the sediment yield with R2 of 0.79 and 0.71, NSE of 0.72 and 0.66, and PBIAS of 2.7 and −8.6, respectively. According to the calibrated model result in the period 1992–2020, the mean annual sediment yield of the watershed was estimated as 17.9 t ha−1yr−1. Spatially, around 22% of the Andassa watershed was severely eroded, and more than half of the watershed (55%) was moderately eroded. The remaining 23% of the watershed was free of erosion risk. Therefore, the findings suggests that applying the sediment rating curve equation, in conjunction with hydrological and sediment modeling, can be used to estimate sediment yield and identify erosion hotspot areas in data-scarce regions of the Upper Blue Nile Basin in particular, and the Ethiopian highlands in general with similar environmental settings.

Urban built form shapes avian richness in green spaces

Urban biodiversity plays an important role in ecological processes and ecosystem services within cities, making conservation a priority in many municipal sustainability plans. Urban green spaces (UGS) have been a key strategy for conservation by providing habitat for wildlife, including avian communities. While the ecological attributes necessary to enhance the habitability of UGS for avian communities are relatively well known, an understanding of how variation in the surrounding urban matrix influences avian richness outcomes in these spaces, is less understood. As new urban areas continue to develop and UGS become increasingly important habitat areas, urban designers and planners will need a better understanding of the ways in which urban built form patterns at the matrix-level influence avian biodiversity outcomes at the site-level in UGS. To that end, this study investigates the influence of 4 urban built form matrix-level variables , capturing three-dimensional (3D) configuration patterns, and 6 UGS site-level variables on total avian richness and avian richness by foraging guild using generalized linear model methods in 22 UGS. This analysis was conducted using high resolution land cover data, LiDAR data, and twenty years of bird occurrence data from the eBird community science program in well-surveyed UGS in Los Angeles, California. The size of the UGS was the best predictor of richness patterns across all groups, confirming previous findings. However, several urban form metrics, when included with UGS size, improved model prediction for Carnivores, Insectivores, and Omnivore foraging guilds, indicating that urban built form does influence some avian groups within UGS. The results of this study suggest that the most important factor for avian richness in UGS is the size of the green space, but that urban built form plays a role too, especially when considered from a 3D perspective.

Global inventory of suitable, cultivable and available cropland under different scenarios and policies

Where land-use change and particularly the expansion of cropland could potentially take place in the future is a central research question to investigate emerging trade-offs between food security, climate protection and biodiversity conservation. We provide consistent global datasets of land potentially suitable, cultivable and available for agricultural use for historic and future time periods from 1980 until 2100 under RCP2.6 and RCP8.5, available at 30 arc-seconds spatial resolution and aggregated at country level. Based on the agricultural suitability of land for 23 globally important food, feed, fiber and bioenergy crops, and high resolution land cover data, our dataset indicates where cultivation is possible and how much land could potentially be used as cropland when biophysical constraints and different assumptions on land-use regulations are taken into account. By serving as an input for land-use models, the produced data could improve the comparability of the models and their output, and thereby contribute to a better understanding of potential land-use trade-offs.

Spatiotemporal variation patterns and aggregation of crop residue burning at county scale in Northeast China

Crop residue burning (CRB) has been a major environmental concern in key agricultural regions of China. To reveal spatiotemporal patterns of CRB events, a county-level assessment in Northeast China was conducted using Moderate Resolution Imaging Spectroradiometer (MODIS) thermal anomalies and land cover data. The results revealed that cropland fire occurrence increased significantly from 2003 to 2019, and annual CRB occurrence from 2014 to 2019 was significantly higher than that from 2003 to 2013. The majority of detected CRB events occurred in spring and autumn, accounting for 58.50% and 28.98% of the total cropland fires, respectively. The time scan statistics showed that CRB events lasted for approximately 40 days each year and were more severe in spring than autumn. The number of cropland fires monitored by Terra and Aqua satellites was relatively more balanced, accounting for 49.74% and 50.26% of the total, respectively, indicating that farmers may intentionally burn crop residue at night. The counties with higher CRB intensity were mainly in the Songnen Plain, Sanjiang Plain, and along the main stream of the Liaohe River. The Moran’s I value of 0.054 to 0.452 for CRB intensity demonstrated a strong spatial autocorrelation at the county level. The local autocorrelation Getis-Ord showed 86 counties with significant hot spots and 91 counties with significant cold spots. The county-level temporal and spatial patterns of CRB events may be the results of policies and socio-economic conditions. This study could provide support for identifying the influencing factors of CRB and offer suggestions for crop residue management practices in this agriculture-dominant region.

