Development of a participatory Green Infrastructure design, visualization and evaluation system in a cloud supported jupyter notebook computing environment

Leonard, Lorne; Miles, Brian; Heidari, Bardia; Lin, Laurence; Castronova, Anthony M.; Minsker, Barbara; Lee, Jong; Scaife, Charles I.; Band, Lawrence E.

doi:10.1016/j.envsoft.2018.10.003

Cited by 27 publications

(15 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Working in the 3D environment of the Blender software makes it easy to add new objects that can be considered in the visual impact analysis. It can be very helpful for planning new infrastructure, expanding green infrastructure [41], planning decision support systems for smart cities [42,43], and for performing an environmental impact assessment of potential investments. For example, three potential buildings were added to the CDSM (Figure 10a) and assigned with a different material of known RGB values (Figure 10c).…”

Section: Resultsmentioning

confidence: 99%

Quantitative Landscape Assessment Using LiDAR and Rendered 360° Panoramic Images

Wróżyński

Pyszny²,

Sojka

2020

Remote Sensing

View full text Add to dashboard Cite

The study presents a new method for quantitative landscape assessment. The method uses LiDAR data and combines the potential of GIS (ArcGIS) and 3D graphics software (Blender). The developed method allows one to create Classified Digital Surface Models (CDSM), which are then used to create 360° panoramic images from the point of view of the observer. In order to quantify the landscape, 360° panoramic images were transformed to the Interrupted Sinusoidal Projection using G.Projector software. A quantitative landscape assessment is carried out automatically with the following landscape classes: ground, low, medium, and high vegetation, buildings, water, and sky according to the LiDAR 1.2 standard. The results of the analysis are presented quantitatively—the percentage distribution of landscape classes in the 360° field of view. In order to fully describe the landscape around the observer, graphs of little planets have been proposed to interpret the obtained results. The usefulness of the developed methodology, together with examples of its application and the way of presenting the results, is described. The proposed Quantitative Landscape Assessment method (QLA360) allows quantitative landscape assessment to be performed in the 360° field of view without the need to carry out field surveys. The QLA360 uses LiDAR American Society of Photogrammetry and Remote Sensing (ASPRS) classification standards, which allows one to avoid differences resulting from the use of different algorithms for classifying images in semantic segmentation. The most important advantages of the method are as follows: observer-independent, 360° field of view which simulates human perspective, automatic operation, scalability, and easy presentation and interpretation of results.

show abstract

Section: Resultsmentioning

confidence: 99%

Quantitative Landscape Assessment Using LiDAR and Rendered 360° Panoramic Images

Wróżyński

Pyszny²,

Sojka

2020

Remote Sensing

View full text Add to dashboard Cite

show abstract

“…RHESSys has a lot of potentials to quantify ecosystem services more accurately and to be a useful tool for studies on watershed sustainability. However, there are few relevant studies in the literature [25,78].…”

Section: Key Challengesmentioning

confidence: 99%

The Regional Hydro-Ecological Simulation System for 30 Years: A Systematic Review

Chen

Liu

et al. 2020

Water

View full text Add to dashboard Cite

As the Regional Hydro-Ecological Simulation System (RHESSys) is a tool to simulate the interactions between ecological and hydrological processes, many RHESSys-based studies have been implemented for sustainable watershed management. However, it is crucial to review a RHESSys updating history, pros, and cons for further improving the RHESSys and promoting ecohydrological studies. This paper reviewed the progress of ecohydrological studies employing RHESSys by a bibliometric analysis that quantitatively analyzed the characteristics of relevant studies. In addition, we addressed the main application progress, parameter calibration and validation methods, and uncertainty analysis. We found that since its release in 1993, RHESSys has been widely applied for basins (<100 km2) within mainly seven biomes. The RHESSys model has been applied for evaluating the ecohydrological responses to climate change, land management, urbanization, and disturbances, as well as water quality and biogeochemical cycle. While most studies have paid their attention on climate change, the focus has shifted to the application for land management in recent years. This study also identified many challenges in RHESSys such as the inaccessible data and parameters, oversimplified calibration approach, few applications for large-scale watersheds, and limited application fields. Therefore, this study proposed a set of suggestions to overcome the limitations and challenges: (1) Developing a new approach for parameter acquisition and calibration from multi-source data, (2) improving the applicability for a large-scale basin, and (3) extending the scope of application fields. We believe RHESSys can improve the understandings of human–environment relationships and the promotion of sustainable watersheds development.

show abstract

“…Development of a participatory Green infrastructure design, visualization, and evaluation system in a cloud supported Jupyter notebook computing environment [12].…”

Section: Environmentmentioning

confidence: 99%

Digital signal processing course on Jupyter–Python Notebook for electronics undergraduates

Zúñiga-López

Avilés‐Cruz

2020

Comp Applic In Engineering

View full text Add to dashboard Cite

The Jupyter–Python Notebook open source is currently used world‐wide as a programming environment for courses and labs. This paper describes an application of a set of Python computer programs implemented in the form of Jupyter notebooks to learn the course of digital signal processing and also seeks to contribute to the development of innovative teaching methodologies. A set of user‐friendly Jupyter notebooks workflows allows teachers–students to teach and learn theoretical concepts, description of problems, computer code, and simulation results with a graphical output. The proposed environment allows teachers/students to code their simulations in Python or Octave simulating languages. Additionally, a new approach involves student‐centered learning to teach theoretical and lab concepts.

show abstract

Development of a participatory Green Infrastructure design, visualization and evaluation system in a cloud supported jupyter notebook computing environment

Cited by 27 publications

References 21 publications

Quantitative Landscape Assessment Using LiDAR and Rendered 360° Panoramic Images

Quantitative Landscape Assessment Using LiDAR and Rendered 360° Panoramic Images

The Regional Hydro-Ecological Simulation System for 30 Years: A Systematic Review

Digital signal processing course on Jupyter–Python Notebook for electronics undergraduates

Contact Info

Product

Resources

About