DPM model segregation validation and scaling effect in a rotary drum

Discrete phase method (DPM) model was used to analyse rotary drum systems for segregation behavior. DPM simulations were performed for comparison with a dynamic segregation experimental measurement from the literature. This included dynamic segregation and time-averaged particle velocity field, which were validated with experimental data. In addition, a direct DPM and parcel scaled DPM simulation study was performed to analyse the effect of drum and particle parcel size scaling. The segregation dynamics was evaluated using the Lacey mixing index. This work shows segregation dynamics decreases with increasing drum size while keeping the same particle size. It further shows that for a given drum size the segregation dynamics deviate after a certain particle parcel scaling limit. The parcel scaling limit also increases with increasing drum size.

A GIS- Based Approach on Annual Tractor Use, Soil Type and Crop Pattern Interactions in Some Provinces of the Aegean Region

The objective of this study was to provide the basic data related to the tractor such as power ratios and annual use along with the production pattern, type of farms, farm size and their fragmentation, soil properties. In order to meet the above objective a survey study was conducted in 2013 in four provinces of the Aegean region namely Aydın, Denizli, İzmir and Manisa.Using a GPS device, the coordinates of the location of the agricultural lands were also found in order to analyze the data for further evaluation and GeoMedia Professional 6.0 CBS software. The results from the study indicated that the annual use of tractors is about 650 hours and the surveyed land is a typical example and resembles the whole country in terms of average land size and fragmented land structure. The total land owned by 305 farmers was calculated to be 4023.1 ha and each farmer has 7.47 ha land. On the other hand, the average parcel size was found to be 0.86 ha. The distance between the parcels ranged between 0.5-10 km mostly while %12 of the lands was out of this range.

Establishing a framework of a watershed-wide screening tool to support the development of watershed-based flood protection plans for low-lying coastal communities

Flood risk analysis is the instrument by which floodplain and stormwater utility managers create strategic adaptation plans to reduce the likelihood of flood damages in their communities, but there is a need to develop a screening tool to analyze watersheds and identify areas that should be targeted and prioritized for mitigation measures. The authors developed a screening tool that combines readily available data on topography, groundwater, surface water, tidal information for coastal communities, soils, land use, and precipitation data. Using the outputs of the screening tool for various design storms, a means to identify and prioritize improvements to be funded with scarce capital funds was developed, which combines the likelihood of flooding from the screening tool with a consequence of flooding assessment based on land use and parcel size. This framework appears to be viable across cities that may be inundated with water due to sea-level rise, rainfall, runoff upstream, and other natural events. The framework was applied to two communities using the 1-day 100-year storm event: one in southeast Broward County with an existing capital plan and one inland community with no capital plan.

Parcel Management and Perceived Ecosystem Services and Disservices in the Exurbs of a Midwestern County in the United States

Exurban development is a prominent land use in the United States of America, particularly in the Midwest, where much of it occurs on farmland and remnant woodlands. While exurbanization may affect ecosystem services, its impact could be modulated by management decisions made by residents. We aimed to uncover how exurban residents in a midwestern county perceived ecosystem services and disservices provided by their property based on 26 semi-structured interviews of landowners on parcels between 1 and 20 acres with a pond in unincorporated areas. We found the ecosystem services people associated most with their land are classified as cultural services (dominated by recreation services), while the most common mentioned disservices were classified as regulating disservices. Many ecosystem services that would be categorized as supporting or regulating services were not mentioned by interviewees, including microclimate stabilization, carbon sequestration, disease regulation, and maintenance of genetic diversity. Residents spent an average of 1.4 h/acre each week managing their properties. However, as parcel size and forest cover increased, the residents reported managing less surface area. Our study suggested that residents cultivate landscape features that directly benefit them and view many of the services that benefit regional biodiversity and ecosystem processes as disservices, which, to rectify, may require coordinated landscape-level management or local policies/incentives.

When Small Is Not Beautiful: The Unexpected Impacts of Trees and Parcel Size on Metered Water-Use in a Semi-Arid City

Colorado’s water supply is under threat due to climate change pressures and population growth, however Colorado has been recognized to have some of the most progressive water conservation programs in the country. Limiting outdoor water consumption is an increasingly popular approach to conserving water in semi-arid cities, yet in order to implement effective water reduction and conservation policies, more utilities and city managers need a firm understanding of the local drivers of outdoor water consumption. This research explores the drivers of outdoor water consumption in a semi-arid, medium-sized Colorado city that is projected to undergo significant population growth. We used a combination of correlation and linear regression analyses to identify the key descriptive variables that predict greater water consumption at the household scale. Some results were specific to the development patterns of this medium-sized city, where outdoor water use increased 7% for each additional mile (1.6 km) a household was located from the historic urban center. Similarly, more expensive homes used more water as well. Surprisingly, households with a higher ratio of vegetation cover to parcel size tended toward less water consumption. This result could be because parcels that are shaded by their tree canopy require less irrigation. We discuss these results to assist city managers and policymakers in creating water-efficient landscapes and provide information that can be leveraged to increase awareness for water conservation in a growing, semi-arid city.

Mapping Urban Tree Cover Changes Using Object-Based Convolution Neural Network (OB-CNN)

Urban trees provide social, economic, environmental and ecosystem services benefits that improve the liveability of cities and contribute to individual and community wellbeing. There is thus a need for effective mapping, monitoring and maintenance of urban trees. Remote sensing technologies can effectively map and monitor urban tree coverage and changes over time as an efficient and low-cost alternative to field-based measurements, which are time consuming and costly. Automatic extraction of urban land cover features with high accuracy is a challenging task, and it demands object based artificial intelligence workflows for efficiency and thematic accuracy. The aim of this research is to effectively map urban tree cover changes and model the relationship of such changes with socioeconomic variables. The object-based convolutional neural network (CNN) method is illustrated by mapping urban tree cover changes between 2005 and 2015/16 using satellite, Google Earth imageries and Light Detection and Ranging (LiDAR) datasets. The training sample for CNN model was generated by Object Based Image Analysis (OBIA) using thresholds in a Canopy Height Model (CHM) and the Normalised Difference Vegetation Index (NDVI). The tree heatmap produced from the CNN model was further refined using OBIA. Tree cover loss, gain and persistence was extracted, and multiple regression analysis was applied to model the relationship with socioeconomic variables. The overall accuracy and kappa coefficient of tree cover extraction was 96% and 0.77 for 2005 images and 98% and 0.93 for 2015/16 images, indicating that the object-based CNN technique can be effectively implemented for urban tree coverage mapping and monitoring. There was a decline in tree coverage in all suburbs. Mean parcel size and median household income were significantly related to tree cover loss (R2 = 58.5%). Tree cover gain and persistence had positive relationship with tertiary education, parcel size and ownership change (gain: R2 = 67.8% and persistence: R2 = 75.3%). The research findings demonstrated that remote sensing data with intelligent processing can contribute to the development of policy input for management of tree coverage in cities.