The Imprint of Built-Up Land Expansion on Cropland Distribution and Productivity in Shandong Province

Grain self-sufficiency is a national food security target of China. The way that built-up land expansion impacts upon cropland loss and food provision needs to be explored in the major grain producing areas. Shandong Province is an important agricultural food production region, which is also experiencing rapidly urbanizing. Here we assessed the spatiotemporal distribution of cropland loss due to built-up land expansion and landscape dynamics of cropland during 2000–2020, by using 30 m resolution land cover data. We also analyzed the potential yield change influenced by cropland loss. The results showed that the area of built-up land expanded by 5199 km2 from 2000–2010, and 11,949 km2 from 2010–2020. Approximately 95% of the new built-up land was from cropland during the two stages, and the primary mode of built-up land expansion was the edge expansion. The patch density and the patch size of cropland kept increasing and decreasing, respectively, and the aggregation index kept decreasing from 2000 to 2020, indicating increased cropland fragmentation. The proportion of occupied cropland with potential yield greater than 7500 kg/ha was 25% and 37% during the former and the latter period. Thus, higher quality cropland was encroached in the recent period. The findings could provide meaningful implications for making sustainable land use development strategies in the study area and other similar regions.

US EPA EnviroAtlas Meter-Scale Urban Land Cover (MULC): 1-m Pixel Land Cover Class Definitions and Guidance.

This article defines the land cover classes used in Meter-scale Urban Land Cover (MULC), a unique, high resolution (one meter2 per pixel) land cover dataset developed for 30 US communities for the United States Environmental Protection Agency (US EPA) EnviroAtlas. MULC data categorize the landscape into these land cover classes: impervious surface, tree, grass-herbaceous, shrub, soil-barren, water, wetland and agriculture. MULC data are used to calculate approximately 100 EnviroAtlas metrics that serve as indicators of nature's benefits (ecosystem goods and services). MULC, a dataset for which development is ongoing, is produced by multiple classification methods using aerial photo and LiDAR datasets. The mean overall fuzzy accuracy across the EnviroAtlas communities is 88% and mean Kappa coefficient is 0.84. MULC is available in EnviroAtlas via web browser, web map service (WMS) in the user's geographic information system (GIS), and as downloadable data at EPA Environmental Data Gateway. Fact Sheets and metadata for each MULC Community are available through EnviroAtlas. Some MULC applications include mapping green and grey infrastructure, connecting land cover with socioeconomic/demographic variables, street tree planting, urban heat island analysis, mosquito habitat risk mapping and bikeway planning. This article provides practical guidance for using MULC effectively and developing similar high resolution (HR) land cover data.

Spatio-Temporal Pattern of Land Degradation from 1990 to 2015 in Mongolia

Land degradation is an important environmental problem facing the world. “Land Degradation Neutrality” is one of the core indicators in the 15th goal of the “United Nations Sustainable Development Goals” for 2030. Mongolia is an important country for global land degradation. The increasingly serious land degradation has caused a direct impact on the ecosystem of the entire Mongolian plateau. We analyzed the patterns of land degradation and restoration during 1990–2010 and 2010–2015 and determined the driving forces behind the variations, by using fine resolution land cover data for the first time in Mongolia. The results showed that the spatial distribution of newly increased land degradation and restoration have a strong transitional nature. For the past 25 years, the trend of land change in Mongolia was dominated by land degradation. However, land degradation was accompanied by ongoing restoration of some land areas, and the capacity for land restoration has been gradually improved. This study discovers a series of typical land degradation and restoration regions and provides an interpretation of the driving forces in these areas. The joint effects of natural and socioeconomic factors have been found to result in land degradation and restoration in different regions.

