Robust forest cover indices for multispectral images

Trees occur in many land cover classes and provide significant ecosystem services. Remotely sensed multispectral images are often used to create thematic maps of land cover, but accurately identifying trees in mixed land-use scenes is challenging. We developed two forest cover indices and protocols that reliably identified trees in WorldView-2 multispectral images. The study site in Maryland included coniferous and deciduous trees associated with agricultural fields and pastures, residential and commercial buildings, roads, parking lots, wetlands, and forests. The forest cover indices exploited the product of either the reflectance in red (630 to 690 nm) and red edge (705 to 745 nm) bands or the product of reflectance in red and near infrared (770 to 895 nm) bands. For two classes (trees versus other), overall classification accuracy was >77 percent for the four images that were acquired in each season of the year. Additional research is required to evaluate these indices for other scenes and sensors.

Relationship between Land-Use Type and Daily Concentration and Variability of PM10 in Metropolitan Cities: Evidence from South Korea

Since urban areas with high air pollution are known to have higher mortality rates compared to areas with less air pollution, accurately understanding and predicting the distribution of particulate matter (PM) in cities is important for urban planning policies that seek to emphasize the health of citizens. Therefore, this study aims to investigate the relationship between PM and land use in metropolitan cities in South Korea using the land-use regression model. We use daily data from the air quality monitoring stations (AQMS) in seven cities in South Korea for the year 2018. For analysis, K-means clustering is employed to identify the land-use pattern surrounding the AQMSs and two log-lin regression models are used to investigate the effects of each land-use type on PM. The findings show a statistically significant difference in PM concentration and variability in the business, commercial, industrial, mixed, and high-density residential areas compared to parks and green areas, and that PM concentration and variability were less in mixed areas than in single land use, thus verifying the effectiveness of a mixed land-use planning strategy. Moreover, microclimatic, seasonal, and regional factors affect PM concentration and variability. Finally, to minimize exposure to PM, various policies such as mixed land use need to be established and implemented differently, depending on the season and time.

Behavioral-Based Pedestrian Modeling Approach: Formulation, Sensitivity Analysis, and Calibration

Pedestrians are among the travelers most vulnerable to collisions that are associated with high fatality and injury rates. The increasing rate of urbanization and mixed land-use construction make walking (along with other non-motorized travel) a predominant transportation mode with a wide variety of behaviors expected. Because of the inherent safety concerns seen in pedestrian transportation infrastructures, especially those with conflicting multimodal movements expected (crosswalks, transit platforms, etc.), it is important that pedestrian behavior is modeled as a risk-taking stochastic behavior that may lead to errors and thus collision formation. In previous work, the complexity and cost associated with building pedestrian models in a cognitive-based environment weighted down the construction of simulation tools that can capture pedestrian-involved collisions, including those seen in shared space environments. In this paper, a tool that will help evaluate the safety of pedestrian traffic is initiated: an extended modeling framework of pedestrian walking behavior is adopted while incorporating different physiological, physical, and decision-making elements. The focus is on operational decisions (i.e., path choices defined by longitudinal and lateral trajectories) with a pre-specified set of origins and destinations. The model relies on the prospect theory paradigm where pedestrians evaluate their acceleration and directional alternatives while considering the possibility of colliding with other “particles.” Using a genetic algorithm method, the new model is calibrated using detailed trajectory data. This model can be extended to model the interactions between a variety of different modes that are present in different mixed land-use environments.