Correlations of Stormwater Runoff and Quality: Urban Pavement and Property Value by Land Use at the Parcel Level in a Small Sized American City

As the urban environment keeps growing, stormwater management programs have been adopted to address unregulated nonpoint runoff and pollutants across the world. Extensive studies on stormwater runoff and quality at smaller spatial scales exist, but are rare at larger spatial scales. Using the City of Corvallis, Oregon, a small sized American city, as a test-bed, this study estimates urban stormwater runoff and quality by zoning, which specifies land uses, and by parcel, which defines land ownership using the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) model and high resolution land use and land cover data. The correlations between stormwater runoff volume, stormwater quality, parcel land cover sizes, and values are then analyzed and visualized in RStudio. The results indicate that stormwater runoff and quality are determined by complex biophysical processes, with strong correlations between urban spatial sizes and property values for some land uses being observed. The research results provide suggestions for low impact development applications for different land uses, and the findings in this research can be used to suggest stormwater management policy for various land uses in small sized cities.

Uncertainties in assessing the extent and legal compliance status of riparian forests in the eastern Brazilian Amazon

Riparian forests play a vital role in conserving biodiversity, maintaining ecological connectivity and assisting the delivery of key ecosystem services (i.e. maintenance of water flows). Nevertheless, riparian forests are particularly threatened across the tropics, including in Brazil following widespread forest conversion for pasturelands and agriculture. Despite the significance of the landmark Brazilian Forest Code as the basis of environmental protection on private properties, the effectiveness of the legal protection afforded to riparian vegetation in permanent preservation areas (APP, in Portuguese) has been very poorly assessed. We provide the first fine-scale estimation of the total riparian APP distribution and deficit for the 1.25 Mkm2 Brazilian state of Pará, which has the highest deforestation rate in the Brazilian Amazon in recent years, using the available properties boundaries. We demonstrate how coarse-resolution mapping data (for landscape scale mapping, e.g. 1:100,000) consistently underestimates the extent of APP areas, highlighting the scale of the challenge involved in achieving compliance with the Brazilian Forest Code. Moreover, despite nearly half (49%) of the total extent of riparian APPs being forested, following the 2012 revision of the Forest Code, the area that does not need to be restored is six times the area obligated for restoration (940,000 ha). This suggests that some catchments in Pará may not recover fully functioning hydrological and ecological services, as around 5.7 Mha of already cleared APP are likely to remain deforested because there was a law amnesty. We also found that deforestation within APPs was greater than outside APPs for most municipalities in Pará, indicating that implementation of the Forest Code has not been sufficient to protect riparian forests to date. This shortfall in the legal requirement to restore riparian forest cover suggests that under the current enforcement regime regulatory measures on their own are insufficient and incentive-based mechanisms are needed to encourage restoration – even within the existing legal framework. Our work demonstrates the importance of using finer resolution land cover data and improved hydrological models for ensuring the effective implementation of Brazilian legislation and the safeguarding of the environmental benefits provided by these critically important ecosystems.

Who has access to urban vegetation? A spatial analysis of distributional green equity in 10 US cities

This research examines the distributional equity of urban vegetation in 10 US urbanized areas using very high resolution land cover data and census data. Urban vegetation is characterized three ways in the analysis (mixed vegetation, woody vegetation, and public parks), to reflect the variable ecosystem services provided by different types of urban vegetation. Data are analyzed at the block group and census tract levels using Spearman’s correlations and spatial autoregressive models. There is a strong positive correlation between urban vegetation and higher education and income across most cities. Negative correlations between racialized minority status and urban vegetation are observed but are weaker and less common in multivariate analyses that include additional variables such as education, income, and population density. Park area is more equitably distributed than mixed and woody vegetation, although inequities exist across all cities and vegetation types. The study finds that education and income are most strongly associated with urban vegetation distribution but that various other factors contribute to patterns of urban vegetation distribution, with specific patterns of inequity varying by local context. These results highlight the importance of different urban vegetation measures and suggest potential solutions to the problem of urban green inequity. Cities can use our results to inform decision making focused on improving environmental justice in urban settings.

National scale spatiotemporal land-use regression model for PM2.5, PM10 and NO2 concentration in China

BackgroundAir pollution epidemiological studies increasingly rely on high-resolution exposure prediction models. However, to date, few models of this type exist for use in China. ObjectivesWe produced a national land-use regression model (LUR) to estimate monthly average PM2.5, PM10 and NO2 from 2014 to 2016 in China. MethodsWe developed a spatiotemporal semi-parametric model using generalized additive mixed models. A variety of predictor variables were included in model: time varying meteorological data, high resolution land cover data from Globaland30, satellite measures of aerosol optical depth, and Geographic Information System (GIS)-derived predictors. We assessed model performance with two cross-validation (CV) approaches, including hold-out CV, and 10-fold CV. ResultsOver 22,000 monthly observations at 1382 monitoring locations were included to estimate the air pollution exposure. The time-varying spatial terms explained 87%, 71%, and 69% of variability with a hold-out cross-validated R2 of 0.85, 0.62, and 0.62 for PM2.5, PM10 and NO2 models, respectively. Models show that meteorological variables, population density, elevation, distance to road, and land cover types were important predictors for air pollution exposure. Conclusionswe have developed a new nationwide model to estimate residence-level air pollution exposures, which can be used in studies of the chronic adverse effects of air pollution.

Spatiotemporal nexus between the pattern of land degradation and land cover dynamics in Iran

AbstractIn recent decades, extensive land transformations and environmental and climate change events such as floods and droughts, increasing heat waves, and forest fires have been observed in Iran. Monitoring and intensity analysis of the land cover dynamics and land degradation in Iran are lacking due to the lack of fine resolution land cover data. In this paper, we explore the type, extent, and intensity of land transformations and the intensity of transitions among land cover classes from 2000 to 2010 using GlobeLand30 land cover data. Our results indicate that approximately 35% of Iran changed during 2000–2010, due mainly to the active gain of barren land. Furthermore, the increase in barren targeted grass and shrub. Barren expansion in Iran is alarming because most of the country is located in arid and semi‐arid regions. Iran has actively participated in desertification combat plans, and thus, it is critical to explore extra intervals of land dynamics. This will help to evaluate the temporal rate of land degradation at multiple intervals and assess the effectiveness of desertification management strategies. Additionally, investigating the role of climate and human‐made interventions into the type and extent of land transformation is recommended.

Do Trees in Halifax Grow on Money?: A Comparison of Urban Tree Canopy Cover and Median Household Income in North End and South End Halifax

Trees in the city provide numerous ecological, health, and social benefits to urban residents. Studies from large North American cities have confirmed a spatial pattern that higher urban forest tree canopy positively correlates with higher levels of affluence. The just distribution of trees will become increasingly important for urban planners and foresters as there is a national trend towards living in cities. This research report investigates the equity of distribution of urban tree canopy cover in two neighbourhoods on the peninsula of Halifax, Nova Scotia. High spatial resolution land cover data from 2007 and 2006 Statistics Canada census data was used to create maps and tables to answer the research question. The socio-economic indicators of median household income and population density are represented based on census tract dissemination areas from the 2006 Statistics Canada long survey. Preliminary results indicate lower median household income and higher population density in the chosen study area of North End Halifax compared to higher median household income and lower population density in the chosen study area of South End Halifax. Tree canopy cover density is slightly lower in North End Halifax (5.3%) than in South End Halifax (7.6%). These preliminary results coincide with findings of other researchers that higher household income and lower population density at the neighbourhood level may result in increased urban forest canopy. However, further research and more reliant tree canopy cover data is needed to determine the accuracy of these findings.

Regional to Global Biogenic Isoprene Emission Responses to Changes in Vegetation From 2000 to 2015

AbstractIsoprene, a dominant biogenic volatile organic compound that is mainly emitted by trees, has a significant impact on the atmospheric chemistry. Regional to global changes in biogenic isoprene emission associated with vegetation variations between 2000 and 2015 were estimated using the MEGAN model with satellite land cover data for inputs in this study. The satellite data estimates of land cover changes were compared to results from previous investigators that have either conducted regional studies or have used lower resolution land cover data. The analysis indicates that tree coverage increases of >5% occurred in 13% of locations including in central China and Europe. In contrast, a decrease of >5% was observed in about 5% of locations, especially in tropical regions. The trends in global tree coverage from 2000 to 2015 resulted in a global isoprene emission decrease of only 1.5%, but there were significant regional variations. Obvious decreases in tree coverage in some tropical areas (e.g., Amazon Basin, Western Africa, Southeast Asia) resulted in a ~10% reduction of regional isoprene emission due to agricultural expansion. Distinct increments of isoprene emission (5–10%) were mainly found in Northeast China and India and were associated with afforestation efforts. Deforestation and afforestation associated with managed plantations does not only affect the total forest coverage but also impacts average isoprene emission capacity, which can result in accelerated isoprene emission variations. Consequently, isoprene variation assessments are needed that not only account for changes in vegetation fractions but also consider the changes in plant species compositions of forests and other landscapes.