Mixed Land Use in Delhi: Impact on Infrastructure and Environment and Suggestions for Sustainable Planning

Mixed-use has been a part of our cities since historic times where retail, workshop, etc. all co-existed with the residence. Industrialization and associated adverse impacts led to overtaking of mixed-use concept by zoning. However, the ills of segregation started showing in the cities, and urban planners, sociologists and anthropologists started advocating in favor of re-introducing mixed-use. Now it is being planned worldwide to attain vibrant and cohesive urban development. Despite the well-established benefits of mixed-use, the Master Plan of Delhi’s approach of increasing the ambit of ‘permitting’ mixed-use as against the strategy of ‘planned mixed-use development’ adopted in other global cities makes us question the likely impacts of this liberal shift. Thus the study aims at investigating the environmental implications of such mixed-use development in the city of Delhi. From the assessment of two case study areas, it can be inferred that the nature and magnitude of impact on a mixed-use area is determined by the type of mix and its intensity, for example, in Lajpat Nagar numerous retail establishment led to vehicle-related issues while in Naraina the issues were infrastructure-related due to type of activities. It was recommended that the policies guiding the mixed-use development are in dire need of a mechanism to assess the impacts, identify demand-supply gaps and future needs, and thereafter augment accordingly to mitigate the implications in a case-specific manner.

Assessment of Snowmelt Quality Discharging from a Cold-Climate Urban Landscape During Spring Melt

Stormwater results from precipitation events and melting snow running off urban landscapes and typically being released into receiving water bodies with little to no treatment. Despite evidence of its deleterious impacts, snowmelt (SM) management and treatment are limited, partly due to a lack of quality and loading data. This study examines snowmelt quality during the spring for a cold-climate, semi-arid Canadian city (Saskatoon, Saskatchewan). Four snow storage facilities receiving urban snow plowed from roads in mixed-land-use urban catchments (228 km2) were sampled including snow piles (five events) and SM (twelve events) runoff in 2019 and 2020. Samples were analyzed for pH, EC, TDS, TSS, COD, DOC, metals, chloride, PAHs, and Raphidocelis subcapitata and Vibrio fischeri toxicity. Notable event-specific TSS spikes occurred on April 13th, 2019 (3,513 mg/L) and April 24th, 2019 (3,838 mg/L), and TDS, chloride, and manganese on March 26th, 2020 (15,000 mg/L, 5,800 mg/L, 574 mg/L), April 17th, 2020 (5,200 mg/L, 2,600 mg/L, 882 mg/L), and April 23rd, 2020 (5,110 mg/L, 2,900 mg/L, 919 mg/L), though chloride remained elevated through May 1st, 2020 samples (1,000 mg/L). Additionally, at two sites sampled April 13th, 2019 pulses of aluminum (401 mg/L) and PAHs (pyrene, phenanthrene, anthracene; 71 µg/L, 317 µg/L, 182 µg/L) were detected. The EC50 for R. subcapitata and V. fischeri was observed, if at all, above expected toxicity thresholds.

Mixed Land Use Evaluation and Its Impact on Housing Prices in Beijing Based on Multi-Source Big Data

The tense relationship between the supply and demand of land resources and the past spatial expansion of urban development in Beijing have brought many urban problems. Mixed land use is considered to be able to solve these urban problems as well as promote sustainable urban development. In this context, this study uses multi-source big data such as POI, OpenStreetMap and web crawler data to construct current land-use data of the area within the sixth ring road of Beijing, and then uses the entropy index and type number index to analyze the spatial distribution and aggregation characteristics of the mixed land-use level. Finally, a multi-scale geographically weighted regression is applied to explore the impact of the block and life circle scale mixed land use on housing prices. The results show that: (1) the accuracy of land use data obtained by using multi-source big data is high, and the consistency with the real land use situation is as high as 82.67%. (2) the mixed land use level in the study area is higher in the urban center and lower in the periphery of the city. However, it does not show the spatial distribution characteristics gradually decreasing with the increase of the distance from the urban center but shows that the area from the third to the fifth ring road is the highest. (3) the impact of block scale and life circle scale mixed land use on housing price is different. The type number index has a negative effect on the housing price in block scale mixed land use, while the entropy index has a positive effect on the housing price in life circle scale mixed land use. Based on the existing “bottom-up” individual-dominant development mode, the government of Beijing should issue relevant policies and documents to give “top-down” control and guidance in the future, so as to promote the maximization of the benefits of mixed land use. Furthermore, in the practice of mixed land use in Beijing, land use types should be reduced at the block scale and the area of different land use types should be balanced at the life circle scale.