Mapping land cover change over continental Africa using Landsat and Google Earth Engine cloud computing.

Quantifying and monitoring the spatial and temporal dynamics of the global land cover is critical for better understanding many of the Earth’s land surface processes. However, the lack of regularly updated, continental-scale, and high spatial resolution (30 m) land cover data limit our ability to better understand the spatial extent and the temporal dynamics of land surface changes. Despite the free availability of high spatial resolution Landsat satellite data, continental-scale land cover mapping using high resolution Landsat satellite data was not feasible until now due to the need for high-performance computing to store, process, and analyze this large volume of high resolution satellite data. In this study, we present an approach to quantify continental land cover and impervious surface changes over a long period of time (15 years) using high resolution Landsat satellite observations and Google Earth Engine cloud computing platform. The approach applied here to overcome the computational challenges of handling big earth observation data by using cloud computing can help scientists and practitioners who lack high-performance computational resources.

Spatial and temporal variability of air temperature across urban neighborhoods with varying amounts of tree canopy

Recent studies have emphasized the presence of microclimates in urban settings, but most do not have the high resolution observations necessary to understand the interactions taking place at a neighborhood scale. This study used a network of 10 identical weather stations and high resolution land cover data in Knoxville, Tennessee, to analyze the microclimates of a medium-sized city with a temperate climate. Two stations were installed in each of four urban neighborhoods in locations with varying localized tree cover, and two additional stations were installed in the center of downtown and in a nearby urban nature center. The intra-neighborhood results suggested that there is significant temperature variability within a single neighborhood based on the tree canopy density immediately surrounding a given weather station. However, the inter-neighborhood variability (differences between neighborhoods) was similar in magnitude, which suggests that the overall differences in neighborhood characteristics also have an effect on climate. Land cover at the neighborhood scale (in particular tree canopy percentages at the 500-m radii) had the highest correlation with the minimum daily temperature (Tmin) during the summer season. Maximum daily temperature (Tmax) relied most on the distance of each station from Downtown and the amount of impervious area in the 50m surrounding each station. Tmax was also most influenced by surrounding land cover during dry conditions (a Dry Moderate air mass). Overall, highly localized impervious land cover percentages and larger-scale forested canopy were important in explaining temperature fluctuation, pointing to the importance of scale in microclimate assessments. Dry air masses enhanced the relationship between land cover and temperature during the day, while moist air masses did the same overnight. These data can be used to better inform planning strategies to build resiliency to extreme heat into urban environments by considering the influence of tree canopy.

Habitat change in the lower Columbia River estuary, 1870–2009

We conducted a spatial analysis of long term land cover change for the lower Columbia River estuary and its floodplain by comparing GIS representations of late 1800’s maps (Office of Coast topographic sheets and General Land Office survey maps) with recent, high resolution land cover data from 2009. In terms of combined spatial and temporal extents, ours is the most comprehensive of similar studies that have been done for the region in recent decades. Losses of 68–70% were noted for vegetated tidal wetlands, which are critical habitats for juvenile salmonids that utilize the estuary. These values are consistent with those derived from previous studies. A loss of 55% of forested uplands was also noted. The majority of loss of these habitats was due to conversion of land for agriculture and urban development. Also significant was conversion of tidal wetlands to non-tidal wetlands. Tidal flats have changed more with respect to location than overall areal coverage, which could be expected for this high energy environment. Spatial patterns of change in these habitats were variable throughout the study area, which may have practical implications for guiding restoration and conservation practices. Uncertainties with our analysis are present as a result of differences in methodologies used to develop the historical and present day data sets as well as unknowns about, and difficulties interpreting, the historical data sources. Despite these uncertainties, the analysis provides useful insight into the extent of change which has occurred in the lower Columbia River Estuary and in particular the significant declines in vegetated tidal wetlands that have occurred.

Combining citizen science and land use data to identify drivers of eutrophication in the Huangpu River system.

In recent years, the massive land use changes and urbanization of Shanghai City have coincided with a growing eutrophication and an overall degradation of Huangpu River, with related risks to the city's drinking water supply and economic development. However, there is only limited information to evaluate the spatial and temporal changes to the Huangpu River and its many tributaries. In the present study, 400 citizen scientists were trained to monitor water quality and environmental conditions on a monthly basis over three years in the lower (high urbanized) Huangpu River catchment. Their data were integrated with high resolution land cover data using GIS techniques to characterize water quality dynamics of the Huangpu River system with respect to main environmental drivers. Environmental driver analysis indicated that up-catchment conditions dominate river dynamics while typical urban impacts (first flush, impermeable land cover…) have only limited influence. According to these results, the city's investments to improve wastewater treatment and mitigate lower river impacts need to be extended throughout the catchment to reduce nutrient concentrations that are near or above thresholds for rivers and streams. The positive impact of in-stream vegetation pointed to the possibilities that local scale ecological remediation activities to reduce runoff could be viable approaches to improve river conditions throughout the catchment.

Utility of Fine Resolution Land Cover Data for Modeling Northern Bobwhite Abundance in the Oaks and Prairies of Oklahoma

Management of northern bobwhite (Colinus virginianus) requires landscape-level planning to promote sustainable populations. Limitations in time and resources necessitate the use of large geographic datasets to efficiently evaluate habitat suitability across landscapes. Many such datasets, however, are limited by a lack of detailed and current information relevant to regional management efforts. To meet this need, regional partners recently released the Oklahoma Ecological Systems Mapping (OESM) product, which offers high spatial and thematic resolution vegetation data, current to 2015. We evaluated the utility of the new OESM product for modelling abundance of northern bobwhite and other grassland birds, relating percent cover types to bird survey data from the Oaks and Prairies Joint Venture. Using an information-theoretic approach (AIC), we compared the performance of OESM to the more widely known National Land Cover Dataset (NLCD). The OESM data provided information on 20 land cover types at 10-m resolution compared 7 types and 30-m resolution for NLCD. We conducted a total of 2,367 individual counts for breeding birds from May–July, 2014–2016, across 10 counties in the Oklahoma Oaks and Prairies ecoregion. We used generalized linear mixed models to control for random effects of year and AICc to evaluate model performance. OESM models vastly outperformed NLCD for 6/7 species (AIC weights >0.99). Northern bobwhite was the only species where NLCD was the highest performing model. These results suggest that, in contrast to other grassland species, added levels of habitat complexity do not inform our ability to model local bobwhite abundance.

Street-scale green infrastructure and physical activity

Introduction: Urban roadside nature, an important component of ecosystem services, provides the opportunity for individuals to experience the natural world while going about their everyday lives. Despite mounting evidence that green space can improve health and health determinants, these studies are often limited with respect to the spatial resolution of analysis. This study examines the association between green infrastructure with active transportation and recreational activity using fine-scale estimates of the urban green environment. Methods: Percent green space and tree cover along walkable roads was calculated using 1-meter resolution land cover data within 500 and 1000 meter network buffers around study participants’ homes. Data on self-reported physical activity were from a prospective cohort of survey respondents residing in Milwaukee and Green Bay, WI (n=752). Logistic regression was used to assess the association between neighborhood environment and physical activity, adjusting for socio-demographics, duration of residence, intersection density, and neighborhood destinations and economic hardship index. Results: Greater than 35% green space along walkable roads within 500 and 1000 meter buffers doubled the odds of recreational physical activity at least once within a 30 day window (adjusted odds ratio (AOR)=2.0, p=0.03, 95% CI: 1.1, 3.9 and AOR=2.2, p=0.04, 95% CI: 1.0, 4.6 respectively). Study participants with greater than 15% tree cover along walkable roads within 500 and 1000 meters of their homes had approximately 1.5 times greater odds of choosing active transportation once than those with less tree cover (AOR=1.5, p=0.22, 95% confidence interval (CI): 0.8, 2.9 and AOR=1.5, p=0.35, 95% CI: 0.7, 3.3 respectively). Conclusions: These findings suggest that the proximate neighborhood environment may influence an individual’s decision to exercise for recreation and potentially transportation, however, more work on neighborhood preference is needed.